Abstracts

Final Program and Abstracts 

ASM Conference on 

Rapid Next-Generation Sequencing 

and Bioinformatic Pipelines for 

Enhanced Molecular Epidemiologic 

Investigation of Pathogens 

September 24 – 27, 2015 

Washington, DC

© 2015 American Society for Microbiology 

1752 N Street, N.W. 

Washington, DC 20036-2904 

Phone: 202-737-3600 

World Wide Web: www.asm.org 

All Rights Reserved 

Printed in the United States of America

Table of Contents 

ASM Conferences Information....................................... 2 

Conference Organization................................................ 3 

Acknowledgments........................................................... 3 

General Information........................................................ 4 

Travel Grants................................................................... 5 

Scientific Program........................................................... 6 

Oral Presentation Abstracts........................................... 15 

Poster Abstracts............................................................. 39 

Index........................................................................... 112 

ASM Conference on Rapid Next-Generation Sequencing and Bioinformatic 

Pipelines for Enhanced Molecular Epidemiologic Investigation of Pathogens 

1

ASM Conferences Committee 

Sean Whelan, Chair 

Harvard Medical School 

Joanna Goldberg, Vice Chair 

Emory University 

Victor DiRita 

University of Michigan 

Lora Hooper 

University of Texas Southwestern 

Medical Center 

Petra Levin 

Washington University in Saint Louis 

Gary Procop* 

Cleveland Clinic 

Curtis Suttle 

University of British Columbia 

Theodore White 

University of Missouri – Kansas City 

*Indicates Committee Liaison for this Conference 

ASM Conferences Mission 

To identify emerging or underrepresented topics of broad scientific significance. 

To facilitate interactive exchange in meetings of 100 to 500 people. 

To encourage student and postdoctoral participation. 

To recruit individuals in disciplines not already involved in ASM to ASM 

membership. 

To foster interdisciplinary and international exchange and collaboration with 

other scientific organizations. 

2 

ASM Conferences

Program Committee 

Marc Allard 

U.S. Food and Drug 

Administration 

Silver Spring, MD 

Eric Brown 

U.S. Food and Drug 

Administration 

Silver Spring, MD 

Dag Harmsen 

University of Muenster 

Muenster, Germany 

Acknowledgments 

The Conference Program Committee and the American Society for Microbiology 

acknowledge the following for their support of the ASM Conference on Rapid 

Next-Generation Sequencing and Bioinformatic Pipelines for Enhanced Molecular 

Epidemiologic Investigation of Pathogens. On behalf of our leadership and members, 

we thank them for their financial support: 

Platinum Supporters 

Illumina OpGen ThermoFisher 

Gold Supporters 

Applied Maths 

PathoNGenTrace 

Qiagen 

Ridom Bioinformatics 

Silver Supporters 

Geneious 

Genologics 

Bronze Supporters 

Microbial Genomics – 

a journal from the Society 

for General Microbiology 

(SGM) 

New England Biolabs 

Sponsored Seminars 

The Platinum sponsors are hosting additional educational opportunities during the 

conference. Please stop by the tabletop displays of Illumina, OpGen and ThermoFisher 

to learn more about how you can participate. Space is limited. 



3

General Information 

REGISTRATION AND NAME BADGES 

ASM Staff will be available at the 

registration desk in the Blue Room 

Pre-function at the Omni Shoreham 

Hotel during posted registration hours. 

Participants may collect name badges and 

program materials at the registration desk. 

A name badge is required for entry into all 

sessions and meals. Each participant may 

register one guest to attend the Welcome 

Reception at the National Zoo and/or 

the Conference Party. Guest tickets are 

$50 per event. Guests may not attend 

sessions, poster sessions, lunches or 

coffee breaks. 

GENERAL SESSIONS 

All general sessions will be held in the 

Blue Room in the Omni Shoreham Hotel. 

POSTER SESSIONS 

Poster boards are located in the Hampton 

Room at the Omni Shoreham Hotel. 

Posters will be available for viewing 

informally throughout the conference, 

with official poster sessions scheduled on 

Friday and Saturday. 

All posters may be mounted starting on 

Thursday after 3:00 pm, and should be 

available to view by no later than 10:00 

am Friday morning. Posters are to be 

removed after 6:30 pm on Saturday, 

September 26, but by no later than 12:30 

pm on Sunday, September 27. Posters not 

removed in time may be discarded. 

Odd-numbered posters (1,3,5…) will 

be officially presented in Session A on 

Friday, September 25, and even-numbered 

posters (2,4,6…) will be officially 

presented in Session B on Saturday, 

September 26. 

Please check your assigned number in the 

abstract index. The same number is used 

for the presentation and board number. 

EXHIBITS 

Please be sure to visit the supporter’s 

display tables in the Blue Room 

Pre-Function. Supporting company 

representatives will be available to talk 

with you during coffee breaks. 

NETWORKING MEALS AND SOCIAL 

EVENTS 

Registration includes attendance at the 

Welcome Reception at the National 

Zoo on Thursday evening, Networking 

Lunches on Friday and Saturday, and the 

Conference Party with DJ and Dancing 

on Saturday night. Ample time has also 

been scheduled for participants to network 

during coffee breaks. 

CERTIFICATE OF ATTENDANCE 

Certificates of Attendance can be found 

in the registration packet received at the 

registration desk. 

Note: Certificates of Attendance do not 

list session information. 

CAMERAS AND RECORDINGS 

POLICY 

Audio/video recorders and cameras are 

not allowed in session rooms or in the 

poster areas. Taking photographs with 

any device is prohibited. 

CHILD POLICY 

Children are not permitted in session 

rooms, poster sessions, conference meals 

or social events. Please contact the hotel 

concierge to arrange for babysitting 

services in your hotel room. 

4 

ASM Conferences

Travel Grants 

ASM STUDENT TRAVEL GRANTS 

ASM encourages the participation of graduate students and new postdocs at ASM 

Conferences. To support the cost of attending the conference, ASM has awarded travel 

grants of $500 to each of the following individuals: 

Levent Albayrak 

Nabil-Fareed Alikhan 

Philip Ashton 

Ellsworth Campbell 

Laura Carroll 

Hattie Chung 

Madeline Galac 

John Haydek 

Mathis Hjelmsø 

Sung Im 

Marianne Kjeldsen 

Denis Kutnjak 

Ana Lauer 

Kara Levinson 

An-Dong Li 

Helena Jaramillo Mesa 

Muhammad Shafiq 

Dylan Storey 

Anni Zhang 

ASM-LINK UNDERGRADUATE FACULTY RESEARCH INITIATIVE 

FELLOWSHIPS 

The ASM-LINK Undergraduate Faculty Research Initiative (UFRI) Fellowship is a 

professional development resource that trains STEM faculty to initiate and sustain 

successful research partnerships. Through interactive training, structured mentoring, 

and deliberate networking at ASM-sponsored research conferences, UFRI fellows gain 

access to resources and networks to advance their undergraduate research programs. 

We congratulate the 2015 ASM Conference on Rapid Next-Generation Sequencing 

UFRI Fellows: 

Olga Calderon 

LaGuardia Community College, CUNY, Long Island, NY 

Robert Furler 

Florida SouthWestern State College, Ft. Myers, FL 

Olabisi Ojo 

Southern University at New Orleans, New Orleans, LA 



5

Scientific Program 

Thursday, September 24, 2015 

5:00 pm – 7:00 pm Opening Keynote Session 

Blue Room 

Session Chair: Steven Musser 

5:00 – 5:15 pm Welcome Remarks 

Joseph Campos; Secretary, American Society for 

Microbiology, Washington, DC 

Gary Procop; ASM Conferences Committee, Washington, DC 

Eric Brown; US Food and Drug Administration, Silver 

Spring, MD 

5:15 – 6:00 pm Microbial Genomics and Beyond 

George Weinstock; Jackson Laboratory, Farmington, CT 

6:00 – 6:45 pm Translating Genomics from Research to Clinical Application 

Julian Parkhill; Univ. of Cambridge, Cambridge, United 

Kingdom 

7:15 – 9:00 pm Welcome Reception 

National Zoo, Hors d’oeuvres and an open bar will be provided. You must 

Bird House 

have your name badge to enter. 

(10 min. walk from Omni) 

To get there: Walking out the front entrance of the Omni 

Shoreham, walk to the right on Calvert Street (the street 

in front of the hotel), and at the major intersection with 

Connecticut Avenue turn left to walk up Connecticut Avenue. 

The National Zoo entrance will be on your right. The Bird 

House is the first major building you will reach as you enter 

the Zoo. Staff will be on hand to direct you once you arrive at 

the Zoo. 

A small shuttle van will also run on a continuous loop between 

the hotel and the zoo for attendees who elect not to walk (note 

the van is small so wait times could be significant). 

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ASM Conferences


Friday, September 25, 2015 

8:00 – 8:45 am Surveillance Keynote I 

Blue Room 

Session Chair: Eric Brown 

Priming the Innovation Pump: FDA’s Role in Advancing and 

Using NGS 

Stephen Ostroff; US Food and Drug Administration, Silver 

Spring, MD 

8:45 – 10:00 am Session 1: Genomics for Food and Veterinary Pathogen 

Blue Room 

Surveillance 


8:45 – 9:15 am GenomeTrakr: A Pathogen Database to Build a Global 

Genomic Network for Pathogen Traceback and Outbreak 

Detection 

Marc Allard; US Food and Drug Administration, Silver 

Spring, MD 

9:15 – 9:45 am Global Microbial Identifier – is Global Harmonization and 

Comparison of WGS Data Feasible? 

Frank Aarestrup; Technical Univ. of Denmark, Lyngby, 

Denmark 

9:45 – 10:00 am Three Months of Surveillance of S. Typhimurium and 

S. 1,4,[5],12:i:- in Denmark Based on Whole-genome 

Sequencing and MLVA Typing 

Marianne Kjeldsen; Statens Serum Institut, Copenhagen, 

DENMARK 

10:00 – 10:30 am Coffee Break 

Blue Prefunction Room 

and Patio 

10:30 – 12:00 pm Session 2: One Health, Regulation and Assuring the 

Blue Room 

Quality of NGS for Pathogen Surveillance 


10:30 – 11:00 am Assuring the Quality of Next-Generation Sequencing in 

Clinical and Public Health Laboratories 

Amy Gargis; Centers for Disease Control and Prevention 



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11:00 – 11:15 am Long Reads Sequencing for Better Short Reads SNP Analysis 

Deborah Moine; Nestlé Institute of Health Sciences, 

Lausanne, SWITZERLAND 

11:15 – 11:30 am Subtractive-hybridization for Enrichment of Non-host Nucleic 

Acid for Improvement of Sequence-based Detection of 

Pathogens 

Roger Barrette; Plum Island Animal Disease Center (USDA/ 

APHIS), Greenport, NY 

11:30 – 11:45 am The Salmonella in silico Typing Resource (SISTR): Rapid 

Analysis of Salmonella Draft Genome Sequence Data 

Catherine Yoshida; Public Health Agency of Canda, Guelph, 

ON, CANADA 

11:45 – 12:00 pm Revolutionising Public Health Reference Microbiology Using 

Whole Genome Sequencing: A Case Study with Salmonella 

Philip Ashton; Public Health England, London, UNITED 

KINGDOM 

12:00 – 1:15 pm Lunch 

Palladian Ballroom 

1:15 – 3:15 pm Session 3: Genomics for Public Health Pathogen Surveillance 

Blue Room 

Session Chair: Amy Gargis 

1:15 – 1:45 pm The Application of Genomics to Public Health—an 

Epidemiologist’s Point of View 

Greg Armstrong; Centers for Disease Control and Prevention, 

Atlanta, GA 

1:45 – 2:15 pm Tracing Evolution and Spread of Mycobacterium tuberculosis 

Strains in Times of Antibiotic Treatment 

Stefan Niemann; Research Center Borstel, Borstel, Germany 

2:15 – 2:30 pm Benchmark Datasets for Validating Foodborne Outbreak 

Investigations: Integrating WGS and Phylogenomic Analyses 

Ruth Timme; US Food and Drug Administration, College 

Park, MD 

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2:30 – 2:45 pm Integrating Core Genome Phylogenetic Relationships 

and Isolate Geographic Data to Trace the 2012 Neisseria 

meningitidis Outbreak in New York City 

Madeline Galac; Univ. of North Carolina at Charlotte, 

Charlotte, NC 

2:45 – 3:00 pm Whole Genome Sequencing Provides Rapid Traceback of 

Clinical to Food Sources During a Foodborne Outbreak of 

Salmonellosis 

Maria Hoffmann; US Food and Drug Administration, College 

Park, MD 

3:00 – 3:15 pm Transforming Public Health Microbiology in the United States 

with Whole Genome Sequencing (WGS) - PulseNet and 

Beyond 

Eija Trees; CDC, Atlanta, GA 

3:15 – 3:30 pm Coffee Break 


and Patio 

3:30 – 6:00 pm Session 4: Bioinformatic Pipelines and Tools for Genomic 

Blue Room 

Pathogen Surveillance 

Session Chair: Bill Klimke 

3:30 – 4:00 pm Pathogen Genomics at NCBI 

David Lipman; National Center for Biotechnology 

Information, Bethesda, MD 

4:00 – 4:30 pm Overview of Tools for Microbial NGS Data Analysis 

Dag Harmsen; Univ. of Munster, Munster, Germany 

4:30 – 5:00 pm Community and Social Data / Applications for Genomic 

Pathogen Surveillance 

David Aanensen, Imperial College, London, United Kingdom 

5:00 – 5:15 pm Salmonella Serotype Determination Utilizing High-throughput 

Genome Sequencing Data 

Xiangyu Deng; Univ. of Georgia, Griffin, GA 

5:15 – 5:30 pm PATRIC Pipeline 

Fangfang Xia; Univ. of Chicago, Chicago, IL 




9


5:30 – 5:45 pm CFSAN SNP Pipeline: A Whole Genome Sequence Data 

Analysis Pipeline for Food-borne Pathogens 

Yan Luo; FDA/CFSAN, College Park, MD 

5:45 – 6:00 pm Assembling Whole Genomes from Mixed Microbial 

Communities Using Hi-C 

Ivan Liachko; Univ. of Washington, Seattle, WA 

6:00 – 7:30 pm Poster Session A 

Hampton Room Odd-numbered posters will be officially presented. 

Saturday, September 26, 2015 

8:00 – 8:45 am Surveillance Keynote II 

Blue Room 

Session Chair: Greg Armstrong 

Integrating Advanced Molecular Technologies into Public 

Health 

Rima Khabbaz; Centers for Disease Control and Prevention, 

Atlanta, GA 

8:45 – 9:45 am Session 5: Omni-Omics for Pathogen Surveillance 

Blue Room 

Session Chair: Martin Maiden 

8:45 – 9:15 am SURPI: A Deep Sequencing Clinical Analysis Tool for 

Infectious Disease 

Charles Y. Chiu; Univ. of California, San Francisco, San 

Francisco, CA 

9:15 – 9:45 am Genomics & Transcriptomics in the Clinical Microbiology 

Laboratory 

Randall J. Olsen; Houston Methodist, Houston, TX 



and Patio 

10:15 – 11:45 am Session 6: Genomics for Microbial Taxonomy 

Blue Room 

Session Chair: Dag Harmsen 

10:15 – 10:45 am A New Genomics Driven Taxonomy. Are We There, Yet? 

George Garrity; Michigan State Univ., East Lansing, MI 

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10:45 – 11:15 am Beyond Typing and Phylogeny: the Population and Functional 

Genomics of the Neisseria 

Martin Maiden; Univ. of Oxford, Oxford, United Kingdom 

11:15 – 11:30 am Next Generation Sequencing of Brucella melitensis Isolates 

from Kuwait and Comparative Genome Analyses 

Abu Mustafa; Kuwait Univ., Jabriya, KUWAIT 

11:30 – 11:45 am Microbial Genomic Taxonomy at GenBank 

Scott Federhen; NCBI, Bethesda, MD 

11:45 am – 1:15 pm Lunch 

Empire Ballroom 

1:15 – 3:45 pm Session 7: Pathogen Surveillance Software Demonstration 

Blue Room 

Session Chairs: Kathryn Holt, Bill Klimke, Errol Strain 

1:15 – 1:30 pm What Do We Need from Microbial Genomics Surveillance 

Software? 

Kathryn Holt; Univ. of Melbourne, Melbourne, AUSTRALIA 

1:30 – 1:39 pm A Biosurveillance Analysis Pipeline for Genomic Sequence 

Data 

Christian Olsen; Biomatters, Inc., Newark, NJ 

1:39 – 1:48 pm A Universal Whole Genome Sequencing Approach Using 

Whole Genome MLST and Whole Genome SNP Analysis in 

the Cloud 

Hannes Pouseele; Applied Maths NV, Sint-Martens-Latem, 

BELGIUM 

1:48 – 1:57 pm Typing and Epidemiological Clustering of Common Pathogens 

Based on Whole Genome NGS Data 

Arne Materna; QIAGEN, Aarhus, DENMARK 

1:57 – 2:06 pm wgsa.net: Whole Genome Sequence Analysis 

David Aanensen; Imperial College London, London, UNITED 

KINGDOM 

2:06 – 2:15 pm SeqSphere+ Software for Prospective Bacterial Genomic 

Surveillance and Resistome or Virulome Analysis 

Jörg Rothgänger; Ridom GmbH, Münster, GERMANY 



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2:15 – 2:24 pm Nullarbor: Rapid Analysis of Bacterial Outbreak Sequence 

Data 

Torsten Seemann; Univ. of Melbourne, Melbourne, 

AUSTRALIA 

2:24 – 2:33 pm EnteroBase: A Powerful, User-friendly Online Resource for 

Analysing Genomic Variation 

Nabil-Fareed Alikhan; Univ. of Warwick, Coventry, UNITED 

KINGDOM 

2:33 – 2:42 pm SnapperDB: A Scalable Database for Routine Sequencing of 

Bacterial Isolates 

Philip Ashton; Public Health England, London, UNITED 

KINGDOM 

2:42 – 2:51 pm Phylogenetic Reconstruction and Outbreak Investigation 

Using IRIDA and SNVPhyl 

Aaron Petkau; Public Health Agency of Canada, Winnipeg, 

MB, CANADA 

2:51 – 3:00 pm PanCore: A Flexible Workflow for the Comparison and 

Assignment of Genomes to Outcomes 

Dylan Storey; Univ. of California, Davis, CA 

3:00 – 3:09 pm Reference-free Pan-genomic Epidemiology using Cortex 

Zamin Iqbal; Wellcome Trust Centre for Human Genetics, 

Univ. of Oxford, Oxford, UNITED KINGDOM 

3:10 – 3:15 pm Quality Issues and Standards for Next Generation Sequencing 

Bill Klimke; NCBI/NLM/NIH, Bethesda, MD 

3:15 – 3:35 pm Retrospective WGS of Two Foodborne Outbreaks 

Errol Strain; US Food and Drug Administration, College 

Park, MD 

3:35 – 4:00 pm Coffee Break 


and Patio 

4:00 – 5:00 pm Session 8: Genomics for Microbial Forensics 

Blue Room 

Session Chair: Marc Allard 

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4:00 – 4:30 pm Microbial Forensics and Its Needs for Standards and 

Standardization 

Bruce Budowle; Univ. of Texas, Austin, TX 

4:30 – 5:00 pm Anthrax – Molecular Epidemiology and Forensics from Whole 

Genome Sequencing and Metagenomic Sampling of Complex 

Specimens 

Paul Keim; Northern Arizona Univ., Flagstaff, AZ 

5:00 – 6:30 pm Poster Session B 

Hampton Room Even-numbered posters will be officially presented. 

7:00 – 10:00 pm Conference Party 

Blue Room 

Enjoy a relaxed and fun evening with your colleagues. A DJ 

will play a range of music and dancing is encouraged. Heavy 

hors d’oeuvres, a carving station and a tickets/cash bar will be 

provided. 

Sunday, September 27, 2015 

8:00 – 10:00 am Session 9: Genomics for Clinical Microbiology Pathogen 

Blue Room 

Surveillance 

Session Chair: Stefan Niemann 

8:00 – 8:30 am Full Genome Sequencing to Track Carbapenem Producing 

Organisms within a Hospital 

Julie Segre; National Institutes of Health, Bethesda, MD 

8:30 – 9:00 am Prospective Genome Sequencing for Real-time Surveillance of 

Resistant Bacteria in a Univ. Hospital 

Alexander Mellmann; Univ. of Munster, Munster, Germany 

9:00 – 9:15 am Whole-genome Sequence Analysis of Pseudomonas 

aeruginosa in Acute Infection Reveals Widespread withinpopulation 

Diversity and Rapid Transmission within the Body 

Hattie Chung; Harvard Medical School, Boston, MA 

9:15 – 9:30 am Beyond the SNV: Integrating Multiple Data Types into 

Genomic Epidemiology 

Meredith Wright; J. Craig Venter Institute, La Jolla, CA 



13


9:30 – 9:45 am Defining Clonality in Acinetobacter baumannii Using Whole 

Genome Sequencing of Outbreak Strains Associated with the 

Conflict in Iraq 

Erik Snesrud; Walter Reed Army Institute of Research, Silver 

Spring, MD 

9:45 – 10:00 am Direct from Sputum: Next Gen Analysis of Mycobacterium 

tuberculosis in Clinical Samples 

David Engelthaler; TGen North, Flagstaff, AZ 



and Patio 

10:30 – 12:30 pm Session 10: Genomics for Virus Surveillance 

Blue Room 

Session Chair: Charles Y. Chiu 

10:30 – 11:00 am Genomic Surveillance of the Ebola Epidemic in Western 

Africa 

Shirlee Wohl; Harvard Univ., Cambridge, MA 

11:00 – 11:30 am Small Game Hunting 

W. Ian Lipkin; Columbia Univ., New York, NY 

11:30 – 11:45 am Development of an Efficient Next-generation Sequencing 

Platform for Charting the Evolution of Norovirus Strains 

Gabriel Parra; National Institutes of Health, Bethesda, MD 

11:45 – 12:00 pm Genome-wide Comparison of Cowpox Viruses Reveals a New 

Clade Related to Variola Virus 

Livia Schuenadel; Robert Koch Institute, Berlin, GERMANY 

12:00 – 12:15 pm Virome Analyses Among Children with Acute Respiratory 

Infection in China 

Wenjie Tan; National Institute for Viral Disease Control and 

Prevention, China CDC, Beijing, CHINA 

12:15 – 12:30 pm Using Wastewater to Monitor Viral Pathogens in the Slum City 

of Kibera 

Mathis Hjelmsø; Technical Univ. of Denmark, Kgs. Lyngby, 

DENMARK 

12:30 – 12:35 pm Concluding Remarks and Plans for ASMNGS 2017 

Blue Room 

Dag Harmsen 

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ASM Conferences

Oral Presentation Abstracts 

n S1:3 

THREE MONTHS OF SURVEILLANCE OF S. 

TYPHIMURIUM AND S. 1,4,[5],12:I:- IN 

DENMARK BASED ON WHOLE-GENOME 

SEQUENCING AND MLVA TYPING 

M. Kjeldsen, P. Gymoese, M. Torpdahl; 

Statens Serum Institut, Copenhagen, DEN- 

MARK. 

Introduction: Salmonella enterica subsp. enterica 

Typhimurium (S. Typhimurium) and its 

monophasic variant 1,4,[5],12:i:- are zoonotic 

pathogens of significance in both humans and 

animals worldwide. In Europe, Salmonella 

cause the majority of food-borne outbreaks. 

Currently, several laboratories primarily use 

pulsed-field gel electrophoresis (PFGE) and 

Multiple-locus variable-number tandem repeat 

analysis (MLVA) for surveillance and outbreak 

investigations of Salmonella. Surveillance 

studies based on whole-genome sequences 

(WGS) shows good results and are promising 

alternatives to conventional methods. In this 

study, we evaluate SNP analysis in comparison 

to MLVA for surveillance of S. Typhimurium 

and S. 1,4,[5],12:i:-. Materials and Methods: 

We analyzed all S. Typhimurium and S. 

1,4,[5],12:i:- human clinical isolates from the 

Danish surveillance program from January to 

March 2015. This collection comprises of 40 

monophasic S. Typhimurium and 66 S. Typhimurium 

isolates, hereunder three outbreaks 

defined by MLVA-typing and epidemiological 

findings. The relatedness of the strains was 

examined by core genome SNP analysis, and 

results were compared with those of MLVA 

and Multi-locus sequence typing (MLST). 

Results: WGS analysis on the collection of 

106 strains resulted in close to 5900 SNPs. 

A clear correlation between SNP and MLST 

analysis was observed. S. Typhimurium ST36 

was separated by a deep branch from ST19 

and ST34. Isolates of ST34 mainly comprised 

monophasic variants and were separated by 

440 SNPs, indicating a close relationship 

within this group. In correspondence with the 

MLVA defined S. Typhimurium outbreaks, 

the SNP based tree revealed three clusters of 

closely related strains with a few SNP differences. 

In one of the outbreaks, MLVA included 

35 isolates while the SNP analysis added two 

potential outbreak isolates to this cluster. In the 

ST34 group, SNP analysis dispersed all MLVA 

clusters, including the outbreak cluster (of 

eight isolates) located within this group; SNP 

queried if one of the eight defined outbreak 

isolates should be included. Conclusion: Our 

results show that strains with identical MLVA 

profiles can be either unrelated or closely related 

based on SNP distance determined from 

WGS. Using WGS analysis for outbreak detection 

seems reliable and in addition, it provides 

a higher resolution of the strains relationships. 

At present, defining an outbreak solely on SNP 

differences is problematic, since the number 

of SNP differences allowed within a cluster 

have to be considered. This study highlights 

the challenges with both SNP and MLVA based 

cluster detection and emphasizes the importance 

of combining molecular methods with 

epidemiological data. 

n S2:2 

LONG READS SEQUENCING FOR BETTER 

SHORT READS SNP ANALYSIS 

D. Moine 1 , L. Baert 2 , C. Barretto 2 , C. Ngom- 

Bru 2 , M. Kasam 1 , C. Fournier 1 , L. Michot 2 , J. 

Gimonet 2 , C. Chilton 2 ; 

1 

Nestlé Institute of Health Sciences, Lausanne, 

SWITZERLAND, 2 Nestle Research Center, 

Lausanne, SWITZERLAND. 

Whole genome sequencing (WGS) is an 

emerging tool for foodborne pathogen characterization. 

It can help to identify and type 

the bacteria for investigative purposes (source 

attribution), factory ecology and trend analysis 

in the food industry. This novel approach is 



15


more precise and faster than former methods 

like ribotyping or 16S gene sequencing. The 

sequencing of the isolates (e.g. Salmonella, 

Listeria, Cronobacter) was performed using 

Illumina short read sequencing technology. 

Regarding bioinformatic analysis, the approach 

of single nucleotide polymorphism (SNP) 

called from a reference is a good way to obtain 

precise result. One key parameter when doing 

SNP analysis is to have a valuable reference 

genome that is closely related to the targeted 

strain. Often those complete reference genomes 

are not available. Only a few strains of 

Salmonella, Listeria or Cronobacter have complete 

publicly available genomes of high quality. 

To create the reference genomes, we used 

the Pacific Biosciences long reads sequencing 

technology followed by hierarchical genome 

assembly process (HGAP) for de novo genome 

assembly. The assemblies were then validated 

using a quality checking pipeline (mega blast 

and dot plot comparison). 

n S2:3 

SUBTRACTIVE-HYBRIDIZATION FOR 

ENRICHMENT OF NON-HOST NUCLEIC ACID 

FOR IMPROVEMENT OF SEQUENCE-BASED 

DETECTION OF PATHOGENS 

R. W. Barrette, F. R. Grau, M. T. McIntosh; 

Plum Island Animal Disease Center (USDA/ 

APHIS), Greenport, NY. 

Next generation sequencing is a powerful tool 

for detection and characterization of pathogens. 

This technology is rapidly reducing the 

sequencing cost per base while simultaneously 

increasing the amount of sequence data 

produced. Next generation sequencing is 

particularly well suited to screening complex 

mixtures of nucleic acids, which can be exploited 

for pathogen identification. However, 

such nucleic acid mixtures are often biased to 

host-derived, rather than pathogen-derived, 

nucleic acid. This can greatly reduce the 

number of sequence reads that are relevant to 

infectious agents. By hybridization of random 

cDNA from samples to host RNA immobilized 

on paramagnetic beads, we have developed a 

subtracted-hybridization method that may be 

used to enrich microbial or viral cDNA from 

diagnostic specimens. To test this method, 

samples from cattle exhibiting clinical signs 

similar to foot-and-mouth disease (FMD) were 

cultured in primary lamb kidney cells (LK) and 

a monkey kidney cell line (Vero) for isolation 

of potential viruses. FMD virus was rapidly 

ruled out by conventional diagnostic methods 

including real-time RT-PCR of the original and 

cultured samples; however, electron microscopy 

of LK and Vero cell cultures revealed the 

presence of picornavirus-like particles. LK cultured 

material was subsequently subjected to 

random cDNA amplification, host subtraction 

and next-generation sequencing. Total cDNA 

was simultaneously sequenced to compare the 

effectiveness of the new method. Results from 

the host subtracted cDNA library resolved 74% 

of the genome of a novel isolate of Enterovirus 

Type 1A. This was compared to 34% virus 

genome coverage by direct NGS. Phylogenetic 

analysis revealed that only 84% nucleotide sequence 

identity was shown between the newly 

identified enterovirus and the next most closely 

related virus. This work illustrates the utility 

of next generation sequencing and host nucleic 

acid subtraction in sequence-based detection 

and characterization of new viruses. 

n S2:4 

THE SALMONELLA IN SILICO TYPING 

RESOURCE (SISTR): RAPID ANALYSIS OF 

SALMONELLA DRAFT GENOME SEQUENCE 

DATA 

C. Yoshida 1 , P. Kruczkiewicz 2 , J. H. Nash 1 , E. 

N. Taboada 2 ; 

1 

Public Health Agency of Canda, Guelph, ON, 

CANADA, 2 Public Health Agency of Canda, 

Lethbridge, AB, CANADA. 

Salmonella is an important food safety and 

public health concern worldwide. Serotyping 

has historically been essential in human 

disease surveillance and outbreak prevention 

and control. Although laboratories around the 

16 

ASM Conferences


world rely on serotyping as a primary means 

of isolate characterization, there is significant 

consensus that current means of Salmonella 

subtyping often lack the specificity and discriminatory 

power required in the context of 

epidemiologic investigations. Whole-genome 

sequencing (WGS) is increasingly being adopted 

as a front-line laboratory tool, promising to 

revolutionize our ability to perform advanced 

pathogen characterization in support of enhanced 

public health surveillance and epidemiologic 

investigations. In an effort to overcome 

the significant barrier that exists for laboratories 

without the necessary bioinformatics infrastructure 

we have developed the Salmonella In 

Silico Typing Resource (SISTR), an open webaccessible 

bioinformatics platform for rapidly 

analyzing draft Salmonella genome sequence 

data. In addition to serovar prediction by 

genoserotyping, Multi-Locus Sequence Typing 

(MLST) and ribosomal MLST (rMLST), the 

SISTR platform incorporates a novel core genome 

MLST (cgMLST) scheme that we have 

developed, the first such scheme described for 

Salmonella. The platform incorporates metadata-driven 

visualizations to examine the phylogenetic, 

geospatial and temporal distribution of 

genome-sequenced isolates. The resource also 

incorporates a database comprising over 4,000 

publicly available genomes, allowing users to 

place their isolates in a broader phylogenetic 

and epidemiological context. We have used a 

dataset comprised of 4,188 finished genomes 

and WGS draft assemblies to examine correlation 

between an isolate’s cgMLST cluster 

and its serovar and show how phylogenetic 

context from cgMLST analysis can supplement 

the genoserotyping analysis to increase 

the accuracy of in silico serovar prediction 

to over 94.6%. As sequencing of Salmonella 

isolates at public health laboratories becomes 

increasingly common, rapid in silico analysis 

of minimally processed draft genome assemblies 

provides a powerful replacement for 

current methods of isolate characterization. 

The SISTR platform can be used to generate 

the requisite serovar information and subtyping 

data currently used by epidemiologists and 

public health practitioners, while also providing 

some of the genome-based analyses that 

are the primary motivation for a move towards 

WGS. This type of integrated analysis allows 

for continuity with historical serotyping and 

subtyping data as we transition towards the 

increasing adoption of genomic epidemiology 

in public health. The SISTR web-application is 

accessible online at https://lfz.corefacility.ca/ 

sistr-app/. 

n S2:5 

REVOLUTIONISING PUBLIC HEALTH 

REFERENCE MICROBIOLOGY USING WHOLE 

GENOME SEQUENCING: A CASE STUDY 

WITH SALMONELLA 

P. Ashton, S. Nair, T. Peters, A. Waldram, E. de 

Pinna, R. Elson, K. Grant, T. Dallman; 

Public Health England, London, UNITED 

KINGDOM. 

Background: Salmonella is a major human 

pathogen and a global public health issue. 

Currently, presumptive Salmonella isolates 

received by Public Health England (PHE) are 

typed by slide agglutination against specific 

anti-sera to determine their serotype based 

on their lipopolysaccharide and flagella. The 

microbiological typing data generated in 

the laboratory is fed into the public health 

workflow at PHE that involves the use of a 

sophisticated statistical algorithm for detection 

of pathogen outbreaks. A more discriminatory 

typing method would lead to more accurate 

and focussed public health investigation. Here, 

we address the question of whether whole 

genome sequencing (WGS) can improve on 

results obtained via this current workflow. 

Methods: Public Health England have adopted 

WGS for the routine identification and 

epidemiological investigation of Salmonella. 

The sequence type (ST) of each isolate is determined 

via a reference mapping approach. 

For the more common STs, the whole genome 

is analysed for Single Nucleotide Polymorphisms 

that are used to construct phylogenetic 



17


trees, giving the highest possible resolution. 

To date, more than 11000 isolates have been 

analysed in this fashion. Findings: There are 

three primary results to report. Firstly, as there 

is a link between underlying genetics and serotype, 

the serotype can be determined from 

the sequence type with 98.5% concordance. 

This allows for backwards compatibility with 

existing outbreak detection methods. Secondly, 

clusters of genetically related cases can be 

identified; between 01/04/2014 and 11/08/2014 

there were seven clusters of more than 10 Salmonella 

Enteritidis isolates within a 10 SNP 

cluster. Only one of these was identified as an 

outbreak by traditional means. These clusters 

were retrospectively investigated for common 

exposures. Finally, a WGS analysis of clusters 

identified based on traditional typing will be 

presented. Interpretation: Although WGS is 

being embraced and adopted by PHE, it is not 

being treated as a panacea for public health 

microbiology, but rather as a paradigm shift in 

microbiological typing which has the potential 

to transform, rather than replace, traditional 

epidemiological investigation. 

n S3:3 

BENCHMARK DATASETS FOR VALIDATING 

FOODBORNE OUTBREAK INVESTIGATIONS: 

INTEGRATING WGS AND PHYLOGENOMIC 

ANALYSES 

R. E. Timme 1 , H. Rand 1 , E. Trees 2 , R. Agarwala 

3 , S. David 1 , M. Shumway 3 , M. Simmons 4 , 

G. Tillman 4 , P. Bronstein 4 , H. Carleton 2 , S. 

Defibaugh-Chavez 4 , W. Klimke 3 , L. S. Katz 2 ; 

1 

US Food and Drug Administration, College 

Park, MD, 2 Center for Disease Control, Decatur, 

GA, 3 National Center for Biotechnology 

Information, Bethesda, MD, 4 USDA-FSIS, 

Athens, GA. 

Background: As US regulatory agencies begin 

to rely on whole genome sequencing (WGS) 

data and analyses for foodborne pathogen 

surveillance, and public health applications for 

WGS expand, a critical need has emerged for 

benchmark datasets of well-curated outbreaks. 

Such datasets can serve as validation standards 

for training purposes, ensuring analytical consistency 

among participating labs, and allowing 

researchers to compare parameter choices 

and understand the inherent biases of different 

methods and workflows. Having analytical 

consistency and the flexibility to use a variety 

of methods will give epidemiologists and regulators 

the best possible support for their efforts. 

What we have done: We identified one retrospective 

outbreak dataset for each of the most 

common foodborne pathogens (Salmonella, 

Listeria, and E.coli) that met criteria for wellvetted 

epidemiological data, sequence quality, 

and completeness of metadata. Chosen outbreaks 

represented the size (8-30 isolates) and 

degrees of sequence divergence seen in recent 

years. The isolates from which these genomes 

came are vouchered in culture collections at 

the FDA and CDC, allowing further validation 

of identified variants. Our standard format 

provides BioSample, SRA, and GenBank 

accession numbers, a classification of the isolates 

as in or out of the outbreak, a suggested 

reference genome, and a phylogenetic tree in 

Newick format. To ensure that labs can use the 

same materials for validating current or future 

analysis tools, we created detailed instructions 

to exactly replicate these datasets. Results: Using 

our detailed instructions, we downloaded 

each standardized dataset on a wide variety 

of Linux platforms (CentOS6, Ubuntu 14.4, 

Mac OS, etc) and verified that these downloads 

were both correct (using sha256sum values) 

and compatible with many current analysis 

methods for SNP finding and reconstructing 

phylogenies (CFSAN SNP Pipeline, Lyve- 

SET, BioNumerics, wgMLST, Wombac, kSNP, 

and SNVPhyl). We then created a strategy 

for comparing the results of different analysis 

methods. Conclusion: Government, university, 

and industry labs now have a shared baseline 

against which we can assess new methods, 

enabling researchers to work together more 

effectively and with greater confidence in each 

other’s results. The process of building this 

first benchmark dataset has helped four US 

18 

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government agencies (CDC, FDA, USDA, 

NCBI) work together and refine their methodologies 

together. Additional curated datasets 

can and should be incorporated into this schema: 

phylogenetic data are context-dependent: 

as more sequence data accumulate, we will 

need to revise our analyses. In this way, our 

work can ensure a sound footing for WGSbased 

regulatory decision-making. 

n S3:4 

INTEGRATING CORE GENOME 

PHYLOGENETIC RELATIONSHIPS AND 

ISOLATE GEOGRAPHIC DATA TO TRACE THE 

2012 NEISSERIA MENINGITIDIS OUTBREAK 

IN NEW YORK CITY 

M. R. Galac 1 , I. Ezeoke 2 , M. D. Krepps 3 , Y. 

Lin 2 , J. Kornblum 2 , H. S. Gibbons 3 , D. Weiss 2 , 

D. A. Janies 1 ; 

1 

University of North Carolina at Charlotte, 

Charlotte, NC, 2 New York City Department 

of Health and Mental Hygiene, Queens, NY, 

3 

Edgewood Chemical and Biological Center, 

Aberdeen, MD. 

Between 2012 and 2014, a serogroup C Neisseria 

meningitidis (Nm) outbreak was detected 

among men who have sex with men (MSM) 

in New York City (NYC). To determine if 

geographic paths of isolates from this outbreak 

were distinct, we compared them to previous 

and concurrent isolates of Nm from NYC. We 

sequenced 102 isolates (79 serogroup C) covering 

an 11 year period (2003-2013). Bacterial 

whole genomes were sequenced with Illumina 

at ~250x coverage, assembled de novo, and 

annotated by xBASE. OrthoMCL identified 

orthologous clusters in our 102 genomes plus 

14 complete Nm genomes from GenBank. 

Sequences from clusters containing a gene 

from every isolate were individually aligned 

with MAFFT and concatenated to produce 

one core genome. Based on this alignment, we 

employed RAxML to infer a phylogenetic tree 

using maximum likelihood optimality criterion 

which was then integrated with geocoded patient 

addresses. We then focused on 73 NYC 

genomes with geographic data that were not 

multiple samples from the same patient. To 

build a network of transmission events, we 

used PAUP* to calculate the ancestor descendent 

changes in geographic location on the tree 

and to count the changes and directionality of 

isolation location at ancestral nodes. This created 

a network of bacterial movement. At the 

NYC borough resolution, Brooklyn is integral 

to the movement of all Nm city-wide over the 

11 years examined. We then subdivided the 

boroughs into neighborhoods. We found that 

almost all transmission events were unique 

movements between neighborhoods and did 

not represent repeated or reciprocal movement 

of the bacteria from one neighborhood to another. 

The most closely related non-outbreak 

isolate to the monophyletic MSM outbreak 

isolates originated in Brooklyn-neighborhood- 

A then spread to Manhattan-neighborhood-B. 

From Manhattan-neighborhood-B, the infection 

moved to different neighborhoods in Manhattan, 

Brooklyn and the Bronx. This included 

Manhattan-neighborhood-C from which 6 

isolates with nearly identical core genomes 

radiated outward to additional neighborhoods 

in 4 boroughs. GeoGenes was used which assigns 

higher network betweenness to locations 

that are repeatedly the shortest path between 

all possible pairs of locations. We found that 

Manhattan-neighborhood-B and C were hubs 

for the spread of the MSM outbreak. In summary, 

whole genome sequencing, phylogenetic, 

and betweenness analyses allowed us 

to determine that the outbreak was the result 

of a single highly similar group of Nm and 

enabled us to track the spread of the bacteria 

throughout NYC. These analyses could be 

an integral component of the public health 

response against meningococcal transmission. 

The results can further elucidate the relation 

between cases and the phylogeographic related 

networks, allowing for more targeted and efficient 

interventions. 



19


n S3:5 

WHOLE GENOME SEQUENCING PROVIDES 

RAPID TRACEBACK OF CLINICAL TO 

FOOD SOURCES DURING A FOODBORNE 

OUTBREAK OF SALMONELLOSIS 

M. Hoffmann 1 , Y. Luo 1 , S. R. Monday 1 , T. 

Muruvanda 1 , D. Janies 2 , I. Senturk 3 , U. V. 

Catalyurek 3 , W. J. Wolfgang 4 , R. Myers 5 , P. S. 

Evans 1 , J. Meng 6 , M. W. Allard 1 , E. W. Brown 1 ; 

1 


Park, MD, 2 University of North Carolina, 

Charlotte, NC, 3 Ohio State University, Columbus, 

OH, 4 New York State Department of 

Health, Albany, NY, 5 Department of Health and 

Mental Hygiene, Baltimore, MD, 6 University of 

Maryland, College Park, MD. 

Salmonella serovar Bareilly is responsible for 

numerous outbreaks among humans. In 2012 

a widespread foodborne outbreak associated 

with scraped tuna imported from India occurred 

in the United States. A comparative 

genomic analysis within the serovar was 

performed to explore, on a global scale, how 

effectively whole-genome sequencing (WGS) 

can differentiate outbreak isolates of S. Bareilly 

from non-outbreak isolates sharing the 

same Xbal PFGE pattern. We sequenced, on 

different platforms, 100 S. Bareilly isolates 

including 41 isolates from the 2012 outbreak. 

A single isolate was sequenced on the Pacific 

Biosciences RS II Sequencer to determine the 

first complete genome sequence of S. Bareilly 

that served as the reference genome. Subsequent 

raw reads were mapped to this reference 

genome to build a single-nucleotide polymorphism 

(SNP) matrix and construct a phylogenetic 

tree. Pathogen genomes were linked 

with geography by projecting the phylogeny 

on a virtual globe. Using the phylogenetic tree 

and the pathogen metadata a transmission network 

was reconstructed for S. Bareilly. Using 

SNP analyses, we were able to distinguish 

and separate highly clonal S. Bareilly strains 

sharing the same XbaI PFGE pattern. The isolates 

from the recent 2012 outbreak clustered 

together, sharing only a few SNPs differences 

between them. Our results revealed a common 

origin for the outbreak strains, indicating that 

the patients in the U.S. were infected from 

sources originating at the India facility. In addition, 

we identified a unique arsenic resistance 

operon carried by many of these strains. Our 

data strongly suggests that WGS, combined 

with geographic mapping and the novel use of 

transmission networks for genetic data, vastly 

improves the rapid source tracking and surveillance 

of a bacterial outbreak that are critically 

important for characterizing outbreak dynamics 

and, ultimately, protection of the public 

health. 

n S3:6 

TRANSFORMING PUBLIC HEALTH 

MICROBIOLOGY IN THE UNITED STATES 

WITH WHOLE GENOME SEQUENCING (WGS) 

- PULSENET AND BEYOND 

E. K. Trees, H. Carleton, P. Gerner-Smidt; 

CDC, Atlanta, GA. 

Background: A number of different methods 

and technologies are used in public health 

laboratories to characterize foodborne bacterial 

pathogens ranging from phenotypic tests 

e.g., growth characteristics, serotyping and 

antimicrobial susceptibility testing, to PCR 

and other molecular methods for detection 

of e.g., virulence genes, and for subtyping in 

outbreak investigations e.g., pulsed-field gel 

electrophoresis (PFGE). This traditional strain 

characterization is labor and resource intensive 

and has a turn-around-time (TAT) of up to 

several months. Most of this information may 

be extracted from the genome sequence of any 

organism. With the introduction of next generation 

sequencing technologies and advances in 

bioinformatics it is now possible to characterize 

foodborne pathogens in a single workflow 

with a TAT of ≤ four working days in a costefficient 

manner. Methods: The Enteric Diseases 

Laboratory Branch at CDC is working 

with partners in federal agencies, public health 

20 

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laboratories in the states and internationally to 

build applications to extract information about 

genus, species, lineage, serotype, pathotype, 

virulence profile and antimicrobial resistance 

markers and subtyping using gene by gene approaches 

(whole genome multi-locus sequence 

typing, wgMLST) from the genome sequences. 

The PulseNet foodborne disease surveillance 

network infrastructure and analytical platform 

(BioNumerics, Applied Maths) and database 

will be used because of its versatility and 

because the public health laboratories have 

worked with it for > 15 years. Additionally, 

the use of this analytic software does not require 

extensive bioinformatics skills or local 

high performance computing capacity. The 

method will also be validated and will replace 

conventional CLIA related reference identification 

activities in the branch and public health 

laboratories. Results: WGS has successfully 

been tested for the US national surveillance 

of listeriosis since September 2013 and the 

wgMLST analysis has been implemented in 

the daily routine. The technology has proven 

its power supplementing traditional methods in 

outbreak investigations by adding phylogenetic 

relevance and increased resolution compared 

with current methods thereby enhancing case 

definitions and source tracking. The wgMLST 

method is currently being piloted in-house 

for surveillance of Campylobacteraceae, 

Shiga toxin-producing E. coli, and Salmonella 

with the remainder of foodborne bacteria to 

be added over the next three years. A betatesting 

pilot with the PulseNet public health 

laboratory partners will start in summer 2015. 

Conclusion: WGS will revolutionize the surveillance 

of both sporadic and outbreak related 

foodborne infections by replacing and enhancing 

current laboratory methods in use in public 

health laboratories. 

n S4:4 

SALMONELLA SEROTYPE DETERMINATION 

UTILIZING HIGH-THROUGHPUT GENOME 

SEQUENCING DATA 

S. Zhang 1 , Y. Yin 2 , M. Jones 3 , Z. Zhang 4 , B. 

Deatherage Kaiser 5 , B. Dinsmore 6 , C. Fitzgerald 

6 , P. Fields 6 , X. Deng 1 ; 

1 

University of Georgia, Griffin, GA, 2 Illinois 

Institute of Technology, Chicago, IL, 3 J. Craig 

Venter Institute, Rockville, MD, 4 University of 

Michigan, Ann Arbor, MI, 5 Pacific Northwest 

National Laboratory, Richard, WA, 6 Centers 

for Disease Control and Prevention, Atlanta, 

GA. 

Serotyping forms the basis of national and 

international surveillance networks for Salmonella, 

one of the most prevalent foodborne 

pathogens worldwide. Public health microbiology 

is currently being transformed by whole 

genome sequencing (WGS) which opens the 

door to serotype determination using WGS 

data. SeqSero (www.denglab.info/SeqSero) 

is a novel web-based tool for determining 

Salmonella serotypes using high-throughput 

genome sequencing data. SeqSero is based 

on curated databases of Salmonella serotype 

determinants (rfb gene cluster, fliC and fljB 

alleles) and is predicted to determine serotype 

rapidly and accurately for nearly the full spectrum 

of Salmonella serotypes (more than 2,300 

serotypes), from both raw sequencing reads 

and genome assemblies. The performance of 

SeqSero was evaluated by testing: 1) raw reads 

from genomes of 308 Salmonella isolates of 

known serotype; 2) raw reads from genomes 

of 3,306 Salmonella isolates sequenced and 

made publicly available by GenomeTrakr, a 

U.S. national monitoring network operated 

by the Food and Drug Administration; and 3) 

354 other publicly available draft or complete 

Salmonella genomes. We also demonstrated 

Salmonella serotype determination from raw 

sequencing reads of fecal metagenomes from 

mice orally infected with this pathogen. Seq- 

Sero can help to maintain the well-established 



21


utility of Salmonella serotyping when integrated 

into a platform of WGS-based pathogen 

subtyping and characterization. 

n S4:5 

PATRIC PIPELINE 

F. Xia 1 , T. Brettin 2 , S. Boisvert 2 , N. R. Conrad 2 , 

J. J. Davis 1 , T. Disz 1 , J. Edirisinghe 2 , R. A. Edwards 

3 , C. Henry 1 , R. W. Kenyon 4 , D. Machi 4 , 

C. Mao 4 , G. J. Olsen 5 , R. Olson 2 , R. Overbeek 6 , 

B. Parrello 6 , G. D. Pusch 6 , M. P. Shukla 2 , B. W. 

Sobral 4 , R. L. Stevens 1 , V. Vonstein 6 , A. Warren 

4 , R. Will 4 , H. Yoo 4 , A. R. Wattam 4 ; 

1 

University of Chicago, Chicago, IL, 2 Argonne 

National Laboratory, Lemont, IL, 3 San Diego 

State University, San Diego, CA, 4 Virginia 

Tech, Blacksburg, VA, 5 University of Illinois 

at Urbana and Champaign, Urbana, IL, 6 Fellowship 

for Interpretation of Genomes, Burr 

Ridge, IL. 

Recent advances in DNA sequencing technology 

accompanied by plummeting per-base cost 

is making sequence-based applications more 

amenable. While a plethora of bioinformatics 

databases and workflows exist, their capabilities 

are often hampered by the inconsistent 

use of analysis tools. PATRIC, the NIAIDfunded 

comprehensive bacterial bioinformatics 

resource, has integrated more than 30,000 

consistently annotated prokaryote genomes 

with a focus on human pathogenic species. 

Here we present PATRIC’s new computational 

services that support the assembly, annotation 

and metabolic modeling of user-supplied 

genomes in the same consistent fashion. These 

services, integrated with PATRIC’s collections 

of specialty genes such as antibiotic resistance 

determinants and virulence factors, will enable 

users to rapidly process newly sequenced 

pathogens and investigate key pathogenic 

determinants in foodborne outbreaks using the 

powerful visualization and comparative analysis 

tools in PATRIC. We have implemented 

the new services with three principles in mind. 

(1) Controlled vocabulary. At the heart of 

PATRIC’s annotation service is a controlled 

vocabulary for functional annotation derived 

from the curated subsystems and protein families 

in the RAST and SEED systems [2]. Similarly, 

the new model reconstruction service 

relies on our curated biochemistry data [5]. 

These curation efforts ensure newly sequenced 

genomes can be automatically annotated and 

modeled and readily compared with existing 

reference data. (2) Modular design. In genome 

assembly as well as other bioinformatic analyses, 

there is often no single tool best suited 

for all occasions [4]. We have added support 

for more than 30 tools for error correction, 

contig assembly, scaffolding, contig evaluation, 

consensus building, gene calling, overlap 

removal, as well as many custom algorithms 

[3]. These modules are condensed into a few 

curated workflows to ensure convenient and 

efficient execution as well as consistent quality 

control. (3) Integrated analysis. The new 

workspace allows users to upload their own 

data for analysis, and upon completion the 

private results are immediately integrated into 

PATRIC. This enables users to take advantage 

of PATRIC’s data (drug targets, omics, AMR 

and other clinical metadata) and comparative 

tools (protein family sorter, phylogeny, heat 

maps, etc). In addition to these services, we 

are actively building support for batch analysis 

and SNP-level comparative analysis for closely 

related genomes. URL: https://www.patricbrc. 

org. References: [1] Gillespie, et al. “PATRIC: 

... (2011). [2] Overbeek, et al. “The SEED 

... (RAST).” Nucleic acids research (2014): 

D206-D214. [3] Brettin, et al. “RASTtk ...” 

Scientific reports 5 (2015). [4] Earl, et al. “Assemblathon 

...” Genome research (2011). [5] 

Henry, et al. “High-throughput ... models.” 

Nature biotechnology (2010). 

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n S4:6 

CFSAN SNP PIPELINE: A WHOLE GENOME 

SEQUENCE DATA ANALYSIS PIPELINE FOR 

FOOD-BORNE PATHOGENS 

Y. Luo, J. Pettengill, J. Baugher, H. Rand, S. 

Davis; 

FDA/CFSAN, College Park, MD. 

In support of the analysis of whole genome sequence 

data (WGS) for closely related pathogens 

in food-borne outbreaks, the Center for 

Food Safety and Applied Nutrition (CFSAN) 

at the FDA has developed a reference-based 

software pipeline for high quality SNP identification 

and analysis. This software pipeline 

combines into a single package the mapping of 

WGS reads to a reference genome, processing 

of those mapping files, identification of variant 

sites, and production of a SNP matrix. Additional 

features include a summary table of the 

results, soft-links to minimize data storage, and 

the ability to switch between workstations and 

computer clusters with minimal effort. The CF- 

SAN SNP Pipeline is currently used in production 

mode to analyze WGS data from isolates 

related to food-borne illnesses. The pipeline is 

used when outbreak investigations are ongoing 

to link samples and to provide information for 

decision-makers. It is also used retrospectively 

to aid in the analysis of closed outbreaks. The 

CFSAN SNP Pipeline is reference-based, 

and so a reference must be provided. Isolate 

sequence data must be in fastq format but can 

either be paired-end or single-read data. All 

analysis steps are run automatically, and only 

depend on the proper organization of the input 

files and identification of a suitable reference. 

Additionally, each of the analysis steps can be 

run using individual shell scripts. The addition 

of new samples is very straightforward, and result 

files from previous portions of the analysis 

that do not need to be regenerated are reused. 

This greatly reduces the computational time 

when adding new samples as the mapping and 

pileup steps are not redone. The pipeline will 

run without problems on current workstations, 

and will run on high performance computing 

clusters with either Torque or Grid Engine job 

schedulers. The CFSAN SNP Pipeline is written 

in a combination of Bash and Python. The 

code is designed to run on Linux platforms 

with bash and python. BioPython must be 

installed in tandem with three executable software 

dependencies, Bowtie2, SAMtools, and 

VarScan. Substantial effort has been devoted to 

making the software robust, well-documented, 

and easy to use. The following links provide 

for access to the source code, the documentation, 

and the Python package. Also provided is 

the current publication reference. Source code: 

https://github.com/CFSAN-Biostatistics/snppipeline. 

Documentation: http://snp-pipeline. 

rtfd.org. PyPI package: https://pypi.python. 

org/pypi/snp-pipeline. Reference publication: 

Pettengill JB, Luo Y, Davis S, Chen Y, Gonzalez-Escalona 

N, Ottesen A, Rand H, Allard 

MW, Strain E An evaluation of alternative 

methods for constructing phylogenies from 

whole genome sequence data: A case study 

with Salmonella. 

n S4:7 

ASSEMBLING WHOLE GENOMES FROM 

MIXED MICROBIAL COMMUNITIES USING 

HI-C 

I. Liachko 1 , J. N. Burton 1 , L. Sycuro 2 , A. H. 

Wiser 2 , D. N. Fredricks 2 , M. J. Dunham 1 , J. 

Shendure 1 ; 

1 

University of Washington, Seattle, WA, 2 Fred 

Hutchinson Cancer Research Center, Seattle, 

WA. 

Assembly of whole genomes from next-generation 

sequencing is inhibited by the lack of 

contiguity information in short-read sequencing. 

This limitation also impedes metagenome 

assembly, since one cannot tell which sequences 

originate from the same species within 

a population. We have overcome these bottlenecks 

by adapting a chromosome conformation 

capture technique (Hi-C) for the deconvolution 

of metagenomes and the scaffolding of de novo 

assemblies of individual genomes. In modeling 

the 3D structure of a genome, chromosome 



23


conformation capture techniques such as Hi-C 

are used to measure long-range interactions of 

DNA molecules in physical space. These tools 

employ crosslinking of chromatin in intact 

cells followed by intra-molecular ligation, 

joining DNA fragments that were physically 

nearby at the time of crosslink. Subsequent 

deep sequencing of these DNA junctions generates 

a genome-wide contact probability map 

that allows the 3D modeling of genomic conformation 

within a cell. The strong enrichment 

in Hi-C signal between genetically neighboring 

loci allows the scaffolding of entire chromosomes 

from fragmented draft assemblies. 

Hi-C signal also preserves the cellular origin 

of each DNA fragment and its interacting partner, 

allowing for deconvolution and assembly 

of multi-chromosome genomes from a mixed 

population of organisms. We have used Hi-C 

to scaffold whole genomes of animals, plants, 

fungi, as well as prokaryotes and archaea. We 

have also been able to use this data to annotate 

functional features of microbial genomes, such 

as centromeres in many fungal species. Additionally, 

we have applied our technology to 

diverse metagenomic populations such as craft 

beer, bacterial vaginosis infections, soil, and 

tree endophyte samples to discover and assemble 

the genomes of novel strains of known 

species as well as novel prokaryotes and 

eukaryotes. The high quality of Hi-C-based 

assemblies allows the simultaneous closing of 

numerous unculturable genomes, placement of 

plasmids within host genomes, and microbial 

strain deconvolution in a way not possible 

with other methods. Reference: Burton JN*, 

Liachko I*, Dunham MJ, Shendure J. Specieslevel 

deconvolution of metagenome assemblies 

with Hi-C-based contact probability maps. G3. 

2014, May 22;4(7):1339-46. 

n S6:3 

NEXT GENERATION SEQUENCING OF 

BRUCELLA MELITENSIS ISOLATES FROM 

KUWAIT AND COMPARATIVE GENOME 

ANALYSES 

A. S. Mustafa, F. Shaheed, N. Habibi, M. W. 

Khan; 

Kuwait University, Jabriya, KUWAIT. 

Human brucellosis is a zoonotic disease of 

worldwide occurrence. In Kuwait, almost all 

cases are caused by Brucella melitensis. Three 

different strains (biovars) of B. melitensis have 

been identified using classical techniques but 

the presence of very limited variation across 

strains makes it difficult to identify a particular 

strain using the classical molecular methods, 

e.g. PCR and Sanger sequencing. The aim of 

this study was to exploit the potential of next 

generation sequencing to identify the strain(s) 

of B. melitensis present in Kuwait, and also 

to find the extent of differences among the 

isolates of the same strain by comparative 

genome analyses. B. melitensis were isolated 

from 15 patients suspected of human brucellosis. 

The bacterial colonies from culture 

plates were suspended in saline and heated at 

95°C for 10 minutes. DNA released from the 

bacterial cells were purified using the QIAamp 

DNA Mini Kit (Qiagen). The isolated DNA 

were quantitated and checked for purity using 

a spectrophotometer (Epoch) and a fluorometer 

(Qubit), respectively. DNA libraries were 

prepared using the Nextera XT DNA Sample 

Preparation Kit (Illumina) and sequenced using 

the next generation sequence platform of 

MiSeq (Illumina) using standard procedures. 

The obtained sequence files were aligned to 

the sequences of three known biovars of B. 

melitensis available in the NCBI data base, 

i.e. biovar 1 str. 16M, biovar 2 str. 63/9, and 

biovar 3 str. Ether. The alignment and variant 

calling were performed using ‘bwa mem’ 

and SAMtools/VCFtools, respectively. The 

results showed that the genome size of all the 

isolates was around 3.3 mega base pairs, and 

24 

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all of them belonged to B. melitensis biovar 

2 str. 63/9. A neighbor-joining tree analysis 

identified one of the isolates as an outlier. Furthermore, 

variations (SNPs and indels) were 

spread all over the genome; but 138 SNPs 

were common among the 14 isolates, supporting 

the same ancestral origin. In addition, 

SNPs (2 - 478) unique to each isolate were 

also identified, which divided the B. melitensis 

biovar 2 into two major variant groups. In 

conclusion, this study suggest that biovar 2 is 

the most prevalent biovar of B. melitensis in 

Kuwait. Furthermore, at least two major variant 

groups exist within biovar 2. Supported 

by Kuwait University Research Sector grant 

SRUL02/13. 

n S6:4 

MICROBIAL GENOMIC TAXONOMY AT 

GENBANK 

S. Federhen; 

NCBI, Bethesda, MD. 

Incorrectly identified genomes at GenBank 

are a problem for users of the data. Some 

genomes are submitted with incorrect species 

identifications. Others were correctly identified 

when they were submitted but should now 

be updated based on a subsequent taxonomic 

publication, for example the description of a 

new species. GenBank has traditionally relied 

on the submitters to provide the correct 

taxonomic identifications for their sequence 

submissions. Two developments have combined 

to change this situation in the domain 

of microbial genomes. First, the curation of 

type material in the NCBI taxonomy database 

allows us to flag sequences from type in the 

nucleotide and genome domains of Entrez. 

Second, current sequencing technology makes 

it fast and easy to generate microbial genomes. 

It has been clear for some time that the current 

paradigm of species delimitation by 16S rRNA 

sequence and DNA-DNA hybridization (DDH) 

would eventually be replaced with a model 

based on whole genome analysis. We present 

a proposal to find and correct misidentified 

genomes based on average nucleotide identity 

(ANI) from type and proxytype. Sequences 

from type are reliably identified (by definition) 

once we have verified that they are free from 

contamination and are actually from the strain 

with which they are annotated. All other identifications 

are a matter of opinion, and will be 

subject to verification. We have genomes from 

type (both finished and WGS) for 4000 species, 

including 3500 bacteria. This represents 

25% of bacterial species with validly published 

names. The other 75% of bacterial species 

will generally have an assortment of short sequences 

from type in GenBank - at least a 16S 

sequence, but often more. These sequences are 

used to probe our existing genomes and predict 

where the genome from type will appear once 

we do get one. In many cases we can designate 

a proxy for the missing type from among 

the genomes that we do have - we call these 

‘proxytype’ genomes. Taken together, these 

genomes from type and proxytype represent a 

scaffold of reliably identified sequences that 

we can use in conjunction with some simple 

genome-wide comparison measures to validate 

the identifications in our other genomes. 

Once we have identified genomes that need 

taxonomic updates, we plan to correct the entries, 

add a structured comment detailing the 

evidence for the update, and notify the submitters 

of the change. This represents a significant 

change in policy for GenBank - a new genomic 

paradigm for validating taxonomic identifications, 

some new types of analysis, as well as 

a shift in the boundary for database-driven 

source feature updates. We convened a workshop 

to present the proposal, with representation 

from a broad spectrum of the bacterial 

taxonomic community (GenBank genomic 

taxonomy workshop, 12-13 May 2015). This 

group unanimously endorsed our genomic approach 

to validating taxonomic identifications 

in genomes at GenBank. 



25


n S7:2 

A BIOSURVEILLANCE ANALYSIS PIPELINE 

FOR GENOMIC SEQUENCE DATA 

C. Olsen 1 , K. Qaadri 1 , H. Shearman 2 , R. Moir 2 , 

M. Kearse 2 ; 

1 

Biomatters, Inc., Newark, NJ, 2 Biomatters, 

Ltd., Auckland, NEW ZEALAND. 

Next-generation sequencing (NGS) approaches 

have numerous applications for biosurveillance 

programs and outbreak investigation. 

However, there are significant challenges for 

analyzing the data accurately without the aid 

of high performance compute resources in a 

timely fashion. Often times the users are not 

bioinformaticians who are comfortable running 

sequence analysis pipelines. Biomatters’ 

Geneious R9 is a bioinformatics software 

platform that allows researchers the use of 

industry-leading algorithms for their genomic 

and protein sequence analyses. Geneious offers 

a comprehensive suite of functions, including 

a robust collection of peer-reviewed tools, 

that enable researchers to be more efficient 

with their sequence analysis workflows. The 

recent addition of the 16S Biodiversity tool 

and Sequence Classifier plugin provides tools 

which can be incorporated into an easy to use 

pathogen identification workflow. The 16S 

Biodiversity tool identifies high-throughput 

16S rRNA amplicons from environmental 

samples using the RDP database, and visualizes 

biodiversity as an interactive chart using 

a secure web viewer. The Sequence Classifier 

plugin taxonomically classifies an organic 

sample by how similar its DNA is to your 

own database of known sequences using a 

BLAST-like algorithm with multiple loci and 

trees to assist with identification. By utilizing 

Geneious R9, biologists can easily streamline 

their sequence analysis workflows for mixed 

sample analysis. This demonstration is for a 

sequence analysis pipeline for identification of 

bacterial pathogens from mixed metagenomic 

data generated from outbreaks. The pipeline 

can also be extended to include eukaryotic and 

fungal pathogens. 

n S7:3 

A UNIVERSAL WHOLE GENOME 

SEQUENCING APPROACH USING WHOLE 

GENOME MLST AND WHOLE GENOME SNP 

ANALYSIS IN THE CLOUD 

H. Pouseele, K. De Bruyne, B. Pot, K. Janssens; 

Applied Maths NV, Sint-Martens-Latem, BEL- 

GIUM. 

Introduction: While whole genome sequencing 

(WGS) is becoming very attractive for 

research as well as for routine analyses, current 

challenge is to extract whole genome typing 

information relevant for e.g. outbreak surveillance 

without the need for cluster facilities or 

highly specialized staff. Methodology: Here 

we present a cloud-based, high throughput 

(HT) environment for WGS data processing. 

Raw sequences are processed with standardized 

and validated pipelines to produce WG 

multi-locus sequence typing (wgMLST) and 

WG single nucleotide polymorphism (wg- 

SNP) results. Whereas wgMLST is typically 

used to identify potential outbreak clusters, 

wgSNP analysis is considered useful for final 

subtyping. The wgMLST pipeline uses WGS 

data (assembled or not) to perform MLST on 

a genome-wide scale. For each sample, locus 

presence is analyzed and allelic variants are 

determined. Per locus, new sequences are 

submitted, curated and assigned new allele 

numbers. WgMLST provides the possibility to 

obtain traditional typing results such as MLST, 

rMLST, etc, yielding full compatibility with 

former typing schemes, without additional 

cost or time delay. A ‘pan-genome’ wgMLST 

scheme has the advantage over the core genome 

of maximizing resolution and leads to 

stability, as adding new samples does not influence 

existing information. The core schema is 

implemented as a subschema of the pan-genome 

and was shown to have a high epidemiological 

relevance and stability, necessary for 

long-term surveillance and outbreak investigation. 

For the wgSNP approach, a dual reference-based 

approach is used: reads are mapped 

26 

ASM Conferences


on organism- and outbreak-specific references, 

yielding maximum resolution for detecting 

outbreak isolates. Once allele assignments and/ 

or SNPs are calculated, traditional BioNumerics® 

analysis tools are used for phylogenetic, 

statistical and comparative follow-up analyses. 

Access to the Amazon cloud is integrated in 

BioNumerics®, allowing easy point-and-click 

analysis. Confidential metadata remain at all 

times at the local BioNumerics® database, 

while anonymous WGS data are processed 

in the cloud. Results: We will evaluate the 

wgMLST and wgSNP approaches for L. monocytogenes 

and Salmonella using the Amazon 

cloud solution. wgMLST combined with wg- 

SNP analysis identified reliably strains belonging 

to documented outbreaks. Public NCBI 

SRA data will provide additional sequences 

that add context to more precisely understand 

and position the outbreak. Conclusion: NGS 

combined with automated data analysis and 

interpretation tools holds great promise for 

rapid, accurate and comprehensive identification 

of outbreaks. BioNumerics® 7.6* together 

with its scalable HT calculation environment, 

offers a powerful, user-friendly, easy-access 

environment where both wgMLST and wgSNP 

analyses can be performed and interpreted, 

lowering the thresholds for the use of WGS in 

routine applications. 

n S7:4 

TYPING AND EPIDEMIOLOGICAL 

CLUSTERING OF COMMON PATHOGENS 

BASED ON WHOLE GENOME NGS DATA 

A. Materna, K. Einer-Jensen, P. Liboriussen, 

J. Johansen, L. Schauser, A. C. Materna; 

QIAGEN, Aarhus, DENMARK. 

Next generation sequencing (NGS) data from 

whole pathogen genomes is frequently used for 

enhanced surveillance and outbreak detection 

of common pathogens. Version 1.5 of the CLC 

Microbial Genomics Module for CLC Genomics 

Workbench and CLC Genomics Server 

introduces new functionality for molecular 

typing and epidemiological analysis of bacterial 

isolates. The latest update to the module 

enables the user to perform stepwise (tool-bytool) 

analysis or to take advantage of included 

multistep workflows. With a few clicks workflows 

can be optimized for routine analysis of 

a specific pathogen or outbreak. New features 

include for instance streamlined tools for NGSbased 

Multilocus Sequence Typing (MLST), 

resistance typing, as well as detection of genus 

and species information. New tools for phylogenetic 

tree reconstruction generate trees based 

on single nucleotide polymorphisms (SNPs) or 

infer K-mer trees from NGS reads or genomes. 

A new table format, acting as a database, collects 

typing results and associates these results 

with metadata such as sample information, 

geographic origin, treatment outcome, etc. Results 

generated using e.g. MLST and resistance 

typing can furthermore be associated with the 

original sample metadata. Users can filter for 

results and metadata to find and select relevant 

subsets of samples for downstream analysis. 

Results and metadata available during tree 

generation can further be used to explore phylogeny 

in the context of this epidemiologically 

relevant information. Version 1.5 of the CLC 

Microbial Genomics Module aims to facilitate 

tasks commonly carried out during outbreak 

investigation, such as typing or source tracking 

based on whole genome data. Preconfigured 

workflows and simple deployment via integration 

into the widely used CLC Genomics 

Workbench and CLC Genomics Server ecosystem 

offer a user-friendly platform for scientists 

engaged in outbreak prevention and control. 

n S7:5 

WGSA.NET: WHOLE GENOME SEQUENCE 

ANALYSIS 

D. M. Aanensen 1 , S. Argimon 2 , C. A. Yeats 1 , A. 

Fedosejev 1 , C. Glasner 2 , R. Goater 2 , D. Garcia 

2 , J. NT 2 ; 

1 

Imperial College London, London, UNITED 

KINGDOM, 2 Centre for Genomic Pathogen 

Surveillance, Wellcome Genome Campus, 

Cambridgeshire, UNITED KINGDOM. 



27


WGSA.net [1] provides an intuitive interface 

for the uploading, processing, clustering and 

visualization of microbial genomic assemblies. 

Uploaded data are run through a number of 

analysis modules, including MLST, detection 

of genes and variants for identifying 

potential antibiotic resistance and virulence, 

and profiling of gene family membership for 

the production of clustering and identification 

of strict core and non-core genes. Metadata 

included during upload includes required (eg 

location and date) and non-required datatypes. 

Once processed, assemblies are presented 

to users in, firstly, a population context and 

then secondly, through selection of specific 

sub-parts of a population, relatedness to other 

very closely related genomes allowing further 

investigation by a user (eg outbreak detection). 

All results are available to download allowing 

further investigation and utility. An intuitive 

user interface presenting inferred clustering 

(using PhyloCanvas), geographic location (using 

Google maps) and data tables, allows the 

overlay on clustering of both user metadata 

and also results of anlaysis modules, based on 

the visualization tool microreact.org [2]. Wgsa. 

net is available via the web and runs in any 

modern browser. [1] http://www.wgsa.net [2] 

http://microreact.org 

n S7:6 

SEQSPHERE+ SOFTWARE FOR 

PROSPECTIVE BACTERIAL GENOMIC 

SURVEILLANCE AND RESISTOME OR 

VIRULOME ANALYSIS 

J. Rothgänger; 

Ridom GmbH, Münster, GERMANY. 

SeqSphere+ was introduced in the year 2013 

(Nat Biotechnol. 31: 294, 2013) and supports 

genome-wide allele and single nucleotide 

variant (SNV) calling from whole genome 

sequence (WGS) data either on core genome 

and/or accessory genome level. However, 

the recommended (initial) analysis is a core 

genome MLST (cgMLST) allele typing as a 

global and uniform nomenclature service is 

maintained to ensure for a ‘molecular typing 

Esperanto’. A number of cgMLST schemes using 

the software have been published recently; 

e.g., for M. tuberculosis (JCM 52: 2479, 2014) 

or L. monocytogenes (JCM 54: Jul 1. pii: 

JCM.01193-15, 2015 [ahead of print]). These 

schemes are available for download within 

the software. In addition, users can define 

with the included cgMLST Target Definer on 

the fly own ‘ad hoc’ schemes. Furthermore, 

the software supports setup of resistome and 

virulome schemes. Place, time, ‘person’, and 

type dimensions can be visualized with built-in 

geographic information system (GIS), epicurve, 

coloring, and phylogenetic tree (among 

others the minimum spanning tree algorithm is 

supported) functionality. All dimension views 

are inter-linked and exportable in publication 

quality SVG format. Finally, the software 

can generate sample-reports for senders with 

a summary of the analytical results (e.g., 

MLST, cgMLST), QC/QA data, and extensive 

documentation of the analytical procedure. 

SeqSphere+ is designed for distributed workgroups 

(client/server model with encryption of 

all data in transmission) and requires no scripting 

or bioinformatics skills. It allows automatic 

processing and analyzing of next generation 

sequence (NGS) and Sanger data for prospective 

bacterial genomic surveillance. De novo 

assembly or reference mapping of NGS read 

data is achieved with the incorporated Velvet 

or BWA tools, respectively. Defining and starting 

a pipeline to down-sample, assemble, and 

analyze data processes NGS data fully automated, 

e.g., by fetching the raw reads from a 

benchtop-sequencer as soon as data are generated. 

For speeding- and scaling-up the analysis 

simply an additional computer can be added 

for processing data in parallel. Experiment 

and epidemiologic meta-data are stored in an 

integrated searchable SQL database together 

with the DNA data. New sequence entries can 

be compared against stored data and automatic 

cluster alerts of possible outbreaks can be triggered. 

Meta- and sequence data can be (semi)- 

automated submitted to the EBI ENA archive. 

A backup plan for all data can be defined and 

28 

ASM Conferences


automatically executed. An audit trail of all 

user actions including the execution of analysis 

and (manual) data editing is also maintained. 

SeqSphere+ is commercially available from 

Ridom GmbH (Münster, Germany) for Windows 

and Linux operation systems. Further 

information and a request for a fully functional 

trial version can be found at http://www.ridom. 

de/seqsphere/. 

n S7:7 

NULLARBOR: RAPID ANALYSIS OF 

BACTERIAL OUTBREAK SEQUENCE DATA 

T. Seemann, J. Kwong, D. M. Bulach, B. P. 

Howden; 

University of Melbourne, Melbourne, AUS- 

TRALIA. 

The modern public health microbiology laboratory 

has embraced whole genome sequencing 

as the primary assay for pathogen surveillance 

and outbreak analysis. Here we present Nullarbor, 

a software pipeline for turning a set 

of isolate sequence data into a single report 

summarizing the key information about each 

isolate and the relationship between isolates. 

This report is then used by epidemiologists 

and laboratory staff to make a final recommendation 

or actionable decision. Nullarbor 

first performs quality control on each isolate. 

The reads are adaptor and quality trimmed 

then measured for yield whereby low coverage 

isolates are quarantined. The cleaned reads are 

scanned with Kraken to identify the likely species 

and the level of contamination, and offtarget 

or mixed samples are quarantined. The 

read are then de novo assembled into contigs 

using MegaHit and annotated using Prokka 

(*). The contigs are also used to calculate the 

MLST with the mlst tool (*) and the resistome 

profile using ABRicate (*). Next the relationship 

between the isolates is determined. Each 

isolate is aligned against a reference genome 

and variants called using Snippy (*) and a 

core genome SNP alignment produced. The 

reference can be provided or the isolate assemblies 

can be used, and the SNP alignment 

may be optionally filtered of recombination 

using ClonalFrameML. A phylogenetic tree is 

created using FastTree and various statistics 

including SNP distances are calculated. The 

annotated genomes are used to calculate the 

pan-genome with Roary and visualized using 

FriPan (*). The use of the pan-genome augments 

the investigation with data on mobile 

genetic elements otherwise missed by core 

SNP analysis. Nullarbor then generates a report 

which can rendered in multiple formats 

using PanDoc. The Nullarbor pipeline follows 

the Unix philosophy of using and combining 

existing standalone tools in an efficient manner. 

The input is a spreadsheet-like text file, 

and the default output is a clean HTML report. 

The pipeline utilises the Unix make dependency 

system to enable highly parallel analyses 

on a single machine, to maximize efficiency 

for the typical laboratory having only a single 

bioinformatics workstation. It is currently used 

by the Microbiological Diagnostics Unit Public 

Health Laboratory in Australia routinely for all 

investigations. Nullarbor is open-source software 

released under a GPL licence and runs 

on Unix systems. It is available from https:// 

github.com/tseemann/nullarbor. Simple installation 

of Nullarbor and its dependencies may 

be achieved via Homebrew Science https:// 

github.com/Homebrew/homebrew-science. 

Future plans include a Docker image based on 

CoreOS, and a virtual machine image for use 

with Amazon, OpenStack and VirtualBox. (*) 

denotes software written by the first author. 

n S7:8 

ENTEROBASE: A POWERFUL, USER- 

FRIENDLY ONLINE RESOURCE FOR 

ANALYSING GENOMIC VARIATION 

N. Alikhan, M. Sergeant, Z. Zhou, A. Millard, 

M. J. Pallen, M. Achtman; 

University of Warwick, Coventry, UNITED 

KINGDOM. 

The decreasing cost of next-generation sequencing 

promises to revolutionise molecular 

epidemiology. For example, Salmonella en- 



29


terica has over 40,000 sets of short reads available 

within GenBank. Sequencing at this scale 

can potentially encompass sufficient genomic 

variation across a species to elucidate evolutionary 

lineages and virulence factors such 

as antimicrobial resistance. These data could 

provide a basis for a global perspective of 

microbial pathogens, which not only identifies 

outbreak-associated strains but also long-term 

transmission trends and novel environmental 

reservoirs. However, the paucity of analytical 

tools to handle data at such scales remains a 

key limitation. Here we present EnteroBase, 

which addresses such logistical challenges and 

facilitates access to data for a general audience 

of clinicians and epidemiologists. EnteroBase 

is a user-friendly online resource, where users 

can upload their own sequencing data for 

de novo assembly by a stream-lined pipeline. 

The assemblies are used for calling MLST and 

wgMLST patterns, allowing users to compare 

their strains to publically available genotyping 

data from other EnteroBase users, GenBank 

and classical MLST databases. EnteroBase 

was designed to exclude low quality data. Assemblies 

are screened for contamination, poor 

sequencing quality and misassemblies, and 

linked to standardised and curated strain metadata, 

including geographic, host and temporal 

information. Curation will be by experts from 

the microbial research community. Curated 

metadata will support exploring associations 

between strain genotype and phenotypic or 

geographic features. EnteroBase will also 

include SNP and pan-genome based genome 

comparisons, including virulence factors and 

antimicrobial resistance. Visualisation approaches 

will be integrated, including minimal 

spanning trees and geographic mapping. Many 

approaches implemented in EnteroBase will 

also be applicable to metagenomic data, which 

we intend implementing. We will provide 

integration with existing databases, such as 

BIGSdb, and existing analytical tools, such as 

Bionumerics, in addition to providing a standardized 

ontology implemented through APIs 

allowing for easy interoperability with other 

similar resources. EnteroBase is accessible 

through all modern web browsers, and is available 

at http://enterobase.warwick.ac.uk. 

n S7:9 

SNAPPERDB: A SCALABLE DATABASE FOR 

ROUTINE SEQUENCING OF BACTERIAL 

ISOLATES 

P. Ashton, A. Al-Shahib, A. Jironkin, A. Underwood, 

T. Dallman; 

Public Health England, london, UNITED 

KINGDOM. 

As routine sequencing of bacterial isolates becomes 

a reality, scalable data storage solutions 

are required. Analysis of bacterial populations 

often requires re-computing the likely variants 

across all isolates in a dataset and this is 

not feasible in rapidly growing, large datasets. 

Public Health England has embarked on the 

implementation of high throughput sequencing 

for the surveillance of several important 

human pathogens and aims to leverage the 

high discriminatory power of single nucleotide 

polymorphisms (SNPs) to detect linked 

cases and outbreaks of infectious disease. For 

this software demonstration we will present 

SnapperDB, a set of tools to store and query 

bacterial variant data to facilitate reproducible 

and scalable analysis of bacterial populations. 

The use of a relational database enables highly 

efficient queries that can generate SNPs in the 

core genome for phylogenetic analysis, or the 

whole genome consensus sequence for output 

into e.g. recombination detection tools. As part 

of SnapperDB, a pairwise distance matrix is 

maintained from which hierarchical clustering 

is performed. This allows the assignment of a 

‘SNP address’, which locates the isolate within 

‘SNP space’ and enables the rapid identification 

of closely related isolates. SnapperDB is 

a stable application which is easily installed 

from the Github repository by Unix power 

users. For those who are less familiar with the 

command line, there are pre-configured instances 

on both the MRC CLIMB (http://www. 

climb.ac.uk/) and Amazon Web Services cloud 

computing infrastructures. 

30 

ASM Conferences


n S7:10 

PHYLOGENETIC RECONSTRUCTION AND 

OUTBREAK INVESTIGATION USING IRIDA 

AND SNVPHYL 

A. Petkau 1 , P. Mabon 1 , L. S. Katz 2 , F. Bristow 1 , 

T. Matthews 1 , J. Adam 1 , J. Cabral 3 , C. Sieffert 1 , 

N. Knox 1 , D. Dooley 4 , E. Griffiths 5 , G. Winsor 5 , 

M. R. Laird 5 , M. Courtot 5 , P. Kruczkiewicz 6 , 

E. Taboada 6 , J. A. Carriço 7 , A. Keddy 8 , R. G. 

Beiko 8 , C. Berry 1 , A. Reimer 1 , M. Graham 1 , W. 

Hsiao 4 , F. Brinkman 5 , G. Van Domselaar 1 ; 

1 

Public Health Agency of Canada, Winnipeg, 

MB, CANADA, 2 Centers for Disease Control 

and Prevention, Atlanta, GA, 3 University of 

Manitoba, Winnipeg, MB, CANADA, 4 BC 

Public Health Microbiology and Reference 

Laboratory, Vancouver, BC, CANADA, 5 Simon 

Fraser University, Burnaby, BC, CANADA, 

6 

Laboratory for Foodborne Zoonoses, Lethbridge, 

AB, CANADA, 7 University of Lisbon, 

Lisbon, PORTUGAL, 8 Dalhousie University, 

Halifax, NS, CANADA. 

Whole Genome Sequencing (WGS) based 

methods for disease surveillance and outbreak 

investigation are poised to replace existing 

typing methods such as pulsed-field gel electrophoresis 

(PFGE) and multi-locus sequence 

typing (MLST). The wealth of information 

obtained from WGS provides a typing method 

enabling significantly increased resolution 

between outbreak-associated and non-outbreak 

concurrent isolates. However, the routine 

use of WGS data for outbreak investigation 

has been hindered due to complexity in the 

management and quality assessment of WGS 

data, execution of analysis pipelines, and the 

visualization and interpretation of results. SN- 

VPhyl is a pipeline for building whole genome 

phylogenies from single nucleotide variants 

(SNVs). SNVPhyl accepts a set of pathogen 

WGS sequence reads, an assembled reference 

genome, and a collection of QA/QC parameters. 

The sequence reads are mapped to the 

reference genome, high-quality variants are 

identified within the core genome, and a table 

of all variants together with a quality report of 

the data is generated. The identified variants 

are used to generate a multiple sequence alignment 

of variant sites along with a maximum 

likelihood phylogeny. SNVPhyl is an integrated 

suite of tools that are implemented within 

a Galaxy workflow. Galaxy, a web-based 

bioinformatics analysis platform, supports 

execution of workflows on a variety of different 

high-performance computing environments 

which enables, together with parallelization 

of the pipeline tools, rapid analysis of large 

datasets. SNVPhyl is also integrated into 

IRIDA (Integrated Rapid Infectious Disease 

Analysis) a genomic epidemiology platform. 

IRIDA provides a web interface for the storage 

and management of WGS data and epidemiological 

metadata, a simplified interface for 

executing pipelines such as SNVPhyl, and the 

storage and visualization of analysis results. In 

addition, IRIDA provides support for integration 

with external tools using a REST API. In 

particular, a plugin has been developed for the 

software GenGIS, a phylogeographic analysis 

and visualization tool, allowing the in-depth 

exploration of SNVPhyl pipeline results. SN- 

VPhyl is the culmination of nearly five years 

of development on a pipeline to construct 

whole genome phylogenies at the National 

Microbiology Laboratory (NML) in Canada, 

with contributions from the US Centers for 

Disease Control and Prevention. Both IRIDA 

and SNVPhyl are actively in use at the NML 

for outbreak response and are available as free 

and open source software. More information 

can be found at http://irida.ca. 

n S7:11 

PANCORE: A FLEXIBLE WORKFLOW FOR 

THE COMPARISON AND ASSIGNMENT OF 

GENOMES TO OUTCOMES 

D. B. Storey, B. C. Weimer; 

University of California, Davis, CA. 

The identification and assignment of bacterial 

pathogens to specific outbreaks is a task 

of importance for the continued security and 

safety of our food supply. As high throughput 



31


sequencing technologies continue to increase 

in throughput, decrease in cost, and increase 

in mobility; it is clear that they will play an 

increasing role in our ability to identify and 

characterize bacteria in our food supply in a 

pro-active manner. By applying robust datascience 

techniques to these problems we can 

attempt to begin regulating our food supply in 

a prescriptive manner instead of a reactive one. 

With these goals in mind we present PanCore; 

a work-flow that utilizes: high throughput sequencing 

data, reference free assembly, global 

annotations, machine learning techniques, and 

predictive algorithms to classify and assign 

bacterial genomes to a phenotype and identify 

outlier isolates in a fast and robust manner. 

The work-flow makes use of publicly available 

sequencing data from the SRA/ENA and data 

generated as part of the 100kPathogen genomes 

project as the underlying data for building 

global models of what the potential genetic 

landscape is for an organism of interest. We 

incorporate a number of measures including: 

genomic distance, kmer distributions, genetic 

content, gene polymorphisms, and allele frequencies 

into this database. These expansive 

databases are subjected to dimensionality 

reduction techniques, and informative features 

are extracted. Using these reduced data representations 

and user input data (i.e. serotype, 

presence in an outbreak, geographic location 

etc.) and a training set the program undergoes 

a second round of clustering and feature extraction. 

These refined features are then used 

to train a Naive Bayesian classifier which can 

be directly applied to new incoming sequencing 

data. This two step approach allows for the 

classification of isolates directly into classes 

and identification of outliers in a data dependent 

manner. This means that unknown and/or 

mis-classified isolates can be quickly identified 

and subjected to more directed analyses and reincluded 

in the underlying database in an efficient 

manner. By front-loading computing and 

applying dimensionality reduction techniques 

prior to training the classifier we also make it 

possible to provide fast scalable classification 

that doesn’t require large infrastructure for 

support. We have successfully applied these 

methods as a way to identify Campylobacter 

isolates that have been mis-classified by biochemical 

methods and identify potential markers 

for identifying isolate host range. 

n S7:12 

REFERENCE-FREE PAN-GENOMIC 

EPIDEMIOLOGY USING CORTEX 

Zamin Iqbal 1 , Henk den Bakker 2 , Phelim 

Bradley 1 , Rachel Norris 1 , Jennifer Gardy 3 , 

Sarah Walker 4 , Tim Peto 4 , Derrick Crook 4 ; 

1 

Wellcome Trust Centre for Human Genetics, 

Univ. of Oxford, UK, 2 Department of Animal 

and Food Sciences, Texas Tech University, 

Lubbock, Texas, 3 Communicable Disease 

Prevention and Control Services, British Columbia 

Centre for Disease Control, Vancouver, 

BC, Canada, 4 Nuffield Department of Medicine, 

University of Oxford, UK 

Bacterial outbreak studies based on genetic 

epidemiology alone are fundamentally focussed 

on looking at genetic similarity and 

differences within sets of samples, both in 

the core genome, and also at shared accessory 

genome elements (e.g. due to plasmid 

transfer). Standard approaches require one to 

compare all samples against a reference genome, 

which can add artefacts and noise, and 

cost compute time. We therefore developed a 

reference-free multi-sample approach called 

Cortex [1] , allowing partial assembly of many 

samples into a joint de Bruijn graph, followed 

by rapid and accurate determination of SNP, 

indel and structural variants segregating within 

the samples, and extremely simple assaying 

of accessory genome content. In terms of the 

species involved in this challenge, Cortex has 

been used for outbreak analysis of Salmonella 

[2] and sequence presence/absence testing in 

Listeria [3]. In our experience, this approach 

leads to highly accurate and sensitive call sets 

for bacteria (where coverage is not generally 

limiting) without the need for manual curation 

32 

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or parameter tuning, allowing the user to focus 

on subsequent analyses. 

The software runs on Linux or Mac OS X, and 

is freely available under the GPLv3 license at 

http:/github.com/iqbal-lab/cortex. All analyses 

done for this bioinformatics challenge will be 

available on a secondary github repository. 

[1] De novo assembly and genotyping of variants 

using colored de Bruijn graphs Iqbal et al, 

Nature Genetics (2012) 

[2] Rapid whole-genome sequencing for surveillance 

of Salmonella enterica serovar enteritidis, 

den Bakker et al, EID (2014) 

[3] Whole genome sequencing allows for 

improved identification of persistent Listeria 

monocytogenes in food associated environments. 

Stasiewicz et al, AEM (2015) 

n S9:3 

WHOLE-GENOME SEQUENCE ANALYSIS OF 

PSEUDOMONAS AERUGINOSA IN ACUTE 

INFECTION REVEALS WIDESPREAD WITHIN- 

POPULATION DIVERSITY AND RAPID 

TRANSMISSION WITHIN THE BODY 

H. Chung 1 , K. B. Flett 2 , M. Anderson 2 , R. Kishony 

3 , G. P. Priebe 2 ; 

1 

Harvard Medical School, Boston, MA, 2 Boston 

Children’s Hospital, Boston, MA, 3 Technion 

- Israel Institute of Technology, Haifa, 

ISRAEL. 

Bacterial pathogen populations mutate and 

adapt during the course of an infection. Strong 

selective pressures such as host-adaptation 

and antibiotic treatment lead to genetic diversification 

within a population. Examining 

this diversity in pathogen populations originating 

from chronic infections such as cystic 

fibrosis has revealed that many mutations are 

polymorphic rather than fixed. Theoretical 

works have shown that such within-population 

diversity can hinder accurate reconstruction 

of epidemiological transmission networks. 

Here we show that even in an acute infection, 

we observe within-population diversity due to 

pre-existing polymorphisms in the infecting 

population, as well as de novo mutations that 

arise rapidly during treatment. We describe a 

pipeline for rapidly collecting and sequencing 

populations of a bacterial pathogen from 

patients over multiple time points. Focusing on 

ventilator-associated tracheitis in mechanically 

ventilated children, we sampled Pseudomonas 

aeruginosa populations from the respiratory 

tract (and in some cases the gut) of eight patients 

prior to and after antibiotic treatment. 

Using a low-cost library preparation method 

we developed, we prepared in just 4 days the 

whole-genomes of 636 Pseudomonas isolates 

for next-generation sequencing on the Illumina 

HiSeq platform. Comparing diversity 

of pathogen populations between the airways 

and the gut reveals fast transmission of newly 

generated genotypic variants across the body. 

While analyzing de novo mutations is useful 

for inferring genes that confer selective advantage 

in adapting to the human host, uncovering 

pre-existing diversity of a pathogen across 

the body prior to treatment could be key for 

tailoring patient-specific treatment strategies. 

Furthermore, incorporating within-population 

diversity into current epidemiological models 

will improve the accuracy of reconstructing 

transmission events, especially in rapidly occurring 

outbreaks. 

n S9:4 

BEYOND THE SNV: INTEGRATING 

MULTIPLE DATA TYPES INTO GENOMIC 

EPIDEMIOLOGY 

M. S. Wright 1 , G. G. Sutton 2 , R. A. Bonomo 3 , 

M. D. Adams 1 ; 

1 

J. Craig Venter Institute, La Jolla, CA, 2 J. 

Craig Venter Institute, Rockville, MD, 3 University 

Hospitals Case Medical Center,Louis 

Stokes Cleveland Department of Veteran Affairs 

Medical Center, Cleveland, OH. 

Single nucleotide variant (SNV) analyses can 

be useful for identifying transmission routes 

during outbreaks, characterizing pathogen 

population structure, and providing taxonomic 

discrimination for strain identification. The 

inclusion of gene content analysis adds further 



33


resolution to evolutionary relationships and 

yields phenotypically significant information 

during investigations that use genomic 

epidemiology. To demonstrate this we have 

sequenced > 50 Klebsiella pneumoniae (Kp) 

and >200 Acinetobacter baumannii (Ab) 

isolates from the Midwestern US. Core SNV 

phylogenies for both species indicated population 

mixing across hospital locations, with the 

maintenance of lineages distinct from other 

geographical locations. Gene content analysis 

additionally revealed population-specific 

plasmids in both species. A significant founder 

effect was observed in Kp, where all ST258b 

strains likely originated from a common ancestor 

that had an entS deletion unique to this 

lineage. Mapping of insertion sequence (IS) 

locations identified lineage- and strain-specific 

IS events. Longitudinal sequence analysis of 

Ab isolates originating from the same patient 

over time highlighted gene content variability 

mediated by isolate-specific IS events including 

the loss of phenotypically relevant antibiotic 

resistance genes, as well as antibiotic 

resistance plasmid gain events that would be 

missed by conventional SNV analysis. Using 

patient-specific SNVs, IS events, and gene 

content analyses, we were able to determine 

whether persistent Ab infections were the result 

of treatment failure or reinfection by new 

strains. Thus the combination of SNV, gene 

content, and IS mapping data lead to a more 

complete picture of transmission dynamics, 

pathogen populations, and evolution. Challenges 

remain in integrating these data types in 

clinically-relevant settings, given the requirements 

for rapid assessments and minimal data 

curation efforts. Computational tools are under 

development so that new strains can be rapidly 

placed in the genotypic and phenotypic context 

of local and global strains. 

n S9:5 

DEFINING CLONALITY IN ACINETOBACTER 

BAUMANNII USING WHOLE GENOME 

SEQUENCING OF OUTBREAK STRAINS 

ASSOCIATED WITH THE CONFLICT IN IRAQ 

E. Snesrud, P. Mc Gann, L. Appalla, F. 

Onmus-Leone, A. C. Ong, R. Maybank, R. 

Clifford, M. K. Hinkle, P. E. Waterman, E. P. 

Lesho; 

Walter Reed Army Institute of Research, Silver 

Spring, MD. 

Multi-drug resistant (MDR) A. baumannii 

emerged as a significant source of infection 

during the conflict in Iraq. Unravelling the 

epidemiology of these strains using conventional 

typing methods, such as multi-locus 

sequence typing (MLST), is difficult, as these 

methods lack the resolution to detect small 

genetic changes, such as single nucleotide 

polymorphisms (SNPs). Whole genome sequencing 

(WGS) offers the prospect of providing 

definitive data on strain relatedness, but 

studies to define what constitutes clonality 

are lacking. Here, WGS was employed on 

a large collection of A. baumannii cultured 

from patients treated at the Walter Reed Army 

Medical Center (WRAMC) from 2003-2011 in 

an effort to determine a baseline for clonality 

in this species. From 2003-2011, carbapenem 

resistance among clinical isolates of A. 

baumannii rose from 12% to >95%. WGS 

was performed using the Illumina MiSeq and 

NextSeq benchtop sequencers on every deduplicated 

carbapenem-resistant A. baumannii 

(CRAB) archived during this period (N=394). 

An additional 142 carbapenem-sensitive A. 

baumannii from the same time period were 

sequenced in tandem. Comparative genomics 

were performed on all isolates to determine 

clonality. Carbapenem resistance was mediated 

by the Class D oxacillinases in all isolates, 

with bla OXA-23 

identified in 312 strains (79.2%). 

In silico MLST revealed 12 different sequence 

types (ST) carrying bla OXA-23 

, with ST-1, 2, 20, 

25, 81 and 94 the most prevalent. SNP-based 

analysis revealed that ST-1 was composed of 

34 

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4 different clonal clusters that were temporally 

separated. Within each clonal group, SNP accumulation 

occurred over time, with an average 

of 23 SNPs separating the first and last 

isolate identified in each cluster. In contrast, 

isolates of ST-2, 20, 25, 81, and 94 were all 

caused by a single clone that persisted and 

spread throughout the healthcare facility over 

1 to 6 years. Accumulation of SNPs in these 

strains was consistent with that observed for 

ST-1; strains varied by 0 to 25 SNPs, with the 

number of SNPs increasing as time progressed. 

At the height of the Iraq conflict (2004-2006), 

a new patient with a CRAB infection was 

being identified almost daily. Remarkably, 

despite the large number of patients involved, 

WGS revealed that the majority of infections 

over the 9 years were caused by just 9 different 

strains, which appear to have entered, 

persisted and disseminated within the facility 

over periods ranging from 1 to 6 years. SNPbased 

phylogeny demonstrated that every ST 

accumulated SNPs at a comparable rate, with 

an average of 8 SNPs accumulating every year. 

Thus, we define a baseline for clonality in A. 

baumannii as two isolates sharing ≤ 8 (± 3) 

SNPs over the course of a year. 

n S9:6 

DIRECT FROM SPUTUM: NEXT GEN 

ANALYSIS OF MYCOBACTERIUM 

TUBERCULOSIS IN CLINICAL SAMPLES 

D. M. Engelthaler 1 , R. E. Colman 1 , V. Crudu 2 , 

D. Catanzaro 3 , A. Catanzaro 4 , P. Keim 5 , T. 

Cohen 6 , T. C. Rodwell 4 ; 

1 

TGen North, Flagstaff, AZ, 2 Phthisiopneumology 

Institute, Chișinău, MOLDOVA, REPUB- 

LIC OF, 3 University of Arkansas, Little Rock, 

AR, 4 University of California San Diego, San 

Diego, CA, 5 Northern Arizona University, 

Flagstaff, AZ, 6 Yale University, New Haven, 

CT. 

The incidence of drug-resistant (DR) tuberculosis 

(TB) continues to increase worldwide. 

With the presence of multi-drug resistance it 

has become critical to quickly identify the appropriate 

treatment regimen, in order to effectively 

treat disease and prevent further transmission 

of DR-TB. Tabletop DNA sequencers 

now allow for rapid and robust sequencing 

of pathogen isolates as well as direct analysis 

of clinical samples. However, applying this 

technology directly to complex samples, such 

as sputum, currently has limitations due to the 

complexity of biological samples. We have 

developed accessible tools and methodologies 

for direct sequencing of clinical sputum samples 

which enable us to detect Mycobacterium 

tuberculosis (Mtb), produce a rapid drug susceptibility 

profile, detect heteroresistance and 

conduct additional analyses related to the nature 

of TB infection and transmission. Targeted 

sequencing allows for Next Gen Drug Susceptibility 

Testing (Next Gen-DST), an amplicon 

sequencing method for generating a rapid and 

inexpensive DST profile straight from positive 

sputum samples. Additionally we have devised 

Single Molecule Overlapping Read (SMOR) 

analysis – an advanced amplicon sequencing 

approach for detecting and measuring heteroresistance 

(i.e., resistance allele mixtures) to 

0.1% minor resistance component. Lastly, a 

more detailed analysis of the generated with 

these techniques allows for haplotype analysis 

leading to an understanding of the nature of an 

infection (e.g., whether a patient has a superinfection 

of multiple strains or is infected with 

multiple lineages of the same strain). We have 

employed these techniques on DNA extracted 

from >150 remnant clinical Mtb sputum 

samples from The Republic of Moldova. The 

Next Gen-DST assay provided comparable 

drug sensitivity profiles as culture-based DST 

on 36 well established target loci; the SMOR 

analysis identified the presence of mixtures 

in samples at


n S10:3 

DEVELOPMENT OF AN EFFICIENT NEXT- 

GENERATION SEQUENCING PLATFORM FOR 

CHARTING THE EVOLUTION OF NOROVIRUS 

STRAINS 

G. I. Parra, C. K. Karangwa, S. V. Sosnovtsev, 

K. Y. Green; 

National Institutes of Health, Bethesda, MD. 

Noroviruses (NoV) are important pathogens 

of acute gastroenteritis. An effective vaccine 

could save thousands of lives each year, but 

the number of antigenic components needed 

for the development of efficacious vaccines 

as well as the immune correlates of protection 

against NoV infections are still unknown. Like 

many other RNA viruses, NoV are genetically 

diverse with seven major genogroups containing 

over 30 different genotypes. To gain insight 

into the rules that govern intra- and interhost 

NoV evolution and antigenic diversity, 

we developed an efficient platform to analyze 

complete NoV genomes by Next-Generation 

Sequencing (NGS), and analyzed differences 

in the population dynamics of NoV infecting 

healthy individuals. The first set of samples 

was collected from patients with GII.3, GII.4, 

or GII.6 infection that, despite resolving symptoms 

within days, shed NoV for up to 4 weeks. 

The GII.6 and GII.3 strains were stable and did 

not show evidence of adaptive changes during 

the prolonged shedding phase, while the 

GII.4 viruses showed a number of nucleotide 

changes as infection progressed. A second set 

of samples was obtained from cases of personto-person 

transmission of non-GII.4 strains, 

which showed that although minor changes 

could be detected during transmission (acute 

phase), the virus would often revert to the original 

virus sequence during the recovery phase 

of the infection. Finally the new amplification 

and genome sequence method developed allowed 

us not only to amplify archival samples 

but also describe and characterize a new GII.17 

norovirus (Hu/GII.17/GaithersburgD1/2014/ 

USA) that is currently causing large outbreaks 

of gastroenteritis in countries from Asia. Taken 

together, our data suggests different patterns of 

evolution among NoV strains; with some viruses 

(like GII.4) more prone to change, while 

others remain static over time, limiting their 

antigenic diversity and prevalence. Population 

dynamics offers a new tool in the development 

of NoV vaccines. 

n S10:4 

GENOME-WIDE COMPARISON OF COWPOX 

VIRUSES REVEALS A NEW CLADE RELATED 

TO VARIOLA VIRUS 

P. Dabrowski, A. Radonic, A. Kurth, L. 

Schuenadel, A. Nitsche; 

Robert Koch Institute, Berlin, GERMANY. 

Zoonotic infections caused by several Orthopoxviruses 

(OPV) like Monkeypox virus or 

Vaccinia virus have a significant impact on 

human health. In Europe, the number of diagnosed 

infections with Cowpox viruses (CPXV) 

is increasing in animals as well as in humans. 

CPXV used to be enzootic in cattle; however, 

such infections were not being diagnosed over 

the last decades. Instead, individual cases of 

cowpox are being found in cats or exotic zoo 

animals that transmit the infection to humans. 

Both animals and humans reveal local exanthema 

on arms and legs or on the face. Although 

cowpox is generally regarded as a self-limiting 

disease, immunosuppressed patients can develop 

a lethal systemic disease resembling smallpox. 

To date, only limited information on the 

complex and, compared to other OPV, sparsely 

conserved CPXV genomes is available. Since 

CPXV displays the widest host range of all 

OPV known, it seems important to comprehend 

the genetic repertoire of CPXV which in 

turn may help elucidate specific mechanisms 

of CPXV pathogenesis and origin. Therefore, 

about 50 genomes of independent CPXV 

strains from clinical cases involving several humans, 

rats, cats, jaguarundis, beaver, elephant, 

marah and mongoose were sequenced. All 

samples were collected as part of the routine 

36 

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diagnostics at the German Consultant Laboratory 

for Poxviruses over the last decade. The 

first genomes were gained by using massive 

parallel pyrosequencing (GS FLX) while Illumina 

sequencing in combination with Nextera 

library generation was utilized later on. The 

extensive phylogenetic analysis showed that 

the CPXV strains sequenced clearly cluster 

into several distinct clades, some of which are 

closely related to Vaccinia viruses while others 

represent different clades in a CPXV cluster. 

Particularly one CPXV clade is more closely 

related to Camelpox virus, Taterapox virus and 

Variola virus than to any other known OPV. 

These results support and extend recent data 

from other groups who postulate that CPXV 

does not form a monophyletic clade and should 

be divided into multiple lineages. 

n S10:5 

VIROME ANALYSES AMONG CHILDREN 

WITH ACUTE RESPIRATORY INFECTION IN 

CHINA 

W. Tan, Y. Wang; 

National Institute for Viral Disease Control 

and Prevention, China CDC, Beijing, CHINA. 

Acute respiratory infection (ARI) of children 

is known to be caused by several recognized 

respiratory viruses. Global understanding of 

virome of respiratory tract in children with 

ARI is limited, especially in Beijing, China, 

though infection by individual viruses is well 

characterized. To define the virome of respiratory 

tract in children with ARI, we carried 

out next-generation sequencing (NGS) of 

nasopharyngeal swabs by Illumina Hiseq 2500 

followed by phylogenetic analysis. A total of 

42,951,290 reads were obtained with 25% 

of all the generated reads assigned to recognized 

respiratory viruses. Respiratory tract of 

children with ARI contains complicated viral 

populations which are mainly dominated by 

seven families including Paramyxoviridae, 

Comnaviridae, Parvoviridae, Orthomyxoviridae, 

Picornaviridae, Adenoviridae and Anelloviridae. 

Various viruses of different genotypes 

were detected in respiratory samples. In detail 

HRSV, HCoVs (HCoV-229E, HCoV-OC43, 

HCoV-HKU1), HBoV1, influenza A/B, HRVs, 

HAdVs, anelloviruses (TTVs and TTMVs) 

and HPIVs represented the most abundant 

and common viruses harbored by childhood 

respiratory tract in Beijing. In contrast HMPV, 

HCoV-NL63 and measles virus were occasionally 

detected. Contigs sequence analysis 

indicated that HRSV detected in this study 

mainly belonged to BA and GA2 subtypes. At 

least three genotypes of HCoV-OC43 circulating 

in Beijing were determined including B, 

C/D and UNT subgroups and genotype UNT 

is the predominant genotype in recent years. 

Influenza A, B and C viruses were all detected 

in this study and mainly included H1N1 and 

H5N1. Most of the reads related to HRVs belong 

to HRV-A and HRV-C. Diverse types of 

anelloviruses (TTVs and TTMVs) were found 

in respiratory samples including TTV5, TTV7, 

TTV10, TTV21, TTMV5, TTMV7, TTMV8 

and TTV-like mini virus. The viral population 

and dominant genotypes detected differed significantly 

between ours and previous reports. 

This research firstly provides a comprehensive 

understanding of virome of children with ARI 

in China and indicated a high heterogeneity of 

known viruses present in respiratory tract of 

children, which may benefit detection and prevention 

of respiratory disease in China. 

n S10:6 

USING WASTEWATER TO MONITOR VIRAL 

PATHOGENS IN THE SLUM CITY OF KIBERA 

M. H. Hjelmsø 1 , O. Lukjancenko 1 , L. Bergmark 

1 , E. Ngeno 2 , F. M. Aarestrup 1 , R. S. Hendriksen 

1 ; 

1 

Technical University of Denmark, Kgs. Lyngby, 

DENMARK, 2 Center for Disease Control, 

Nairobi, KENYA. 

Pathogenic viruses are a huge burden to 

mankind. Enteric viruses, the major cause of 

gastroenteritis, indisposes millions of people 

each year and kills hundreds of thousands in 

developing countries. Other important diseases 



37


caused by viruses include MERS, SARS, Aids 

and Ebola. To combat these diseases, health 

authorities need an understanding of the spread 

and epidemiology of the viruses in question. 

Classic surveillance relies on data from local 

health centers and diagnostic labs. This 

is problematic as many people in the world 

either fail to contact or have no access to such 

facilities, leading to an under-reporting of the 

problem. Alternative monitoring methods are 

needed to limit the spread of viral pathogens in 

the future. The objective of this study was to 

establish if deep sequencing of wastewater can 

be used as a surveillance tool for viral pathogens. 

Infected individuals shed virus particles 

in large numbers in their feces, which ends up 

contaminating the wastewater. In this regard, 

wastewater is most often seen as a problem, 

but in this project we use it to monitor the 

health state of the population it originates 

from. To test this novel approach, a proof of 

concept study was made in the slum city of 

Kibera, Kenya. This city has very poor sanitary 

conditions and limited medical facilities, leading 

to a high incidence of infective diseases. 

Wastewater was sampled daily at two central 

locations for three months. Samples were 

frozen at -80 °C directly after sampling and 

shipped frozen to our laboratory in Denmark. 

The virus particles were then isolated from the 

rest of the wastewater content, concentrated 

with PEG8000 precipitation and treated with 

nucleases to degrade extracellular DNA and 

RNA. The nucleotides from the pure viral 

concentrate were then extracted with the Nucleospin 

RNA XS kit and the High Pure Viral 

Nucleic Acid Kit selecting for RNA and DNA 

viruses, respectively. The nucleotides were 

then amplified with 40 cycles of PCR using a 

random primer before library creation with the 

Nextera XT kit and sequenced on the Illumina 

MiSeq creating 250bp paired-end reads. The 

reads were then mapped to a custom database 

containing all viral sequences and genomes 

from the NCBI and ViPR databases. We were 

able to detect 456 different virus species, 

including the human pathogens: Enterovirus, 

Rotavirus, Norovirus, Astrovirus, Hepatitis C 

virus and Human Herpes Virus. Several of the 

human viral pathogens exhibited a clear rise 

and fall in numbers during the study period. 

Similar epicurves were seen at both sampling 

points, suggesting that our results were indeed 

a reflection of an outbreak in the city. Unfortunately, 

no clinical data exists to confirm this. 

With this approach, the public health of large 

populations can be monitored cost effectively. 

Better monitoring could be instrumental in 

combating the diseases locally and from 

preventing global outbreaks of existing and 

emerging viral pathogens. 

38 

ASM Conferences

Poster Abstracts 

n 1 

A BIOSURVEILLANCE ANALYSIS PIPELINE 

FOR GENOMIC SEQUENCE DATA 

C. Olsen 1 , K. Qaadri 1 , H. Shearman 2 , R. Moir 2 , 

M. Kearse 2 , S. Markowitz 2 , J. Kuhn 2 , S. Dunn 2 , 

A. Cooper 2 ; 

1 

Biomatters, Inc., Newark, NJ, 2 Biomatters, 

Ltd., Auckland, NEW ZEALAND. 

Next-generation sequencing (NGS) approaches 

have numerous applications for biosurveillance 

programs and outbreak investigation. 

However, there are significant challenges for 

analyzing the data accurately without the aid 

of high performance compute resources in a 

timely fashion. Often times the users are not 

bioinformaticians who are comfortable running 

sequence analysis pipelines. Biomatters’ 

Geneious R9 is a bioinformatics software 

platform that allows researchers the use of 

industry-leading algorithms for their genomic 

and protein sequence analyses. Geneious offers 

a comprehensive suite of functions, including 

a robust collection of peer-reviewed tools, 

that enable researchers to be more efficient 

with their sequence analysis workflows. The 

recent addition of the 16S Biodiversity tool 

and Sequence Classifier plugin provides tools 

which can be incorporated into an easy to use 

pathogen identification workflow. The 16S 

Biodiversity tool identifies high-throughput 

16S rRNA amplicons from environmental 

samples using the RDP database, and visualizes 

biodiversity as an interactive chart using 

a secure web viewer. The Sequence Classifier 

plugin taxonomically classifies an organic 

sample by how similar its DNA is to your 

own database of known sequences using a 

BLAST-like algorithm with multiple loci and 

trees to assist with identification. By utilizing 

Geneious R9, biologists can easily streamline 

their sequence analysis workflows for mixed 

sample analysis. This poster aims to describe a 

sequence analysis pipeline for identification of 

bacterial pathogens from mixed metagenomic 

data generated from outbreaks. The pipeline 

can also be extended to include eukaryotic and 

fungal pathogens. 

n 2 

THE IMPORTANCE OF ASSESSING THE 

PERFORMANCE OF METHODS FOR VARIANT 

DETECTION AND CONSTRUCTING SNP 

MATRICES: INSIGHTS FROM A VALIDATION 

EXPERIMENT OF THE CFSAN SNP PIPELINE 

J. B. Pettengill, S. Davis, Y. Luo, H. Rand, E. 

Strain; 

FDA, College Park, MD. 

The CFSAN SNP Pipeline combines into a single 

package the steps necessary to generate a 

SNP matrix with which a phylogenetic tree can 

be inferred to assist in traceback and foodborne 

outbreak investigations. The pipeline works 

with next-generation sequencing reads from 

a group of individuals and uses a referencebased 

approach to identify variant sites. Given 

the importance of decisions that may be based 

on the topology inferred from the SNP matrix, 

it is paramount that the method be validated. 

With this objective in mind, we developed a 

simple python package that when given a reference 

genome will generate variants of known 

position against which we validate our pipeline. 

We created 1000 simulated Salmonella 

enterica sp. enterica Serovar Agona genomes 

at 100x and 20x coverage, each containing 

500 SNPs, 20 single-base insertions and 20 

single-base deletions. For the 100x dataset, 

the CFSAN SNP Pipeline recovered 98.9% of 

the introduced SNPs and had a false positive 

rate of 1.04 x 10-6; for the 20x dataset 98.8% 

of SNPs were recovered and the false positive 

rate was 8.34 x 10-7. Interestingly, failure to 

meet the consensus frequency threshold rather 

than the coverage threshold was the primary 

explanation for false negatives. Additionally, 

false negatives were not randomly distrib- 



39


uted across the genome, which suggests that 

hotspots within the genome may exist where it 

will be difficult to accurately detect differences 

between two samples. These results provide 

critical metrics that show the CFSAN SNP 

Pipeline to be a robust method for constructing 

a SNP matrix and further reinforces the utility 

and importance of validation exercises. 

n 3 

TGS-TB: TOTAL GENOTYPING SOLUTION 

FOR MYCOBACTERIUM TUBERCULOSIS 

USING SHORT-READ WHOLE-GENOME 

SEQUENCING 

T. Sekizuka 1 , A. Yamashita 1 , Y. Murase 2 , T. 

Iwamoto 3 , S. Mitarai 2 , S. Kato 2 , M. Kuroda 1 ; 

1 

National Institute of Infectious Diseases, Shinjyuku-ku, 

JAPAN, 2 Japan Anti-Tuberculosis Association, 

Kiyose-shi, JAPAN, 3 Kobe Institute 

of Health, Kobe-shi, JAPAN. 

Background: Whole-genome sequencing 

(WGS) with next-generation DNA sequencing 

(NGS) is an increasingly accessible and affordable 

method for genotyping hundreds of Mycobacterium 

tuberculosis (Mtb) isolates, leading 

to more effective epidemiological studies 

involving single nucleotide variations (SNVs) 

in the core genomic sequences based on molecular 

evolution. Methods: We developed an 

all-in-one web-based tool for genotyping Mtb, 

referred to as Total Genotyping Solution for 

TB (TGS-TB), to facilitate multiple genotyping 

platforms using NGS for spoligotyping and 

the detection of phylogenes with core genomic 

single nucleotide variations (SNVs), IS6110 

insertion sites, and VNTRs (our customized 

short TR on 43 loci) through a user-friendly 

simple click interface. In addition, this methodology 

is implemented with a KvarQ script 

to predict MTBC lineages/sublineages and 

potential antimicrobial resistance. Findings: 

The results of in silico analyses using TGS-TB 

are completely consistent with those obtained 

using conventional molecular genotyping 

methods, suggesting that MiSeq NGS short 

reads could provide multiple genotypes to 

discriminate multiple strains of Mtb. Indeed, 

seven Mtb isolates showing the same VNTR 

profile were accurately discriminated through 

median joining network analysis using specific 

SNVs unique to those isolates. Furthermore, 

an additional IS6110 insertion was detected 

in one of those isolates as supportive genetic 

information in addition to core genomic SNVs. 

The results obtained from all in silico analyses 

can be downloaded from the website. Interpretation: 

TGS-TB provides more accurate 

and discriminative strain typing for clinical 

and epidemiological investigations; NGS strain 

typing offers a total genotyping solution for 

Mtb outbreak and surveillance. The genotype 

information obtained for all Mtb isolates can 

be deposited into an integrated database for 

the surveillance of future outbreaks and global 

infections. TGS-TB web site: http://gph. 

niid.go.jp/tgs-tb Funding: This research was 

funded through a Grant-in-Aid for Research 

on Emerging and Re-emerging Infectious Diseases 

(H25-Shinko-Ippan-015) from the Ministry 

of Health Labour and Welfare Programs 

of Japan. 

n 4 

MARA: THE MULTI-ANTIBIOTIC RESISTANCE 

ANNOTATOR 

S. Partridge 1 , G. Tsafnat 2 ; 

1 

Westmead Millennium Institute, Sydney, AUS- 

TRALIA, 2 Centre for Health Informatics, Macquarie 

University, Sydney, AUSTRALIA. 

Much of the increasingly problematic multiresistance 

in Gram-negative bacteria is due 

to resistance genes associated with different 

mobile elements (mainly gene cassettes/ 

integrons, insertion sequences, transposons) 

that tend to cluster together in complex multiresistance 

regions (MRR). MRR in turn are 

found on plasmids that can spread between 

cells, including different species, or sometimes 

in islands integrated into the chromosome. 

Increasing numbers of MRR sequences are 

becoming available as part of large projects 

using next-generation methods, enabling 

40 

ASM Conferences


comparative analysis to better understand how 

resistance genes are spreading. However, many 

such sequences are poorly and inconsistently 

annotated, as available software often only 

focuses on identifying genes and the putative 

functions of the proteins that they encode. In 

the resistance domain, gene functions are often 

well known, but resistance gene nomenclature 

is confusing, with different naming systems 

for the same genes and relationships between 

genes often not evident from their names 

alone. Consistently naming genes, identifying 

minor variations that have important effects 

on resistance phenotype and simultaneously 

identifying boundaries of mobile genetic elements 

are needed to provide the most useful 

annotations. We previously developed an automated 

tool, Attacca, which uses a database 

of ‘features’ and computational grammars 

to accurately and consistently annotate gene 

cassettes and integrons, and the Repository of 

Antibiotic-resistance Cassettes (RAC) website 

http://www.rac.aihi.mq.edu.au/), which provides 

a database of resistance and other gene 

cassettes and allows sequences to be submitted 

for annotation by Attacca. We have now 

extended Attacca to annotate other resistance 

genes and associated mobile elements using 

an expanded database (compiled from existing 

resistance gene repositories, where possible) 

and additional grammar rules. Attacca annotations 

were compared with manually annotated 

sequences to refine the grammars. Attacca is 

able to accurately and consistently annotate 

resistance genes, the boundaries of complete 

mobile elements and their fragments, insertions 

of one mobile element inside another and 

direct repeats indicative of insertion, as well 

as providing diagrammatic representations of 

annotations. The Multi-Antibiotic Resistance 

Annotator (MARA) website (linked to RAC) 

will provide access to the extended resistance 

gene database, as browsable lists that include 

information such as relationships within different 

resistance gene families, and a service for 

annotating MRR sequences. 

n 5 

MOLECULAR EPIDEMIOLOGY OF 

CAMPYLOBACTER ISOLATES FROM 

SOUTH AFRICA INVESTIGATED USING 

WEB-BASED BIOINFORMATICS PIPELINES 

DEVELOPED AT THE CENTER FOR GENOMIC 

EPIDEMIOLOGY OF THE TECHNICAL 

UNIVERSITY OF DENMARK 

A. M. Smith, M. Thobela, A. Ismail, K. H. 

Keddy; 

National Institute for Communicable Diseases, 

Johannesburg, SOUTH AFRICA. 

Background: Campylobacter is a leading 

cause of bacterial gastroenteritis globally. The 

molecular epidemiology of Campylobacter is 

well described in developed countries. However, 

in developing countries very little data exist 

on the molecular epidemiology of Campylobacter. 

Objectives: A proof-of-concept study 

was conducted to determine the feasibility of 

investigating the molecular epidemiology of 

Campylobacter by using web-based bioinformatics 

pipelines to analyze whole-genome 

sequence (WGS) data. Methods: Sixteen human 

isolates of Campylobacter jejuni were 

investigated. Isolates were recovered in 2014 

from the Gauteng and Western Cape provinces 

of South Africa. Genomic DNA was isolated 

from strains using the QIAGEN QIAamp DNA 

Mini Kit and sequenced using Illumina MiSeq 

next generation sequencing technology. Raw 

data generated on the MiSeq were assembled 

into contiguous DNA sequences using CLC 

Genomics Workbench Software. Assembled 

data was analyzed using ‘multi-locus sequence 

typing (MLST)’ and ‘acquired antimicrobial 

resistance gene finder’ pipelines developed at 

the Center for Genomic Epidemiology (CGE) 

of the Technical University of Denmark. Results: 

Genomic data were successfully uploaded 

to CGE servers and the resulting analyses 

were returned via e-mail messages containing 

website links to the analyzed data. Analysis 

of a single data file was typically completed 

within 5-10 minutes. Our 16 isolates showed 

the following MLST subtypes: ST-4063 (n=2), 



41


1737 (n=1), ST-4624 (n=1), ST-6091 (n=1), 

ST-5809 (n=1), ST-658 (n=1), ST-356 (n=1), 

ST-257 (n=1), ST-883 (n=1), ST-474 (n=1), 

ST-829 (n=1), ST-51 (n=1), and ST-unknown 

(n=3). Analysis for acquired antimicrobial resistance 

genes showed the following: 3 isolates 

had no evidence of known resistance genes; 9 

isolates contained bla OXA-61 

; 1 isolate contained 

tet(O); 2 isolates contained both bla OXA-61 

and 

tet(O); and 1 isolate contained bla OXA-61 

, tet(O), 

tet(B), sul2, strA and strB. Conclusions: 

Our proof-of-concept study was successful. 

Analysis of WGS data using web-based bioinformatics 

pipelines at the CGE provided a 

single, rapid and cost-effective approach to 

investigate the molecular epidemiology of 

Campylobacter. We showed a wide diversity 

of MLST subtypes among 16 Campylobacter 

isolates suggesting that a heterogeneous population 

of Campylobacter may exist in South 

Africa. A diversity of acquired antimicrobial 

resistance genes was shown in the population 

with bla OXA-61 

(coding for β-lactam resistance) 

most commonly described. 

n 6 

GENETIC TYPING AND EPIDEMIOLOGICAL 

CLUSTERING OF COMMON PATHOGENS 

BASED ON WHOLE GENOME NGS DATA 

K. Einer-Jensen, P. Liboriussen, J. Johansen, 

L. Schauser, A. C. Materna; 


Next generation sequencing (NGS) data from 

whole pathogen genomes is frequently used for 

enhanced surveillance and outbreak detection 

of common pathogens. Version 1.5 of the CLC 

Microbial Genomics Module for CLC Genomics 

Workbench and CLC Genomics Server 

introduces new functionality for molecular 

typing and epidemiological analysis of bacterial 

isolates. The latest update to the module 

enables the user to perform stepwise (tool-bytool) 

analysis or to take advantage of included 

multistep workflows. With a few clicks workflows 

can be optimized for routine analysis of 

a specific pathogen or outbreak. New features 

include for instance streamlined tools for NGSbased 

Multilocus Sequence Typing (MLST), 

resistance typing, as well as detection of genus 

and species information. New tools for phylogenetic 

tree reconstruction generate trees based 

on single nucleotide polymorphisms (SNPs) or 

infer K-mer trees from NGS reads or genomes. 

A new table format, acting as a database, collects 

typing results and associates these results 

with metadata such as sample information, 

geographic origin, treatment outcome, etc. Results 

generated using e.g. MLST and resistance 

typing can furthermore be associated with the 

original sample metadata. Users can filter for 

results and metadata to find and select relevant 

subsets of samples for downstream analysis. 

Results and metadata available during tree 

generation can further be used to explore phylogeny 

in the context of this epidemiologically 

relevant information. Version 1.5 of the CLC 

Microbial Genomics Module aims to facilitate 

tasks commonly carried out during outbreak 

investigation, such as typing or source tracking 

based on whole genome data. Preconfigured 

workflows and simple deployment via integration 

into the widely used CLC Genomics 

Workbench and CLC Genomics Server ecosystem 

offer a user-friendly platform for scientists 

engaged in outbreak prevention and control. 

n 7 

NEEDS ASSESSMENT AND ONTOLOGY 

DEVELOPMENT FOR INTEGRATING WHOLE 

GENOME SEQUENCING INTO ROUTINE 

OUTBREAK INVESTIGATIONS 

E. J. Griffiths 1 , M. Courtot 1 , D. Dooley 2 , J. 

Adam 3 , F. Bristow 3 , J. A. Carrico 4 , B. Dhillon 

1 , M. Graham 3 , M. Laird 1 , T. Matthews 3 , A. 

Petkau 3 , L. Schriml 5 , J. Shay 1 , E. Taboada 6 , G. 

Winsor 1 , G. Van Domselaar 3 , F. Brinkman 1 , W. 

Hsiao 2 ; 

1 

Simon Fraser University, Burnaby, BC, 

CANADA, 2 BC Public Health Microbiology 

and Reference Laboratory, Vancouver, BC, 

CANADA, 3 National Microbiology Laboratory, 

Winnipeg, MB, CANADA, 4 University of 

Lisbon, Lisbon, PORTUGAL, 5 University of 

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Maryland School of Medicine, Baltimore, MD, 

6 

Laboratory for Foodborne Zoonoses, Lethbridge, 

AB, CANADA. 

Whole-genome sequencing (WGS) can provide 

increased pathogen typing discriminatory 

power as well as additional genomic information 

using comparative genomics tools. Canada’s 

Integrated Rapid Infectious Disease Analysis 

(IRIDA) platform will equip public health 

workers with user friendly tools for incorporating 

WGS into isolate typing and epidemiological 

pipelines to support real-time infectious 

disease investigation. In order to understand 

the practical requirements for implementing 

this platform within the Canadian health care 

network, a needs assessment was conducted 

by interviewing public health stakeholders 

and domain experts. User activities, lab 

management software, pathogen-tracking and 

reporting systems were profiled to better characterize 

levels of user computational expertise, 

required functionalities and information flow. 

Key gaps were identified in personnel training, 

data processing and sharing, data integration, 

as well as governance. Consistent capture of 

structured metadata is crucial for integrated 

data analyses, human health risk assessments, 

source attribution, ecosystems modelling and, 

in the simplest terms, to make sense of the genome 

data. In addition to the needs assessment, 

an ontology resource review was conducted 

to assess the utility of different community 

standards for fulfilling the needs of a genomic 

epidemiology program. No single ontology is 

sufficient to cover all attributes required for 

genomic epidemiology and the very breadth 

of many ontologies hinders their practical use 

in real-time by users with little bioinformatics 

expertise. User profiles and data requirements 

were harmonized with different sources 

in order to produce an OWL file containing 

metadata fields and terms describing isolate 

source attribution, lab analytics, sequencing/ 

assembly/annotation processes as well as 

quality metrics, and patient demographics (or 

environmental descriptors). By adhering to 

the best practices of the Open Biomedical and 

Biological Ontology (OBO) Consortium, the 

application ontology we are developing allows 

consolidation of various existing ontological 

efforts into a resource directly compatible with 

IRIDA. The data integration capabilities of 

the IRIDA ontology are currently being tested 

in a Canadian government Genome Research 

and Development Initiative and will shortly be 

expanded to include antimicrobial resistance. 

This research is a key component of the IRIDA 

platform allowing for automated data integration 

alleviating the burden of manual analyses. 

Furthermore, outbreak response can be expedited 

after auto-detecting deviations above 

pre-defined biosurveillance thresholds. The 

lessons learned from the needs assessment and 

ontology development reported here should be 

informative for other countries with autonomous 

health regions. 

n 8 

IRIDA: A FEDERATED PLATFORM ENABLING 

RICHER GENOMIC EPIDEMIOLOGY 

ANALYSIS IN A PUBLIC HEALTH 

ENVIRONMENT 

F. Bristow 1 , J. Adam 1 , J. A. Carrico 2 , M. Courtot 

3 , B. Dhillon 3 , D. Dooley 4 , E. Griffiths 3 , J. 

Isaac-Renton 5 , A. Keddy 6 , P. Kruczkiewicz 7 , M. 

Laird 3 , T. Matthews 1 , A. Petkau 1 , L. Schriml 8 , 

J. Shay 3 , E. Taboada 7 , P. Tang 4 , J. Thiessen 1 , 

G. Winsor 3 , R. G. Beiko 6 , M. Graham 1 , G. Van 

Domselaar 1 , W. Hsiao 4 , F. S. Brinkman 3 ; 

1 

National Microbiology Laboratory, Winnipeg, 

MB, CANADA, 2 University of Lisbon, Lisbon, 

PORTUGAL, 3 Simon Fraser University, Burnaby, 

BC, CANADA, 4 BC Public Health Microbiology 

and Reference Laboratory, Vancouver, 

BC, CANADA, 5 BC Public Health Microbiology 

and Reference Laboratory, Burnaby, BC, 

CANADA, 6 Dalhousie University, Halifax, NS, 

CANADA, 7 Laboratory for Foodborne Zoonoses, 

Lethbridge, AB, CANADA, 8 University of 

Maryland School of Medicine, Baltimore, MD. 

Most public data analysis tools/pipelines for 

genomic epidemiology require considerable 

technical knowledge to execute, or cannot eas- 



43


ily integrate rich epidemiologic data, providing 

a barrier to wider use by public health workers. 

Canada’s Integrated Rapid Infectious Disease 

Analysis (IRIDA) web-based bioinformatics 

platform provides a model approach that 

aims to address these issues and complement 

other international bioinformatics pipelines 

for genomic epidemiology. The IRIDA development 

team comprises five interconnected 

working groups: 1) Ontology and Database; 

2) Microbial Typing; 3) Architecture and API; 

4) Tools Development; 5) User Experience. 

Teams are embedded in Canadian national and 

provincial public health agencies, and in academia, 

to engage end users and stakeholders 

during design and implementation phases of 

the project. IRIDA implements secure storage 

of WGS data, epidemiological and application 

metadata, data analysis pipelines, visualization 

of results, and a federated data sharing model 

intended to facilitate secure communication 

within and between provincial and federal public 

health institutions in Canada. Metadata is 

encoded in an application ontology following 

community recognized standards and extending 

existing OBO domain ontologies (http:// 

www.obofoundry.org/) to promote interoperability. 

Data analysis pipelines and execution 

provenance are transparently implemented 

using Galaxy, and federated data sharing and 

analysis is realized with a common REST 

API across platform instances. Data analysis 

tools being further developed include SNV- 

Phyl for phylogeographic analysis, in silico 

microbial typing capability, and IslandViewer/ 

visualization tools for antimicrobial resistance, 

virulence factor, and genomic island analysis. 

Linkage with other international genomic epidemiology 

initiatives, involving public genomic 

data release with more limited metadata, is 

also envisaged. A publicly available academic 

version of IRIDA that does not provide access 

to potentially sensitive epidemiologic metadata 

will provide IRIDA’s analysis tools for wider 

research use. An initial IRIDA version is being 

tested in the public health environment 

using current outbreak data, enabling further 

refinement of ontology and tool development. 

IRIDA is free, open-source software that may 

be a useful platform for other countries with 

autonomous health regions that wish to empower 

their public health workers’ genomic 

analysis capabilities. See http://www.irida.ca 

for more information. 

n 9 

PIPELINE FOR THE AUTOMATIC 

IDENTIFICATION OF PATHOGENS 

A. Andrusch, P. Dabrowski, A. Nitsche; 


Special diagnostics of infectious diseases 

nowadays rely on the gold standard of nucleic 

acid detection that is the polymerase chain 

reaction (PCR). The specific detection by PCR 

has its limit in that every pathogen has to be 

tested for with an own assay. This limitation 

can be overcome by adopting the molecular 

open view capacities that next-generation 

sequencing (NGS) can provide. NGS-based 

methods allow for the representative sequencing 

of all nucleic acids contained in clinical 

samples, enabling the downstream analysis 

of all generated reads for various known 

pathogens at once. This comes at the price of 

necessary filtering steps for the removal of 

background reads originating from the patient. 

Beyond that, NGS cannot only extend the 

diagnostic possibilities provided by PCR, but 

also serve as a stepping stone in the detection 

of hitherto unknown and novel pathogens. The 

‘Pipeline for the Automatic Identification of 

Pathogens’ (PAIPline) presented here, is a new 

complete workflow for the pathogen search in 

NGS datasets. It includes several steps for the 

preprocessing and quality control of the raw 

data to ensure that only information-rich reads 

are evaluated. It furthermore includes steps 

for the assignment of reads to their respective 

taxons based on reliable, established referencebased 

algorithms. Filtering of background 

reads, contaminants and organisms of low 

interest as well as the evaluation of ambiguous 

read information is automatically done before 

44 

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the results are presented. Analysis results are 

shown in a highly accessible manner, allowing 

the user to gain a quick overview as well 

as permitting deep analysis. The performance 

of the PAIPline was benchmarked on real and 

artificial datasets of known compositions and 

compared to competing tools. The results and 

discussed features show that the presented approach 

is a viable strategy for the identification 

of pathogen sequences in NGS datasets. 

n 10 

SEPARATION OF FOREGROUND AND 

BACKGROUND READS IN MIXED NGS 

DATASETS 

S. Tausch, A. Nitsche, B. Renard, P. Dabrowski; 


NGS is a valuable technology for rapid and indepth 

analysis of clinical samples, as it allows 

sequencing of a pathogen’s whole genome 

directly from patient material within as little 

as 26 hours. However, the follow-up analysis 

is severely slowed down by the abundance of 

reads originating from the host. Thus, in order 

to exploit the full potential of the technology 

for rapid diagnostics, a method for rapid in 

silico removal of host reads is necessary. Commonly, 

a mapping-based approach is used to 

separate reads: either reads mapping to a background 

reference or reads not mapping to a 

foreground reference are discarded. However, 

while the former approach is highly specific 

in discarding only true background reads and 

the latter is highly sensitive in only keeping 

foreground reads, neither offers a good balance. 

Hence we have aimed at developing a 

novel tool specifically geared towards both 

specific and sensitive separation of foreground 

and background reads. In order to determine 

whether a read belongs to the foreground or 

the background, we train markov chains of 

an order k from 4 to 12 on user-provided sets 

of foreground and background reference sequences, 

where each state is a k-mer of length 

k and each transition is one of the four possible 

bases A, C, G and T. We then calculate the 

difference of log likelihoods of each transition 

observed within a read with regards to 

the foreground and the background markov 

chains. This difference is then used as a score 

for the separation of reads, with scores smaller 

than 0 indicating a background read and scores 

larger than 0 indicating a foreground read. 

We have tested our tool on several datasets, 

including Cowpoxvirus sequenced from a 

human host. In all cases, our tool was faster 

than any competing tool (achieving speeds of 

up to 10 Megabases/second using 4 CPUs), 

including Kraken and mapping via bowtie2. 

At the same time, we consistently achieved 

the best F-Score of all tested tools. Our tool is 

developed in python and java and available for 

download from http://sourceforge.net/projects/ 

rambok/ We have developed a freely available, 

easy to use, rapid and both highly sensitive and 

specific tool for the separation of foreground 

and background reads in mixed NGS datasets. 

We believe that this will be highly useful as an 

initial filtering step for anyone analyzing viral 

sequences via NGS. 

n 11 

A RAPID AND SCALABLE SINGLE 

NUCLEOTIDE POLYMORPHISM DISCOVERY 

AND VALIDATION PIPELINE FOR OUTBREAK 

INVESTIGATION OF BACTERIAL PATHOGENS 

B. Rusconi 1 , A. L. Rodriguez 2 , S. S. Koenig 1 , 

M. Eppinger 1 ; 

1 

University of Texas at San Antonio - South 

Texas Center For Emerging Infectious Diseases 

(STCEID), San Antonio, TX, 2 University of 

Texas at San Antonio -Computational Biology 

Initiative, San Antonio, TX. 

Background: Assuring a timely and effective 

response in the control of bacterial outbreaks 

is challenging, as discriminatory power becomes 

of particular importance to distinguish 

outbreak isolates that form tight clonal complexes 

with only few genetic polymorphisms. 

The increase of throughput and concomitant 



45


cost reduction of next generation sequencing 

has allowed researchers to migrate from 

analyzing distinct loci in only few prototypical 

isolates to whole genome sequencing typing 

(WGST) of large bacterial collections. The 

growing sequence databases fortified with 

strain-associated metadata require efficient 

and well-integrated bioinformatics tools that 

will explore and harvest the information 

content of WGS data to enhance the traceability 

of microbes in support of informed and 

timely countermeasures. Particularly single 

nucleotide polymorphism (SNP) discovery 

and typing often surpass labor-intensive molecular 

typing schemes in offering both robust 

long- and short-term high-resolution variant 

markers. Methods: Our group has developed 

a bioinformatics SNP Discovery and Validation 

Pipeline (SNPDV) implemented on the 

open source platform Galaxy coded in Python 

that makes use of the wealth of sequence data 

for the whole genome sequence typing of 

bacterial pathogens. Its modular architecture 

guarantees high flexibility and scalability for 

SNP discovery and validation and can be run 

in serial, multiprocessor, or threaded version 

depending on available server capabilities. 

This pipeline allows to rapidly type strains of 

unknown provenance by testing allelic states 

in already established SNPs panels, or for unbiased 

de novo discovery. Results: Given the 

clonal nature of outbreaks, we have successfully 

deployed this pipeline in the investigation 

of the 2010 Haiti cholera and 2006 spinach 

outbreaks, and diverse other enteric and 

emerging pathogens, such as Yersinia pestis, 

Bacillus spp., Vibrio spp., and Acinetobacter 

baumannii, achieving improved phylogenetic 

accuracy and resolution. Discussion: Following 

the concept of genomic epidemiology 

the gathered phylogenomic data have major 

public health relevance in utilizing sequencebased 

information for improved surveillance, 

strain attribution, and source identification 

to contain outbreaks. Canonical SNPs can be 

implemented in efficient typing assays offering 

robust phylogenetic signals for outbreak inand 

exclusion that surpass classical technologies. 

The phylogenomic framework is a further 

critical resource to precisely cluster pathogens 

into subgroups distinguished by different genotypic, 

epidemiological, and phenotypic traits, 

such as whether particular genotypes reflect 

highly pathogenic subpopulations. Future 

developments are directed towards the cloud 

implementation of this pipeline in collaboration 

with the UTSA Cloud and Big Data Laboratory 

(NSF Cloud). 

n 12 

COMBINING LABORATORY AND 

EPIDEMIOLOGICAL DATA FOR OUTBREAK 

INVESTIGATION AND PUBLIC HEALTH 

SURVEILLANCE: LESSONS FROM AN 

OUTBREAK OF HIV-1 INFECTION LINKED TO 

INJECTION DRUG USE OF OXYMORPHONE _ 

INDIANA, 2015 

E. M. Campbell 1 , R. R. Galang 1 , J. Gentry 2 , P. 

J. Peters 1 , S. J. Blosser 2 , E. L. Chapman 2 , C. 

Conrad 2 , J. W. Duwve 2 , L. Ganova-Raeva 3 , W. 

Heneine 1 , D. Hillman 2 , H. Jia 1 , L. Lui 2 , J. Lovchik 

2 , A. Perez 2 , P. Peyrani 4 , P. Pontones 2 , S. 

Ramachandran 3 , J. C. Roseberry 2 , M. Sandoval 

2 , A. Shankar 1 , H. Thai 3 , G. Xia 3 , Y. Khudyakov 

3 , W. H. Switzer 1 ; 

1 

Division of HIV/AIDS Prevention, National 

Center for HIV/AIDS, Viral Hepatitis, STD, 

and TB Prevention, CDC, Atlanta, GA, 2 Indiana 

State Department of Health, Indianapolis, 

IN, 3 Division of Viral Hepatitis, National Center 

for HIV/AIDS, Viral Hepatitis, STD, and 

TB Prevention, CDC, Atlanta, GA, 4 Division of 

Infectious Diseases, University of Louisville, 

Louisville, KY. 

In January 2015, a cluster of HIV-1 infections 

was detected in rural Indiana among persons 

who reported injecting the prescription opioid, 

oxymorphone. As of May, HIV-1 infection 

was diagnosed in 153 individuals. Molecular 

analyses of HIV-1 and HCV sequences were 

combined with epidemiological data via a 

novel bioinformatics pipeline to infer the 

timing of HIV transmission relative to HCV 

and to explore risk factors associated with 

46 

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the inferred transmission network. Interviews 

were conducted with HIV-1-positive patients 

to capture high-risk contacts with respect to 

needle-sharing events and sexual encounters. 

HIV polymerase (pol) and HCV NS5B gene 

sequences obtained from blood specimens 

from newly diagnosed infections were phylogenetically 

analyzed. Clusters were defined 

when HIV-1 pol sequences were highly genetically 

related (0.99). 

Genetic distances < 1.5% were used to infer 

the HIV-1 transmission network. Molecular, 

demographic, behavioral, and high-risk contact 

data were combined to discern transmission 

and haplotype networks. Interactive exploration 

of these networks through open source 

tools informed public health response and 

helped to prioritize resources. One large cluster 

of HIV-1 subtype B infections was identified 

(n= 55) that was comprised of three primary 

haplotypes. Of 36 HIV-infected specimens 

with HCV antibody results, 34 (94%) were 

HCV co-infected. Among all HCV infections, 

genotype 1a (n=82) was most common, followed 

by 3a (n=29), 2b (n=5), and 1b (n=3). 

Three unique clusters of HCV strains were 

identified (Cluster 1, n = 45; Cluster 2, n =9; 

Cluster 3, n = 7. Of 118 HCV-infected specimens 

with HIV antibody results, 38 (32.2%) 

were HIV co-infected. The overwhelming 

majority of HIV transmission events (98.3%) 

occurred during needle-sharing rather than 

sexual encounters (1.7%). Positive assortativity 

(r=0.17) among needle-sharing encounters 

was observed between individuals who 

identified as commercial sex workers. In this 

outbreak, a single strain of HIV-1 was introduced 

into a population infected with multiple 

HCV strains. Due to the lack of molecular 

diversity, transmission network reconstruction 

was uninformative until contextualized 

with epidemiological data afforded by patient 

interviews. The heterogeneity of HCV strains 

(clustering and non-clustering) suggests earlier 

introduction of HCV compared with HIV. 

These data demonstrate the outbreak potential 

with introduction of HIV-1 into a community 

where HCV prevalence is high among persons 

who inject prescription opioids. These results 

also highlight the utility of combining multiple 

bioinformatics methods into a single pipeline 

to rapidly synthesize data from local, state, and 

federal public health institutions for characterizing 

transmission networks and to provide a 

rapid public health response. 

n 13 

AN AUTOMATED PIPELINE FOR MICROBIAL 

GENOME SEQUENCE ASSEMBLY AND 

CHARACTERIZATION 

F. Onmus-Leone, E. Snesrud, L. Appalla, P. 

McGann, A. Ong, R. Maybank, M. Hinkle, E. 

Lesho, R. Clifford; 


Spring, MD. 

Background: The Multidrug-resistant organism 

Repository & Surveillance Network 

(MRSN) collects and characterizes multidrug 

resistant bacteria isolated throughout the US 

military healthcare system. To complement 

species identification, antibiotic susceptibility 

testing and molecular assays, the MRSN has 

begun to employ whole genome sequencing 

(WGS) for routine bacterial characterization. 

Objective: To perform WGS on the hundreds 

of isolates received each month, MRSN has 

developed an automated analysis pipeline built 

from commercial, open source and custom 

software. Methods: Sequencing platforms 

used by MRSN are the Illumina MiSeq, Illumina 

NextSeq and PacBio RSII. Assembly begins 

with the merging of overlapping sequencing 

reads using FLASh and the filtering of lowquality 

portions of reads with Btrim. Filtered 

reads are then assembled with GS Assembler 

software. Assembly quality is measured by 

read coverage, the number of contigs and contig 

N50. Species identification is performed by 

searching against the SILVA 16S rDNA sequence 

database; contamination is indicated by 

the presence of 16S sequences from multiple 

species or the existence of multiple contigs 

with a complete 16S gene from one species. 



47


Assemblies that pass quality control continue 

through the pipeline for the next phase of analysis, 

which includes identification of resistance 

genes, identification of virulence genes, plasmid 

Inc typing and multilocus sequence typing. 

These analyses are BLAST-based searches 

against nucleotide and protein databases from 

public sources that are quality checked, expanded 

with the addition of novel genes reported 

from the literature and curated by MRSN. 

The RAST annotation server and Prokka are 

used for “working” whole genome annotation. 

Final genome annotation is performed by the 

NCBI upon sequence submission. Determining 

strain relatedness is critical for hospital infection 

control and disease epidemiology. For 

whole-genome phylogenetic analysis, sequencing 

reads from isolates are aligned to a reference 

genome using Bowtie. High quality SNPs 

and indels in the core genome are used to build 

evolutionary trees. Overall genome similarity 

(chromosomes and plasmids) among strains is 

measured using BLAST-based methods; similarity 

matrices are used for UPGMA clustering. 

Results: Our pipeline allows the automated 

analysis of 50 genomes per week and can be 

readily scaled up. We have processed ~1800 

bacterial genomes sequenced on Illumina platforms 

to date. Custom scripts control the pipeline, 

handle file management, produce reports 

and prepare results for loading into the MRSN 

database. Conclusion: The value of WGS data 

to infectious disease surveillance is unequaled, 

allowing comprehensive identification of resistance 

loci and definitive determination of strain 

relatedness. The analytic pipeline developed 

by MRSN will be used to characterize all bacteria 

in our repository. 

n 14 

ARMOR-D: A DATABASE LINKING 

DEMOGRAPHIC, PHENOTYPIC AND 

GENOMIC DATA FOR MULTIDRUG- 

RESISTANT BACTERIA 

R. Clifford 1 , M. Julius 1 , Y. Kwak 1 , F. Onmus- 

Leone 1 , L. Appalla 1 , E. Snesrud 1 , J. Padilla 1 , 

G. Ward 1 , M. Sparks 1 , P. Waterman 2 , M. Hinkle 

1 , E. Lesho 1 ; 

1 


Spring, MD, 2 Armed Forces Health Surveillance 

Center, Silver Spring, MD. 

Background: In response to Executive Order 

#13676 Combating Antibiotic Resistant Bacteria, 

interagency efforts to link phenotypic, 

demographic and sequence data in a central 

database similar to Genbank have recently 

begun. Objective: To assist, we describe the 

Antimicrobial Resistance Monitoring and Research 

Database (ARMoR-D) developed for 

the Department of Defense (DoD) healthcare 

system. Methods: ARMoR-D is a relational 

database built upon SQL Server®, C#, .NET® 

and other enterprise technologies. Data objects 

are highly normalized and modeled after those 

naturally falling in this domain - primarily isolates 

and results. Related business rules are not 

part of the data model, but are implemented in 

the application code. User managed “dictionary” 

database tables enhance data quality control. 

ARMoR-D has a web-based user interface 

to support characterization and archiving of 

isolates. It imports data from submitting facilities 

and test results from the central referral 

laboratory, and manages repository inventory. 

ARMoR-D stores high-level whole genome 

sequencing data from a semi-automated pipeline, 

including 16S-based species identification, 

MLST typing, antibiotic resistance and 

virulence genes, quality control metrics, and 

the locations of sequence assembly files on the 

system. ARMoR-D has a generic data model 

that can be easily extended to support new 

organisms, sample types, laboratory test results 

and other data that pertain to the characterization 

of biological isolates and/or isolate reposi- 

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tories. Due to its unique mission, the ARMoR 

Program typically collects multiple results 

for the same test per isolate; thus there is no 

limit on the number of data records associated 

with a given data object. Special features 

in ARMoR-D flag and resolve discrepancies 

among replicate test results. ARMoR-D also 

allows users to work effectively with de-identified 

data while retaining restricted access to 

personally identifiable information necessary 

for epidemiology and transmission analyses. 

Results: Currently, ARMoR-D contains 2 million 

test results from the centralized analyses 

of more than 30,000 clinical isolates, along 

with their associated demographic information 

and repository locations. The database also 

contains sequence-based results for more than 

1000 of these bacterial samples. Conclusion: 

ARMoR-D provides a useful model for those 

seeking to link demographic, phenotypic, and 

genomic data as they respond to the national 

strategy to combat antibiotic resistant bacteria. 

Future versions of ARMoR-D will include a 

public-facing webpage with decision support 

tools and calculators for optimized antibiotic 

dosing, and machine based learning algorithms 

for identifying genetic determinants of antimicrobial 

resistance and predicting drug-resistant 

phenotypes from the genome sequences of 

isolates that have not yet undergone automated 

susceptibility testing. 

n 15 

A WEB-HOSTED R WORKFLOW TO 

SIMPLIFY AND AUTOMATE THE ANALYSIS 

OF 16S NGS DATA 

J. Bradshaw 1 , T. Purucker 2 , K. Wong 1 , M. 

Molina 2 ; 

1 

ORISE, Athens, GA, 2 U.S. EPA, Athens, GA. 

Next-Generation Sequencing (NGS) produces 

large data sets that include tens-of-thousands 

of sequence reads per sample. For analysis of 

bacterial diversity, 16S NGS sequences are 

typically analyzed in a workflow that containing 

best-of-breed bioinformatics packages that 

may leverage multiple programming languages 

(e.g., Python, R, Java, etc.). The process to 

transform raw NGS data to usable operational 

taxonomic units (OTUs) can be tedious due to 

the number of quality control (QC) steps used 

in QIIME and other software packages for 

sample processing. Therefore, the purpose of 

this work was to simplify the analysis of 16S 

NGS data from a large number of samples by 

integrating QC, demultiplexing, and QIIME 

(Quantitative Insights Into Microbial Ecology) 

analysis in an accessible R project. User command 

line operations for each of the pipeline 

steps were automated into a workflow. In addition, 

the R server allows multi-user access 

to the automated pipeline via separate user 

accounts while providing access to the same 

large set of underlying data. We demonstrate 

the applicability of this pipeline automation 

using 16S NGS data from approximately 

100 stormwater runoff samples collected 

in a mixed-land use watershed in northeast 

Georgia. OTU tables were generated for each 

sample and the relative taxonomic abundances 

were compared for different periods over storm 

hydrographs to determine how the microbial 

ecology of a stream changes with rise and fall 

of stream stage. Our approach simplifies the 

pipeline analysis of multiple 16S NGS samples 

by automating multiple preprocessing, QC, 

analysis and post-processing command line 

steps that are called by a sequence of R scripts. 

n 16 

DOING PIPELINES BETTER 

M. J. Stanton-Cook, T. J. Robinson, N. L. Ben 

Zakour, S. A. Beatson; 

The University of Queensland, Brisbane, AUS- 

TRALIA. 

Over the last three years we have developed 

the Banzai Microbial Genomics Pipeline 

(https://github.com/mscook/Banzai-MicrobialGenomics-Pipeline). 

Banzai aims to 

simplify the analysis of microbial next-gen 

sequencing (NGS) datasets. It was specifically 

designed to distribute workload over internal 

and external High Performance Computing 



49


(HPC) resources. Banzai (in most cases) does 

not provide new NGS algorithms, but it harnesses 

the power of tried and tested NGS tools. 

Banzai simplifies, automates and distributes 

computational workloads, which is the typical 

bottleneck in analysis of large NGS datasets. 

Our local Banzai Microbial Genomics Pipeline 

install was initially tightly coupled to an aging 

HPC resource. Here, I will discuss how we 

future proofed and migrated our pipeline using 

DevOPs approaches. In particular, how we 

restructured the code base, abstracted out the 

details of the underlying compute resources, 

and how we now treat the entire Banzai Pipeline 

and underlying compute resources as an 

Infrastructure as Code model. This is great 

first hand lesson how we had to significantly 

change our development practice to consider 

long term sustainability of our pipeline. In less 

than three years, the Beatson Laboratory has 

had to scale from the simultaneous analysis of 

10 to 100 to 1000 genomes. By understanding 

and employing DevOps based approaches the 

genomics community will be able to build and 

scale reproducible analysis pipelines with minimal 

modification to their existing processes. 

n 17 

DEVELOPMENT OF A PLATFORM FOR 

PLANT VIRUS DIAGNOSTICS AND 

CHARACTERIZATION USING NEXT 

GENERATION SEQUENCING 

P. A. Gutiérrez, L. Muñoz Baena, H. Jaramillo 

Mesa, D. Muñoz Escudero, M. A. Marín Montoya; 

Universidad Nacional de Colombia, Medellin, 

COLOMBIA. 

Next generation sequencing methods are becoming 

an essential tool for the discovery and 

characterization of plant viruses as well as in 

the diagnostics of infected material in seed 

certification and virus management programs. 

Unfortunately, for traditional plant pathologists, 

efficient use and interpretation of the 

massive amount of data obtained from NGS 

is still a daunting task that generally requires 

of expert bioinformaticians to extract useful 

information. In this work, PVDT (Plant Virus 

Diagnostic Tool), an automated diagnostic 

platform for plant virus detection and characterization 

is presented. PVDT allows for raw 

data quality checking, sampling of reads and 

outputs an easy to interpret diagnosis of virus 

genera and their relative levels in the sample. 

A secondary analysis allows for virus assembly 

using either a sequential de novo method 

or mapping with a reference genome. Finally, 

PVDT determines whether detected viruses 

could be a novel species using ICTV criteria 

or an uncharacterized variant. The program 

was tested succesfully using simulated data 

sets and plant transcriptomes of Potato (Solanum 

tuberosum, S. phureja), Cape gooseberry 

(Physalis peruviana), Tamarillo (S. betaceum), 

Bell pepper (Capsicum annuum), Tomato (S. 

lycopersicum), Angel´s trumpet (Brugmansia 

candida) and Pinto peanut (Arachis pintoi). 

This platform is a contribution to the development 

of user-friendly tools for diagnostic 

purposes using NGS data and hopefullly will 

be useful to agricultural decision-making institutions. 

This work was funded by Universidad 

Nacional de Colombia (Grant VRI: 19438) and 

International Foundation for Science (Sweden, 

Grant: C/4634-2). 

n 18 

COMPARISON OF PHYLOGENIC 

METHODS FOR IDENTIFYING PUTATIVE 

TRANSMISSIONS OF ENTEROCOCCUS 

FAECIUM IN A HOSPITAL 

H. C. Lin 1 , T. Mi 1 , G. Wang 2 , W. Huang 2 , P. 

Mayigowda 1 , K. Murugesan 1 , A. Gupta 1 , J. T. 

Fallon 2 , N. Dimitrova 1 ; 

1 

Philips Research North America, Briarcliff 

Manor, NY, 2 New York Medical College, Valhalla, 

NY. 

Background: Enterococcus faecium is a major 

cause of hospital acquired infection in the 

US. Recently, whole genome sequencing data 

has been used for molecular typing, antibiotic 

resistance characterization and transmission 

50 

ASM Conferences


route reconstruction. For the purpose of reconstructing 

putative transmission routes, a 

variety of phylogeny building methods have 

been developed and can help identify potential 

transmission events within a hospital environment. 

Method: We sequenced 149 E. faecium 

isolates of MLST type ST736 from 106 patients 

over 55 months at Westchester Medical 

Center, and reconstructed phylogenies on the 

samples with various distance based methods. 

To construct the phylogenies, we used a variant 

calling pipeline to determine likely SNPs 

within the samples, and then we applied various 

filters to remove inaccurate SNPs, such 

as removing SNPs in repetitive regions and 

phage regions. Afterwards, a pairwise distance 

matrix was built based on the SNP calls, and 

finally, we applied various phylogeny building 

methods to the distance matrix. The methods 

we experimented with were neighbor-joining, 

maximum likelihood, and Prim’s (undirected) 

and Edmond’s (directed) minimum spanning 

tree (MST) algorithms. The results of these 

methods were then compared to each other to 

determine how robust and accurate our results 

may be. Lastly, we also evaluated how frequently 

antibiotic resistance increased along 

the predicted transmission events in our tree. 

Results: The trees we generated mostly have 

good concordance with each other often with 

more than 80% of subtrees matching between 

results. Additionally, we also computed how 

frequently antibiotic resistance increased along 

transmissions suggested by the MST methods. 

Both MST methods showed that antibiotic 

resistance generally increased along transmission 

edges in the trees, and Edmond’s directed 

MST method showed a higher percentage of 

the predicted transmission events resulting in 

increased daptomycin resistance, with about 

3/4 of transmissions indicating an increase in 

resistance. As we expect resistance to increase 

as transmissions occur, this may indicate that 

Edmond’s directed MST produced a more 

accurate tree. Conclusion: Our preliminary 

results show that Edmond’s directed MST may 

produce an accurate phylogeny tree, although 

further verification may be needed, as other 

methods yield slightly different trees. In the future, 

we plan to examine clinical data in order 

to help validate the correctness of the transmissions 

predicted by our phylogeny trees. 

n 19 

ANALYSIS OF AN ENTEROVIRUS D68 

OUTBREAK THROUGH METAGENOMIC 

SEQUENCING 

H. Lin 1 , Q. Wan 1 , W. Huang 2 , G. Wang 2 , J. 

Zhuge 2 , S. M. Nolan 2 , J. T. Fallon 2 , N. Dimitrova 

1 ; 

1 



NY. 

Background: Metagenomic sequencing can be 

used to detect the presence of microbial organisms. 

Many metagenomic techniques have 

been developed to explore and verify microbial 

ecology, evolution and diversity, especially for 

investigating infectious viruses and bacteria. 

By obtaining the population distribution of 

classified microbial genomes in certain samples, 

metagenomic tools have the potential to 

provide insights into the causality of infectious 

disease outbreaks. During the Enterovirus D68 

outbreak in 2014, 93 nasopharyngeal swab 

samples were obtained from patients with 

symptoms of respiratory infection and were 

sequenced at Westchester Medical Center. 

Methods: We used Kraken for the purpose of 

classifying reads based on taxonomic orders. 

By using Kraken, microbial reads can be identified 

and subsequently enable the investigation 

of certain infectious disease outbreak. To 

obtain the best performance from Kraken, we 

build a pipeline to automatically fetch publicly 

available reference genomes from NCBI at 

user’s demand to keep Kraken’s classification 

results up-to-date for detecting pathogens present 

within a sample. The pipeline is not only 

able to extract reads by viruses, bacteria, and 

fungi species, but can also generate aggregate 

analysis indicating the most prevalent microorganisms 

within any taxonomic group. We also 

provide analysis about possible cohabitations 



51


of different pathogens, in terms of a pairwise 

correlation ranking between any species, as 

well as an overall plot of co-existing species 

within samples. Moreover, the distribution 

of reads classified into any species across all 

patients can be plotted. Results: Our pipeline 

detects 1148 species among reads from the 

93 patient samples. Overall, the number of 

identified Enterovirus D68 reads ranks the 4th 

(8.79%) among all microbial species, but dominates 

viral sequences found in our samples 

with a proportion of 96.40% of viral reads. The 

next highest occurrence of viral reads consists 

of Human respiratory syncytial virus (2.37%) 

and Rhinovirus B (0.32%). Additionally, the 

pipeline reveals a possible cohabitation between 

Enterovirus D68 and other bacteria and 

viruses, such as Rhinovirus, which can both 

cause symptoms of respiratory infection. We 

observe that EVD68 is exceeded by Moraxella 

catarrhalis (41.34%), Haemophilus influenzae 

(20.79%) and Pseudomonas aeruginosa 

(16.55%), which can be pathogenic bacteria. 

Conclusion: We built an automatic metagenomic 

analysis pipeline designed for detecting 

pathogens that can cause infectious disease 

outbreaks at hospitals. The pipeline provides 

tools to visualize and investigate sequence read 

classification to conveniently discover pathogens 

residing within samples and discover 

microbial cohabitations. 

n 20 

MUTATION RATE ANALYSIS TO AID 

IN IDENTIFYING TRANSMISSIONS OF 

ENTEROCOCCUS FAECIUM IN A HOSPITAL 

SETTING 

P. Mayigowda 1 , A. Gupta 1 , H. Lin 1 , K. Murugesan 

1 , T. Mi 1 , G. Wang 2 , A. Dhand 2 , J. Fallon 2 , 

N. Dimitrova 1 ; 

1 



NY. 

Next-generation sequencing (NGS) has proved 

instrumental in tracing the spread and evolution 

of bacterial populations through time and 

geographical locations. With genome-wide 

analysis of single nucleotide polymorphisms 

(SNPs) we can measure genetic evolution by 

comparing SNP changes in a genome and estimate 

the mutation rate over time of a particular 

pathogen of interest. This estimated mutation 

rate can then be used to determine if there may 

have been a transmission between any two 

patients. Namely, if the number of changes 

occurring over time in a pathogen sequenced 

from two patients is within the typical number 

of changes we expect to see based on the expected 

mutation rate, then we can infer that a 

transmission may have occurred. If the number 

of changes is outside the typical mutation rate, 

then we can conclude that a transmission likely 

did not occur. We analyzed 149 non-duplicate 

Enterococcus faecium isolates belonging to 

clone ST736 to estimate the mutation rate for 

E. faecium isolates to help identify a probable 

path of the spread of the pathogen. These isolates 

were collected from 106 patients at Westchester 

Medical Center, New York, on various 

dates from 2009 to 2013. Out of these 106 

patients, 28 had two or more isolates, which 

were compared pairwise to estimate the mutation 

rate using linear regression on the number 

of SNP differences over time for 59 pairs of 

isogenic samples. To visualize the mutation 

rates of multidrug resistant isolates, separate 

mutation rates were calculated for 7 pairs of 

samples that maintained their status as daptomycin 

susceptible (dap S), and 32 pairs that 

maintained daptomycin non-susceptible (dap 

NS) status. We also analyzed pairs of isolates 

that converted from dap S to dap NS (8 pairs) 

or vice-versa (12 pairs). The mutation rate was 

observed to be 10.0 Substitutions per Mb per 

Year (SMY) after filtering for outliers (mean ± 

3SD) through the mean shift approach. Paired 

isolates maintaining dap S status had a mean 

mutation rate of 5.85 SMY, while the paired 

dap NS isolates had a mutation rate of 12.3 

SMY. For conversion of isolates from dap S 

to dap NS the mutation rate was 12.6 SMY 

while dap NS to dap S had a particularly high 

mutation rate of 53.2 SMY. These results are 

52 

ASM Conferences


comparable to a recent study which reported 

mutation rates for different clades of a phylogenetic 

tree constructed from 73 enterococci 

isolates. The rate was 49 ± 3 SMY for a clade 

belonging to clonal complex CC17, and was 

3.6 ± 0.6 SMY and 13 ± 2 SMY for two 

clades with mixed STs. We have developed 

a method for mutation rate inference using 

SNPs obtained from NGS data. Standardizing 

the filtering process will allow expanding this 

approach to other species. With studies reporting 

variation in mutation rates within species 

such procedural methodology becomes all the 

more important. This bioinformatics approach 

to identify disease transmission paths can help 

recognize, control, and diagnose bacterial 

clinical outbreaks. 

n 21 

COMPARISON OF RNA AND DNA 

EXTRACTION KITS FOR VIRUS DETECTION 

BY NEXT GENERATION SEQUENCING (NGS) 

J. Klenner, C. Kohl, P. Dabrowski, A. Nitsche; 


A crucial step in the molecular detection of 

viruses in clinical specimens is the efficient 

extraction of viral nucleic acids. The total yield 

of viral nucleic acid from a clinical specimen 

is dependent on the specimens’ volume, the 

initial virus concentration and the effectiveness 

provided by the extraction method. Recent 

Next Generation Sequencing (NGS)-based 

diagnostic approaches provide a molecular 

‘open view’ into the sample, as they generate 

sequence reads of any nucleic acid present 

in a specimen in a statistically representative 

manner. However, since a higher virus-related 

read output promises better sensitivity in the 

subsequent bioinformatic analysis, the extraction 

method selected determines the reliability 

of diagnostic NGS. In this study four commercially 

available nucleic acid extraction 

kits (QIAGEN, Hilden, Germany: QIAamp 

Viral RNA Mini Kit, QIAamp DNA Blood 

Mini Kit, QIAamp cador Pathogen Mini Kit 

and QIAamp MinElute Virus Spin Kit) were 

evaluated by NGS. The nucleic acid yields 

and sequence read output were compared for 

four different model viruses comprising Reovirus, 

Orthomyxovirus, Orthopoxvirus and 

Paramyxovirus, each at defined but varying 

concentrations in the same sample. The total 

nucleic acid extracted was divided into two 

aliquots; one was subjected to RNA and the 

other to DNA processing for NGS. The yield 

of nucleic acids was determined by Qubit and 

virus-specific quantitative real-time PCR. NGS 

libraries were prepared for sequencing on 

the Illumina HiSeq 1500 system. Finally, the 

percentage of reads which could be assigned 

to each virus after extraction with the different 

kits was determined via mapping. As presented 

here, evaluation of the different commercial 

nucleic acid extraction kits indicates little 

deviation in the numbers for RNA and DNA 

reads, depending on the kit used. 

n 22 

TRANSMISSION OF HIGH RISK K. 

PNEUMONIAE CLONES IN HEALTH CARE 

NETWORKS LARGELY CHALLENGES THE 

CURRENT INFECTION PREVENTION AND 

CONTROL SYSTEM 

K. Zhou, M. Lokate, R. H. Deurenberg, G. C. 

Raangs, H. Grundmann, A. W. Friedrich, J. W. 

Rossen; 

University of Groningen, University Medical 

Center Groningen, Groningen, NETHER- 

LANDS. 

Controlling dissemination of multidrugresistant 

pathogens remains one of the major 

challenges in hospitals and public health. Here 

we describe an inter-institutional transmission 

of an ESBL-producing ST15 Klebsiella pneumoniae 

between patients caused by patient 

referral. An epidemiological link between 

the patient isolates was supported by patient 

contact tracing and phylogenetic analysis of 

the isolates obtained from May to November 

2012 using next generation sequencing (NGS). 

By May 2013, a patient treated in two institutions 

in two cities was involved in expanding 



53


the cluster. A clone-specific multiplex PCR 

was developed for patient screening by which 

another patient was identified in September 

2013. Environmental surface contamination 

and lack of consistent patient screening were 

identified as risk factors. Our study highlights 

the challenge of controlling the transmission 

of K. pneumoniae high risk clones (HiRiCs), 

suggesting the necessity for active surveillance 

and inter-institutional collaboration for 

outbreak management. In addition, the use of 

NGS for typing and for developing an outbreak-specific 

multiplex PCR facilitated rapid 

patient screening procedures and was important 

for optimizing outbreak management. 

n 23 

WHOLE GENOME SEQUENCING OF FOUR 

ENTEROPATHOGENIC E. COLI STRAINS 

ASSIGNED TO A NEW SEQUENCE TYPE 

ST4554 

M. Ferdous 1 , K. Zhou 1 , A. M. Kooistra-Smid 2 , 

A. W. Friedrich 1 , J. W. Rossen 1 ; 

1 



LANDS, 2 University Medical Center Groningen 

and Certe Laboratory for Infectious Diseases, 

Groningen, NETHERLANDS. 

Enteropathogenic Escherichia coli (EPEC) is a 

leading cause of infantile diarrhoea in developing 

countries. They are comprised of a large 

heterogeneous group of strains and serotypes. 

EPEC strains assigned to a new sequence type 

(ST4554) were isolated from four different 

patients with gastrointestinal complaints in 

two different regions of the Netherlands during 

July 2013-February 2014. No epidemiological 

link was found between the four patients. 

All four isolates were non-motile, beta glucuronidase 

negative, sorbitol fermenting and 

of phylogenetic group B2. All were resistant 

to ampicillin, three to trimethoprim and trimethoprim/sulfamethoxazole 

whereas the 

fourth one was resistant to tetracycline. Whole 

genome sequencing (WGS) was performed on 

the four strains for detailed characterization 

and to compare them with their closest relative 

E. coli strain. The serogenotype and the 

presence of antibiotic resistance and virulence 

genes were determined using the Centre for 

Genomic Epidemiology (CGE) web tool. Phylogenetic 

analysis was done using Ridom Seq- 

Sphere+. The serogenotype of the isolates was 

O157:H39. Virulence profiling revealed the 

presence of adhesin genes eae (type kappa), tir, 

iha, and secretion system genes espA, espC, 

espI, espJ, and etpD. Virulence genes were 

located on a pathogenicity island, plasmid or 

insertion element. The antibiotic resistance 

genes strA, strB, sul and dfrA were located on 

a pCERC1-like resistant plasmid found in E. 

coli strain S1.2.T2R, whereas the tetA gene 

was located on transposon Tn121. Phylogenetic 

analysis using core genome MLST revealed 

that three of our isolates contained no different 

alleles but differed from the fourth one in 62 

alleles. Subsequent SNP analysis revealed 20 

SNPs among the three isolates: 5 non-synonymous, 

4 synonymous and 11 in intergenic 

regions. E. coli strain E2348/69 was found as 

the most closely related of our isolates when 

determining the phylogeny including 60 complete 

genomes of E. coli available in NCBI. A 

genome-wide comparison of the four isolates 

with E. coli E2348/69 shows that they lacked 

most of the mobile genetic elements (MGEs) 

found in E. coli E2348/69 except one prophage 

pp1, one insertion element IE5 and two pathogenicity 

islands (LEE and espC pathogenicity 

island). They possess additional MGEs as, e.g., 

prophage p16 of EPEC O26:H11 strain 11368, 

plasmid pO55 of E. coli O55:H7 and plasmid 

pSS_O46 of Shigella sonnei. Therefore, dissemination 

of virulence genes by MGEs may 

have resulted in these new pathogenic strains. 

WGS allowed us to get insight into the virulent 

and resistant determinants of the new EPEC 

isolates and to reveal their phylogenetic relationship 

with known E. coli strains. 

54 

ASM Conferences


n 24 

MOLECULAR CHARACTERIZATION OF SHIGA 

TOXIN PRODUCING ESCHERICHIA COLI 

(STEC) ISOLATES IN THE NETHERLANDS 

M. Ferdous 1 , A. M. Kooistra-Smid 2 , R. F. de 

Boer 2 , P. D. Croughs 3 , I. H. Friesema 4 , J. W. 

Rossen 1 , A. W. Friedrich 1 ; 

1 



LANDS, 2 University Medical Center Groningen 

and Certe Laboratory for Infectious 

Diseases, Groningen, NETHERLANDS, 3 Star- 

MDC, Rotterdam, NETHERLANDS, 4 National 

Institute for Public Health and the Environment, 

Bilthoven, NETHERLANDS. 

Shiga toxin producing Escherichia coli (STEC) 

is one of the major causes of human gastrointestinal 

disease and has been implicated in 

sporadic cases and outbreaks of diarrhoea, 

hemorrhagic colitis and haemolytic uraemic 

syndrome worldwide. Whole genome sequencing 

(WGS) was performed on 131 STEC 

isolates obtained from faeces of patients with 

gastrointestinal complaints from the regions 

Groningen and Rotterdam of the Netherlands, 

during April 2013 to March 2014 as part of the 

STEC-ID-net study. Multilocus sequence type, 

serogenotype, Shiga toxin encoding gene (stx) 

subtype and the presence of antibiotic resistance 

and virulence genes were determined 

using the Centre for Genomic Epidemiology 

(CGE) web tool. Phylogenetic analysis was 

done using Ridom SeqSphere+. Based on clinical 

symptoms (available for 76 patients), patients 

were divided into severe (n=28), moderate 

(n=35) and mild (n=13) groups. Statistical 

analyses were done by the Pearson Chi-Square 

and Fisher’s exact test. Based on WGS data a 

diversity of serotypes and sequence types (ST) 

were found with serotypes O91:H14 (14.5%), 

O157:H7(13%), O26:H11(11%), O103:H2 

(8%), O128:H2 (5%), O63:H6 (4%), O5:H9 

(3%) and sequence types ST33 (14.5%), ST11 

(13%), ST21 (13%), ST17 (7.6%), ST25 

(4.5%), ST583 (4%) being the most predominant 

ones. Several stx subtype combinations 

including stx1a (41%), stx2c, (8%), stx2f 

(8%), stx1c+stx2b (8%), stx1a+stx2c (7%), 

stx1c (7%), stx1a+stx2a (6%) and stx2a (6%) 

being predominant were found among the 

isolates. The presence of stx1 and stx2 gene 

together was significantly (P=0.02) associated 

with the severe patient group (46%). Among 

virulence genes other than stx, the toxin encoding 

genes toxB, astA and non-LEE-encoded 

effector protein encoding gene nleC were 

detected with high prevalence in this group 

(P


n 25 

RAPID DIAGNOSTIC TESTING OF 

MYCOBACTERIUM TUBERCULOSIS 

CULTURES USING WHOLE-GENOME 

SEQUENCING 

P. Lapierre, J. Shea, T. Halse, M. Shudt, P. Van 

Roey, V. Escuyer, K. Musser; 

NY DOH Wadsworth Center, Albany, NY. 

Mycobacterium tuberculosis (MTB) remains 

an important pathogen today, infecting more 

than a third of the world population. The cost 

associated with the diagnostic and treatment 

of MTB can be considerable, specifically in 

cases involving MDR or XDR strains. Weeks 

to months are usually required to identify 

mycobacterial species, determine drug susceptibilities, 

and generate molecular genotyping 

for epidemiological purposes using traditional 

methods due to the slow growth rate of 

MTB. Whole genome sequencing (WGS) is 

perceived as a potential alternative for MTB 

diagnostics that will greatly improve the time 

required for the complete molecular profiling 

and antibiotic resistance prediction of new 

MTB cases. We have conducted a retrospective 

study on 87 clinical isolates, consisting of 51 

pure isolates on solid media and 36 early positive 

liquid cultures (MGIT), to determine the 

feasibility of using WGS as part of our routine 

testing algorithm for MTB samples. We have 

developed an efficient DNA extraction and 

library preparation method that yield consistent 

depth and data quality from WGS, utilizing 

Illumina MiSeq instrumentation. We built 

a bioinformatics pipeline that compares the 

Illumina reads against M. tuberculosis H37Rv 

reference strain for identification, in silico 

spoligotyping, resistance profiling and phylogenetic 

analysis. Our results show an almost 

complete concordance for strain identification 

and spoligotype determination when compared 

with current molecular methods, as well as 

more than 90% concordance between our in 

silico resistance prediction and our current 

molecular and conventional drug susceptibility 

testing methods. We estimate the total time 

required using WGS as a complete diagnostic 

test for culture samples to be approximately 7 

days. Given the diagnostic accuracy and reproducibility 

of this method, and the significant 

reduction in time from receipt of specimen to 

WGS results to predict antibiotic resistance, it 

is financially appealing to adopt this technology 

in a public health setting. 

n 26 

CHARACTERIZATION OF AN IMI-1 

CARBAPENEMASE-PRODUCING COLISTIN- 

RESISTANT ENTEROBACTER CLOACAE BY 

GENOME SEQUENCING AND INSERTIONAL 

MUTAGENESIS 

Z. Zong; 

West China Hospital, Sichuan University, 

Chengdu, CHINA. 

Background: A carbapenem-resistant Enterobacter 

cloacae, WCHECl-1060, was recovered 

from blood of a patient after stem cell transplantation. 

It was found carrying blaIMI-1, a 

carbapenemase gene by PCR and sequencing 

and was high-level resistance to colistin (MIC, 

64 mg/L). However, its genetic context of 

blaIMI-1 and mechanisms for colistin resistance 

were unclear. Methods: WCHECl-1060 

was subjected to whole genome sequencing 

with a ca. 100× coverage using the Hiseq 2500 

sequencer. Reads were assembled to contigs 

using Spades. MLST was performed using 

the assembled contigs. Phages and genomic 

islands were predicted using PHAST and IslandViewer 

tools. Insertional mutagenesis by 

Tn5 transoposon was performed and colistinsusceptible 

mutants were selected using replica 

plating. Inverse PCR and sequencing were 

used to identified genes interrupted by Tn5 

insertion. Results: A total of 5,591,800 reads 

and 698,975,000 bases were obtained with 

a 55.8% GC content. Reads are assembled 

to 21 contigs that are ≥500 bp (N50 metric, 

714,400 bp) and contain 4,808,707 bases. 

WCHECl-1060 belonged to a new ST, ST420. 

In addition to blaIMI, WCHECl-1060 also has 

quinuolone-resistance genes oqxA and oqxB 

56 

ASM Conferences


and fosfomycin-resistance gene fosA. blaIMI-1 

was located on chromosome and the 16.2-kb 

region containing blaIMI-1 (8.1 kb upstream 

and 7.3 kb downstream of blaIMI-1) has much 

lower GC content (37.1%), suggesting a foreign 

origin. Indeed, the 16.2-kb region was 

identified as a genomic island by IslandViewer. 

Two genes responsible for colistin resistance, 

phoP and yqjA, were identified by Tn5 insertional 

mutagenesis. phoP is part of the phoP/ 

PhoQ two-component system and mutations 

of phoP can lead to colistin resistance among 

other Enterobacteriaceae species in previous 

reports. yqjA encodes an inner membrane protein 

but has not been associated with colistin 

resistance before. The 675-bp phoP and 660-bp 

yqjA of WCHECl-1060 have at least 6 and 3 

nucleotides differences from those of other 

Enterobacter strains with sequences available 

in GenBank, respectively. Conclusions: 

This is the first genome sequence of blaIMI- 

1-carrying Enterobacter strain. blaIMI-1 was 

on a genomic island, which may explain why 

only few E. cloacae carry this gene. Colistin 

resistance in WCHECl-1060 are likely due to 

mutations of phoP and yqjA. 

n 27 

COMPUTATIONAL PIPELINE FOR NEXT 

GENERATION SEQUENCING-BASED 

PATHOGEN DETECTION IN CLINICAL 

SETTINGS 

L. Albayrak 1 , M. Rojas 1 , K. Khanipov 1 , S. M. 

Chumakov 2 , G. Golovko 1 , M. Pimenova 1 , Y. 

Fofanov 1 ; 

1 

University of Texas Medical Branch, Galveston, 

TX, 2 University of Guadalajara, Guadalajara, 

Jalisco, MEXICO. 

In recent past, next-generation sequencing 

(NGS) had been exclusively performed in 

large sequencing centers; however, dramatic 

progress in sequencing technology resulted in 

cost reduction and the improvement of quality 

throughput allowing it to be used by universities 

and even individual labs. Moreover, the 

latest “benchtop” sequencers have progressed 

into extremely cost-effective tools that can be 

used for pathogen detection in clinical settings. 

In order to transform NGS technology from 

being an exclusively research tool to being 

routinely used for clinical diagnostics, major 

challenges must be resolved, including: 1. The 

vast amount of data and computational complexity 

of NGS analysis tools requiring data to 

be stored away from place of origin, separating 

sequencing from analysis and the decision 

making process (as well as raising security 

and privacy concerns); 2. The absence of standardized 

analysis and task specific reference 

databases; 3. The large and often complicated 

reports generated by NGS data analysis pipelines 

which usually require Ph.D. level scientists 

to interpret. To address those challenges, 

we present a bioinformatics pipeline capable 

of evaluating NGS samples quickly and accurately, 

with relatively low computational infrastructure 

requirements. By utilizing a novel 

data format that reduces the size of sequenced 

reads files and keeps only high quality sequences 

in binary format, we can easily store, 

manipulate, and transfer large amounts of data 

produced by NGS instruments. The use of a 

robust collection of reference gene sequences 

instead of complete genomes greatly reduces 

the size of the reference database without 

compromising ability to detect and identify 

pathogens. Clustering together genes based on 

nucleotide similarity and using just a single 

representative sequence further reduces redundancy 

among gene sequences. We propose to 

use a reference-by-reference search against 

sequenced reads rather than a read-by-read 

search algorithm as used by BWA and Bowtie 

which generate reads-centric files (e.g., BAM/ 

SAM). The output of the proposed algorithm 

is much smaller than a read-centric approach 

and it is tied to the reference sequences instead 

of sequencing reads. The proposed methods 

of clustering, curation, compression and 

reference-by-reference sequence search paves 

way to bringing NGS based pathogen identification 

methods to clinical and field diagnostic 

settings. 



57


n 28 

EVALUATION OF METAGENOMIC RNA-SEQ 

FOR DETECTION OF ENTEROVIRUS D68 

AND RESPIRATORY VIRAL PATHOGENS IN 

CLINICAL SAMPLES 

W. Huang 1 , G. Wang 1 , H. Lin 2 , J. Zhuge 3 , S. M. 

Nolan 1 , N. Dimitrova 2 , J. T. Fallon 1 ; 

1 

New York Medical College, Valhalla, NY, 

2 


Manor, NY, 3 Westchester Medical Center, Valhalla, 

NY. 

Background: Next-generation sequencing 

(NGS) is becoming a novel approach 

for identifying the causative pathogens of 

infectious diseases directly from clinical 

specimens. However, practical application of 

NGS is hindered by lack of proven sensitive 

and reliable preparation protocols and by the 

bioinformatics challenge of building accurate 

and complete databases for data analysis. 

Here, we explored the potential application of 

NGS in detection of viral pathogens in clinical 

samples. Methods: A simple metagenomic 

RNA-Seq protocol, with reverse transcription 

and Illumina Nextera XT technology incorporated 

but without amplification or enrichment, 

was employed to analyze 93 nasopharyngeal 

swab specimens collected from patients in 

the lower Hudson Valley, New York during 

an outbreak of enterovirus D68 (EV-D68)- 

associated respiratory illness in 2014. Among 

these samples, 72 were positive for EV-D68 

using real-time reverse-transcriptase PCR 

(rRT-PCR) (J. Clin. Microbiol., 2015; 53:1915- 

1920). To detect EV-D68 and other pathogenic 

viruses, we employed three bioinformatics 

tools for analysis of the NGS data: alignment/ 

mapping using Illumina MiSeq Reporter, the 

PathSEQ Virome, and the sequence-based 

ultra-rapid pathogen identification (SURPI) 

viral software. Results: NGS sequencing 

yielded an average of 103,531 clusters passing 

filter per sample. Compared to the results from 

rRT-PCR, we detected 65, 66 and 69 samples 

positive for EV-D68 using the three bioinformatics 

tools, respectively. We also found 

that the scores in the PathSEQ Virome were 

reversely correlated to the cycle thresholds 

in the rRT-PCR assay. Both PathSEQ Virome 

and SURPI viral excelled in the simultaneous 

detection of multiple viruses. PathSEQ stood 

out in the final report, convenient for clinical 

use; whereas SURPI viral was more sensitive 

in identification of EV-D68 and other viruses. 

In addition, from these NGS sequence reads 

we were able to detect a variety of inhabiting 

bacteria by using SURPI Bacteria. Conclusion: 

Our results support NGS approach is 

comprehensive and efficient tool for pathogen 

detection. With improved sequencing protocols 

and bioinformatics tools, the NGS-based assay 

has a great potential in actionable identification 

of pathogens in clinical and public health 

laboratory settings. 

n 29 

A NEXT GENERATION SEQUENCING 

METHOD FOR THE PAN-IDENTIFICATION 

OF VIRAL PATHOGENS IN CSF FROM 

ENCEPHALITIS CASES 

Y. Sun 1 , C. Parikh 1 , Y. Ku 1 , D. Lamson 2 , J. Au- 

Young 1 , K. St. George 2 , A. Felton 1 ; 

1 

Thermo Fisher Scientific, South San Francisco, 

CA, 2 New York State Department of Health, 

Albany, NY. 

Encephalitis is commonly caused by viral infections 

and can be associated with significant 

morbidity and mortality. While variable at 

different times of the year, overall, the causative 

agent fails to be identified in more than 

75% of cases with current diagnostic methods. 

PCR- and serology- based approaches are 

limited by the requirement of prior knowledge 

and decisions on the pathogens to be tested. 

We demonstrate here a deep-sequencing based 

method with the potential for a more universal 

approach to testing of viral encephalitis 

cases. Initially, synthetic cerebrospinal fluid 

(sCSF) samples were spiked with several human 

encephalitic viruses, at a range of viral 

58 

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loads commonly seen in clinical CSF samples. 

Cultured, genomically quantitated, DNA and 

RNA viruses were used for spiking. Testing on 

the new platform was performed in a blinded 

manner. Samples were sequenced on the Ion 

Proton platform at a depth of approximately 

70 million per sample. Based on these data, we 

developed a bioinformatics pipeline to detect 

viral genome in the samples. Sequencing reads 

of human origin were filtered by aligning to 

human genome (hg19). The remaining reads 

were mapped to a reference database comprising 

viral genomes from NCBI. From a panel 

of 13 samples, virus was successfully and 

accurately identified in all but one of the 11 

virus-positive samples, which contained very 

low viral titers. In addition, the read depth of 

viral sequences correlated inversely with the 

Ct values obtained from virus-specific qPCR 

assays of the samples. To establish limit of 

detection with this approach, the 70 million 

sequencing reads were subsampled randomly 

to a range of 5-50 million for each sample. The 

bioinformatics analysis demonstrated that 5 

million reads is sufficient for detection of viral 

genomes in these samples. Once the workflow 

pipeline was established, the same approach 

was applied in a blinded manner, to 8 human 

CSF samples from patients with encephalitis. 

The samples had been tested previously for 14 

pathogens in a qPCR panel designed to detect 

viral encephalitic agents. Using 5 million 

reads, viral genomes were accurately identified 

in 4 samples. . When read depth was increased 

to 40-45 million, viral genome was accurately 

identified in one of the remaining 4 samples. 

The other 3 samples, which had tested negative 

for 14 target agents in the qPCR panel, 

remained negative for viral genomes in the 

new assay. The described new technique was 

demonstrated to be very effective for the detection 

of viral pathogens in CSF samples. Future 

experiments are planned to refine the technology 

for the detection and identification of 

causative pathogens in CSF from patients with 

encephalitis. 

n 30 

APPLYING WHOLE GENOME SEQUENCING 

TO DETECT MOLECULAR EVENTS LEADING 

TO THE ACQUISITION OF CARBAPENEM 

RESISTANCE IN CLINICAL SAMPLES 

L. Appalla 1 , P. Mc Gann 1 , E. Snesrud 1 , A. 

C. Ong 1 , F. Onmus-Leone 1 , M. Koren 2 , Y. I. 

Kwak 1 , P. E. Waterman 1 , E. P. Lesho 1 ; 

1 


Spring, MD, 2 Walter Reed National Military 

Medical Center, Bethesda, MD. 

Background: Multi-drug resistant organisms 

(MDROs) have emerged as a global threat to 

public health. Tracking the dissemination of 

MDROs and controlling their transmission are 

major challenges for the healthcare community 

worldwide. We have applied whole genome 

sequencing (WGS) and analysis to a collection 

of serial isolates from a single patient to 

identify the molecular events that led to the 

acquisition of carbapenem resistance during 

treatment. Initial cultures from the patient 

contained extended-spectrum β-lactamase 

(ESBL)-producing, carbapenem-susceptible, 

Escherichia coli. Multiple rounds of ertapenem 

treatment were administered, but the infection 

recurred after each course of antibiotics. Subsequent 

cultures yielded carbapenem-resistant 

E. coli and Morganella morganii. Methods: 

All isolates were sequenced using the Illumina 

MiSeq desktop sequencer. Sequencing data 

was assembled using an in-house pipeline that 

incorporates FLASh, Btrim and the GS De 

Novo Assembler. Assemblies were then passed 

through quality control filters that check read 

coverage, contig number and contig size. Further 

analysis using the bioinformatics pipeline 

included 16S species identification, multilocus 

sequence typing, comprehensive identification 

of antibiotic resistance genes and the detection 

of single nucleotide changes, insertion/deletion 

events and large-scale genomic rearrangements 

that distinguish the isolates. Results: All E. 

coli isolates were multilocus sequence type 

131 and carried 9 antibiotic resistance genes 

(including blaCTX-M-27, which confers an 



59


ESBL phenotype) on an IncF plasmid. The 

isolates were identical by genome sequencing, 

with the exception of 150 kb of plasmid 

DNA present only in the carbapenem resistant 

isolates. This DNA sequence included a sixty 

kilobase IncN plasmid carrying the carbapenemase 

gene blaOXA-181, present in M. morganii. 

In the M. morganii plasmid, blaOXA-181 

was flanked by IS3000 and ISKpn19, but in all 

but one of the carbapenem resistant E. coli isolates, 

a second copy of ISKpn19 had inserted 

adjacent to IS3000. Conclusion: blaOXA-181 

was acquired by a member of the virulent 

sequence type 131 E. coli clonal group via an 

IncN plasmid from M. morganii. Because M. 

morganii tends to have high intrinsic resistance 

to imipenem, and because blaOXA-181 has 

relatively weak carbapenemase activity and a 

substrate profile that includes penicillins but 

not extended-spectrum cephalosporins, the 

presence of blaOXA-181 in these strains was 

nearly overlooked. However, WGS and advanced 

sequence analysis techniques revealed 

that this gene was responsible for the acquisition 

of carbapenem resistance by E. coli. WGS 

represents a powerful approach for the surveillance 

of multidrug resistant microbes. 

n 31 

THE UTILITY OF WHOLE GENOME 

SEQUENCING IN CHARACTERIZING 

ACINETOBACTER EPIDEMIOLOGY AND 

ANALYZING HOSPITAL OUTBREAKS 

M. A. Fitzpatrick, E. A. Ozer, A. R. Hauser; 

Northwestern University, Chicago, IL. 

Background: Acinetobacter baumannii is a 

frequent cause of nosocomial infections and 

outbreaks. Whole genome sequencing (WGS) 

is a promising new technique for bacterial 

strain typing and outbreak investigation. Here 

we compare the performance of conventional 

methods with WGS for strain typing 

clinical Acinetobacter isolates and analyzing 

a carbapenem-resistant A. baumannii (CRAB) 

outbreak. Methods: We performed band-based 

typing, multi-locus sequence typing (MLST), 

and WGS on 148 Acinetobacter calcoaceticus- 

Acinetobacter baumannii complex bloodstream 

isolates collected from 2005-2012. Clustering 

dendrograms and phylogenetic trees were 

constructed using the results of band-based 

and sequence-based typing, respectively. 

Discriminatory power and level of agreement 

of the techniques were compared. WGS was 

then used to analyze an ICU CRAB outbreak 

that occurred in our hospital during the study 

period. Results: Phylogenetic trees inferred 

from core genome SNPs confirmed three 

Acinetobacter species within this collection. 

Four major A. baumannii sequence types (STs) 

circulated in our hospital over the course of the 

study, three of which have a global distribution 

pattern and one of which is novel. WGS 

indicated that a threshold of 2500 core SNPs 

accurately distinguished A. baumannii isolates 

with the same ST from those with different 

STs. Conventional band-based techniques performed 

less well in accurately assigning isolates 

to ST lineages and exhibited poor agreement 

overall with sequence based techniques. 

When WGS was applied to a CRAB outbreak, 

we found that a threshold of 2.5 core SNPs distinguished 

non-outbreak strains from outbreak 

strains. WGS was more discriminatory than 

conventional band-based techniques and was 

used to construct a more accurate transmission 

map that resolved many of the plausible 

transmission routes suggested by PFGE and 

epidemiologic links. More detailed accessory 

genome analysis identified a plasmid that was 

circulating among isolates over the course of 

the outbreak. Conclusion: Our study demonstrates 

that WGS is superior to conventional 

techniques for A. baumannii strain typing and 

outbreak analysis. These findings support incorporation 

of WGS into healthcare infection 

prevention efforts. 

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n 32 

KLEBSIELLA PNEUMONIAE IN ITALY: 

INSIGHTS FROM SHORT- AND LONG-TERM 

SCALE STUDIES 

F. Comandatore; 

University of Milan and University of Pavia, 

Milan and Pavia, ITALY. 

The diffusion of multi-drug resistant (MDR) 

pathogenic bacteria represents one of the most 

important issues for global public health. 

Indeed, during the last twenty years, a dramatic 

increase of nosocomial infections and 

outbreaks due to MDR pathogens has been 

reported world-wide. Klebsiella pneumoniae 

(Kp) isolates able to resist to third-generation 

cephalosporins and carbapenems have been 

reported in Countries spanning from Asia 

through Europe to America. Kp infections result 

in high morbidity and mortality in people 

with weak immune systems, such as inpatients 

of hospital intensive care units (ICUs). 

In Italy, since the first 2000s, an increasing 

number of MDR Kp nosocomial infections 

has been reported. We built an epidemiological 

network, connecting together six hospital 

microbiology units, a public health veterinary 

laboratory, and two university bioinformatic 

groups. During the first year (2014-2015), 

we developed in-house pipelines to perform 

SNP and wide-genome analyses on hundreds 

(up to thousands) bacterial genomes. Our first 

research project (AAC, 2015, vol. 53(4), 389- 

396) regarded 89 isolates from hospital collections. 

We included those genomes in a 319 

Kp genomes worldwide database, spanning 

the genetic variability across the species. The 

analysis of this genomic database provided 

important insights into the origin of the pathogenic 

clonal group Kp CG258, and about the 

emergence of Kp in Italy. Indeed, we described 

a huge genomic recombination (~1.3Mb size) 

that occurred during the emergence of the 

Kp CG258. The time-calibrated phylogenetic 

analysis allowed us to date that recombination 

around 1985. On the basis of that phylogenetic 

reconstruction, we were able to describe the 

four major Kp CG258 clades in Italy, and date 

their emergences from 2009 to 2010. The second 

research project was focused on a shorttime 

scale: we used genomic epidemiology 

approach to study a Kp outbreak that occurred 

in an Italian hospital during 2013 (JCM, 2015, 

00545-15). We were able to identify, and genetically 

describe, this pathogenic Kp clone. It 

resulted to belong to the Kp CG258 and to be 

phylogenetically associated to one of the four 

Italian major lineages described above. Furthermore, 

the SNP analysis showed that this 

Kp clone spread across the ICU through a sole 

carrier, and not from inpatient to inpatient. In 

conclusion, genomic studies allowed us to obtain 

insights on Kp epidemiology at short- and 

long-time scale providing useful information 

for outbreak control and genome evolution of 

this important nosocomial pathogen. 

n 33 

COMPARATIVE GENOMIC ANALYSIS OF 

THE FIRST TWO VAND-TYPE VANCOMYCIN- 

RESISTANT ENTEROCOCCUS FAECIUM IN 

THE NETHERLANDS 

M. Rogers, J. Sinnige, J. Top, E. Brouwer, M. 

Bonten, R. Willems; 

UMC Utrecht, Utrecht, NETHERLANDS. 

Introduction: Enterococcus feacium has 

rapidly become an important nosocomial 

pathogen. Vancomycin-resistant E. feacium 

(VRE) strains are of particular importance, as 

these are often multi-resistant, which drastically 

limits treatment options. To date, there 

have been nine different types (vanA-G, vanL- 

N) of vancomycin resistance gene clusters 

described of which vanA and vanB are most 

frequently found. Recently, two epidemiologically 

unrelated vanD VRE isolates were found 

in two Dutch hospitals. Here we report on the 

phylogenetic analyses of the first vanD VRE 

in the Netherlands. Methods: Whole genome 

sequencing of these two strains was performed 

using the Nextera XT DNA Library Prep 

Kit (Illumina) for library preparation and sequenced 

on the MiSeq System (Illumina) with 



61


a 2x250 bp MiSeq Reagent Kit v2 (Illumina). 

Quality filtering of the reads was performed 

using Nesoni 0.109 and reads were assembled 

using SPAdes 2.5.1 genome assembler. Genes 

were predicted and annotated using PROKKA. 

For the phylogenetic analysis, a total of 104 

strains were used (73 publicly available strains, 

29 strains from our lab and the 2 vanD positive 

strain). Core alignment was performed using 

Bowtie2 and SAMtools was used for SNP calling 

between the two vanD-type VRE strains 

(E7962 and E8043). OrthAgogue was used 

for prediction of gene orthology relations, followed 

by clustering of orthogroups via MCL. 

A phylogenetic tree was constructed based on 

the core genome of the 104 strains using RAx- 

ML. Results: Phylogenetic inferences revealed 

that both vanD VRE clustered in clade A1 containing 

mostly clinical strains. Whole genome 

analysis revealed considerable SNP difference 

(2981 SNPs; 1.1*10-3 SNPs/Mb) between the 

two strains’ recombination-free core genome, 

indicating that both strains were not clonally 

related. Furthermore, both strains carried the 

entire vanD gene cluster (vanRD, vanSD, 

vanYD, vanHD, vanD, vanXD) on their largest 

scaffold (size: ~222kb for E7962 and ~192kb 

for E8043) and SNP analysis of these scaffolds 

revealed only 9 SNPs (4.6*10-5 SNPs/Mb) between 

them. Further analysis showed the presence 

of a 76kb core-region (present in all 104 

strains) in these vanD-scaffolds, followed by 

an accessory-region (of 116kb for E8043 and 

146 kb for E7962) containing the vanD gene 

cluster and phage-related genes, present only 

in the two vanD-VRE. Conclusion: These 

results suggest that the vanD gene cluster is 

located on a mobile genetic element that was 

acquired by two clonally unrelated strains from 

a common third source or from each other. 

n 34 

ACUITAS ® RESISTOME TEST - A HIGH 

THROUGHPUT TRIAGE TOOL FOR STRAIN 

TYPING BY WHOLE GENOME SEQUENCING 

R. K. Kersey, G. T. Walker, T. Rockweiler, W. 

Chang; 

OpGen, Gaithersburg, MD. 

Multi-drug resistant organisms (MDROs) are a 

global healthcare issue associated with an increase 

in morbidity and mortality. Acuitas ® Resistome 

Test, a high throughput multiplex PCR 

test, detects approximately 50 antibiotic resistance 

genes in Gram-negative bacilli including 

genes for carbapenemases, Extended Spectrum 

Beta Lactamases (ESBLs) and AmpC enzymes 

carried by MDROs. The Resistome Test is 

useful for genotyping carbapenem and cephalosporin 

resistance genes for surveillance of 

transmission events in hospitals. We validated 

gene specificity of the Resistome Test through 

evaluation of 265 culture isolates with reported 

gene subtypes that were adjudicated by whole 

genome sequencing to resolve discrepancies, 

which fell into three categories (missed genes, 

incorrect genotype and additional genes reported). 

Genomic DNA was extracted from broth 

cultures of isolates followed by Nextera library 

preparation, Illumina MiSeq sequencing, genomic 

assembly and sequence analysis using 

Ridom SeqSphere+ (MLST+) software. 

Results showed 100% concordance between 

gene results from the Resistome Test and 

whole genome sequencing. Our second objective 

was to illustrate that the Resistome Test 

was also useful as a triage tool to select culture 

isolates for strain typing by whole genome 

sequencing (WGS). We tested 65 Klebsiella 

pneumoniae isolates from two studies by the 

Resistome Test. Each K. pneumoniae isolate 

was positive for two to five of the following 

antibiotic resistance genes in various combinations: 

KPC, CTX-M-1, ACT, CMY, OXA-50, 

OXA-2, FOX, SHV and TEM plus ESBL 

variants of TEM and SHV. The Resistome 

Test provided a level of genotypic resolution 

62 

ASM Conferences


that resolved clinical strains of K. pneumoniae 

prior to whole genome sequencing. Clinical 

isolates with distinct Resistome Test results 

were shown to be distinct strains by whole 

genome sequencing while isolates with identical 

Resistome Test results were often identical 

strains by whole genome sequencing. The Resistome 

Test was able to resolve 40% of the 65 

isolates as distinct strains, thereby identifying 

two potential groups of strain types for higher 

resolution by whole genome sequencing. We 

concluded that Acuitas ® Resistome Test is useful 

for detecting carbapenem and cephalosporin 

resistance in Gram-negative bacilli and as 

a triage tool to select culture isolates for strain 

typing by WGS. 

n 35 

PATHOGEN DISCOVERY IN TRAVELERS’ 

DIARRHEA OF UNKNOWN ETIOLOGY BY 

METAGENOMIC SEQUENCING 

Q. Zhu, M. Jones, S. Highlander; 

J. Craig Venter Institute, La Jolla, CA. 

Infectious diarrhea is responsible for about 

million deaths each year. We are studying 

travelers’ diarrhea (TD) where the known 

causative agents are members of Enterobacteriaceae, 

such as enterotoxigenic Escherichia 

coli, Shigella and Salmonella, viruses such 

as norovirus, and parasites such as Giardia. 

Nevertheless, in over 40% of cases, a known 

pathogen cannot be identified by traditional 

clinical tests. “Pathogen negative” diarrhea is 

enigmatic, although this due, in part to a lack 

of appropriate cultivation and screening tests 

and poor sensitivity of the tests. DNA sequencing 

is increasingly being applied in attempts to 

characterize agents of infectious disease. We 

hypothesize that unrecognized pathogens are 

responsible for a significant proportion of TD. 

These may be known organisms with unrecognized 

pathogenic potential or may be completely 

new species with new mechanisms of 

virulence. We have performed deep NextSeq 

paired-end sequencing of DNAs from stools of 

eight pathogen-negative and two healthy traveler 

controls in an attempt to identify pathogens. 

A total of 132.8 Gb (ca. 12-20 Gb raw 

data/sample) of sequencing data were retained 

after quality filtering. Reads were mapped to 

the NCBI RefSeq genomic database, resulting 

in an average mapping rate of 72.4% (min: 

33.1%, max: 93.8%). In the samples where 

mapping was low, we believe that many of 

the unmapped reads likely represent new uncharacterized 

organisms. Taxonomic profiles 

were generated based on the mapping results, 

and revealed significantly uneven distribution 

of microbial groups among samples. The low 

complexity samples appear to be dominated by 

a single pathogen, while the high complexity 

samples may be the result of a mixed etiology. 

Three TD samples are enriched for E. coli sequences, 

despite the fact that enterotoxins were 

not detected in clinical screens. Two of these 

carry genes for the Shiga toxin. Additional 

TD samples had, for example, high abundance 

of Akkermansia muciniphila, Streptococcus 

spp., Campylobacter jejuni, or Alistipes shahii 

reads, while the two healthy traveler controls 

had high abundance of reads that mapped to 

several Bifidobacterium species, which were 

not present in the diarrheal samples. De novo 

assembly was performed for each sample 

(average N50 statistic: 6516.4), followed by 

contig binning and scaffolding. Near complete 

draft genomes were successfully recovered 

from the metagenomes. Some represent known 

species (such as E. coli and Campylobacter), 

while others could not be taxonomically placed 

in proximity to any known bacterial groups. 

Our results provide a glimpse into the microbiome 

diversity composition and potential etiological 

sources in “no pathogen identified” TD 

samples, and demonstrate the power of highthroughput 

DNA sequencing in the discovery 

of pathogens in infectious disease. 



63


n 36 

WHOLE GENOME SEQUENCING OF PORCINE 

EPIDEMIC DIARRHEA VIRUS BY ILLUMINA 

MISEQ PLATFORM 

L. Wang 1 , T. Stuber 2 , M. Prarat 1 , P. Camp 2 , S. 

Robbe-Austerman 2 , Y. Zhang 1 ; 

1 

Animal Disease Diagnostic Laboratory, Ohio 

Department of Agriculture, Reynoldsburg, OH, 

2 

National Veterinary Services Laboratories, 

Animal and Plant Health Inspection Service, 

United States Department of Agriculture, 

Ames, IA. 

Porcine epidemic diarrhea virus (PEDV) belongs 

to the genus Alphacoronavirus of the 

family Coronaviridae. PEDV was identified as 

an emerging pathogen in US pig populations in 

2013. Since then, this virus has been detected 

in at least 31 states in the US and has caused 

significant economic loss to swine industry. 

Active surveillance and characterization of 

PEDV are essential for monitoring the virus. 

Obtaining comprehensive information about 

the PEDV genome can improve our understanding 

of the evolution of PEDV viruses, 

the emergence of new strains, and improve 

vaccine designs. This study investigated the 

use of deep sequencing by the next-generation 

sequencing (NGS) Illumina MiSeq platform to 

obtain complete genome sequence information 

from PEDV virus isolates. Clinical samples 

were first subjected to a real-time RT-PCR assay 

specific for PEDV. Positive samples were 

then amplified using 19 pairs of PEDV specific 

primers (targeted amplification method). The 

amplified PCR products were mixed and used 

as input DNA to prepare a DNA library using 

the Nextera XT kit for NGS. Alternatively, 

a random-priming method was applied to 

prepare the input DNA for clinical samples 

with high viral loads (real-time PCR with Ct 

value


to cluster strains based on distribution of 1,281 

accessory genes. We identified three major 

clades (A - C) characterized by a large variation 

in r/m ratio: 22.7 (all uncommon STs from 

UK), 0.9 and 3.7, respectively. Within Clade 

B and C, with few exceptions (e.g. ST-11 and 

ST-230), sequence types (ST) did not form 

monophyletic lineages and were composed by 

numerous BAPS populations characterized by 

a clonal structure, limited genetic variation and 

no temporal or geographical signals. Further 

GWAS analysis, which includes both core 

and accessory genome, did not detect overall 

genetic features correlated to geographical 

separation. On the contrary, both phylogenetic 

analysis based on the distribution of accessory 

genes showed geographical clustering of the 

isolates within each BAPS group. They also 

revealed several events of consecutive gene 

flow between isolates of the two countries, 

suggesting migration within a population. 

Our genomic study supported the theory of 

homogeneous global distribution of C. jejuni 

genotypes probably associated with rapid 

animal and/or human movement. Contrary to 

what expected, several lineages within ST45cc 

appear to be genetically monomorphic pathogens 

and were persistently detected over the 

years independently from geographical origin. 

Epidemiological investigations based on core 

genes might be affected by small resolution 

due to the clonal nature of certain lineages and 

a pangenome approach is recommended. 

n 38 

COMPLETE GENOME SEQUENCE OF 

STAPHYLOCOCCUS AUREUS STRAIN FROM 

A PIG, A UNIQUE T324-ST541-V KOREAN 

METHICILLIN RESISTANT S. AUREUS 

CLONE 

S. Lim, D. Moon, G. Jang, H. Lee; 

Animal and Plant Quarantine Agency, Anyang, 

KOREA, REPUBLIC OF. 

Methicillin-resistant Staphylococcus aureus 

(MRSA) has been a major causative agent 

of nosocomial infection, and it has also been 

reported from non-human sources. Livestock 

associated MRSA such as sequence type (ST) 

398 and ST541 has been reported in pigs with 

a high frequency in Korea. Especially, a spa 

type t324, sequence type ST541, and staphylococcal 

cassette chromosome mec element (SC- 

Cmec) type V (t324-ST541-V) was one of the 

predominant clones in pig production industry 

in Korea. To better understand of occurrence, 

genetic repertoire, and relatedness with other 

MRSA types, we sequenced and assembled 

the complete genomes of this predominant 

clone. A t324-ST541-V MRSA isolate designated 

K12PJN53 was isolated from healthy 

pig in 2012. The draft genome sequence of 

K12PJN53 was obtained by combined analyzing 

the results of Illumina MiSeq and Roche 

454 FLX sequencing systems. Each sequencing 

reads were assembled by the CLC genomic 

workbench 5.5 and the GS Assembler 2.6. A 

total of 458 genome sequences were obtained 

from S. aureus subsp. aureus in EzGenome 

database were compared with K12PJN53 by 

calculating average nucleotide identity (ANI) 

values. The genome of K12PJN53 consists of 

a single circular 2,880,108 bp chromosome 

with 32.88% GC content and two plasmids. A 

total of 2,042 protein coding regions, 57 tRNA 

genes, and 10 rRNA genes were detected. 

Among the annotated contigs, 14, 17, and 20 

contigs were annotated to antibiotic resistance, 

adherence and toxin genes, respectively. 

Tetracycline, macrolide, lincosamide and 

streptogramin B, and aminoglycoside resistant 

genes were found outside of the SCCmec elements 

with insertion sequence (IS) 256 or 431. 

In addition, metal-resistant genes were also 

identified in the internal and external regions 

of SCCmec elements. Several virulence genes 

such as elastin binding protein, fibrinogen 

binding protein, clumping factors A, hemolysin, 

and exfoliative toxin A were presented 

in K12PJN53, however, Panton-Valentine 

leukocidin was not detected. The genomic 

distance based on ANI of K12PJN53 strain 

was similar to the ST398 strains, which have 

emerged in European countries. This study is 



65


the first report of the draft genome sequence of 

novel livestock-associated MRSA ST541 strain 

isolated from a pig in Korea. This genome 

sequence assists to understand features of 

ST541 lineage including antibiotic resistance 

and virulence genes. 

n 39 

WHOLE GENOME SEQUENCING OF 

SALMONELLA NEWPORT CLONE 

JJPX01.0061 REVEALS PHYLOGENETIC 

EVIDENCE FOR ENDEMIC PERSISTENCE 

AND EXTENSIVE MICROEVOLUTIONARY 

DIVERSIFICATION AMONG EASTERN SHORE 

SURFACE WATERS 

R. L. Bell, C. Ferreira, E. Reed, C. Wang, E. 

Burrows, T. Muruvanda, J. Zheng, M. W. Allard, 

J. Pettengill, E. Strain, E. Brown; 

FDA, College Park, MD. 

Recurrent outbreaks of Salmonella enterica 

serovar Newport, XbaI PFGE pattern 

JJPX01.0061, have been linked to the consumption 

of tomatoes grown along the Eastern 

Shore of Virginia (VES) at least 6 times since 

2002. Environmental surveys of this region 

suggest that this subtype is endemic, persisting 

in surface waters of the VES microcosm. 

The genomic diversity of a large population of 

JJPX01.0061 isolated from disparate surface 

waters across the VES was investigated using 

whole genome sequencing (WGS) approaches. 

More than 70 environmental JJPX01.0061 

isolates spanning seven years from the VES 

were subjected to whole genome shotgun 

sequencing, assembled, and aligned using 

a reference-based mapping approach to a 

closed Newport genome (CFSAN024225). A 

maximum likelihood tree was then constructed 

based on total SNP variation and evaluated 

in light of geographic variation based on the 

location from which each isolate was collected. 

Genomic diversity within this PFGE 

subtype clustered the strains into four distinct 

clades or “genomovars” separated by a range 

of only 5 to more than 100 SNPs. Two clades 

(C and D) sorted isolates uniquely based on 

specific creeks and were identical (C, 0 SNPs 

intraclade variation) or nearly identical (D, 1 

SNPs). Two additional clades (A and B) were 

polyphyletic with respect to creek location 

suggesting two independent introductions 

of JJPX01.0061 variants into each of these 

locales. Finally, clade B, comprised largely 

of Newports from 2007, was separated from 

more recent Newport groupings by at least 100 

SNPs suggesting that substantial microevolutionary 

change has accrued within this lineage, 

a find consistent with its establishment and 

prolonged environmental persistence. Genetic 

diversification of JJPX01.0061supports its 

long-term and endemic persistence within this 

regional microcosm. Occasional reintroduction 

of distinct genomic variants into common 

creek environments is also seen pointing to 

a potential role for geese or other water fowl 

species in the local mixing of isolates. 

n 40 

MOLECULAR TYPING OF BRUCELLA 

MELITENSIS BY WHOLE GENOME 

SEQUENCING 

G. Garofolo 1 , J. T. Foster 2 , K. Drees 2 , I. Platone 

1 , K. Zilli 1 , M. Marcacci 1 , M. Ancora 1 , C. 

Cammà 1 , I. Mangone 1 , F. De Massis 1 , P. Calistri 

1 , E. Di Giannatale 1 ; 

1 

Istituto Zooprofilattico Sperimentale 

dell’Abruzzo e del Molise, Teramo, ITALY, 

2 

Department of Molecular, Cellular, and Biomedical 

Sciences, University of New Hampshire, 

Durham, NH, USA, NH. 

Brucella melitensis is the causative agent of 

brucellosis in sheep and goats, causing public 

health concerns through the consumption of 

contaminated milk or in people in close contact 

with livestock. Despite concerted eradication 

campaigns, the disease remains endemic 

throughout much of the Mediterranean basin. 

Brucella is highly clonal so single nucleotide 

polymorphism (SNPs) in a phylogenetic 

framework can be used for determining its 

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evolution. Determining which lineages are 

present throughout Italy is crucial to understanding 

brucellosis epidemiology as well as to 

place Italian samples into a global evolutionary 

context. The aim of this study was to evaluate 

an NGS approach using different analyses 

to investigate B. melitensis diversity in Italy. 

Previous genetic assessment using variable 

number tandem repeats (VNTRs) revealed the 

presence of the west Mediterranean lineage 

structured in several clades that were sometimes 

geographically constrained. We selected 

16 B. melitensis isolates for whole genome 

sequencing representing maximum genetic and 

geographic diversity. Libraries were sequenced 

with both paired-end Illumina and Ion Torrent 

sequencing, and our analyses included 59 

publicly available B. melitensis genomes. We 

performed SNP analysis in read alignments 

and whole genomes using NASP pipeline, and 

in parallel we applied a gene-based approach 

using MLST+. Approximately 22,000 putative 

SNPs were identified among the B. melitensis 

samples. The MLST+ revealed 1,748 targets 

for the first chromosome and 876 targets for 

the second chromosome totaling 2,624 loci. 

Both approaches found that the Italian isolates 

formed 3 sub-clades as part of the B. melitensis 

strain Ether lineage, a strain that was isolated 

in Italy fifty years ago, suggesting that this 

lineage has been well established and successful 

for at least over half century in the Italian 

peninsula. Finally, we compared our results to 

a recently published shotgun metagenomic B. 

melitensis sequence from bones from a medieval 

grave in Sardinia (Italy) and confirm that 

this same lineage has been present in the region 

for centuries. This study is a step forward 

in understanding of Brucella evolution in the 

Mediterranean area, and demonstrates the utility 

of WGS SNP analysis, and the feasibility 

of MLST+ as a fast and reliable typing system 

for analyzing the epidemiology of Brucella in 

endemic regions. 

n 41 

GENOMIC EPIDEMIOLOGY AND 

TRANSMISSION OF SALMONELLA 

CHOLERAESUIS VAR. KUNZENDORF IN 

EUROPEAN PIGS AND WILD BOAR 

P. Leekitcharoenphon, F. M. Aarestrup, R. S. 

Hendriksen; 

Technical University of Denmark, Kgs. Lyngby, 

DENMARK. 

Salmonella Choleraesuis is a relative infrequent 

serovar adapted to pigs but also have a 

propensity to cause extraintestinal infections 

in humans. S. Choleraesuis var. Kunzendorf 

are responsible for the majority of outbreaks 

among pigs. The global transmission was 

believed to be a result of imported breeding 

pigs from Canada and the USA into Taiwan. 

In Europe, S. Choleraesuis is a relatively rare 

serovar, both in slaughter pigs and in breeding 

herds. In Denmark, only a few outbreaks have 

been reported among pig herds within the last 

decade; 1999 - 2000 and 2012 - 2013 and in 

both cases it has been impossible to identify 

the route of transmission and source of infection. 

In order to understand transmission and 

epidemiology of S. Choleraesuis, we have 

sequenced 108 S. Choleraesuis isolates from 

pig and wild boar from 12 European countries 

and USA. We applied SNP-based phylogenetic 

methods based on whole genome sequences to 

identify the population structure. We used Baysian 

phylogeny to estimate dates of divergence 

and phylogeographic analyses of lineages by 

using BEAST with Bayesian Skyline model of 

population size change and relaxed uncorrelated 

lognormal clock as the molecular clock. 

The S. Choleraesuis isolates yielded 2,428 

SNPs. We estimated that the ancestral emergence 

of S. Choleraesuis was in 1946. The 

mean evolutionary rates were approximated to 

be 1.58 x 10-6 SNPs/site/year corresponding 

to 7.5 SNPs/year. The isolates were divided 

into two complex clusters and they resided 

in sub-clusters according to countries and 

neighbour countries of isolation. According to 

the source of isolation, the wild boar isolates 



67


from Austria were clustered together but the 

wild boar isolates from Germany, Hungary 

and Estonia were clustered with strains from 

pig. The outbreak isolates from Denmark were 

distantly divided into two groups according to 

outbreak period. The recent outbreak isolates 

(2012-2013) contained an extra Q1 replicon, 

whereas some earlier outbreak isolates (1991- 

2000) had an extra I1 replicon. Some of the 

earlier outbreak strains were isolated from 

different organs from the same animal. Those 

isolates clustered by the pig where they were 

isolated from. Danish isolates together with 

farm geographical information were subjected 

to the discrete phylogeographic analysis using 

a standard Continuous-Time Markov Chain 

(CTMC). The spatial and temporal transmission 

of S. Choleraesuis isolates between different 

farms in Denmark was epidemiologically 

concordant with the data showing the contact 

between farms. These results provide the advantage 

of using WGS for elucidating evolution 

and transmission of S. Choleraesuis and 

emphasize the usefulness of the phylodynamic 

approaches to monitor the emergence and 

spread over time of these particular strains. 

These findings may help to promoting and 

establishing future prevention and control 

measurement of similar successful clones. 

n 42 

USING WHOLE GENOME SEQUENCING 

AND PHYLOGENETIC METHODOLOGIES 

TO CLUSTER SALMONELLA ENTERITIDIS 

ISOLATES BY SOURCE 

E. L. Stevens; 

Food and Drug Administration, College Park, 

MD. 

The Center for Food Safety and Applied Nutrition 

(CFSAN) collaborated with other state 

and federal labs to develop GenomeTrakr 

which houses the genomic sequences of thousands 

of clinical and environment/food isolates 

at the National Center for Biotechnology Information 

(NCBI). This repository of genetic data 

facilitates both surveillance and response to 

foodborne outbreaks. Whole genome sequencing 

(WGS) allows clusters of clinical isolates 

to be grouped and linked to the environmental/ 

food source using the science of molecular 

phylogenetics and has been successfully applied 

in resolving outbreaks. Source attribution 

is the ultimate goal of combining sequence and 

epidemiological data for outbreak resolution. 

One important pathogen, Salmonella enterica 

serotype Enteritidis (SE), which caused 19% 

Salmonella illnesses in 2013 (19%), has been 

highlighted by epidemiologic insight as frequently 

coming from shell eggs. The Food 

and Drug Administration (FDA) published 

the 2010 Egg Rule to reduce Salmonella incidence 

rates caused by SE. However, other food 

and non-food sources such as beef, turkey, 

and chicken consumption, contact with live 

poultry, and international travel have been 

implicated in SE illness. Past methods have 

been unable to fully resolve some outbreaks 

because of the genetic similarity of SE, which 

WGS can resolve. Therefore, SE WGS data 

from GenomeTrakr was collected with the 

following parameters: (1) if it came from the 

United States; (2) had associated metadata 

indicating source, geographic location, clinical/environmental 

status, isolation source (e.g. 

chicken), and collection date. From there, SE 

isolates were summarized according to isolation 

source (e.g. specific to egg products), and 

a cluster analysis was performed to identify the 

variability of SE strains among the different 

food commodities to see if further analysis can 

determine genetic loci that may be predictive 

of SE source attribution. These aims involved 

using phylogenetic methods based on WGS 

to link environmental/food isolates with each 

other due to their shared similarity (i.e. few 

single nucleotide polymorphisms (SNPs) 

between them). This work may ultimately provide 

information in which the sequence data 

of a single isolate derived from a clinical case 

of salmonellosis could help inform the epidemiologic 

analysis by suggesting a potential 

source of the illness. Furthermore, these results 

can help to inform the effect of The 2010 Egg 

Rule. 

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n 43 

CORRELATING PROPORTIONAL 

ABUNDANCES OF SALMONELLA, IN 

A COMPLEX METAGENOMES, WITH 

COVERAGE OF THE SALMONELLA GENOME 

K. G. Jarvis 1 , N. Daquigan 1 , J. R. White 2 , C. J. 

Grim 1 , D. E. Hanes 1 ; 

1 

FDA/CFSAN, Laurel, MD, 2 Resphera Biosciences, 

Baltimore, MD. 

Background: Culturing Salmonella from fresh 

produce requires resuscitation in nonselective 

pre-enrichment broths, followed by parallel 

selective enrichments in Tetrathionate (TT) 

and Rappaport-Vassiliadis (RV) broths, to inhibit 

background microflora. The cilantro microbiome 

consists mainly of Bacteroidetes and 

Proteobacteria. However, overnight nonselective 

pre-enrichment results in a shift to a lower 

proportional abundance of Proteobacteria and 

a dramatic increase in Firmicutes. Purpose: 

This study evaluates microbiome shifts over 

time in spiked cilantro cultures, and correlates 

the relative abundance of Salmonella to coverage 

of the 4.8 Mb genome. Methods: The 

microbiome of cilantro, spiked with S. enterica 

Newport at ~4CFU/25gm, was sequenced after 

0, 6, and 24-hours in nonselective Tryptic Soy 

Broth, utilizing an Illumina MiSeq. High-coverage 

control microbiomes, which were transferred 

to selective TT and RV broths, in parallel, 

and incubated for 24-hours following nonselective 

pre-enrichment, were also sequenced. 

Most Probable Number (MPN) analysis was 

performed at 6 and 24-hours to estimate CFU/ 

ml Salmonella. MetaPhlAn and Resphera Insight 

software were used to analyze shotgun 

metagenomes, and 16S rRNA sequences, respectively. 

Metagenomic sequence reads were 

mapped to an S. enterica Newport genome 

by BLASTn to estimate Salmonella genome 

coverage. Results: The 0-hour cultures had 

9,511 BLASTn hits, with 579,864 (12%) bases 

covered, and an average read coverage of 

3.68. At 6-hours, BLASTn hits to Salmonella 

increased to 59,951 with an average read coverage 

of 16.96 accounting for 832,017 (17%) 

bases. BLASTn hits to Salmonella at 24-hours 

reached 175,532, and accounted for 1,081,888 

(22%) bases, with an average read coverage 

of 39.17. BLASTn hits in RV and TT cultures 

reached 18,110,119 and 21,096,773, representing 

average read coverages of 842 and 1033 

respectively, equating to 4,874,108 (99.94%) 

and 4,876,681 bases (99.99%) of the Newport 

genome represented in the RV and TT cultures. 

Proportional abundances of Salmonella 16S 

rRNA genes in 0, 6, and 24-hours cultures 

were 0.004% (1 of 25,000 reads, or 4 CFU/ 

ml), 0.00% (0 of 25,000 reads, or 9 CFU/ml), 

and 5.81% (1,453 of 25,000 reads or 3.9 X 107 

CFU/ml), respectively. Salmonella proportional 

abundances in shotgun metagenomes were 

0.02%, 0.01%, 0.04%, 92%, and 86% for the 

0, 6, 24-hour, and, RV, and TT cultures, respectively. 

Conclusions: Correlating the proportional 

abundance of Salmonella with coverage 

of the genome, and CFU/ml in metagenomes 

is essential for understanding detection limits 

in complex food matrices such as cilantro. 

Careful consideration of analysis software to 

account for discrepancies in metagenomic databases 

is also important. 

n 44 

COMPARATIVE GENOMICS OF DRUG- 

RESISTANT SALMONELLA ENTERICA 

ISOLATED FROM DAIRY CATTLE AND 

HUMANS 

L. Carroll 1 , M. Wiedmann 1 , H. den Bakker 2 , 

J. Siler 1 , M. Davis 3 , W. Sischo 3 , T. Besser 3 , L. 

Warnick 1 , R. Pereira 1 ; 

1 

Cornell University, Ithaca, NY, 2 Texas Tech 

University, Lubbock, TX, 3 Washington State 

University, Pullman, WA. 

Salmonella enterica is a pathogen of concern 

for both humans and cattle. It can be spread 

from cattle to human populations through direct 

contact with animals shedding Salmonella, 

as well as through the food chain. Infections 

caused by multidrug-resistant isolates can 

be challenging to treat, making multidrugresistant 

Salmonella a relevant human health 



69


hazard. The objective of this study was to use 

whole genome sequencing to study the evolutionary 

relationship of antibiotic-resistant 

S. Typhimurium, S. Newport, and S. Dublin 

isolated from dairy cattle and humans in Washington 

state and New York state from 2008 to 

2012. A total of 90 drug-resistant Salmonella 

isolates were selected for this study, 45 of 

which were from Washington state (20 from 

dairy cattle and 25 from humans) and 45 from 

New York state (21 from dairy cattle and 24 

from humans). Isolates were selected based 

on location, source, and serotype stratified by 

year. All isolates were tested for phenotypic 

antimicrobial resistance to 12 drugs using Kirby-Bauer 

disk diffusion, and all isolates were 

resistant to at least one drug. Genomes of all 

isolates were sequenced at Cornell University 

using the Illumina HiSeq platform and assembled 

de novo using SPAdes. In silico MLST 

and serotyping were performed using SRST2 

and SeqSero, respectively. SRST2 was also 

used in conjunction with the ARG-ANNOT 

database to detect the presence of antibiotic 

resistance genes in the genome of each isolate. 

Maximum likelihood trees were constructed 

using the core genome of each serotype using 

kSNP, and the Cortex variation assembler 

was used to detect SNPs and indels. The most 

common drug classes for which resistance 

genes were detected were aminoglycoside, 

sulfonamide, and tetracycline. Aminoglycoside 

and tetracycline were the two most common 

drug classes for which two or more resistance 

genes for each class were identified within the 

same isolate. Phylogenetic analyses of SNPs 

in the core genome of each serotype showed 

evidence for geospatial clustering. Further 

analyses will evaluate evolutionary phylogeny 

within each serotype and compare genotypic 

and phenotypic antibiotic resistance for all 

isolates to gain further insight into the spread 

of drug-resistant Salmonella between dairy 

cattle and humans in New York and Washington 

state. 

n 45 

NEXT GENERATION SEQUENCING AND 

CITRUS DISEASES; CURRENT STATUS AND 

FUTURE PROSPECTUS 

M. Shafiq, F. Ali, M. Saleem Haider; 

University of the Punjab, Lahore, PAKISTAN. 

Next-generation sequencing (NGS) high 

throughput technologies became available at 

the onset of the 21st century. They provide 

a highly efficient, rapid, and low cost DNA 

sequencing platform beyond the reach of the 

standard and traditional DNA sequencing technologies 

developed in the late 1970s. They are 

continually improved to become faster, more 

efficient and cheaper. They have been used 

in many fields of biology since 2004. Citrus 

is a large genus that includes several major 

cultivated species, including C. sinensis (sweet 

orange), Citrus reticulata (tangerine and mandarin), 

Citrus limon (lemon), Citrus grandis 

(pummelo) and Citrus paradisi (grapefruit). 

The draft genome of sweet orange (Citrus 

sinensis) has been estimated using NGS technologies. 

NGS has also been to study the genomes 

of several varieties of clementine mandarins 

and also to sequence full chloroplast 

citrus genome. NGS using RNAseq analysis 

has also been used to study the regulation of 

citrus genes expression during disease (citrus 

greening and CTV infection) development. It 

is expected that NGS will play very significant 

roles in many research and non-research areas 

of citrus biology and this technology will boost 

the future citrus research in Pakistan. 

n 46 

QUANTIFYING THE SWINE RESISTOME 

USING METAGENOMIC SEQUENCING 

P. Munk, V. D. Andersen, H. Vigre, F. M. Aarestrup; 

Techincal University of Denmark, Kgs. Lyngby, 

DENMARK. 

Monitoring of agricultural antimicrobial resistance 

has traditionally depended on the cultivation 

and phenotypic analysis of indicator 

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bacteria. However, resistance genes are widely 

distributed and present in zoonotic agents as 

well as commensal bacteria that can exchange 

them by horizontal gene transfer. By directly 

sequencing animal fecal DNA, the entire intestinal 

resistome can be characterized at once. 

In this study, we aimed to develop a workflow 

appropriate for quantifying agricultural resistance 

and use it to characterize the Danish 

swine herd resistome. Ten Danish swine herds 

with diverse antimicrobial usage were enrolled 

in the study. In each herd, a fecal floor 

sample was obtained from 30 random pens. 

Those random samples were pooled for each 

herd and DNA was extracted using a modified 

QIAamp stool mini kit protocol. DNA was 

fragmented to 300 bp and prepared with the 

NEXTflex PCR-Free DNA Library Prep kit. 

Libraries were sequenced on a HiSeq2500 (PE, 

2x100 bp), producing roughly 7 billion bp/ 

sample. MGmapper, a BWA-based pipeline 

for metagenomics, was used to map qualitytrimmed 

reads to the ResFinder database (2130 

resistance genes). To ensure high specificity, 

we only counted read pairs where both reads 

aligned with 50+ bp to the same reference 

gene. To minimize unspecific read mapping 

without excluding ambiguous alignments, read 

counts from gene variants were aggregated to 

gene and drug levels. The relationship between 

resistance gene abundances and herd-level 

antimicrobial consumption of more than seven 

drug classes was analyzed using correlation 

analysis. A total of 123 resistance genes were 

observed and 64 were detected in at least half 

of the samples. In all samples, tet(Q), mefA 

and tet(W) comprised over half the detected 

resistance genes. Besides tetracycline and 

macrolide resistance genes, beta-lactam and 

lincosamide resistance genes were also very 

prevalent. Positive correlations between drug 

use and gene abundances were frequently significant 

(p < 0.05). This was not the case for 

negative correlations, suggesting antimicrobial 

use in Danish swine herds is associated with 

an increase in resistance gene abundances. 

In conclusion, our metagenomic approach 

facilitates herd-level resistance monitoring in 

swine. The resolution is sufficient to observe 

antimicrobial-induced effects on the swine 

resistome and quantify more than 100 resistance 

genes. The data may furthermore be well 

suited to study other functional sequences like 

virulence genes and transposable elements, 

making metagenomics an attractive option for 

routine environmental monitoring. 

n 47 

MOLECULAR AND GENOMIC TYPING OF 

POULTRY ASSOCIATED SALMONELLA 

ENTERICA STRAINS FROM NIGERIA 

N. Useh 1 , H. Suzuki 2 , N. Akange 1 , M. Thomas 3 , 

A. Foley 3 , M. Keena 3 , E. Nelson 3 , J. Christopher-Hennings 

3 , J. Scaria 3 ; 

1 

University of Agriculture, Makurdi, NIGERIA, 

2 

Yamguchi University, Yamaguchi, JAPAN, 

3 

South Dakota State University, Brookings, SD. 

Non-typhoidal Salmonellosis is one of the 

common cause of bacterial diarrhea worldwide. 

Globally 94 million cases of gastroenteritis 

and 115,000 deaths each year is estimated 

to be caused as a result of non-typhoidal 

Salmonellosis. Transmission of pathogenic 

Salmonella strains between different countries 

has increased due to global travel and food 

import. While North America and Europe have 

constituted active Salmonella surveillance 

programs, very limited epidemiologic data is 

available in developing countries, particularly 

in sub-Saharan Africa. Therefore, we have 

conducted an epidemiologic investigation of 

Salmonella prevalence in poultry samples from 

Nigeria. Salmonella was isolated by enrichment 

culture in tetrathionate broth followed by 

growth on XLT4 agar. Identify of the strains 

were further confirmed by Matrix Assisted 

Laser Desorption Ionization Time-of-Flight 

(MALDI-TOF). After positive identification, 

virulence of each strain was estimated using 

Human Colo-rectal cell (Caco-2) invasion 

assay. Total of 40 isolates were typed using 

Caco-2 invasion assay. These isolates were further 

typed using Next Generation Sequencing 

(NGS). Sequencing libraries were prepared us- 



71


ing Illumina Nextera XT kit and the sequencing 

was performed using Illumina 2 x 250 base 

paired end sequencing chemistry. Genome 

assembly of the strains was performed using 

Spades Assembler v 3.5.0 and annotated using 

Prokka v1.11. Comparative genomic analysis 

of the isolates was performed using ITEP tool 

kit. We further mapped the presence/absence 

of 200 Salmonella virulence associated genes 

in the sequenced genomes. Strain typing 

results revealed that the Salmonella isolates 

we collected belong to serotypes Anatum and 

Newington. There were several we fold differences 

in the Caco-2 invasiveness of the strains. 

We find that while few strains were not invasive, 

most strains were highly invasive. There 

was no clear correlation between Caco-2 invasiveness 

of the strains and the presence and 

absence of virulence genes in their genomes. 

This might indicate the presence of unknown 

virulence genes in these strains or the condition 

specific expression of known virulence 

genes. Currently, we are performing the detailed 

genomic comparison of invasive and 

non-invasive isolates, and this might give better 

understanding about the virulence mechanisms 

in Salmonella enterica serotype Antaum 

and Salmonella enterica serotype Newington. 

n 48 

16S RRNA SEQUENCING OF FOOD AND 

ENVIRONMENTAL SAMPLES ON THE 

ILLUMINA MISEQ 

N. Daquigan 1 , J. White 2 , C. Grim 1 , D. Hanes 1 , 

K. Jarvis 1 ; 

1 

U.S. Food and Drug Administration, Laurel, 

MD, 2 Resphera, Baltimore, MD. 

Purpose: Microbiome profiling of food and 

environmental samples can enhance our understanding 

of associated microbial community 

complexities with the potential for pathogen 

identification. Sequencing low diversity amplicon 

libraries on the Illumina MiSeq requires 

introduction of diversity into the primer set. 

Here, a 16S rRNA sequencing protocol was 

developed and optimized for enteric pathogen 

surveillance in environmental and food 

samples. Methods: Four pairs of 16S rRNA 

gene primers, with 0-3 additional nucleotides 

to increase sequence diversity, were designed 

to span the V1-V3 regions of the 16S rRNA 

gene and Illumina adapter and Nextera index 

sequences. Cucumber, naturally contaminated 

Masala spice mix, and cilantro samples were 

cultured following FDA BAM methods. Some 

cilantro samples were spiked with Salmonella 

enterica at ~4 CFU/25g. DNA was extracted 

using the QIAcube for food and a standard 

CTAB protocol for hospital biofilm samples. 

16S rRNA amplicon libraries were normalized 

manually or with SequelPrep plates, pooled at 

8-11pM, multiplexed up to 96 samples per run, 

spiked with 10% PhiX at 12.5 or 20pM, and 

sequenced with Illumina 600-cycle v3 chemistry. 

Sequences were quality filtered using the 

Quantitative Insights in Microbial Ecology 

package and analyzed for taxonomic composition 

using Resphera Insight. Results: Newly 

designed 16S rRNA primers demonstrated 

identity to S. enterica, Campylobacter jejuni, 

Listeria monocytogenes, Shigella dysenteriae, 

Shigella sonnei, and Vibrio cholera using 

BLASTn analysis. In 96 sample MiSeq runs, 

SequelPrep plates reduced sample preparation 

time and provided consistent sample normalization 

(0.0005 - 2.0% reads passing filter per 

sample). Increasing library concentrations 

to 11pM with 20pM PhiX resulted in average 

data yields of 9.0G as compared to 7.7G 

with 8pM libraries. Quality filtering identified 

higher levels of non-specific amplicon contaminants 

in Masala (28%) as compared to cilantro 

(2%), cucumber (0.6%), and biofilm (0.7%) 

samples. Microbiome proportional abundances 

included: Staphylococcus (15%), Enterobacter 

(15%), and Bacillus (10%) species for Masala 

(10,000 reads); Pseudomonas (40%), Flavobacterium 

(19%) and Janthinobacterium (8%) 

species in cilantro (25,000 reads); Acinetobacter 

(15%), Rhizobium (12%), and Pseudomonas 

(12%) species in cucumbers (10,000 

reads); and Elizabethkingia (25%), Pseudomonas 

(13%), and Serratia (8%) species in 

biofilm samples (15,000 reads). S. enterica 

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averaged 11.3% (2835 of 25,000 reads, n=6) 

and 0.46% (46 of 10,000 reads, n=2) in spiked 

cilantro and culture-positive Masala, respectively. 

Conclusions: Pathogens associated with 

food and environmental contamination, such 

as S. enterica, are important disease outbreak 

sources. Optimizing all aspects of our 16S 

rRNA sequencing protocol enables sequencing 

of multiple sample types important for public 

health safety. 

n 49 

SALMONELLA ENTERICA SEROVAR 

KENTUCKY ISOLATES FROM DAIRY COWS 

AND POULTRY DEMONSTRATE DIFFERENT 

EVOLUTIONARY HISTORIES AND HOST- 

SPECIFIC POLYMORPHISMS 

B. J. Haley, J. S. Van Kessel, J. S. Karns; 

USDA, ARS, EMFSL, Beltsville, MD. 

Salmonella enterica subsp. enterica serovar 

Kentucky is commonly isolated from dairy 

cows and poultry in the United States. Although 

it is not among the most frequently 

isolated serovars from cases of human salmonellosis, 

its high prevalence in livestock and 

poultry indicate it is a potential public health 

threat, particularly in light of the global spread 

of multi-drug resistant S. Kentucky ST198. To 

investigate genomic differences between S. 

Kentucky isolated from dairy farms and poultry 

operations, the genomes of 41 S. Kentucky 

ST152 isolates recovered from dairy cows and 

four S. Kentucky ST152 isolates recovered 

from poultry were sequenced using an Illumina 

NextSeq 500. Publically available S. Kentucky 

data were retrieved from the NCBI Sequence 

Read Archive and phylogenies were inferred 

after SNPs were detected using both ParSNP 

and the CFSAN SNPfinder. Phylogenetic inference 

demonstrated that S. Kentucky ST152 

isolates from poultry evolved from those 

frequently recovered from dairy cows. The S. 

Kentucky genomes in the clades dominated by 

cow isolates are differentiated from those in 

the clade dominated by poultry isolates by nine 

conserved single nucleotide polymorphisms. 

A significant number of the SNPs were located 

in open reading frames identified as iron-scavenging 

genes suggesting these differences are 

at least partly responsible for the host-specificity. 

Interestingly, among the isolates, there was 

no evidence of mixing of cow and poultry S. 

Kentucky. However, there was one isolate that 

appeared as intermediate and was rooted near 

the most recent common ancestor. Within the 

cow-specific clade, isolates were, in general, 

clustered by geography, however some geographical 

mixing was observed. Results of this 

analysis indicate a geographical location and 

source (cow or poultry) could be inferred from 

the genome sequences of S. Kentucky. 

n 50 

GENOMIC EPIDEMIOLOGY OF SALMONELLA 

ENTERICA SEROTYPE CERRO 

A. Thachil 1 , H. Suzuki 2 , A. Glaser 1 , M. 

Thomas 3 , S. Das 3 , G. Gopinath 4 , J. Jean-Gilles 

Beaubrun 4 , N. Addy 4 , H. Chase 4 , A. Jayaram 4 , 

Y. Yoo 4 , T. Chung 4 , D. Hanes 4 , J. Scaria 3 ; 

1 

Cornell University, Ithaca, NY, 2 Yamaguchi 

University, Yamaguchi, JAPAN, 3 South Dakota 

State University, Brookings, SD, 4 FDA, Laurel, 

MD. 

Salmonella enterica is classified into more 

than 2500 serotypes. Salmonella enterica serotype 

Cerro is a strain type mainly associated 

with dairy cattle. In 1990s the incidence of this 

serotype in cattle was relatively rare. However, 

in the last decade Salmonella enterica serotype 

Cerro has emerged as a common serotype 

found in lactating cows in Northeastern United 

States. Although primarily adapted to cows, 

Salmonella enterica serotype Cerro has also 

caused human infections and outbreaks. This 

includes the 1985 ‘Carne Seca’-associated 

outbreak in New Mexico and 2012 outbreak 

in Arkansas prisons. To obtain better insights 

into the possible cause of the increased incidence 

of Salmonella enterica serotype Cerro, 

we performed a Next generation Sequencing 

(NGS) based comparative genomic analysis of 

Cerro strains isolated from farms in New York, 



73


Pennsylvania, Vermont and South Dakota. 

Strains were isolated and were identified as 

Salmonella using standard protocols. Serotyping 

of the strains was performed at National 

Veterinary Service Laboratory, Ames, Iowa. 

After serotyping over 200 isolates, 70 isolates 

were chosen for NGS. Sequencing libraries 

were prepared using Illumina Nextera XT 

kit and the sequencing was performed using 

Illumina 2 x 250 base paired end sequencing 

chemistry. The strains were assembled 

independently using CLC genome workbench 

and Spades Assembler v 3.5.0, and annotated 

using RAST and Prokka v1.11. Comparative 

genomic analysis of the isolates from our collection 

and other Cerro genomes available 

in NCBI was performed using ITEP tool kit. 

Also, 2800 Salmonella core gene dataset was 

used with in-house perl scripts to identify 

whole genome core gene SNP profiles of Cerro 

isolates and representative strains from other 

serovars. Phylogenetic analysis and illustration 

were completed using MEGA6 suite. To 

determine whether changes in virulence genes 

contributed to the higher incidence of this 

serotype, we mapped the presence/absence of 

nearly 200 Salmonella virulence associated 

genes in the sequenced genomes. Our preliminary 

results indicate that Salmonella enterica 

serotype Cerro of bovine and swine origin has 

similar genome properties. We are currently 

refining the final results from the comparative 

genome analysis and the complete data will be 

presented in the conference presentation. 

n 51 

USE OF NEXT GENERATION SEQUENCING 

TO EXPLORE THE DIVERSITY OF 

TOXINOTYPE V CLOSTRIDIUM DIFFICILE 

STRAINS ORIGINATING FROM A CLOSED 

POPULATION OF HUMANS AND SWINE 

K. N. Norman, H. M. Scott; 

Texas A&M University, College Station, TX. 

Clostridium difficile typically causes nosocomial 

infections; however, there have been 

increasing reports of community-acquired C. 

difficile infection (CA-CDI). These community-acquired 

cases have no recent history of 

hospitalization. The finding of similar strains 

in humans and animals, as well as retail meat, 

has raised concern that C. difficile is a potential 

foodborne pathogen. Previously we isolated 

C. difficile from a closed population of humans 

and swine to investigate the potential for 

C. difficile to transfer from swine to humans 

through occupational exposure. We found 

that there was not a significant difference in 

the prevalence of C. difficile in wastewater 

from humans who worked with swine versus 

humans who did not work with swine. Interestingly, 

the majority of strains isolated from both 

the human wastewater and swine fecal samples 

were classified as toxinotype V, North American 

Pulsed-field type 7 (NAP7). Pulsed-field 

gel electrophoresis (PFGE) and ribotyping 

are the standard methods used to differentiate 

between C. difficile strains; however these 

typing methods may not be the best suited 

methods for C. difficile, particularly in regards 

to toxinotype V strains. Typing of C. difficile is 

further complicated because PFGE is generally 

used in the United States, while ribotyping is 

commonly used in Europe, making comparisons 

between strains and studies difficult. Next 

generation sequencing may provide a more 

discriminatory method to differentiate between 

strains and will also provide evolutionary 

information about the strains. We conducted 

whole genome sequencing on 36 swine and 

28 human epidemiologically related, toxinotype 

V, NAP7 strains isolated from the closed 

population on the Illumina MiSeq sequencing 

platform. Library preparation was performed 

using Nextera XT DNA sample prep kits and 

each strain was individually indexed. MiSeq 

Reporter and Geneious Pro Software were 

used to assemble and align sequences, identify 

potential nucleotide polymorphisms, and 

facilitate phylogenetic analyses. We are currently 

analyzing the data including the single 

nucleotide polymorphisms found in the 64 

sequenced strains. Whole genome sequencing 

data will facilitate the comparison between 

PFGE-derived and genome sequencing-derived 

74 

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diversity. The diversity and evolution of toxinotype 

V, NAP7 strains are especially important 

because these strains are commonly found 

in both food animals and humans and many 

questions still remain about the potential role 

of food animals in CA-CDI. Understanding the 

true diversity within C. difficile toxinotypes 

and North American Pulsed-field types is also 

essential for discussions regarding appropriate 

and standardized typing methods. 

n 52 

A PATHOGENOMICS APPROACH TO 

IMPROVE THE ACCURACY OF VETERINARY 

CLINICAL DIAGNOSTICS 

S. Das 1 , M. Thomas 1 , A. Pillatzki 1 , L. Holler 1 , 

M. Keena 1 , A. Foley 1 , E. Nelson 1 , J. Christopher-Hennings 

1 , H. Suzuki 2 , J. Scaria 1 ; 

1 

South Dakota State University, Brookings, SD, 

2 

Yamaguchi University, Yamaguchi, JAPAN. 

The recent advances in Next Generation Sequencing 

(NGS) technology promise cheap 

and fast whole genomic data and offer the 

possibility to revolutionize veterinary clinical 

diagnostics. It is now possible to improve the 

accuracy of clinical diagnostics when pathology 

data is combined with NGS based clinical 

specimen sequencing. This approach offers 

faster results and avoids the need for many 

different tests to obtain the final interpretation. 

We test such an approach in the clinical 

evaluation of Salmonellosis cases in Animal 

Disease Research and Diagnostic Laboratory 

(ADRDL), South Dakota. All bovine 

and swine salmonellosis cases submitted to 

ADRDL from September 2014 till date was 

included in this study. Salmonella isolates were 

identified using standard microbiological protocols. 

Further characterization of Salmonella 

strains were then performed using NGS. Sequencing 

libraries for NGS was prepared using 

Illumina Nextera XT kit and the sequencing 

was performed using Illumina 2 x 250 base 

paired end sequencing chemistry. Genome 

assembly of the strains was performed using 

Spades Assembler v 3.5.0 and annotated 

using Prokka v1.11. Comparative genomic 

analysis of the isolates was performed using 

ITEP tool kit. Antibiotic resistance gene profile 

and virulence gene profile of the isolates was 

determined using a custom PERL script. These 

results were then combined with pathological 

data. This combined approach revealed that in 

most cases Salmonella infection was accompanied 

by presence of other enteric pathogens 

such as rotavirus, Clostridium perfringens, 

hemolytic Escherichia coli and porcine epidemic 

diarrhea virus. Salmonella enterica serotype 

Dublin was found to be associated with 

septicemia and colitis in the absence of other 

pathogens. Antibiotic resistance gene profile 

obtained based NGS data provided further 

insights into the possible antibiotic susceptibility/resistance 

pattern of the strains. 

n 53 

QUALITY ASSESSMENT OF SINGLE 

NUCLEOTIDE POLYMORPHISM IN THE 

HIGHLY CLONAL SALMONELLA ENTERITIDIS 

D. Ogunremi; 

Canadian Food Inspection Agency, Ottawa, 

ON, CANADA. 

Single nucleotide polymorphism (SNPs) has 

emerged as the most informative genetic 

variation for the characterization of the highly 

clonal Salmonella Enteritidis (SE) to meet 

the goals of epidemiological and evolutionary 

investigations. Raw reads, contigs, draft 

or polished genomes are used to identify SNP 

variants typically by aligning the nucleotide 

reads of an isolate to a reference genome. 

Two relatively well-assembled genomes can 

be compared to determine the number of SNP 

variants between them. In addition, referencefree 

SNP detection from raw reads has also 

been proposed and used. There are limitations 

to detecting SNPs from the currently available 

software and bioinformatics pipelines 

and these include the use of a significantly 

disparate reference genome to align raw reads, 

sequencing errors, mis-assembly and repeats. 

To that end, large scale studies investigating 



75


SNPs typically involve Sanger sequencing of 

PCR amplicons representing the targets for the 

purpose of confirming any variants. Because of 

the responsibility imposed on food safety regulatory 

agencies to act promptly in forestalling 

human exposure to contaminated food, rapid 

but accurate SNP detection is required and 

this precludes lengthy testing procedures such 

as Sanger sequencing before acting to protect 

consumers. Bioinformatics pipelines for SNP 

detection will not only benefit from rigorous 

evaluation and routine quality assurance 

methods but may also require a systematic 

assessment using biologically relevant isolates 

as part of a pre-deployment procedure. To this 

end, polished genomes were developed for 

the chromosomes of three related SE isolates 

obtained from the same poultry facility on the 

same day, and were deposited in the GenBank 

(Accession numbers CP009084.2, CP009085.2 

and CP011942). Using whole genome analysis 

tools, bi-directional Sanger sequencing 

of target amplicons, and pyrosequencing, the 

three isolates were shown to differ from each 

other by 3-9 SNPs. Investigation was done to 

determine whether true SNPs can be accurately 

and consistently detected using the same bioinformatics 

platform applied at the different processing 

stages of each genome from raw reads 

to polished genomes. Variant calling from raw 

reads mapped to a reference genome led to the 

detection of a higher number of SNPs when 

compared to finished genomes, many of which 

could not be confirmed by Sanger sequencing 

(i.e., false SNPs). Progressive analysis of the 

nucleotide data refined the detection of SNPs 

by reducing the number of false positives arising 

from mis-assembly or repeats. The development 

of a wet chemistry, high-throughput, 

cost-efficient SNP-PCR was useful as a quality 

tool for confirming true SNPs and relatedness 

among isolates, providing confidence in testing 

results and averting the need to routinely 

develop polished genomes or carry out further 

lengthy testing procedures. 

76 

n 54 

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COMPARATIVE SEQUENCE ANALYSIS OF 

MULTI-DRUG RESISTANT INCA/C PLASMIDS 

FROM SALMONELLA ENTERICA 

M. Hoffmann 1 , J. Pettengill 1 , J. Miller 1 , N. 

Gonzalez-Escalona 1 , S. L. Ayers 2 , J. Payne 1 , S. 

Zhao 2 , J. Meng 3 , M. W. Allard 1 , P. F. Dermott 2 , 

E. W. Brown 1 , S. R. Monday 1 ; 

1 


Park, MD, 2 US Food and Drug Administration, 

Laurel, MD, 3 University of Maryland, College 

Park, MD. 

Multidrug-resistant (MDR) determinants are 

often encoded on mobile plasmids. Presently, 

there are minimal data regarding antibiotic 

resistance and dissemination, especially as it 

applies to plasmid evolution and maintenance 

in zoonotic bacterial pathogens. Using next 

generation sequencing it is quite difficult to 

filter out large plasmid from chromosomal 

contigs, particularly if the plasmid has a 

low copy number approaching one, as with 

the chromosome. The Pacific Biosciences 

(PacBio) RS II Sequencer provides very long 

reads that greatly facilitates distinguishing 

plasmid sequences. We report here a very efficient 

plasmid isolation protocol for use with 

Salmonella enterica serovars that produces 

purified DNA that meets the criteria necessary 

for sequencing with PacBio technology. 

A total of six different Salmonella enterica 

isolates, representing six different serovars and 

containing the MDR-AmpC resistance profile, 

isolated from retail poultry meats, were used in 

this study. Salmonella plasmids were obtained 

using a modified mini preparation and transformed 

into Escherichia coli DH10Br. A Qiagen 

Large-Construct kit was used to recover 

highly concentrated and purified plasmid DNA 

that was sequenced using PacBio technology. 

The size of the closed IncA/C plasmids ranged 

from 104 kb to 191 kb and shared a stable, 

conserved backbone containing 98 core genes, 

with only six core gene differences. The six 

IncA/C plasmids encoded a number of antimicrobial 

resistance genes, including those for 

quaternary ammonium compounds and mer-


cury. The numbers of resistance determinants 

varied from 8 to 17 (S. Newport (13), S. Typhimurium 

(13), S. Heidelberg (17), S. Infantis 

(14), S. Agona (8), and S. Kentucky (14)) 

with some having two copies of blacmy-2, 

quacEΔ1, sugE, merE, merD, and merA. Additionally, 

we performed a comparative sequence 

analyses that included 1) 14 additional IncA/C 

plasmids derived from Salmonella enterica and 

2) 38 IncA/C additional plasmids derived from 

different genera to provide an evolutionary 

picture of antimicrobial resistance mediated by 

this common plasmid backbone. These findings 

shed light on the variations of the IncA/C 

in resistant bacteria from different sources. 

n 55 

APPLICATION OF WHOLE-GENOME 

SEQUENCING APPROACHES TO THE 

REAL-TIME CHARACTERIZATION OF 

BACTERIAL PATHOGENS IN FOOD-TESTING 

LABORATORIES 

C. D. Carrillo, D. Lambert, A. Koziol, P. Manninger, 

M. Gauthier, B. W. Blais; 

Canadian Food Inspection Agency, Ottawa, 

ON, CANADA. 

The timely identification and characterization 

of foodborne bacteria for risk assessment 

purposes is a key operation in a food safety 

investigation. Current methods require several 

days and/or provide low-resolution characterization. 

We have implemented procedures 

for the rapid production of whole-genome sequence 

(WGS) data for the characterization of 

bacterial pathogens (Salmonella spp., Listeria 

monocytogenes and Shiga-toxigenic Escherichia 

coli (STEC)) isolated in food-testing 

laboratories at the Canadian Food Inspection 

Agency (CFIA). To demonstrate the feasibility 

and accuracy of WGS as an alternative 

to traditional procedures, an analysis of over 

500 historical and contemporary isolates was 

conducted to compare WGS approaches to 

the characterization achieved through current 

methods. Genomic DNA was isolated from 

single colonies or broth cultures and sequencing 

libraries were constructed using the Nextera 

XT DNA sample preparation kit (Illumina, 

Inc., San Diego, CA). Sequence was generated 

on the Illumina MiSeq instrument with raw 

data sampling from the instrument during the 

sequencing run (for urgent samples) and/or 

following completion of the run. Automated 

pipelines for bioinformatic analysis were generated 

to perform read corrections, de novo 

assembly, quality assessment, sequence vector 

screening and removal, and identification of 

pertinent genetic markers. The entire set of 

input parameters and software versions used 

to conduct these analyses was automatically 

captured in a traceability report. Characteristic 

genetic markers were accurately identified in 

all cases where WGS data met minimum quality 

standards and a report of genomic analysis 

(ROGA) could be generated within 1 to 3 days 

following reception of colony isolates. ROGAs 

were presented in a user-friendly format for 

ease of use by risk assessors and recall specialists. 

Real-time WGS produces high-resolution 

characterization of bacterial pathogens at a 

cost and timeframe similar to methods that are 

currently in use and has the potential to replace 

lengthy biochemical characterization and 

typing procedures used in contemporary foodtesting 

laboratories. 

n 56 

GENERATING FORENSIC INSIGHT THROUGH 

EVIDENCE-ASSOCIATED MICROBIOME 

ANALYSIS 

A. Materna, F. Strino, J. Johansen, P. Liboriussen, 

L. Schauser; 


NGS has revolutionized the field of microbial 

ecology, by revealing insight into environmental 

as well as host-associated microbiomes. 

Techniques for monitoring microbiome 

composition are today being used for a wide 

variety of purposes, including forensics applications. 

Here we present a user-friendly 

and NGS-platform independent bioinformatics 

solution for microbiome profiling and 



77


demonstrate its utility for crime scene investigation. 

Within the EU-funded “MiSAFE” 

research project (FP 7) our analysis solution 

was validated through a mock crime scene 

investigation. Partners extracted DNA from 

soil samples obtained from suspects’ boots 

and crime scenes. Then 2x 300bp paired read 

libraries were generated from 16S rRNA amplicons 

and sequenced on an Illumina MiSeq 

instrument. The resulting NGS data were 

subjected to our analysis workflow consisting 

of i) preprocessing and quality control, ii) clustering 

of data into operational taxonomic units 

(OTUs) and taxonomic assignment of OTUs, 

iii) detection of PCR artifacts (chimeras), iv) 

annotation of results with sample metadata, 

v) rarefaction analysis, beta diversity estimation 

and principal coordinate analysis (PCoA). 

The OTUs can be clustered de novo, or using 

common 16S sequence databases as reference. 

A number of additional statistical tools for 

microbiome analysis and microbial ecology 

complete the analysis solution, including richness 

calculation, hierarchical clustering, or 

the Multiple Response Permutation Procedure 

(MRPP) PERMANOVA testing for significant 

differences between two or more groups. The 

obtained results are richly visualized and can 

be browsed in the context of investigationrelevant 

metadata, which resulted in supporting 

evidence for the suspect being involved in 

the criminal act. 

n 57 

RAPID DETECTION AND GENETIC 

CHARACTERIZATION OF BURKHOLDERIA 

PSEUDOMALLEI AND ITS NEAR-NEIGHBORS 

THROUGH MULTIPLEX AMPLICON 

SEQUENCING 

J. Delisle 1 , J. Schupp 1 , J. Sahl 1 , R. Colman 1 , Y. 

Hueftle 1 , H. Heaton 1 , J. Gillece 1 , A. Vazquez 2 , 

C. Hall 2 , J. Busch 2 , M. Mayo 3 , B. Currie 3 , D. 

Engelthaler 1 , P. Keim 1 , D. M. Wagner 2 ; 

1 

Translational Genomics Research Institute, 

Flagstaff, AZ, 2 Northern Arizona University, 

Flagstaff, AZ, 3 Menzies School of Health Research, 

Darwin, AUSTRALIA. 

Burkholderia pseudomallei, the causative agent 

of melioidosis, is a public health threat and potential 

bioterrorism agent endemic to Southeast 

Asia and Northern Australia. Current methodologies, 

such as real-time PCR, allow for rapid 

detection but only limited characterization. A 

rapid and robust assay for the detection and 

characterization of clinical and forensic materials 

suspected of containing Burkholderia 

pseudomallei would be of enormous benefit 

to epidemiological and forensic investigations. 

Next-generation sequencing of multiple 

informative genetic loci can provide efficient, 

rapid detection and differentiation from near 

neighbor species, as well as fine scale genetic 

characterization. We have developed a 68 locus 

amplicon sequencing assay that results in 

1) detection of B. pseudomallei; 2) differentiation 

from B. mallei and other near-neighbor 

species; 3) potential detection of strain mixtures; 

4) differentiation within B. pseudomallei; 

and 5) virulence gene characterization (11 

gene targets) within 24-48 hours, and from 

both culture and complex environmental or 

clinical samples. The system couples highly 

multiplexed amplification reactions with a 

universal amplicon indexing system, resulting 

in efficient multilocus amplicon sequencing 

from potentially hundreds of samples in a 

single Illumina MiSeq sequencing run. Utilizing 

redundant targets identified with Blast 

Score Ratio analysis for species identification, 

we show virtually 100% specificity using a 

panel of B. pseudomallei, B. mallei and close 

near-neighbors, such as B. thailandensis, B. 

oklahomensis, and B. humptydooensis, among 

others. We demonstrate detection of B. pseudomallei 

from as little as 10 genome copies of 

moderately to highly degraded DNA as well as 

differentiation within B. pseudomallei strains, 

utilizing variation within the targeted speciesspecific, 

MLST and virulence loci. 

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n 58 

SHORT TANDEM REPEATS FOR MOLECULAR 

DETECTION OF PATHOGENS 

X. Guo 1 , Q. Yu 2 , P. Liu 1 , J. Watt 1 , Y. Li 3 , S. 

Sammons 3 ; 

1 

Eagle Medical Service and Centers for Disease 

Control and Prevention, Atlanta, GA, 

2 

Emory University, Atlanta, GA, 3 Centers for 

Disease Control and Prevention, Atlanta, GA. 

With recent development in sequencing technology, 

a large number of pathogen genome 

sequences provide it possible for comparative 

analysis of short tandem repeats (sTR) to 

understand their epidemiologic significance 

as genetic marker on gene expression regulation, 

host-pathogen interaction, new pathogen 

molecular identification and disease diagnosis. 

Here with bioinformatics methods including 

scientific programming and computational 

biological statistics, we were trying to identify 

important sTR in virus genomes and apply 

them on rapid identification of new pathogen 

and their diagnosis grouping. Result indicated 

that, among 36 NCBI-available west and 

central Africa (group A-D) monkeypox virus 

genomes, ten sTR with largest statistically 

significant difference were identified with their 

sequence patterns, copy number change and 

genetic mutations. Clustering MPVs with their 

copy number changes on these 10 sTR, almost 

the same grouping results can be obtained 

as clustering them by the alignment of their 

whole genome sequences. At the same time, 

a 5-nt repeat rearrangement has been found 

as TC-31k representatively among different 

groups of MPV. The TC-31k has their specific 

arrangement of ccatt/ccatc (T/C) within the 

west Africa, group C/D and group A/B of 

central Africa MPVs. Combined with the copy 

number change of a tandem repeat AGATT, the 

grouping property of a newly found MPV can 

be identified by one or two PCR sequencing 

or directly from NGS raw data. Considering 

the fact that TC-31k encodes a Kelch-like 

protein motif which is restricted in pox virus, 

the T/C rearrangement may reflect the variety 

of binding interaction with their host proteins. 

As a summary, through the systemly biological 

analysis of MPV genomes, several significant 

sTR were found to be group-specific for DNA 

sequence clustering and the molecular detection 

of newly found MPV pathogens. 

Keywords: short tandem repeat, pathogen 

identification, monkeypox virus, NGS 

n 59 

A WGMLST APPROACH TO SUBSPECIES 

IDENTIFICATION AND CHARACTERIZATION 

OF BACTERIA IN A CULTURE COLLECTION 

A. Luquette 1 , K. Chase 1 , H. Pouseele 2 , K. De 

Bruyne 2 , M. Wolcott 1 ; 

1 

USAMRIID, Frederick, MD, 2 Applied Maths 

NV, Sint-Martens-Latem, BELGIUM. 

Identification and characterization at the subspecies 

level is a challenge within bacterial 

species with high sequence similarity. There 

are a multitude of methods for subspecies 

identification, each with limitations in the ability 

to resolve close genetic neighbors. Nextgeneration 

sequencing allows for the entire 

genome to be used for sequence typing in a 

cost effective manner. While traditional multilocus 

sequence typing (MLST) schemes use 

only 6-10 housekeeping loci for identification, 

whole genome multi-locus sequence typing 

(wgMLST) schemes typically use 2000-4000 

loci from across the genome, providing greater 

sequence typing resolution. wgMLST pipelines 

were developed using commercial software 

(BioNumerics 7.5 software; Applied Maths 

NV) for Bacillus anthracis and Francisella tularensis 

for use by the Department of Defense 

Unified Culture Collection (UCC). Following 

whole genome sequencing, alleles were identified 

with two approaches: de novo assembly 

followed by a BLAST search and assemblyfree 

identification directly from the sequence 

reads. All calculations were done in the cloud 

so very limited local computational memory 

was needed. Alleles for different subspecies 

were generated and compared with current alleles 

in known sequence types. This approach 



79


also allows multiple schemes and subsets to be 

generated based on specific genome data, such 

as virulence factors. The development, application, 

and results of the typing schemes for 

Bacillus anthracis and Francisella tularensis 

will be presented, demonstrating the usefulness 

of the system. Next-generation sequencing 

and wgMLST will extend our capabilities to 

identify and characterize these and many other 

organisms from the UCC. 

n 60 

EFFECT OF SINGLETON REMOVAL ON THE 

MICROBIAL COMMUNITY STRUCTURE 

RESULTING FROM ILLUMINA PAIRED-END 

MISEQ 16S AMPLICON DATA 

K. Wong 1 , T. Shaw 2 , M. Molina 3 ; 

1 

Oak Ridge Institute for Science and Education, 

Oak Ridge, TN, 2 Institute of Bioinformatics, 

University of Georgia, Athens, GA, 

3 

USEPA, Office of Research and Development, 

Ecosystems Research Division, Athens, GA. 

Application of next generation sequencing 

(NGS) to study the microbial ecology in 

diverse environments has been increasingly 

popular in the last several years. The technique, 

which has been applied in different research 

areas, has the ultimate goal of gaining a 

better understanding of human and ecosystem 

functioning, as well as environmental sustainability. 

Because the bioinformatics analysis of 

NGS data is at the maturing stage, the quality 

control (QC) procedures involved in analyzing 

the data varies among different published studies. 

Since Singleton removal has been recently 

recommended in the QC step to remove artificial 

reads generated during Illumina sequencing, 

we investigated its effects on the community 

structure and diversity index from the 

16S amplicon data produced by the Illumina 

Paired-End technique. Our experimental samples 

were fresh and aged cow manure analyzed 

using the Quantitative Insight Into Microbial 

Ecology (QIIME) platform. Singleton removal 

did not have a significant effect on relative 

abundance results except for the removal of 

unknown sequences in most samples. However, 

while the alpha diversity of fresh manure 

was higher than that of aged ones before singleton 

removal, a decreased in the diversity of 

fresh manure samples was observed after the 

removal took place. Overall, results indicate 

that singleton removal does have a significant 

effect on microbial diversity results; therefore, 

we recommend to add a step for removal of 

singletons to the standard QC procedure when 

analyzing Illumina sequencing data. 

n 61 

COMPARISON OF BACTERIAL DIVERSITY 

BETWEEN ENDOSCOPIC MUSOCAL BIOPSY 

AND FECAL SAMPLE AMONG FECAL 

MICROBIOTA TRANSPLANT RECIPIENTS 

J. P. Haydek 1 , W. M. Tauxe 1 , E. Neish 2 , A. 

Ward 3 , T. Dhere 1 , C. S. Kraft 1 ; 

1 

Emory University School of Medicine, Atlanta, 

GA, 2 Emory University, Atlanta, GA, 

3 

Emory Healthcare, Atlanta, GA. 

Background: Endogenous bacteria on the colonic 

mucosal border play a fundamental role 

in nutritional absorption, epithelial stability 

and innate immunity. However, core questions 

regarding the natural microbiome, including 

diversity among different intestinal sites, usage 

of fecal bacteria as a proxy for gut flora, 

and variation between adherent and planktonic 

bacteria, remain unanswered. Clostridium difficile 

infection (CDI) is a diarrheal disease associated 

with 15-25% of antibiotic associated 

diarrhea cases, and causes between 500,000 

and 3 million new cases each year, with a total 

systematic cost estimated between $436 million 

and $3 billion. Fecal microbiota transplant 

(FMT), a procedure consisting of infusion 

of donor fecal material to a recipient with 

refractory CDI, has been increasingly shown 

to be a safe and effective therapy for chronic 

recurrent CDI, but questions still remain about 

its exact mechanisms of action and the microbial 

shifts associated with the procedure. 

Bacterial diversity of the colonic mucosa and 

feces have long been assumed to be equiva- 

80 

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lent, although this premise has increasingly 

been challenged. Methods: In a prospective 

study, stool samples and endoscopic mucosal 

biopsies were taken from 4 FMT recipients 

at the time of procedure, 2 weeks after the 

procedure, and 10 weeks after the procedure. 

DNA from the samples was lysed using the 

Mo Bio Powersoil® kit followed by extraction 

on the Qiagen EZ1 Advanced XL platform. 

High-throughput sequencing (paired end dual 

indexing, 250 base pair bidirectional reads) 

was performed using an Illumina MiSeq. Data 

analysis was performed using the R software 

suite and Vegan R package. Results: Four 

samples taken from the distal colon at time of 

FMT were compared to stool samples prior to 

the FMT procedure. Bray-Curtis dissimilarly 

metrics were calculated, with an average value 

of 0.77. Shannon diversity was also calculated 

for each individual sample, with no significant 

difference between the stool and endoscopic 

mucosal samples (P=0.15). Using a 1-sample 

t-Test, Bray-Curtis measures between samples 

were compared against the null hypothesis (no 

diversity difference) and was determined to 

be significant (P= 0.003). Conclusions: Highthroughput 

sequencing analysis comparing 

endoscopic mucosal biopsies and fecal samples 

show significant differences in bacterial diversity. 

Larger sample sizes and further analysis 

are needed to characterize the bacterial differences 

among colonic mucosal and fecal sites. 

n 62 

REVEALING HUMAN PATHOGENS IN 

LIVESTOCK FAECES BY METAGENOMES 

BASED ON HIGH-THROUGHPUT 

SEQUENCING 

A. Zhang, Y. Mao, T. Zhang; 

The University of Hong Kong, Hong Kong, 

HONG KONG. 

In recent years, human pathogens have raised 

researcher awareness as one of the pollutants 

in the environment. The diversity and 

abundance of pathogens from livestock faeces 

samples can be used to evaluate the environmental 

pollutant risk of these sources. In this 

study, we investigated 12 metagenomic DNA 

sequencing data sets of pigs and chicken faeces 

and swine wastewater and treated water. The 

metagenomic data set of 2.5G for each sample 

was derived from Illumina HiSeq 2000 2 × 

100 bp paired-end sequencing. Metagenomic 

Phylogenetic Analysis (MetaPhlAn) was conducted 

to classify microbial communities and 

reveal human pathogens at species level for the 

distribution, diversity and abundance analysis. 

In summary, 63 different bacterial pathogen 

species were detected with the maximum 

abundance of 61% among all classified species 

of the database. Both the principal coordinates 

analysis (PCoA) and network analysis demonstrate 

a clear clustering pattern that microbial 

structures of pathogens share little similarity 

among different domestic animals. Pathogenic 

bacteria detected in the same host species of 

different growth periods have various distribution 

and abundance pattern. Relatively strong 

co-occurrence correlation (P-value < 0.01 and 

Spearman’s coefficient ρ > 0.6) was revealed 

between pathogens of Shigella sp. and Fusobacterium 

sp., which were clustered in the 

same module. The methodology demonstrated 

in this study may provide more effective and 

accurate approach for pathogenic pollution 

evaluation, and also suggestions on control of 

potential pathogens in livestock farm management. 

Acknowledgement: This research is 

financed by the Research Grants Council of 

Hong Kong (GRF17209914E). 

n 63 

EXPLORING PATHOGEN PLASMID DNA 

IN WASTEWATER AND SLUDGE USING 

METAGENOMICS APPROACH 

A. Li, T. Zhang; 

The University of Hong Kong, Hong Kong, 

HONG KONG. 

Plasmids work as mobile gene elements for 

genetic materials exchange between different 

microorganisms including human pathogens. 

Virulence plasmids may turn the bacteria 



81


cells into pathogenic strains. Therefore, the 

abundance of pathogen plasmid DNA would 

demonstrate the potential threats of the environmental 

samples to the public health. Municipal 

wastewater treatment plants (WWTPs) 

are hotspots for plasmid horizontal transfer 

because of the highly exchanging rate of biological 

compositions and chemical materials 

in activated sludge and anaerobic digestion 

sludge, while they also play significant roles 

in eliminating and digesting various human 

pathogens. In this study, we collected plasmid 

DNA samples from influent, activated sludge 

and digested sludge of two WWTPs. About 

3 Gb sequences of each sample was derived 

from next generation sequencing with Illumina 

HiSeq 2000 platform using the PE101 strategy. 

All the datasets were uploaded to MG-RAST 

for function annotation, and metagenomic phylogenetic 

analysis was conducted to screen out 

human pathogen at species level for the taxonomy, 

diversity and abundance analysis. The 

plasmid metagenomic datasets were compared 

with four corresponding total DNA metagenomic 

datasets obtained from previous studies 

to reveal the difference between the plasmid 

and the total DNA metagenomes. Compared 

with the total DNA metagenomes, the plasmid 

metagenomes extracted from the same sectors 

of the WWTP had significantly higher annotation 

rates, indicating that the functional genes 

located on plasmids can be commonly shared 

by the known microorganisms. This study also 

showed the distribution pattern of the pathogen 

plasmid DNA in the plasmid metagenomes. 

The abundance of pathogen DNA in the influent 

plasmid metagenomes was much higher 

than those in other metagenomes, and the 

digested sludge plasmid metagenomes had the 

lowest pathogen plasmid DNA abundance. All 

in all, the methodology used in this study indicated 

a novel way to evaluate the potential risk 

of pathogen plasmid DNA to the public health. 

Acknowledgement: This research is financed 

by the Research Grants Council of Hong Kong 

(GRF7190/12E). 

n 64 

A 

C. A. Gulvik, J. J. Avillan, E. Alyanak, M. 

Sjölund-Karlsson, B. M. Limbago; 

Centers for Disease Control and Prevention, 

Atlanta, GA. 

Clostridium difficile isolates were collected 

during 2010-2011 as part of the Emerging Infections 

program C. difficile Infection surveillance. 

Isolates (n = 53) were selected to represent 

the diversity of strain types as determined 

by geographic location of isolation, pulsedfield 

gel electrophoresis (PFGE) type, and PCR 

ribotype; other molecular data included PCR 

detection of toxin genes tcdA, tcdB, cdtA, 

and cdtB, and size of tcdC deletions. Epidemiological 

metadata associated with isolates 

includes patient age, U.S. state of residence, 

isolation year, and epidemiologic classification 

as healthcare- or community-associated). 

Paired-end Illumina sequencing was performed 

on isolate genomes to assess congruence of 

molecular typing methods commonly used for 

surveillance. Systematic comparison of nine 

genome assemblers widely used for C. difficile 

and other bacterial genomes revealed iterativeor 

multi-de Bruijn assembly with IDBA and 

SPAdes provided the fewest contigs, largest 

N50, most predicted genes, and largest contig. 

Maximum likelihood phylogeny inference 

was performed using aligned whole genomes, 

which averaged 60X coverage. Phylogenetic 

trees enabled us to classify five isolates with 

unclassifiable PFGE patterns. The overall 

concordance of genome extracted multi-locus 

sequence types (STs), PCR ribotypes (RT), and 

PFGE groups was very good when compared 

to whole genome phylogeny, and provides 

an illustration of how each group of U.S. C. 

difficile is related to others. This is useful because 

the nomenclature of various C. difficile 

typing methods (e.g., NAP01, ST-3, RT 027) 

lacks evolutionary context, whereas whole 

genome phylogeny provides a single illustra- 

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tive comparator for contextualizing isolates 

regardless of the molecular typing method 

used. Two isolates occurred in clades with 

bootstraps of 65 and 100%, which otherwise 

contained single PCR RTs. Repeat sequencing 

and PCR ribotyping of these two isolates confirmed 

the unusual placement of a single RT 

014 isolate within the RT 020 clade, and vice 

versa. Fluoroquinolone resistance determinants 

were common (82%) among the hypervirulent 

epidemic RT 027 genomes; only one non-027 

isolate (RT 017) harbored a Thr82Ile mutation 

in GyrA, which confers fluoroquinolone resistance 

in other species. These molecular and 

epidemiological data will be publicly available 

through NCBI, and isolates have been deposited 

for distribution with BEI Resources. 

n 65 

IMPLEMENTATION OF WHOLE GENOME 

SEQUENCING (WGS) FOR SURVEILLANCE 

AND OUTBREAK DETECTION OF SHIGA 

TOXIN-PRODUCING ESCHERICHIA COLI 

(STEC) IN THE UNITED STATES 

R. L. Lindsey 1 , H. Carleton 1 , K. Joensen 2 , F. 

Scheutz 3 , L. Garcia-Toledo 1 , D. Stripling 1 , H. 

Martin 1 , N. Strockbine 1 , L. S. Katz 1 , L. Gladney 

1 , T. Griswold 1 , S. Im 1 , E. M. Ribot 1 , E. 

Trees 1 , H. Pouseele 4 , P. Gerner-Smidt 1 ; 

1 


Atlanta, GA, 2 Technical University of Denmark, 

Lyngby, DENMARK, 3 Statens Serum 

Institut, Copenhagen, DENMARK, 4 Applied 

Maths, Sint-Martens-Latem, BELGIUM. 

Introduction: Shiga toxin-producing Escherichia 

coli (STEC) is an important foodborne 

pathogen capable of causing severe disease in 

humans. Current methods for characterization 

of STEC are expensive and time-consuming. 

Work has begun to replace traditional methods 

with those using whole genome sequence 

(WGS) data by developing an allele database 

of individual Escherichia genes in BioNumerics 

7.5, (Applied Maths, Austin, TX). This 

will allow characterization of Escherichia 

in a single workflow using a multi-locus sequence 

typing (MLST) approach. Materials 

and Methods: The Escherichia allele database 

was built with 314 annotated reference 

genomes from a geographically diverse collection 

of human, animal and environmental 

strains as well as genes encoding virulence 

factors, antimicrobial resistance and O and 

H antigens from databases at the Center for 

Genomic Epidemiology (DTU, Lyngby, Denmark). 

The reference genomes represent 50 

E. coli serogroups, four Shigella species and 

four additional Escherichia species. Multiple 

subschema will be built within the database 

to perform identification, characterization and 

subtyping, including classical, extended, core 

and whole genome MLST. To test the ability of 

the BioNumerics-based whole genome MLST 

approach to correctly identify, characterize and 

cluster strains, we analyzed 500 Escherichia 

isolates from sporadic and outbreak-related 

infections and compared the findings to those 

obtained previously with phenotypic and 

molecular subtyping methods. Results and 

Discussion: The Escherichia allele database 

contains 18,883 loci. For the 500 Escherichia 

isolates analyzed, there was 95% concordance 

in the results generated by the traditional and 

wgMLST approaches. Conclusions: The 

BioNumerics-based wgMLST approach provides 

a single, cost effective strategy to identify 

and characterize isolates for surveillance 

and outbreak investigations. The analysis tools 

in BioNumerics will enable end-users in public 

health laboratories to analyze WGS data they 

generate with little bioinformatics expertise, 

making the system equally efficient for local 

and central investigations. The system will be 

refined through continued collaboration with 

domestic and international partners. 



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n 66 

DELINEATING COMMUNITY OUTBREAKS 

OF SALMONELLA ENTERICA SEROVAR 

TYPHIMURIUM USING WHOLE GENOME 

SEQUENCING: INSIGHTS INTO GENOMIC 

VARIABILITY WITHIN AN OUTBREAK 

S. Octavia 1 , Q. Wang 2 , M. Tanaka 1 , S. Kaur 1 , V. 

Sintchenko 2 , R. Lan 1 ; 

1 

University of New South Wales, Sydney, 

AUSTRALIA, 2 Westmead Hospital, Sydney, 

AUSTRALIA. 

Whole genome next generation sequencing 

(NGS) was used to retrospectively examine 57 

isolates from five epidemiologically confirmed 

community outbreaks (designated as Outbreak 

1 to Outbreak 5) caused by Salmonella enterica 

serovar Typhimurium (S. Typhimurium) phage 

type DT170. Most of the human and environmental 

isolates confirmed epidemiologically 

to be involved in the outbreaks were either 

genomically identical or differed by one to two 

single nucleotide polymorphisms (SNPs) with 

the exception of Outbreak 1. The isolates from 

Outbreak 1 differed by up to 12 SNPs, which 

suggests that the food source of the outbreak 

was contaminated with more than one strain 

while the other four outbreaks were caused by 

a single strain. In addition, NGS analysis ruled 

in isolates that were initially not considered to 

be linked with the outbreak, which increased 

the total outbreak size by 107%. The mutation 

process was modelled using known mutation 

rates to derive a cut-off value for the number 

of SNP difference to rule-in or rule-out a case 

being part of an outbreak. For an outbreak with 

less than one month ex vivo/in vivo evolution 

time, the maximum number of SNP differences 

between isolates is two and four SNPs 

using the slowest and the fastest mutation rates 

respectively. NGS of S. Typhimurium significantly 

increases the resolution of investigations 

of community outbreaks. It can also inform 

more targeted public health response by providing 

important supplementary evidence to 

rule-in and rule-out cases of disease associated 

with foodborne outbreaks of S. Typhimurium. 

n 67 

DEFINING CORE GENOME OF SALMONELLA 

ENTERICA SEROVAR TYPHIMURIUM 

FOR GENOMIC SURVEILLANCE AND 

EPIDEMIOLOGICAL TYPING 

S. Fu 1 , S. Octavia 1 , M. Tanaka 1 , V. Sintchenko 2 , 

R. Lan 1 ; 

1 

University of New South Wales, Sydney, 

AUSTRALIA, 2 Westmead Hospital, Sydney, 

AUSTRALIA. 

Salmonella enterica serovar Typhimurium is 

the most common Salmonella serovar causing 

food borne infections in Australia and many 

other countries. Twenty one S. Typhimurium 

strains from Salmonella reference collection A 

(SARA) were analyzed using Illumina highthroughput 

genome sequencing. SNPs in 21 

SARA strains range from 46 SNPs to 11,916 

SNPs with an average of 1,577 SNPs per 

strain. Together with 47 selected from publicly 

available S. Typhimurium genomes, the S. 

Typhimurium core genes (STCG) was determined. 

The STCG consists of 3,846 genes, 

which is much larger than the set of 2,882 

Salmonella core genes (SCG) found previously. 

The STCG together with 1,576 core 

intergenic regions (IGRs) was defined as the S. 

Typhimurium core genome. Using 93 S. Typhimurium 

genomes from 13 epidemiologically 

confirmed community outbreaks, we demonstrated 

that typing based on S. Typhimurium 

core genome (STCG+ core IGRs) provides 

superior resolution and higher discriminatory 

power than that based on SCG for outbreak 

investigation and molecular epidemiology 

of S. Typhimurium. Both STCG and STCG+ 

core IGRs typing achieved 100% separation 

of all outbreaks in comprison to SCG typing 

which failed to separate isolates from two 

outbreaks from background isolates. Defining 

the S. Typhimurium core genome allows 

standardization of genes/regions to be used for 

high resolution epidemiological typing of S. 

Typhimurium for genomic surveillance. 

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n 68 

COMPARATIVE PHYLOGENOMIC ANALYSIS 

AND SEQUENCING OF M. TUBERCULOSIS 

DAYCARE OUTBREAK STRAINS IN CORK, 

IRELAND 

O. O. Ojo 1 , M. B. Prentice 2 ; 

1 

Southern University at New Orleans, New 

Orleans, LA, 2 University College Cork, Cork, 

IRELAND. 

Background and Hypothesis: In 2006, Cork- 

Kerry Health Region of Ireland had a tuberculosis 

(TB) notification rate of 16.3/100,000 

population, the highest nationwide. From 1999 

to 2006, TB rate in children


fore, vast amounts of sequence data must be 

generated and analyzed to identify rare pathogen 

sequences. SPIDR-WEB is a sample-toresult 

process that relies on efficient laboratory 

and in silico steps. Clinical samples mostly 

comprise non-informative host RNAs or abundant 

housekeeping gene transcripts. SPIDR- 

WEB incorporates removal of non-informative 

RNAs (RNR), thereby enriching all other 

RNAs, including those from pathogens. This 

step enables either higher sensitivity and specificity, 

or less expensive and faster sequencing. 

Our custom EDGE bioinformatics data analysis 

platform provides rapid read classification 

at all taxonomic levels, and reliably detects 

all organisms present in a sample. EDGE is 

an efficient process, as it uses databases with 

pre-computed signatures, instead of aligning 

sequencing reads to the entire Genbank. In 

addition to RNR and EDGE, SPIDR-WEB 

includes robust, inexpensive and rapid sample 

lysis, RNA extraction, and library preparation 

steps. 

n 70 

EPIGENOMIC CHARACTERIZATION OF 

NEISSERIA GONORRHOEAE ISOGENIC 

MUTANTS AND CLINICAL ISOLATES TO 

EXAMINE THE ROLE OF DNA METHYLATION 

IN ANTIMICROBIAL RESISTANCE 

D. Trees, A. Abrams; 

CDC, Atlanta, GA. 

The emergence of multidrug-resistant Neisseria 

gonorrhoeae has hampered the control and 

prevention of gonorrhea in the United States 

and globally. Historically, most antimicrobial 

resistance in N. gonorrhoeae has resulted from 

the accumulation of mutations in a variety of 

chromosomal genes. The presence of these 

mutations results in levels of resistance to antibiotics 

that reduce the likelihood of successful 

therapy, and it has resulted in the elimination 

of some antibiotics as therapeutic agents. With 

the appearance of the mosaic form of penA, 

ceftriaxone MIC values increased 4-10 fold 

above those previously noted. An apparent 

consequence of the mosaic form of penA is the 

occurrence of treatment failures with various 

cephalosporins, and strains that contain the 

mosaic form are able to mutate to still higher 

levels of resistance to cefixime, cefpodoxime, 

and ceftriaxone. To increase the number of 

penA mutants available for analysis we used 

an approach, replicative mutagenesis, which 

allowed us to isolate large numbers of mutants 

in the penA gene. We used this approach to 

isolate a set of nine mutants in gonococcal 

strain 3502 (ceftriaxone MIC 0.06 µg/mL) 

which contains a mosaic-type penA gene. The 

MIC values to ceftriaxone for the 3502APMx 

mutants are >1.0 µg/mL. These 3502APMx 

mutants can be manipulated to increase their 

ceftriaxone MIC values to 6.0-8.0 µg/mL 

(3502APMx-x strains). The effects of mutations 

in the mosaic penA can be enhanced by 

second-site mutations to make gonococcal infections 

essentially untreatable. Whole genome 

analyses of the isogenic mutants did not identify 

significant novel genomic mutations that 

were shared among 3502APMx or 3502AP- 

Mx-x mutants. Therefore, genomic mutations 

alone did not fully explain the MIC patterns 

observed. To further elucidate the source 

of these increased MICs we looked at the 

methylation patterns of 3502 and the isogenic 

mutants. Initial results from the PacBio base 

modification detection analyses demonstrated 

that the 3502 reference, the nine AMP mutants, 

and a clinical control sample contained several 

shared m6A motifs and one m4C motif. It was 

also observed that as the ceftriaxone MICs increased, 

the number of modified motifs detected 

expanded, including some novel motifs that 

were classified as “unknown” by the software 

program. Methylated sites found in mutant 

isolates were associated with genes involved in 

transcription, translation, putative restrictionmodification 

systems, membranes, piliation, 

and phase variation. These results suggest that 

methylation might play a role in gonococcal 

antimicrobial resistance. Future study will be 

aimed to characterize the methylation patterns 

of additional clinical and laboratory reference 

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isolates with varying degrees of antimicrobial 

resistance. These results will help to shed light 

on the role of epigenetics in antimicrobial 

resistance. 

n 71 

DEVELOPMENT OF A BIONUMERICS 

DATABASE AND EVALUATION OF AVERAGE 

NUCLEOTIDE IDENTITY USING MUMMER 

(ANI-M), RIBOSOMAL MULTI-LOCUS 

SEQUENCE TYPING (RMLST), AND RPOB 

GENE PHYLOGENY FOR IDENTIFICATION OF 

ENTERIC BACTERIA BY WHOLE GENOME 

SEQUENCE ANALYSIS 

G. Williams 1 , J. Pruckler 1 , R. L. Lindsey 1 , L. 

Gladney 1 , A. Huang 1 , L. S. Katz 1 , L. Garcia- 

Toledo 1 , S. Im 1 , K. Roache 1 , M. Turnsek 1 , 

Z. Kucerova 1 , D. Stripling 1 , H. Martin 1 , B. 

Dinsmore 1 , S. van Duyne 1 , H. Carleton 1 , H. 

Pouseele 2 , N. Strockbine 1 , C. Tarr 1 , P. Fields 1 , 

P. Gerner-Smidt 1 , C. Fitzgerald 1 ; 

1 


Atlanta, GA, 2 Applied Maths, Sint-Martens- 

Latem, BELGIUM. 

Background: Conventional phenotypic and 

genotypic methods employed for identification 

of enteric bacteria, including Campylobacter, 

Escherichia, Shigella, Salmonella and Listeria, 

are labor-intensive, expensive, and require 

multiple workflows. We have begun development 

of an Enteric Identification Whole 

Genome Sequence (WGS) database. The 

PulseNet infrastructure (BioNumerics v 7.5) is 

being used to build the database, with the goal 

of identifying these enteric bacteria in a single 

workflow using WGS. Materials and Methods: 

Three different methods are being evaluated 

for inclusion in the BioNumerics Enteric 

Identification database: 1) Average Nucleotide 

Identity using MUMmer (ANI-m), which 

describes a pairwise distance between two 

genomes, 2) Ribosomal Multi-Locus Sequence 

Typing (rMLST), which is a presence/absence 

binary phylogeny for ribosomal genes, and 3) 

rpoB gene phylogeny, which describes the sequencing 

of rpoB and comparing it in a larger 

phylogeny. A set of genome assemblies - 157 

Campylobacter, 126 Escherichia, 23 Shigella, 

and 73 Listeria genomes, representing 23, 5, 

4, and 15 species for each genus, respectively 

- generated at CDC, provided by external partners, 

or publicly available through NCBI were 

selected to evaluate the methods. Results: 

ANI-m showed identities of ≥95% for members 

within a species for the six most common 

clinically-relevant Campylobacter species and 

≤92% for inter-species comparisons; ≥95% 

for members within a species for Escherichia 

and Shigella, and ≤90% for inter-species 

comparisons; ≥95% for members within a species 

for Listeria and ≤91% for inter-species 

comparisons. Due to the diversity of its four 

lineages, L. monocytogenes had slightly lower 

intra-species identity values (≥92%) and interlineage 

identity values (≥92% and ≤95%). 

Intra-lineage identity values for L. monocytogenes 

were consistent with ANI values of other 

Listeria species (≥95%). Total allele assignments 

for the 53 rMLST loci ranged from 14 

to 53 across the validation set, with fewer loci 

called for species rarely received at CDC. An 

rMLST phylogeny appropriately clustered all 

genomes in this evaluation to the species level 

when two or more genomes were represented 

for a species. Where the full-length rpoB gene 

was annotated, phylogenies appropriately clustered 

each species, with intra-species similarity 

≥91% and ≥95% for subspecies. Conclusions: 

This Enteric WGS Identification BioNumerics 

database will provide a single, unified, 

cost-effective approach for accurate species 

identification. Through continued collaboration 

with domestic and international partners, 

we will continue to test and refine the database 

and CLIA validate the reference identification 

methods within the next year. 



87


n 72 

TRANSFORMING PUBLIC HEALTH 

MICROBIOLOGY FOR CAMPYLOBACTER 

WITH WHOLE GENOME SEQUENCING: 

PULSENET AND BEYOND 

J. Pruckler 1 , D. Wagner 1 , G. Williams 1 , H. 

Carleton 1 , C. Bennett 1 , L. Joseph 1 , E. Trees 1 , 

A. Huang 1 , L. S. Katz 1 , L. Gladney 1 , M. C. 

Maiden 2 , W. Miller 3 , Y. Chen 4 , S. Zhao 4 , P. 

McDermott 4 , J. Whichard 1 , H. Pouseele 5 , E. M. 

Ribot 1 , C. Fitzgerald 1 , P. Gerner-Smidt 1 ; 

1 


Atlanta, GA, 2 University of Oxford, Oxford, 

UNITED KINGDOM, 3 United States Department 

of Agriculture, Albany, CA, 4 United States 

Food and Drug Administration, Laurel, MD, 

5 

Applied Maths, Inc., Sint-Martens-Latem, 

BELGIUM. 

Background: Conventional phenotypic and 

genotypic methods employed for identification 

and subtyping of Campylobacter are labor 

intensive, expensive, and imprecise. We have 

begun development of Enteric Reference Identification 

and Campylobacter subtype characterization 

whole genome sequence (WGS) 

databases. The PulseNet infrastructure (BioNumerics) 

will be used in conjunction with the 

existing BIGSdb platform to build the databases, 

with the goal of characterizing Campylobacter 

in a single workflow using WGS. 

Methods: Reference genomes (n=103) provided 

by FDA and USDA and strains (n=100) 

sequenced at CDC were used to develop the 

database. Assemblies and annotations were 

performed using the Computational Genomics 

Pipeline v0.4. These genomes cover the known 

members of the species and genera within 

Campylobacteraceae and the known genetic 

diversity of C. jejuni. The data will be used 

to set criteria for the current PubMLST.org/ 

campylobacter locus definitions. Multiple subschema 

are being set up within the databases to 

perform identification and scalable, hierarchical 

subtyping that will include seven locus, 

ribosomal, core genome and whole genome 

(MLST, rMLST, cgMLST and wgMLST). 

Results: To date 203 reference genomes 

have been sequenced, annotated and used for 

development of the BioNumerics databases 

including an additional 600 isolates that are 

being used to validate the prototype databases. 

Conclusions: These WGS BioNumerics 

databases will provide a single, unified, costeffective 

approach for accurate species identification 

and subtyping to aid the surveillance of 

sporadic and outbreak related Campylobacter 

infections. Through continued collaboration 

with domestic and international partners, we 

will test and refine the nomenclature, databases 

and CLIA validate the reference identification 

subschema within the next year. 

n 73 

METAGENOMIC PATHOGEN DETECTION AND 

GUT MICROBIOME RESPONSE TO ACUTE 

SALMONELLA INFECTION 

A. D. Huang 1 , M. R. Weigand 1 , A. Pena-Gonzalez 

2 , K. T. Konstantinidis 2 , C. L. Tarr 1 ; 

1 


Atlanta, GA, 2 Georgia Institute of Technology, 

Atlanta, GA. 

Background: Current diagnostic testing 

for bacterial foodborne pathogens relies on 

culture-based techniques even though many 

microorganisms, including known pathogens, 

cannot be cultured. Powerful sequence-based 

approaches such as metagenomics have potential 

to derive epidemiologically-relevant 

information directly from complex samples, 

bypassing the need to isolate individual organisms. 

However, such methods have not been 

systematically applied to foodborne pathogen 

detection because standardized bioinformatics 

techniques for analysis have not been established. 

Methods: We applied shotgun metagenomics 

to anonymized residual stool samples 

collected from foodborne outbreaks attributed 

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to Salmonella to evaluate metagenomics as 

a diagnostic and disease surveillance tool, as 

well as to gain insight into the gut microbial 

community responses to foodborne bacterial 

infection. These outbreaks were geographically 

isolated and the etiologic agents were 

identified by culture methods as distinct strains 

of Salmonella enterica serovar Heidelberg. 

We performed shotgun sequencing on these 

samples using the Illumina MiSeq platform. 

Community and taxonomic analysis were 

performed using Parallel-META, Metaphlan, 

and GOTTCHA. Subspecies analysis was 

performed using BLAST recruitment analysis. 

Further phylogenetic analysis was performed 

on metagenomic assemblies of samples and 

resulting contigs matching S. enterica. Results: 

Sample consistency and human DNA 

sequence abundance varied greatly, often 

reducing the sequencing depth of the targeted 

microbial communities, yet referenced-based 

detection of Salmonella serovar Heidelberg 

was possible by metagenomic read recruitment 

as well as metagenomic assembly, even in 

samples with high human DNA content (90- 

96%). Taxonomic profiling revealed similar 

microbial community structures between individual 

patients from each localized outbreak; 

samples from different outbreaks clustered 

separately and were distinct from a subset of 

‘healthy’ references selected from the Human 

Microbiome Project. Microbial gut communities 

consistently showed reduced species 

diversity in each foodborne outbreak compared 

to ‘healthy’ references. Conclusions: These 

results highlight the potential utility of metagenomic-based 

diagnostic tools for foodborne 

pathogen identification and epidemiologically 

relevant clustering, even in samples with high 

human DNA abundance. Furthermore, shotgun 

metagenomic approaches offer additional insight 

into gut microbial community responses 

to foodborne illness that may hold clues to 

pathogen ecology. 

n 74 

INTEGRATING WHOLE GENOME 

SEQUENCING OF SALMONELLA ENTERICA 

SEROVAR ENTERITIDIS INTO THE PUBLIC 

HEALTH LABORATORY FOR SURVEILLANCE 

AND OUTBREAK INVESTIGATIONS 

K. J. Levinson 1 , M. Dickinson 2 , S. Wirth 2 , M. 

Anand 3 , D. J. Baker 2 , D. Bopp 2 , L. Thompson 2 , 

K. A. Musser 2 , P. Lapierre 2 , W. J. Wolfgang 2 ; 

1 

School of Public Health, SUNY Albany, 

Albany, NY, 2 Wadsworth Center/NYSDOH, 

Albany, NY, 3 Bureau of Communicable Disease 

Control/NYSDOH, Albany, NY. 

Salmonella enterica serovar Enteritidis is 

a leading cause of foodborne illness in the 

United States. Pulsed-field gel electrophoresis 

(PFGE) is the gold standard for outbreak detection 

of enteric pathogens. However, the low 

genetic diversity of S. Enteritidis and frequent 

exchanges of mobile genetic elements limits 

how well PFGE can discriminate between 

isolates and identify clusters that may be 

epidemiologically linked. Two-thirds of all S. 

Enteritidis isolates received at the Wadsworth 

Center have PFGE patterns that are considered 

“endemic” and over half of these are pattern 

JEGX01.0004. Consequently, these cases 

are not routinely investigated by epidemiologists. 

To improve discrimination between 

sporadic and outbreak associated isolates, the 

Wadsworth Center began performing whole 

genome sequencing (WGS) single nucleotide 

polymorphism (SNP) based phylogenetic typing 

on all S. Enteritidis isolates in addition to 

PFGE typing. The goal of this project was to 

explore the utility of incorporating WGS-based 

typing into routine public health laboratory 

surveillance and to establish a standard for reporting 

WGS data in a manner that was useful 

for both laboratorians and epidemiologists. Using 

a pipeline developed in-house, we created 

cumulative SNP based phylogenetic trees from 

514 S. Enteritidis isolates in real time over a 

period of 20 months. Based on retrospective 

studies, we used a SNP diversity of 0-5 to de- 



89


fine a genomic cluster. Within the 80 genomic 

clusters identified, we found that 20% contained 

multiple PFGE patterns and conversely, 

we found 35 genomic clusters within PFGE 

pattern JEGX01.0004. We then analyzed these 

clusters in a time-dependent manner and present 

an intuitive non-tree based plot for rapid 

identification of “clusters of interest” (defined 

as clusters that contained at least 4 isolates 

collected within 60 days of each other). Importantly, 

these parameters can be modified in 

real time based on epidemiological feedback. 

By sequencing all S. Enteritidis isolates concurrently 

with PFGE typing, we show it is 

possible to cluster isolates both genomically 

and epidemiologically in a manner that is more 

discriminatory than PFGE typing. We also 

demonstrate the utility and feasibility of this 

method in the public health laboratory setting. 

As we anticipate that the increase of clusters 

detected will pose a challenge in conducting 

epidemiological follow-up, we are now focused 

on modifying the methods to better prioritize 

these clusters to create a more valuable 

tool for epidemiologic investigations. 

n 75 

DETECTION OF ANTIMICROBIAL 

RESISTANCE MARKERS IN NEISSERIA 

GONORRHOEAE AND MIXED GONOCOCCAL 

INFECTION DIRECTLY FROM CLINICAL 

SAMPLES USING NEXT GENERATION 

SEQUENCING 

R. Graham, A. Jennison; 

Queensland Department of Health, Coopers 

Plains, AUSTRALIA. 

Introduction: The rise in Neisseria gonorrhoeae 

strains with reduced susceptibility to 

antibiotics is a major public health concern, 

and the ongoing surveillance of antimicrobial 

resistance (AMR) in community strains of 

N. gonorrhoeae is essential. In many regions 

however, the majority of N. gonorrhoeae 

cases are now diagnosed by molecular assays 

only, meaning that no isolate is obtained for 

antimicrobial susceptibility testing. A number 

of molecular markers have been identified 

for reduced susceptibility to antimicrobials, 

however, screening for these markers requires 

multiple tests, and these may miss novel mutations. 

By sequencing the entire genome of N. 

gonorrhoeae directly from a clinical sample 

using next generation sequencing (NGS), both 

known and novel mutations associated with 

AMR can be identified. In addition, typing information 

such as NG-MAST and MLST types 

can be collected, and potential mixed gonoccocal 

infections can be detected. Methods: DNA 

was extracted from eleven urine Cobas PCR 

media specimens positive for N. gonorrhoeae 

by the Roche Cobas 4800 CT/NG test. DNA 

was enriched for microbial DNA using the 

NEBNext microbiome kit and sequenced using 

the Ion Torrent PGM workflow. Sequences 

that aligned to the human genome were filtered 

out and the remaining sequences were de novo 

assembled into contigs and searched for the 

regions of interest using Ridom SeqSphere. 

MLST and NG-MAST alleles were assigned 

according to the schemes at PubMLST.net 

and NG-MAST.net respectively. Results: All 

eleven of the clinical samples tested generated 

a sufficient number of N. gonorrhoeae 

sequence reads to provide full coverage of the 

genome at a depth of 6-130x. Complete MLST 

and NG-MAST profiles could be generated 

for each of the samples. None of the samples 

had more than one sequence type detected 

in the one sample, which would have been 

indicative of a same site mixed gonococcal 

infection. However, when samples of two different 

sequence types were artificially created 

in vitro, the two distinct allele sequences could 

be detected, suggesting that naturally occurring 

mixed infections would be identified. The presence 

of ten different AMR markers was investigated, 

and mutations associated with reduced 

susceptibility to cephalosporins, quinolones 

and tetracycline were identified. Conclusions: 

We found that multiple levels of N. gonorrhoeae 

typing information could be generated 

directly from clinical samples using NGS. This 

study also demonstrated proof of principle 

for utilising NGS to identify same site mixed 

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gonococcal infections in a culture independent 

manner. Ten AMR markers were examined, 

here and with the complete genome information 

provided by NGS there is the potential for 

many more to be investigated and for novel 

markers to be identified, highlighting the potential 

of this technology to enable continued 

AMR surveillance in areas where culture is no 

longer performed. 

n 76 

SUBTYPING OF E. COLI STEC BY 

TRADITIONAL LABORATORY METHODS AND 

WGS, A COMPARISON! 

E. Litrup, K. Kiil; 

Statens Serum Institut, Copenhagen, DEN- 

MARK. 

The traditional subtyping of E. coli STEC 

strains consists of many different and somewhat 

laborious and slow methods. In Denmark, 

we began whole genome sequencing of all E. 

coli STEC strains isolated from humans in 

January 2015. From January through June, 

we received and typed more than 70 STEC 

strains by both the traditional methods and 

WGS. The methods performed in our laboratory 

are serotyping, PCR assays and dot blot 

hybridization among others. Whole Genome 

Sequencing was performed in-house on an 

Illumina MiSeq with the Nextera Library 

Preparation kit and 250bp paired reads. For the 

comparison of WGS and traditional subtyping, 

we focused on the serotype, the stx1 and stx2 

subtypes and the presence of several genes 

e.g. the eae and ehxA gene. For detection of 

the serotype and virulence genes, we used the 

CGE finders (Serotype Finder and Virulence 

Finder https://cge.cbs.dtu.dk/services/), but 

we also used reference based mapping to the 

databases behind the finders with srst2 (https:// 

github.com/katholt/srst2). The serotype was 

the subtyping method with the most divergent 

results as expected. The Serotype Finder was 

able to detect all but one serovar detected by 

the traditional serotyping. Additionally, almost 

all the strains typed as rough or smooth 

in the laboratory, were assigned an O-type 

by the Serotype Finder tool. Further, all the 

non-motile strains with no phenotypic H-type, 

were assigned an H-type by the Serotype 

Finder tool. Finally, there were some cross 

reactions between different sera in the laboratory, 

where the Serotype Finder was also able 

to give a result regarding which H type was 

more similar to the one sequenced. Regarding 

the detection of the stx1 and stx2 variants, eae 

and ehxA genes, we found an almost 100% 

correlation to the laboratory results achieved 

by PCR and dot blot hybridization. Only in a 

few cases did the laboratory achieve different 

results. We used the Virulence Finder on 

contigs assembled denovo by CLC and also we 

used reference based mapping to the databases 

behind the finder using SRST2, and we saw 

no differences in the performance of the two 

approaches. It is believed that reference based 

mapping is important for gene detection in E. 

coli as it can be challenging to assemble the 

reads into acceptable contigs, but in the case of 

these four genes and their variants this was not 

the case. This might be due to the location of 

these genes; they are probably located in parts 

of the genome that are easy to assemble. 

n 77 

DR. JEKYLL AND MR. HYDE: THE CASE 

FOR MIXED NOCARDIA POPULATIONS 

IN CLINICAL ISOLATES AS REVEALED BY 

WHOLE GENOME SEQUENCE ANALYSIS 

A. C. Lauer, B. Lasker, J. R. McQuiston; 


Atlanta, GA. 

Nocardia species are aerobic GC-rich, 

partially-acid fast, opportunistic, pathogenic 

actinomycetes. Every year, approximately 200 

nocardiae isolates are received by the Special 

Bacteriology Reference Laboratory (SBRL) at 

CDC, however the true incidence of nocardiosis 

in the U.S. remains unknown. Despite reports 

of resistance to co-trimoxazole, the treatment 

of choice for nocardiosis, the molecular 

mechanisms of resistance in nocardiae are not 



91


well understood. To investigate the molecular 

mechanisms responsible for resistance in nocardiae, 

a total of 144 isogenic isolates from 

7 N. farcinica and 5 N. nova clinical isolates, 

with known genetic relationships and resistance 

profiles, were produced in our laboratory. 

The genomes of these isogenic susceptible and 

resistant isolates were compared to detect genetic 

changes that directly correlated with the 

emergence of resistance. Surprisingly, variant 

detection yielded large numbers of high quality 

SNPs (~150,000) between isogenic isolates. 

Further obstacles were encountered that necessitated 

further investigation into the nature of 

the nocardial genome when only 49% to 84% 

of the sequence reads mapped to the available 

complete genomes. Therefore, genomes were 

also assembled de novo using SPAdes, Velvet, 

Mira, and CLC Genomics Workbench and all 

assemblies were evaluated using QUAST. No 

assembler, however, produced a single contig. 

Optical mapping data indicated that less than 

10% of the contigs from the best assemblies 

mapped to the optical map and revealed a 2 

MB difference between our isolates and the 

published reference strain for N. nova. Mapping 

of reads to housekeeping genes (16S, 

gyrB, rpoB) revealed the presence of a high 

level of minority alleles at specific positions. 

Alignment of the consensus sequences for 

isogenic isolates at specific genes showed that 

the minority alleles of one isolate were the 

dominant alleles in other isolates such that 

nucleotides appeared to alternate at specific 

bases. After contamination by other bacteria 

was ruled out, K-mer based trees show that 

despite the many differences between isolates, 

those which are isogenic group together and 

are phylogenetically closer to each other than 

to other members of the same species but from 

different parental lineages. The data suggest 

that clinical Nocardia isolates may occur naturally 

as mixed populations, and may explain 

why published nocardiae genomes often show 

multiple copies of housekeeping genes that 

are normally single-copy housekeeping genes 

such as rpoB and gyrB. Further investigations 

are needed to verify this hypothesis. A better 

understanding of the ecology of the organisms 

sequenced by SBRL is needed such that DNA 

extraction, genome sequencing, and genome 

analysis can be informative and representative 

of the true biology of the organisms before 

genomics can be solely relied upon for surveillance. 

n 78 

EVALUATING THE POTENTIAL OF USING 

NEXT-GENERATION SEQUENCING FOR 

DIRECT CLINICAL DIAGNOSTICS OF FECAL 

SAMPLES FROM DIARRHEA PATIENTS 

O. Lukjancenko 1 , K. G. Joensen 2 , F. M. Aarestrup 

1 ; 

1 

Technical University of Denmark, Kongens 

Lyngby, DENMARK, 2 Statens Serum Institut, 

Copenhagen, DENMARK. 

Diarrhea is a major global disease burden and 

rapid, precise identification of the causative 

pathogens is important to initiate treatment as 

well as prevent further spread and potential 

outbreaks. The current routine diagnostics 

involve a number of different procedures, 

and often the causative agent is not identified 

in time to guide clinical care. In addition, 

in many clinical cases the causative agent 

is never identified. With next-generation sequencing 

(NGS) becoming cheaper, it has huge 

potential in the routine diagnostic settings. The 

aim of this study was to conduct a pilot project 

to evaluate the potential of performing NGSbased 

diagnostics through direct sequencing of 

fecal samples. A total of 61 clinical diarrheal 

fecal samples, including 48 pathogen-positive 

and 13 pathogen-negative, were obtained 

from diarrhea patients as part of the routine 

diagnostics at the Department of Clinical Microbiology 

at Hvidovre University Hospital in 

Denmark. Ten control samples from healthy 

individuals were additionally included. Complete 

DNA content was extracted from fecal 

samples and sequencing was performed on the 

Illumina MiSeq system. Sequence data was 

analyzed by the MGmapper (http://cge.cbs. 

dtu.dk/services/MGmapper/) software to as- 

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sess the species distribution. Sequence-based 

diagnostic prediction was performed for pathogenic 

bacteria and Giardia by evaluating the 

relative abundance of pathogens in the samples 

as well as the presence of pathogen-specific 

virulence genes. The results obtained from the 

sequence-based diagnostic were compared to 

the conventional findings for 51 (of the total 

61) diarrheal samples, 40 of which by conventional 

diagnostic methods were found positive 

for bacterial pathogens and 11 of which were 

found negative. The NGS-based diagnostic approach 

proved to enable detection of the same 

bacterial pathogens as the classical approach 

in 37 of the 40 clinically-positive samples as 

well predict responsible pathogens in eight of 

the eleven samples from clinically ill patients 

that had been found negative by conventional 

methods. Overall, the NGS-based diagnostic 

approach enabled pathogen-detection similar 

to the current routine diagnostics, and this 

analytical approach has the potential to be 

extended to be applicable for detection of 

other pathogens. With further expansion of 

the analysis and obtainment of more sequence 

data per sample, the method does hold promise 

for better diagnostic detection. At present, 

however, this type of metagenomic analysis is 

too expensive per sample, and the turnaround 

time too long for it to become part of routine 

diagnostics. 

n 79 

EXPLORATION OF WHOLE GENOME 

SEQUENCING OF SHIGA TOXIN PRODUCING 

E. COLI IN THE NEW YORK STATE PUBLIC 

HEALTH LABORATORY TO IDENTIFY 

SEROGROUP, VIRULENCE FACTORS, AND 

GENOMIC CLUSTERS 

S. E. Wirth, T. Quinlan, D. J. Baker, T. Halse, 

P. Lapierre, K. A. Musser, W. J. Wolfgang; 

Wadsworth Center, NYSDOH, Albany, NY. 

In the United States, it is estimated that shiga 

toxin producing E.coli (STEC) is responsible 

for at least 265,000 infections each year. STEC 

serogroup O157 causes about one quarter 

of these infections while the rest are due to 

non-O157 serogroups. Accurate and timely 

identification of STEC is crucial to patient care 

and epidemiological tracebacks that are carried 

out to identify sources. At the Wadsworth 

Center, some virulence factors and the more 

common serogroups are currently characterized 

for each isolate by using up to seven realtime 

PCR assays. Concurrently, pulsed-field 

gel electrophoresis (PFGE) is performed to 

identify the PFGE subtype to aid in epidemiological 

investigations. The use of multiple 

molecular tests for identification and typing is 

time-consuming and expensive. Studies have 

shown that Whole Genome Sequencing (WGS) 

can yield information regarding virulence factors, 

serogroup, and genomic subtyping from 

a single dataset potentially leading to reduced 

costs and improved turn around time. Furthermore, 

genomic subtyping can improve cluster 

resolution compared to the gold standard of 

PFGE. To begin to evaluate WGS as a “onestop-shop” 

for STEC identification and typing, 

we have sequenced and analyzed sporadic and 

outbreak-associated STEC isolates. WGS was 

performed on an Illumina MiseqTM using 2 x 

250 paired end chemistry. After passing quality 

control metrics, sequence reads were analyzed 

using an in-house developed bioinformatic 

pipeline and the Virulence Finder database. 

Serogroup and virulence profiles were assigned 

from raw reads and assembled genomes. In 

addition, the pipeline mapped raw reads to 

a single reference genome and a SNP based 

phylogenetic tree was produced. Our analysis 

of STEC WGS data shows a high degree of 

concordance between SNP-based phylogenetic 

clusters, PFGE clusters, and epidemiologically 

defined outbreaks. Additionally, we can reliably 

ascertain certain isolate serogroups and 

virulence factors. Our next steps are to refine 

the pipeline, analyze isolates prospectively in 

real-time, integrate the process into our clinical 

laboratory information system, and seek New 

York State clinical laboratory certification to 

report serogroup and virulence data. 



93


n 80 

NANOPORE + ION TORRENT SEQUENCING, 

ASSEMBLY, AND ANNOTATION OF CULTURE- 

FREE STREAMBED PLASMIDS REVEALS 

HITCHHIKING GENES FOR RESISTANCE TO 

MULTIPLE HUMAN CLINICAL ANTIBIOTICS 

S. D. Turner 1 , E. Gehr 2 , K. Libuit 2 , C. Kapsak 

2 , J. Herrick 2 ; 

1 

University of Virginia, Charlottesville, VA, 

2 

James Madison University, Harrisonburg, VA. 

Background: Transmissible plasmids affect 

environmental ecosystems by facilitating 

exchange and recombination of antibiotic resistance 

genes. This exchange occurs between 

native bacterial populations and introduced 

fecal pathogens selected for resistance in farm 

animals. As such, native bacteria in aquatic 

and soil habitats may act as incubators and 

sites for recombination of genes that are subsequently 

transferred to human pathogens. 

Methods: Transmissible plasmids were 

captured exogenously from stream sediment 

samples by releasing cells from sediment and 

conjugating with a rifampicin-resistant strain 

of E. coli. Transconjugants were selected on 

tetracycline-and rifampicin-amended medium. 

Plasmids were purified and electroporated 

into an electrocompetent E. coli strain and 

tested for decreased antibiotic susceptibility 

relative to the un-electroporated strain using 

a modified Stokes disk diffusion method. Antibiotics 

tested were tetracycline, gentamicin, 

ciprofloxacin, sulfamethoxazole/trimethoprim, 

imipenem, tobramycin, kanamycin, aztreonam, 

ticarcillin, piperacillin, tazobactam, and 

cefepime. Two plasmids were sequenced using 

both the Oxford Nanopore MinION and 

Ion Torrent PGM. Long-read nanopore data 

was assembled with the Celera assembler, the 

assembly was error-corrected using the more 

accurate short read data, and the resulting 

polished assemblies were annotated against 

UniProt, RefSeq, Pfam, and TIGRFAMs. 

Unassembled reads were further screened 

for genes encoding antibiotic resistance. Results: 

23 of 30 captured plasmids conferred 

decreased susceptibility to multiple antibiotics 

in addition to tetracycline. The most common 

phenotypes were tetR, kanR, ticR, pipR, tetR, 

kanR, ticR, pipR, and fepR. One plasmid conferred 

decreased susceptibility to seven of the 

tested antibiotics (tet, tob, kan, tic, tzp, pip, 

and fep). Nanopore sequencing and assembly 

of this plasmid resulted in two contigs, each 

approximately 15kb. Contigs were screened 

for antibiotic resistance against ResFinder, 

containing 2203 genes across 13 drug classes. 

The following genes were identified with 

>93% identity: tetC, tetG, aadA9, aadA2, and 

sul1 (2X) - suggesting the presence of one or 

more Class 1 integrons - floR, aph(3’)-Ic, strB, 

and blaCARB-2. A second plasmid exhibiting 

decreased susceptibility to tet, cip, kan, tic, and 

pip was also sequenced, resulting in a single 

~90kb contig. Annotation revealed a similar resistance 

profile. Conclusions: The presence of 

genes encoding resistance to multiple human 

clinical antibiotics on transmissible plasmids 

selected using tetracycline suggests that there 

may be a significant reservoir of antibiotic 

resistance genes in stream sediments and that 

these may be capable of transmission to pathogenic 

Enterobacteriaceae. 

n 81 

AVERAGE NUCLEOTIDE IDENTITY 

(ANI) ANALYSIS OF A DIVERSE SET OF 

ESCHERICHIA 

S. B. Im 1 , L. Rishishwar 2 , A. D. Huang 1 , L. S. 

Katz 1 , H. A. Carleton-Romer 1 , E. Trees 1 , N. 

Strockbine 1 , R. L. Lindsey 1 ; 

1 


Atlanta, GA, 2 Georgia Institute of Technology, 

Atlanta, GA. 

The advent of high throughput sequencing 

technologies has provided the opportunity to 

explore new methods to characterize organisms, 

not only more efficiently, but also more 

discriminately than traditional phenotypic 

methods. Average nucleotide identity (ANI) 

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is a value that can be used to compare the 

relatedness of two genomic sequences. Findings 

from ANI analysis have been shown to 

correlate strongly with those using DNA-DNA 

hybridization (DDH) methods; with ANI 

values of ≥ 94% being equivalent to ≥ 70% 

DDH for bacteria belonging to the same species. 

In previous studies with a limited number 

of strains, ANI was reported to successfully 

distinguish members of the genus Escherichia. 

To evaluate the limitations of ANI analysis to 

distinguish members of Escherichia/Shigella, 

we expanded our analysis to include more 

strains between species, as well as within and 

between common serogroups that are relevant 

to public health. One hundred seventy strains 

from nine different species and 20 different 

serogroups were tested; 13 diverse Salmonella 

spp. were included as an outgroup. To detect 

the ANI variability within a serogroup, at least 

seven strains were analyzed from each of the 

common Shiga-toxin producing E. coli serogroups: 

O26, O45, O103, O111, O121, O145 

and O157. ANI between two genomes was 

computed using the BLAST algorithm, where 

each genome in the dataset was compared in 

an all-against-all pairwise fashion. Each comparison 

yielded a two-way ANI value describing 

the genetic relatedness between organisms. 

We found ANI values of ≥ 98% between and 

among E. coli isolates belonging to serogroups 

O26, O69, O118, O146, O103, O121, O145, 

O45, O91 and O128; ≥ 97% between and 

among isolates belonging to serogroups O157, 

O127, and the four Shigella species; ≤ 89% 

between E. coli/Shigella strains and those of 

other Escherichia species; and ≤ 80% between 

isolates of the genera Escherichia and Salmonella. 

Pairwise ANI values showed higher 

percent similarity between closely related serogroups 

indicating a clear distinction between 

Escherichia species and Salmonella. 

n 82 

SEQUENCE AND STRUCTURAL VARIANCE 

IN RECENT BORDETELLA PERTUSSIS 

GENOMES 

M. M. Williams, M. R. Weigand, Y. Peng, K. E. 

Bowden, M. L. Tondella; 


Atlanta, GA. 

Pertussis disease, caused by the bacterium Bordetella 

pertussis, has resurged in the U.S. in 

recent decades, despite high vaccine coverage. 

Genome analysis of 424 isolates was undertaken 

to determine causes for pertussis increase. 

Of these isolates, 411 were obtained from 34 

US states for the period 2000-2014. Three vaccine 

strains were also included, and the remaining 

10 isolates were from other countries 

and historic collections, isolated between 1939 

and 1967. Sequence variants, including insertions, 

deletions, small replacements, SNPs and 

MNPs (single and multiple nucleotide polymorphisms, 

respectively) were determined by 

mapping Illumina sequencing reads against the 

Tohama I vaccine reference. A subset of 171 

complete genomes were assembled from long 

read sequence data obtained using the Pacific 

Biosciences RS II platform. Assemblies were 

validated by comparison to genome optical 

maps and sequence accuracy was confirmed by 

independent analysis using DNA cluster sequencing 

(Illumina MiSeq or HiSeq platform). 

Genome structure variation was determined by 

whole genome alignment with progressiveMauve. 

A total of 4,655 variants were detected 

in 424 genomes, the majority of which were 

SNPs (89%). Twenty-nine percent of variants 

occurred in non-coding regions, 26% were 

synonymous, and 45% were non-synonymous 

variants. The locus displaying the most variation 

was prn, the gene encoding pertactin, 

a component of acellular pertussis vaccines 

that has mutated rapidly in the last five years, 

resulting in pertactin non-expression in the 

majority of current US isolates. Variants were 

found in 951 other open reading frames, involved 

in a wide variety of cellular functions. 



95


Phylogenetic analysis of SNPs clustered isolates 

by time. By contrast, 35 unique genomic 

structural patterns were observed among the 

171 assembled genomes of two vaccine strains 

and 169 isolates obtained from 27 states in 

2000-2014. Pattern variation was due to inversion 

of genome sections, many with IS481, an 

insertion sequence element found in 240-256 

copies per genome, located at the predicted 

boundary sites. Although B. pertussis does 

not demonstrate great variety at the nucleotide 

level, several genomic rearrangements are 

circulating in current strains, even within the 

same epidemic. Implications for improving 

molecular epidemiology and understanding 

pertussis pathogenicity will be explored. 

n 83 

NON-TRAVEL ASSOCIATED SALMONELLA 

TYPHIMURIUM ST313 IN THE UK 

P. Ashton, C. Lane, S. Nair, T. Peters, E. de 

Pinna, K. Grant, T. Dallman; 

Public Health England, London, UNITED 

KINGDOM. 

Salmonella enterica serovar Typhimurium 

typically causes self-limiting gastroenteritis. 

However, invasive non-typhoidal Salmonella 

disease has been recently documented in many 

sub-Saharan African countries, causing significant 

morbidity and mortality. The majority 

of these invasive isolates belong to one of two 

monomorphic lineages within a single Multi- 

Locus-Sequence-Type, ST313. There is also 

evidence that ST313 is more invasive than 

other closely related S. Typhiumrium in both 

humans (clinical reports) and chickens (in vivo 

experiments). Here, we present data on 16 isolates 

of ST313 received by the Public Health 

England Salmonella Reference Service. Three 

of sixteen isolates were associated with travel 

to Africa. All of these isolates caused invasive 

disease and they cluster within the previously 

described ST313 phylogenetic lineages. The 

majority of isolates of ST313 in England 

(13/16) are non-travel associated. Only 1 of 

these 13 isolates was associated with invasive 

disease and they cluster into 4 new phylogenetic 

lineages, significantly increasing the 

diversity observed within ST313. Accessory 

genome analysis reveals that there are genomic 

elements exclusively associated with the sub- 

Saharan African lineages including pro-phage 

and antibiotic resistance determinants. These 

results suggest that ST313 in England is epidemiologically, 

clinically and genomically 

heterogeneous, with a geographic relationship 

underlying this heterogeneity. 

n 84 

EFFECT OF SEQUENCING READ ERROR 

CORRECTION METHOD ON HQSNPS 

DISCOVERY AND PHYLOGENETIC TREE 

TOPOLOGY IN OUTBREAKS 

D. D. Wagner, H. Carleton, E. Trees; 


Atlanta, GA. 

Benchtop next-generation sequencing (NGS) 

machines offered by the Illumina platform 

allow public health laboratories to rapidly 

sequence multiple bacterial strains in multistate 

foodborne outbreaks. High-quality single 

nucleotide polymorphisms (hqSNPs) provide a 

means for inferring phylogenetic associations 

among closely-related outbreak strains. Yet, 

NGS technologies often introduce miscalled 

bases or other types of errors that diminish 

the number of phylogenetically-informative 

hqSNPs. The current study shows how bioinformatics 

methods based upon free software 

packages mitigate NGS sequencing errors 

and increase counts of phylogeneticallyinformative 

hqSNPs. NGS datasets from five 

foodborne pathogen outbreaks were cleaned 

in QUAKE, BayesHammer, and FastX. In 

three Salmonella enterica outbreak clusters 

representing serovars Enteritidis, Newport, 

and Baildon, reads corrected/trimmed through 

Fastx, Quake, and BayesHammer all increased 

the counts of informative hqSNP positions by 

at least 9 positions or at most 195 positions 

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when compared with hqSNPs inferred on 

uncorrected reads. In a cluster of Salmonella 

Paratyphi B var. (L+) tartrate+, the Bayes- 

Hammer-cleaned reads and FastX-trimmed 

reads yielded 314 and 311 informative hqSNP 

positions, respectively. In the same set, the 

uncorrected reads yielded 292 informative 

hqSNP positions, unexpectedly outperforming 

the Quake-cleaned reads with 241 hqSNP 

positions. In the fifth data set, a cluster of E. 

coli O157:H7, FastX-trimmed reads yielded 

66 hqSNP positions and Quake-cleaned reads 

yielded 65 hqSNP positions. BayesHammercleaned 

reads and uncorrected reads both performed 

worse for the O157:H7 set, with 61 and 

59 hqSNP postions, respectively. In the Enteritidis 

and Baildon sets, BayesHammer-corrected 

reads yielded hqSNP phylogenies with 

median bootstrap support values, 61% and 

24%, respectively, across all internal branches 

of each tree. By comparison, FastX-trimmed or 

untrimmed reads had a median bootstrap support 

of at least 49% for the Baildon cluster and 

0% bootstrap support for the Enteritidis cluster. 

For the Paratyphi B hqSNP phylogeny, median 

bootstrap values were 100% across all four 

read-trimming methods, but ranged from 1% 

support in the untrimmed tree up to 20% support 

in the FastX tree. Quake-corrected reads 

yielded hqSNP trees with the highest median 

bootstrap values for the Salmonella Newport 

cluster (median support = 51%) and the E. coli 

O157 cluster (median support =27%). These 

results indicate that BayesHammer enables 

discovery of the largest numbers of hqSNPs 

and while BayesHammer and Quake both perform 

well for inferring hqSNPs phylogenetic 

trees. Yet, as Quake may decrease the counts 

of phylogenetically-informative SNP positions, 

BayesHammer and FastX are likely the best 

first-pass cleaning/correction tools for hqSNPs 

pipelines. 

n 85 

EVALUATION OF WHOLE GENOME 

SEQUENCING TO CONFIRM OR REFUTE 

CLONALITY OF CONVENTIONAL GENOTYPE- 

DEFINED CLUSTERS OF MYCOBACTERIUM 

TUBERCULOSIS 

L. Cowan, J. Posey; 


Atlanta, GA. 

Since 2004, the Division of Tuberculosis Elimination 

has conducted genotyping surveillance 

of Mycobacterium tuberculosis. Genotyping 

data is integrated with patient demographic 

and clinical data and routinely analyzed to 

identify suspected outbreaks. However, the 

discriminatory power of the current genotyping 

methods are sometimes insufficient, and some 

suspected outbreaks identified by genotyping 

include a mix of outbreak and sporadic cases 

or do not represent an outbreak. Genomic surveillance 

has the power to increase the accuracy 

of outbreak detection systems by analyzing 

the entire genome versus less than 1% using 

conventional methods. We conducted whole 

genome sequencing (WGS) for 20 (number of 

isolates per cluster, 10 - 100) suspected large 

outbreaks identified by routine genotyping. 

Reference-guided assemblies of Illumina sequence 

read sets were created using LaserGene 

SeqMan NGen (DNAStar). Polymorphisms 

between the clustered isolates were identified 

by comparing assembled genomes including 

coverage statistics read depth and distribution 

of base calls for reliable differences. The final 

set of polymorphisms were confirmed in each 

assembly by visually checking each position in 

the mapped reads of the assembly. The number 

of single nucleotide polymorphisms (SNPs) 

identified for each cluster ranged from 8 to 

101. WGS exhibited a higher level of resolution 

for each cluster as compared to conventional 

genotyping methods and identified cases 

that were not involved in recent transmission 

among the samples analyzed. The preliminary 

results indicate that WGS data could result in 

more focused targeting of limited public health 



97


resources leading to more effective epidemiologic 

field investigations and an improved 

ability to identify where public health intervention 

will have the greatest impact. This curated 

data set can be used to evaluate bioinformatic 

pipelines for variant detection using whole 

genome SNP or multi-locus sequence typing 

(wgMLST) platforms. 

n 86 

RAPID WHOLE GENOME SEQUENCING 

AND DE NOVO ASSEMBLY PIPELINE FOR 

BORDETELLA PERTUSSIS USING MULTIPLE 

PLATFORMS 

Y. Peng, M. M. Williams, M. R. Weigand, K. 

Bowden, M. L. Tondella; 


Atlanta, GA. 

In the U.S. and many other developed countries, 

pertussis is currently the least well controlled 

vaccine‐preventable bacterial disease 

despite excellent vaccination coverage. Whole 

genome sequences of Bordetella pertussis, the 

causative agent of pertussis disease, will help 

us better understand the epidemiologic and 

clinical relevance of current circulating strains, 

develop novel diagnostic assays, and elucidate 

the possible reasons for the current increase 

in pertussis in the U.S. and around the world. 

However, with hundreds of insertion sequences, 

repeat regions, and large rearrangements 

in the B. pertussis genome, whole-genome 

sequencing and assembly is challenging. There 

are currently over 400 B. pertussis incomplete 

genome assemblies publically available, 

most of them are composed of over hundreds 

contigs assembled from short read sequencing. 

Our group has developed a rapid whole 

genome sequencing and assembly pipeline for 

B. pertussis by taking advantage of the latest 

long read PacBio RSII sequencing platform; 

highly accurate, high coverage and low cost 

Illumina sequencing platforms; and the OpGen 

genome optical mapping system. High quality 

genomic DNA was isolated with the Qiagen 

Puregene Yeast/Bact. Kit. Utilizing P6 PacBio 

chemistry and 240 minutes movie recording, 

one SMRT cell produced over 50 X coverage 

subreads with N 50 

as high as 13 kb, which was 

able to assemble into one complete genome using 

HGAP 3.0. After removing the end overlap 

to close the circular genome, the structure of 

de novo assemblies was further tested and confirmed 

by optical mapping and finally polished 

by mapping with high quality short Illumina 

reads. So far, nearly 200 genomes have been 

completed using this multi-platform pipeline 

and sequencing of more than 200 additional 

isolates is currently underway. Deep genome 

variation analysis (SNPs, rearrangements and 

IS-elements, etc.) will direct future work with 

other ‘omics’ approaches, including RNASeq 

and peptide profiling, to determine causes for 

pertussis increase. 

n 87 

DEVELOPMENT OF A SUBTYPING ASSAY 

FOR DIRECT DETECTION AND TARGETED 

SEQUENCING OF SHIGA TOXIN-PRODUCING 

ESCHERICHIA COLI (STEC) FROM CLINICAL 

SAMPLES 

L. M. Gladney 1 , D. Fasulo 2 , R. L. Lindsey 1 , A. 

Huang 1 , E. Trees 1 , N. Strockbine 1 , E. R. Ribot 1 , 

H. A. Carleton 1 , J. Besser 1 ; 

1 

Centers for Disease Control, Atlanta, GA, 

2 

Pattern Genomics, LLC, Madison, CT. 

Shiga toxin-producing Escherichia coli 

(STEC) is an important foodborne pathogen 

that causes approximately 265,000 infections 

and nearly 30 outbreaks per year in the US. 

STEC has been traditionally diagnosed by 

culture and subtyped using a variety of methods 

to help detect outbreaks. Recently, cultureindependent 

diagnostic tests (CIDTs) have 

been introduced in many clinical labs in place 

of culture-based methods. These methods are 

attractive because they are cost-effective, can 

be performed at point-of-care, and do not require 

culture of the pathogen. As a result, the 

current laboratory-based surveillance system 

in the US, PulseNet, is threatened because it 

relies on pure cultures to perform subtyping by 

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pulsed-field gel electrophoresis (PFGE) and 

whole genome sequencing (WGS). To address 

this issue, we are developing a PCR-based 

subtyping assay that may be used directly with 

complex samples such as stool. First, we identified 

targets that are conserved in, and unique 

to STEC utilizing 342 genome sequences (104 

STEC, 201 other E.coli and 37 other Enterobacteriaceae). 

Using Daydreamer TM software 

(Pattern Genomics), we screened for DNA 

signatures that are unique to STEC and not 

present in any other E.coli. We then filtered 

out any signatures that were not specific due 

to a blast hit to anything other than STEC on 

Genbank. We performed a similar analysis 

to find unique signatures for two additional 

Escherichia species that may be phenotypically 

similar to E.coli and present in stool. We 

identified five targets and designed primers 

for STEC (primarily the Stx1 and Stx2 genes), 

which captures all of the top 7 (100%) and 16 

(80%) of top 20 serogroups as well as ten possible 

targets for closely related species E. fergusonii 

and E. albertii. We performed in silico 

PCR on our 342 genomes to confirm the presence 

and absence of the targets in our training 

dataset and to access the overall successfulness 

of the design. Among STEC serotypes included 

in our training set, the coverage of our 

primers was 98% and 77% for the Stx1 and 

Stx2 targets, respectively, while the accuracy 

was 100%. The low coverage of the Stx2 target 

may be due to fragmented assemblies with 

shorter Stx2 sequences in the reference genomes. 

Three additional targets capture one or 

more of the genomes not accounted for by the 

Stx1 or Stx2 target and are 100% accurate, but 

do not cover all STEC. The coverage and accuracy 

was 100% for the species targets. Next, 

we plan to develop targets to subtype lineages 

of STEC that we identify in our dataset and 

evaluate heterogeneity in adjacent regions to 

the STEC targets which may be used to differentiate 

strains in a targeted amplicon sequencing 

approach. This PCR assay should help 

public health labs bridge the gap between isolate 

characterization and shotgun metagenomic 

sequencing to control foodborne disease. 

n 88 

THE APPLICATION OF WHOLE 

GENOME MULTI-LOCUS SEQUENCE 

TYPING TO CHARACTERIZE LISTERIA 

MONOCYTOGENES 

H. A. Carleton 1 , L. S. Katz 1 , S. Stroika 1 , A. 

Sabol 1 , K. Roache 1 , Z. Kucerova 1 , E. M. Ribot 

1 , P. Evans 2 , U. Dessai 3 , K. Kubota 4 , H. 

Pouseele 5 , J. Besser 1 , C. Tarr 1 , E. Trees 1 , P. 

Gerner-Smidt 1 ; 

1 

Centers for Disease Control, Atlanta, GA, 

2 

Food and Drug Administration, Center for 

Food Safety and Applied Nutrition, College 

Park, MD, 3 United States Department of Agriculture, 

Food Safety and Inspection Service, 

Washington, DC, 4 Association of Public Health 

Laboratories, Washington, DC, 5 Applied 

Maths, Sint-Martens-Latem, BELGIUM. 

Background: The introduction of low cost 

rapid next generation sequencers (NGS) is 

revolutionizing public health microbiology 

because traditional phenotypic and genotypic 

characterization methods now can be replaced 

by whole genome sequencing (WGS). As part 

of the Advanced Molecular Detection (AMD) 

initiative at CDC, we focused on Listeria 

monocytogenes surveillance and transformation 

of PulseNet’s current pulsed-field gel 

electrophoresis (PFGE) -based surveillance 

into a WGS -based infrastructure for public 

health laboratories. In collaboration with FDA 

and USDA-FSIS, we developed a L. monocytogenes 

whole genome multi-locus sequence 

typing database (wgMLST) in BioNumerics 

7.5 and tested the utility of this approach in 

surveillance. Methods: Since September 2013, 

WGS has been performed on over 2000 clinical, 

food and environmental L. monocytogenes 

isolates. Nextera XT DNA libraries were sequenced 

on the Illumina MiSeq platform. After 

applying raw read quality controls, sequences 

with >20x coverage were uploaded in real-time 

to the Sequence Read Archive at NCBI and 

further analyzed using wgMLST. The sequences 

were cleaned and assembled using SPAdes, 

then alleles were identified from raw reads and 



99


assembled genomes. To compare performance 

of wgMLST and high quality single nucleotide 

polymorphisms (hqSNP) methods, a subset 

of the isolates was further characterized using 

hqSNP (LYVE-SET: github.com/lskatz/ 

lyve-SET) approach. Results: The wgMLST 

database was built using 200 well characterized 

annotated reference genomes, representing 

plasmid and chromosomal diversity of L. 

monocytogenes. Originally, over 5800 unique 

loci were identified then further validated by 

removing redundant loci and improving locus 

definitions so presently there are 4814 loci in 

the wgMLST scheme. These loci represent 

on average 88.1% of the coding sequences in 

the reference data set and have 0.155% redundancy 

(2 loci defined in same position on genome). 

Number of loci identified per genome 

ranged from 2600 to 3100. For isolates identified 

as part of a cluster/outbreak, there was 

high correlation in the clustering of isolates by 

wgMLST and hqSNP analysis for epidemiologically 

confirmed outbreaks. Conclusion: 

This preliminary analysis suggests that the 

current Listeria wgMLST database adequately 

captures the diversity of Listeria monocytogenes 

that are characterized as part of real-time 

surveillance. Additionally, wgMLST can be 

used to identify clusters of epidemiologically 

related isolates and the wgMLST results match 

closely to hqSNP analyses. 

n 89 

USE OF WHOLE GENOME SEQUENCE 

K-MER-BASED SNP ANALYSIS FOR 

CLOSTRIDIUM BOTULINUM SURVEILLANCE 

IN THE UNITED STATES 

J. L. Halpin, J. K. Dykes, B. H. Raphael, S. M. 

Maslanka, C. Lúquez; 


Atlanta, GA. 

Clostridium botulinum produces botulinum 

neurotoxin, an extremely potent toxin that 

causes a rare but serious paralytic disease. Efficient 

subtyping methods aid in characterization 

of C. botulinum isolated during outbreaks, 

both in confirming or excluding a suspect 

source, linking patients, and distinguishing 

case patients from sporadic illnesses. There are 

seven serologically distinct BoNTs (designated 

serotypes A through G) defined by neutralization 

of toxicity by specific polyclonal antibodies. 

In the United States, 67% of foodborne 

botulism cases reported from 2001 to 2012 

and 99% of infant botulism cases were due to 

serotypes A and B. We completed whole genome 

sequencing on serotype A and B isolates 

that were sent to CDC in 2014 and 2015 for 

characterization or were isolated at CDC from 

specimens (e.g. stool, foods) sent by the state 

public health laboratories. After strains were 

sequenced with the Ion Torrent PGM platform, 

reads were filtered and assembled de novo 

(Torrent Suite v4.4.3, SPAdes plugin v4.4.0.1). 

Assemblies with >20x average coverage (n = 

57) as well as 13 reference strains from Gen- 

Bank were compared using the program kSNP 

v3.0 (k=23, core SNPs) (http://sourceforge.net/ 

projects/ksnp/) and the resulting SNP matrix 

file was imported into MEGA v6.0 to create a 

maximum parsimony tree with bootstrap values 

(n=500). Isolates from the same specimen 

as well as isolates from different specimens in 

a single botulism outbreak (n = 26) grouped together 

in the same clade and were phylogenetically 

distinct from epidemiologically unrelated 

isolates. kSNP can be a useful tool to quickly 

analyze small groups of isolates for relatedness 

in an outbreak situation, but analysis becomes 

cumbersome when large numbers of isolates 

are involved due to the required computing 

power. 

n 90 

INTERNATIONAL GENOMICS 

COLLABORATION FOR GLOBAL HEALTH 

SECURITY 

H. Cui, T. Erkkila, P. Chain; 

Los Alamos National Laboratory, Los Alamos, 

NM. 

Genomic science and next generation sequencing 

technologies have become a highly desir- 

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able area for international collaboration in 

support of strengthening global health security 

and many other application areas. Los Alamos 

National Laboratory is leveraging a long history 

of genomics research and technology development 

to assist multiple partner nations in 

advancing their genomics and bioinformatics 

capabilities, focusing on pathogen detection, 

characterization, and biosurveillance applications. 

Our current partner countries include Republic 

of Georgia, Kingdom of Jordan, Kenya, 

Yemen, Gabon, Uganda, Peru, Iraq, Egypt, 

Liberia, Republic of Korea, and Thailand. Collaborations 

with other countries and regions 

are also being initiated. Such partnership allows 

us to collaborate with host nations and 

provide assistance in capability development 

to enhance public health surveillance capability 

and developing infectious disease detection 

and characterization techniques that can be 

maintained and further developed by the host 

countries. We continue to develop and provide 

to the partner countries with next generation 

sequencing protocols and bioinformatics pipelines 

that enable efficient sample preparation, 

instrument operation, and next generation 

sequencing data processing and analysis. Such 

collaboration efforts will not only benefit the 

host countries and regions with state-of-the-art 

genomics science methods and technologies, 

but also build a trusted international network in 

addressing global emerging infectious disease 

challenges. Here we detail our initial scientific 

collaboration efforts and highlight the research 

outcomes. 

n 91 

MLST + STRAIN TYPING OF MULTIDRUG- 

RESISTANT ORGANISMS (MDROS) USING 

ACUITAS® WHOLE GENOME SEQUENCE 

ANALYSIS 

W. Chang, R. Kersey, A. Saeed, V. Sapiro, T. 

Walker; 

OpGen, Inc., Gaithersburg, MD. 

Quick and efficient strain typing of MDRO 

clinical isolates is crucial for the prevention 

of outbreaks. In this study we developed 

MLST+ schemas to strain type closely related 

clinical isolates of eight Gram-negative species 

with the highest priority in healthcare 

facilities, by using Acuitas® Whole Genome 

Sequence Analysis (WGS) with next generation 

sequencing technology. We selected eight 

species of microbes based on their prominence 

and clinical relevance: Escherichia coli, Pseudomonas 

aeruginosa, Klebsiella pneumonia, 

Acinetobacter baumanii, Enterobacter cloacae, 

Citrobacter freundii, Klebsiella oxytoca, and 

Serratia marcescens. To create a stable MLST+ 

schema, a reference genome sequence and 

enough query genome sequences of that species 

are required. Among our eight selected 

species, the first four already had reference genomes 

suggested by Ridom. For the remaining 

four species, we selected reference genome 

sequences by following the criteria provided 

by Ridom: the candidate reference genome 

must be finished, annotated, and accessible; 

the reference should ideally be constructed 

using Sanger sequencing; the reference isolate 

should be available from culture collections 

and DNA for sequencing must be available; 

and the reference isolate should preferably be 

the type strain or another well characterized 

strain of the species. Since the schema we developed 

will be used for strain typing MDROs, 

we always selected the strains from among 

human pathogens. The query genome sequences 

were drawn from finished genome and 

scaffold sequences of each species from The 

National Center for Biotechnology Information 

(NCBI). However, there were not enough 

genome sequences for Enterobacter cloacae, 

Citrobacter freundii, Klebsiella oxytoca, and 

Serratia marcescens in NCBI to create stable 

MLST+ schema; we supplemented those query 

sequences with our own assembled WGS to 

complete the query genome set. To test these 

schemas, Illumina MiSeq data were generated 

on a total of 74 clinical isolates of these eight 

species, and the whole genome sequences 

were then assembled and analyzed. The results 

demonstrated that Acuitas Whole Genome 



101


Sequence Analysis MLST+ can strain type 

these closely related clinical isolates of each 

species, evaluate evolutionary relationships 

among the isolates, and reveal the possibility 

of an outbreak occurrence. In conclusion, we 

successfully created MLST+ schemas for eight 

species: Escherichia coli, Pseudomonas aeruginosa, 

Klebsiella pneumonia, Acinetobacter 

baumanii, Enterobacter cloacae, Citrobacter 

freundii, Klebsiella oxytoca, and Serratia 

marcescens. These schemas successfully strain 

typed closely related clinical isolates, demonstrating 

the utility of Acuitas Whole Genome 

Sequence Analysis for transmission investigations 

and outbreak prevention. 

n 92 

COMPREHENSIVE ANALYSIS OF ANTIBIOTIC 

RESISTANCE IN MULTIDRUG-RESISTANT 

ORGANISMS (MDROS) BY WHOLE GENOME 

SEQUENCING USING ACUITAS® WHOLE 

GENOME SEQUENCE ANALYSIS 

W. Chang, R. Kersey, A. Saeed, V. Sapiro, T. 

Walker; 

OpGen, Inc., Gaithersburg, MD. 

The timely and efficient determination of the 

antibiotic resistance genes in clinical isolates 

is crucial for the prevention of outbreaks and 

the treatment of patients. In this study, we developed 

pipelines to comprehensively analyze 

antibiotic resistance genes in carbapenemresistant 

Enterobacteriaceae (CREs) and 

extended spectrum beta-lactamase (ESBL) 

producers using Acuitas® Whole Genome 

Sequence Analysis (WGS) with next generation 

sequencing (NGS) technology. To be able 

to comprehensively determine the resistance 

genes in clinical isolates of MDROs, we built 

a database consisting of all beta-lactamase 

variants with NCBI accession numbers from 

Lahey Clinic (http://www.lahey.org/Studies/). 

All genes of beta-lactamases are manually 

curated for the coding sequences with start 

codon and stop codon if those exist. The database 

was tested using WGS data assembled 

from Illumina MiSeq data generated on eight 

species of clinical isolates: Escherichia coli, 

Pseudomonas aeruginosa, Klebsiella pneumonia, 

Acinetobacter baumanii, Enterobacter 

cloacae, Citrobacter freundii, Klebsiella 

oxytoca, and Serratia marcescens; all such 

isolates were reported to harbor CRE and 

ESBL antibiotic resistance genes based on 

results of Sanger sequencing technology and 

the Acuitas® Resistome Test. Using Acuitas 

Whole Genome Sequence Analysis, we resolved 

closely related gene variants across the 

antibiotic resistance gene families KPC, NDM, 

OXA, CTX-M, CMY, TEM, SHV, ACT, IMP, 

VIM, DHA, PER, and VEB in these clinical 

isolates. For example, WGS resolved single 

nucleotide differences between gene variants 

KPC-2 and KPC-3, or single nucleotide differences 

between NDM-1 and NDM-4. Similarly, 

WGS resolved closely related gene variants 2, 

3, 14, 15, and 79 of CTX-M. The depth of our 

database facilitated our discovery of antibiotic 

resistance genes which were not previously reported 

for these clinical isolates. Furthermore, 

our variant determination isn’t limited to the 

contents of the database we developed. If the 

homologous sequence of a resistance gene is 

identified but not identical to the gene variant 

in the database, the coding sequence will be 

retrieved and searched against NCBI database 

to find the identical gene. If the identical variant 

still can’t be found, the gene is reported as 

a new variant of that family of beta-lactamases 

and then dynamically added to our database. 

In conclusion, we have created a database 

consisting of all beta-lactamase genes from 

Lahey Clinic website. Using the database with 

the Acuitas Whole Genome Sequence Analysis 

pipeline, we can comprehensively determine 

antibiotic resistance in multidrug-resistant 

organisms (MDROs), providing tools to help 

the prevention of outbreaks and the treatment 

of patients. 

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n 93 

NEXT-GENERATION SEQUENCING (NGS) 

TECHNOLOGY TO STUDY VIRAL-HOST 

INTERACTIONS 

M. S. Montasser; 

University of Kuwait, Kuwait, KUWAIT. 

Cucumber mosaic virus (CMV) is a pathogen 

causing diseases in a wide variety of economically 

important vegetable crops worldwide. 

One strain of the virus designated CMV-KU1 

(patent No. US 8,138,390 B2) was isolated 

from tomato plants grown in Kuwait. The viral 

genome of this new isolate was found to be 

associated with a benign viral satellite RNA 

mini-genes. This can be used as a biological 

control agent against plant viruses. The whole 

gene was sequenced using Next-Generation 

Sequencing (NGS) technology. The mini-gene 

sequence analysis revealed that the viral satellite 

consisted of a single-stranded RNA, ranging 

in size from about 350 to 400 nucleotides. 

Based on NGS analysis and bioassay experiments, 

the nucleotide sequence was found to 

be unrelated to the viral genomic RNAs, and 

they do not share any significant sequence 

homology, and it is not required for viral replication 

and spread. However, this viral satellite 

RNA is totally dependent on the helper 

viral genomic RNAs for its own replication 

and spread. This mini-gene was found to be 

highly effective against severe necrotic CMV 

strains. The severity of this disease was greatly 

reduced by the presence of this benign mini 

gene, and this is correlated with a reduction 

in virion production. This reduction caused 

ameliorating disease symptoms in infected 

tomato plants infected with severe viral strains. 

This will provide a clear picture on a better 

understanding of severe viral infections affecting 

vegetable crops. In addition, detecting and 

identification of viral satellites, mini-genes, 

and whole viral genome is rapid and costeffective 

at the whole-genome level using new 

sequencing technologies. 

n 94 

VIRUS SURVEILLANCE IN FOOD AND 

WATER USING NEXT GENERATION 

SEQUENCING 

T. AW, S. Wengert, Y. Kim, J. Rose; 

Michigan State University, East Lansing, MI. 

Background: Contaminated food and water 

are important transmission routes of many different 

viruses, associated with diseases ranging 

from mild gastroenteritis to severe neurological 

symptoms. In addition of unreported outbreaks, 

there is increasing evidence of unrecognized 

waterborne and foodborne illnesses. 

There is currently no established method to 

fully describe the broad diversity of human 

viral pathogens in environmental samples despite 

the increasing importance of environmental 

viral infections in global public health. The 

recent advent of next generation sequencing 

technology coupled with metagenomics offers 

an opportunity to identify human viral pathogens 

in various environmental samples without 

a priori knowledge of what viruses may be 

present. The main objective of this study was 

to evaluate the Illumina sequencing to simultaneously 

detect and characterize viral pathogens 

in untreated and treated wastewater for reuse 

as well as from the food (fresh produce) itself. 

Methods: Various wastewater samples were 

collected from full-scale wastewater treatment 

facilities. Viruses were concentrated from 20 

to 100 liters of treated effluent using ultrafiltration 

method. Fresh produce items were 

collected from a produce distribution center. 

Viruses were recovered using Tris-glycine buffer 

followed by precipitation with polyethylene 

glycol. Viral nucleic acid was extracted and 

pooled for Illumina sequencing. Bioinformatics 

approaches were used to analyze the viral 

metagenomics fingerprints. Results: DNA 

sequences collected from wastewaters revealed 

viral communities that were dominated by 

bacteriophages with the subset of eukaryotic 

viruses. Most of the viral sequences (> 70%) 

were uncharacterized, indicating the presence 

of great viral diversity to be discovered. 15 



103


different human pathogens including emerging 

viruses such as bocavirus, polyomaviruses, 

novel astroviruses (MLB), picobirnavirus, 

salivirus A and Aichi virus were identified. 

Rotaviruses (A, D, F and G) were identified 

in 8 batches of the lettuce sample with high 

protein sequence similarity to the GenBank 

reference genomes. This shows that the contamination 

occurred at some point from the 

field through to the distribution center prior to 

moving to the store and suggests a potential 

risk for foodborne transmission of rotavirus 

from lettuce. Conclusions: The application of 

metagenomics coupled with next generation 

sequencing to describe diversity of the viromes 

in food and wastewater systems provided a 

broader outlook of the viral composition of 

these communities. This approach has the 

potential to (i) provide an appropriate tool to 

discriminate viruses associated with different 

hosts (e.g. human, animal, plant and bacteria) 

for microbial source tracking and (ii) identify 

etiologic agents associated with waterborne or 

foodborne disease outbreak. 

n 95 

CRIMEAN CONGO HEMORRHAGIC FEVER, 

2013 AND 2014 SUDAN 

C. Kohl 1 , M. Eldegail 2 , I. Mahmoud 2 , P. 

Dabrowski 1 , L. Schuenadel 1 , A. Radonic 1 , A. 

Nitsche 1 , A. Osman 2 ; 

1 

Robert Koch Institute, Berlin, GERMANY, 

2 

National Public Health Laboratory, Khartoum, 

SUDAN. 

The German Partnership Program for Excellence 

in Biological and Health Security was 

launched in 2013 and is funded by the German 

Federal Foreign Office. Currently, the program 

funds projects in 18 countries in the fields of 

infectious disease surveillance, detection & 

diagnostics, biosafety & biosecurity, capacity 

building and networking. In Sudan one focus 

of the partnership is the detection of highly 

pathogenic viruses and identification of known 

and yet unknown etiological agents in outbreak 

situations. In 2014 an outbreak of hemorrhagic 

fever in humans was reported from different 

states of Sudan (South Darfur, West Kordofan, 

South Kordofan). The NPHL investigated the 

cases and forwarded 29 sera samples from 

patients suffering from hemorrhagic fever 

to the RKI. The sample-set included a panel 

of 10 sera collected during former hemorrhagic 

fever outbreaks in the same region in 

2013. All sera were tested with qPCR assays 

for Marburg virus, Ebola virus and CCHFV. 

Additionally all samples were subjected to 

metagenomic deep sequencing on an Illumina 

MiSeq sequencer. CCHF was identified by 

two independent qPCR assays in a sample 

from November 2013 and November 2014, 

respectively. Deep sequencing confirmed these 

results. Based on the available sequences the 

novel CCHFV strain ‘Sudan 2014’ shares 96% 

identity (na) with its closest relative CCHFV 

SPU 187/90 from South Africa. CCHFV is 

reported to be transmitted by ticks in Europe, 

Asia and Africa and known as etiological agent 

of severe hemorrhagic fever in humans and 

livestock. Beside insect-repellent no preventive 

measures are available. The pathogenicity 

and characteristics of this novel strain have yet 

to be determined by cell-culture isolation and 

serology. Further molecular analysis will contribute 

to clarify the divergence of the CCHFV 

strains detected in 2013 and 2014. First results 

will be presented. 

n 96 

TRACKING PLANT VIRUS POPULATION 

STRUCTURE CHANGES WITH DEEP 

SEQUENCING OF VIRUS DERIVED SMALL 

RNAS 

D. Kutnjak 1 , M. Rupar 1 , I. Gutierrez-Aguirre 1 , 

T. Curk 2 , J. F. Kreuze 3 , M. Ravnikar 1 ; 

1 

National Institute of Biology, Ljubljana, SLO- 

VENIA, 2 University of Ljubljana, Faculty of 

Computer and Information Science, Ljubljana, 

SLOVENIA, 3 International Potato Center 

(CIP), Lima, PERU. 

RNA viruses exist within a host as a cloud of 

mutant sequences, often referred to as quasi- 

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species. The composition of the mutant cloud 

within a host is an important characteristic 

of the virus, since it represents a reservoir of 

genetic variants, which can be subjected to 

different evolutionary processes. High mutation 

rate of RNA viruses and their quasispecies 

nature drive their quick adaptability and evolution 

rate. Thus, new viruses can emerge as a 

result of different processes, such as e.g. host 

shifts (1). Next generation sequencing technologies, 

with their unprecedented depth, enable 

through investigation of the viral population 

structure within a host. This allows us to detect 

relevant mutations in the viral population 

even before the emergence of new pathogenic 

viruses or viral strains (2). Before being able to 

use such a tool to follow the emergence of new 

viral variants in a viral mutant cloud, efficient 

sample preparation and bioinformatics pipelines 

are required. Deep sequencing of virus 

derived small interfering RNAs (vsiRNAs; the 

derivates of a plant RNA silencing mechanism) 

has been used efficiently for the reconstruction 

of consensus viral genome sequences from 

insects and plants (3). In our research, we are 

focused on potato virus Y, an important potato 

pathogen. Using this virus as a model, we first 

tested if the variation observed in vsiRNAs 

reflects the full diversity of viral populations in 

plants. Using ultra deep Illumina sequencing, 

the diversity of two coexisting Potato virus 

Y sequence pools present within a plant was 

investigated: RNA isolated from viral particles 

and vsiRNAs. Our analysis pipeline included 

state of the art bioinformatic software for low 

frequency variant detection (LoFreq) and recombination 

pattern detection (ViReMa) and 

other commonly used NGS analysis tools. We 

showed that both sequence pools reflect highly 

similar mutational spectrum (4). Currently 

we are employing deep sequencing of small 

RNAs to track the dynamics of virus adaptation 

to several potato cultivars, which differ in 

their response to the virus. Particularly, we are 

interested if the structure of virus population 

is changed in different potato cultivars and if 

there are convergent patterns of virus evolution 

emerging in the same cultivar. References: 1. 

Domingo et al. 2012. Microbiol. Mol. Biol. 

Rev. 76, 159-216 2. Kreuze et al. 2009. Virology 

388, 1-7 3. Kutnjak et al. 2015. J. Virol. 

89, 4761-4769 4. Stapleford et al. 2014. Cell 

Host Microbe 15, 706-716 

n 97 

A SURVEY OF RNA VIRUSES INFECTING 

SOLANACEOUS CROPS IN THE PROVINCE 

OF ANTIOQUIA (COLOMBIA) USING NGS 

H. Jaramillo Mesa 1 , L. Muñoz 1 , J. F. Alzate 2 , 

M. A. Marín Montoya 1 , P. A. Gutiérrez 1 ; 

1 

Universidad Nacional de Colombia, Medellin, 

COLOMBIA, 2 Universidad de Antioquia, Medellin, 

COLOMBIA. 

Solanaceae is a family of flowering plants of 

great economic importance in the food and 

pharmaceutical industries. Some important 

members of this family include potato (Solanum 

tuberosum and S. phureja), eggplant (S. 

melongena), tomato (S. lycopersicum), bell 

pepper (Capsicum annuum), tobacco (Nicotiana 

tabacum) and many garden ornamentals 

such as Angel´s trumpet (Brugmansia candida). 

The Andean region of South America is 

considered to be the center of diversity of several 

of these species and, as such, it is expected 

to have a large diversity of viruses. In this 

work we investigate the presence of viruses 

infecting potato, cape gooseberry (Physalis 

peruviana), tamarillo (Solanum betaceum), bell 

pepper, tomato and Angel´s trumpet in Antioquia 

(Colombia) using NGS. From the analysis 

of 14 transcriptomes evidence was found for 

the presence of six genera of RNA virus infecting 

these plants: Potexvirus (Alphaflexiviridae), 

Carlavirus (Betaflexiviridae), Tospovirus 

(Bunyaviridae), Crinivirus (Closteroviridae), 

Potyvirus (Potyviridae) and Polerovirus (Luteoviridae). 

Potato virus X (PVX, Potexvirus) 

was detected at very high levels in P. peruviana 

(8.9% of reads) and in lower amounts in 

S. lycopersicum (0.74%). Potyviruses were 

detected in S. betaceum (Tamarillo leaf malformation, 

TaLMV; 0.57%), S. lycopersicum (Potato 

virus Y, PVY; 0.031%), S. phureja (Potato 



105


virus V, PVV; 1.15%) and S. tuberosum (PVY, 

1.33%). Potato yellow vein virus (PYVV) 

was detected in S. lycopersicum (0.033%), S. 

phureja (1.90%) and S. tuberosum (0.26%). 

Potato leaf roll virus (PLRV), was found infecting 

B. candida (0.01%), C. annuum (0.001 

%), P. peruviana (0.023%) and S. tuberosum 

(0.029%). A tospovirus closely related to Alstroemeria 

necrotic streak virus (ANSV) was 

found in C. annuum only (0.043%) and seems 

to be the most important virus affecting this 

crop in Antioquia. Finally, the virus with the 

least relative abundance was PVS, found in the 

transcriptomes of S. lycopersicum (0.003%) 

and S. phureja (0.018%). All these viruses 

were detected and assembled using in-house 

Perl scripts and their phylogenetic relationships 

with related species are discussed. The 

information derived from this study was used 

to design specific molecular diagnostic tests 

using RT-PCR and RT-qPCR as a tool that will 

support seed certification and virus management 

programs in Colombia. This work was 

funded by Universidad Nacional de Colombia 

(Grants VRI: 28616, 26737) and International 

Foundation for Science (Sweden, Grant: 

C/4634-2). 

n 98 

DECIPHERING VIRAL RNA GENOMES IN 

METAGENOMIC DATA FROM A WIDE RANGE 

OF CLINICAL SAMPLES 

T. Ng, A. Montmayeur, L. Magana, E. Ramos, 

J. Vinje, P. Rota, S. Oberste; 


Atlanta, GA. 

A significant component of laboratory-based 

surveillance for viral diseases is genetic characterization 

of pathogens by sequencing. Many 

RNA viruses are genetically highly diverse, 

even within a single serotype, therefore present 

a challenge in transforming the raw data 

into useful viral genomes. Furthermore, coinfections 

of multiple viral pathogens often 

occur in clinical samples, necessitating both 

laboratory procedures and a bioinformatic 

pipeline capable of simultaneous detection of 

different pathogen genomes. Here we demonstrate 

the laboratory procedures and bioinformatic 

analysis needed to routinely monitor 

viral pathogens from a wide range of samples, 

as our laboratories handle over 500 samples 

and generated 500M NGS raw reads annually. 

Initially, we hypothesized that NGS reads can 

be mapped to a list of reference genomes using 

a simple reference assembly algorithm. Our 

results indicated reference assembly using a 

list of pre-selected references does not consistently 

generate full genomes, especially for 

the highly divergent regions To analyze these 

complex clinical samples, the NGS data were 

analyzed with the metagenomic approach. Our 

pipeline analyzed the NGS data using de novo 

assembly, as well as BLAST analysis comparing 

against the entire GenBank viral genome 

database. To select an appropriate assembler, 

we compared the output of different de novo 

algorithms, including de bruijn assemblers, 

overlap layout consensus assemblers, and 

combinations of different assemblers. The 

combination approach showed significant 

improvement over single assemblers such as 

SOAPdenovo and ABySS, while the SPAdes 

assembler produced similar results in less time. 

We found, however, the adaptor and primer 

need to be trimmed perfectly first for any assembler 

to work well. Under-trimming or 

over-trimming produce gaps or misalignments 

that will significantly affect downstream de 

novo assembly. We’ll discuss the issues and 

solutions for analyzing datasets with multiple 

pathogens, low sequence coverage, sequence 

misalignment, and cross-sample / cross-run 

contamination. Manual inspection of pipeline 

output, and communication between bioinformaticians 

and laboratorians are the key to obtaining 

accurate output across different sample 

types and genome types. A graphic interface 

to view the NGS output has allowed more 

scientists without any programming skills to 

analyze the data. Our strategies to monitor 

viral pathogens such as poliovirus, measles 

virus and norovirus are applicable to the epidemiologic 

investigation of other emerging 

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pathogens. We’ll also discuss how to use NGS 

to investigate emerging pathogens, exemplified 

by our recent findings of novel astrovirus, 

picornavirus and rotavirus. 

n 99 

METHODS, APPLICATIONS AND 

EXPERIENCES WITH NEXT GENERATION 

WHOLE GENOME SEQUENCING IN PUBLIC 

HEALTH VIROLOGY 

D. M. Lamson 1 , J. Laplante 1 , J. McGinnis 1 , M. 

Shudt 1 , A. Kajon 2 , K. St. George 1 ; 

1 

Wadsworth Center, Albany, NY, 2 Lovelace Respiratory 

Research Institute, Albuquerque, NM. 

Potential uses for Next-Generation/Whole 

Generation Sequencing (NG/WGS) in public 

health virology include improved characterization 

for molecular epidemiology, viral evolution 

studies and genome-wide surveillance. 

The Wadsworth Center Virology Laboratory 

has initiated NG/WGS for several applications, 

initially focusing on adenoviruses (HAdV), 

influenza (InfA) and enterovirus D68 (EV- 

D68). Challenges include the separation of 

viral nucleic acid (NA) from high titer host 

NA in both primary samples and cultured 

isolates. For HAdV isolates cultured in A-549 

cells, high salt precipitation was used to preferentially 

remove host cell DNA prior to viral 

NA extraction. For InfA and EV-D68, primary 

samples or isolates were extracted, then 

RT-PCR amplified for either all 8 genomic 

fragments (InfA) or near whole genome (EV- 

D68). NA concentrations in extracted samples 

were determined using the standard Nextera 

protocol. Libraries were quantified, average 

fragment size determined and paired-end sequencing 

was performed on an Illumina MiSeq 

with MiSeq 500 cycle v2 kit. Sequences were 

de novo-assembled with SPAdes and remapped 

using Geneious Pro. Over 100 HAdV samples 

from each of the 6 species have been processed 

as well as 12 adeno-associated virus samples 

that co-purified with HAdV. Applications have 

included the investigation of multiple epidemic 

keratoconjuctivitis outbreaks, analysis of 47 

HAdV3 samples geographically and temporally 

distributed over 20 years, genetic characterization 

of intertypic recombinants, and 

the investigation of HAdV4 and 14 genomic 

variants in college infections. The analysis 

of 21 EV-D68 strains revealed from 1 to 104 

amino acid changes over the entire coding 

region during the 2014 outbreak, with greatest 

variability observed in the capsid gene. 

Fifteen influenza A/H1pdm09 specimens and 

44 A/H3N2 specimens from 2009-15 have 

been analyzed. Mutations were detected in all 

8 segments of both A/H1 and A/H3 viruses, 

with highest rates in the hemagglutinin and 

neuraminidase genes in both subtypes. Phylogenetic 

analysis showed all A/H1 specimens to 

cluster with the current vaccine strain, whereas 

recent A/H3 viruses were more similar to A/ 

Switzerland/9715293/2013 than to the current 

vaccine strain. As technologies improve and 

processing time is reduced, NGS applications 

will become more routine. The potential for 

clarifying nomenclature, investigating viral 

evolution and performing more comprehensive 

surveillance is vast. By developing techniques 

for applications on primary samples and the 

implementation of more streamlined data analysis, 

better diagnostics and the resolution of 

more outbreak investigations will be obtained. 

Additionally, more extensive viral surveillance 

will enhance the potential for advance warning 

of the emergence of drug-resistant and novel 

strains with pandemic potential. 

n 100 

ASSEMBLING WHOLE GENOMES FROM 

MIXED MICROBIAL COMMUNITIES USING 

HI-C 

I. Liachko 1 , J. N. Burton 1 , L. Sycuro 2 , A. H. 

Wiser 2 , D. N. Fredricks 2 , M. J. Dunham 1 , J. 

Shendure 1 ; 

1 

University of Washington, Seattle, WA, 2 Fred 

Hutchinson Cancer Research Center, Seattle, 

WA. 

Assembly of whole genomes from next-generation 

sequencing is inhibited by the lack of 



107


contiguity information in short-read sequencing. 

This limitation also impedes metagenome 

assembly, since one cannot tell which sequences 

originate from the same species within 

a population. We have overcome these bottlenecks 

by adapting a chromosome conformation 

capture technique (Hi-C) for the deconvolution 

of metagenomes and the scaffolding of de novo 

assemblies of individual genomes. In modeling 

the 3D structure of a genome, chromosome 

conformation capture techniques such as Hi-C 

are used to measure long-range interactions of 

DNA molecules in physical space. These tools 

employ crosslinking of chromatin in intact 

cells followed by intra-molecular ligation, 

joining DNA fragments that were physically 

nearby at the time of crosslink. Subsequent 

deep sequencing of these DNA junctions generates 

a genome-wide contact probability map 

that allows the 3D modeling of genomic conformation 

within a cell. The strong enrichment 

in Hi-C signal between genetically neighboring 

loci allows the scaffolding of entire chromosomes 

from fragmented draft assemblies. 

Hi-C signal also preserves the cellular origin 

of each DNA fragment and its interacting partner, 

allowing for deconvolution and assembly 

of multi-chromosome genomes from a mixed 

population of organisms. We have used Hi-C 

to scaffold whole genomes of animals, plants, 

fungi, as well as prokaryotes and archaea. We 

have also been able to use this data to annotate 

functional features of microbial genomes, such 

as centromeres in many fungal species. Additionally, 

we have applied our technology to 

diverse metagenomic populations such as craft 

beer, bacterial vaginosis infections, soil, and 

tree endophyte samples to discover and assemble 

the genomes of novel strains of known 

species as well as novel prokaryotes and 

eukaryotes. The high quality of Hi-C-based 

assemblies allows the simultaneous closing of 

numerous unculturable genomes, placement of 

plasmids within host genomes, and microbial 

strain deconvolution in a way not possible 

with other methods. Reference: Burton JN*, 

Liachko I*, Dunham MJ, Shendure J. Specieslevel 

deconvolution of metagenome assemblies 

with Hi-C-based contact probability maps. G3. 

2014, May 22;4(7):1339-46. 

n 101 

AN AUTOMATED WGS PIPELINE FOR 

ANALYSIS OF BACTERIAL GENOMES 

M. Thomsen 1 , H. Hasman 2,3 , A. Petersen 3 , R. 

Skov 3 , O. Lund 1 , A.R. Larsen 3 and F.M. Aarestrup 

2 ; 

1 

Danish Technical University – Systems Biology, 

CBS, Lyngby, Denmark, 2 , Danish Technical 

University – National Food Institute, 

Lyngby, Denmark, 3 Statens Serum Institut, 

Copenhagen, Denmark. 

Background New approaches within diagnostics 

and surveillance for species identification, 

clonal clustering, and identification 

of resistance genes are based whole genome 

sequencing (WGS). The Centre for Genomic 

Epidemiology (CGE) has been developing 

stand-alone web-tools for handling WGS information 

for outbreak investigation, epidemiological 

surveillance and diagnostics. 

Material and methods Based on previously 

published web-based CGE tools we developed 

a pipeline for automatic analysis of WGS data 

from bacterial isolate samples https://cge.cbs. 

dtu.dk/services/CGEpipeline-2.0/). The bacterium 

analysis pipeline (BAP) automatically 

identifies the bacterial species and identify 

the multilocus sequence type (MLST), spa 

type (S. aureus only), serotype (E. coli only) 

and antimicrobial resistance genes. To test the 

BAP, a set of 101 S. aureus strains originating 

from bacterial infections at Danish hospitals 

submitted to Statens Serum Institute (SSI) for 

genotypic by traditional Sanger sequencing 

were subjected to WGS on an Illumina HiSeq 

to a minimum coverage of 30x and assembled 

by Velvet prior to being uploaded to the 

BAP. Draft genomes including a mandatory 

metadata spread sheet containing sequenc- 

108 

ASM Conferences


ing information as well as epidemiological 

data was submitted to the BAP through the 

Batch Upload webpage (https://cge.cbs.dtu.dk/ 

services/CGEpipeline-2.0/) and automatically 

analysed by kmerFinder to verify the bacterial 

species and by ResFinder to identify antimicrobial 

resistance genes. When the bacterial 

species was correctly identified as S. aureus, 

MLST and spa typing were performed automatically 

and stored in the Isolate Overview 

page to be extracted as an Excel sheet for 

further analysis. Results and discussion The 

bacterial species was correctly identified as S. 

aureus for all isolates. Therefore, MLST and 

spa typing was performed automatically and 

could be compared to the previous data for the 

strains. An MLST type was assigned for 99% 

of the genomes and good concordance was 

found between the clonal complexes inferred 

from spa types and WGS analysis through the 

pipeline. For spa typing, 92 of the genomes 

showed the same spa type as found by Sanger 

sequencing previously. Seven genomes showed 

a different spa type and 2 genomes failed to 

give a spa type in the pipeline. This was most 

like due to the small size reads (2*100 bp) 

obtained from the HiSeq sequencer, which is 

not optimal for assembly of highly repetitive 

DNA sequences such as the variable region of 

the spa gene. ResFinder successfully identified 

the mecA gene in all MRSA genomes and 

not in any of the MSSA genomes. Conclusion 

A combined bioinformatics platform 

was developed and made publicly available, 

providing easy-to-use automated analysis of 

bacterial whole genome sequencing data. The 

platform may be of immediate relevance for 

investigators using whole genome sequencing 

for clinical diagnostic and surveillance. 

n 102 

ON THE EDGE: ROBUST GENERALIZED 

BIOINFORMATICS FOR NEXT-GEN 

SEQUENCING NOVICES 

Chien-Chi Lo 1 , Po-E Li 1 , Joseph Anderson 2,4 , 

Karen W. Davenport 1 , Kimberly A. Bishop- 

Lilly 3,4 , Yan Xu 1 , Sanaa Ahmed 1 , Shihai Feng 1 , 

Tracey Allen K. Freitas 1 , Vishwesh P. Mokashi 

4 , and Patrick Chain 1 

1 

Los Alamos National Laboratory, 2 Defense 

Threat Reduction Agency, 3 Henry M. Jackson 

Foundation, 4 Naval Medical Research Center 

- Frederick 

With the continuing evolution of sequencing 

platforms and technologies, the so-called democratization 

of sequencing is in fact already 

in full swing. Despite the inherent challenges 

involved, moving sequencing technology into 

the field and closer to the source of diverse and 

interesting biological samples, is an attractive 

idea to many agencies. This even extends to 

OCONUS laboratories that are being equipped 

with next generation sequencing (NGS) platforms 

to complement more traditional molecular, 

cell, and microbiology methods for infectious 

disease research. However, many groups 

new to NGS and/or laboratories in remote or 

austere locations may be ill-equipped to handle 

the bioinformatic requirements associated with 

rapid production of massive, complex datasets 

from sequencing clinical or environmental 

samples. A collaborative effort, named EDGE 

bioinformatics, has begun to research, prototype, 

and program bioinformatic pipelines 

that can be deployed to new NGS laboratories 

in order to enable successful adoption of 

sequencing technologies by allowing robust 

processing of NGS data. These pipelines were 

initially developed with specific use cases and 

sample types in mind, although the modular 

design provides utility beyond these initial 

use cases. The pipelines are being designed 

to make use of the most common file formats 

and run in a linux environment on relatively 

inexpensive hardware so that barriers to adoption 

are minimal. A pilot program to install and 



109


utilize EDGE at an OCONUS DoD facility has 

already taken place, with successful processing 

of locally-generated data using a recently 

locally-installed MiSeq, and demonstrated 

reachback capability using CONUS support. 

n 103 

SUPERPHY: PREDICTIVE GENOMICS FOR 

THE PATHOGEN ESCHERICHIA COLI 

M. D. Whiteside 1 , C. R. Laing 1 , A. Manji 1 , J. 

Masih 1 , P. Kruczkiewicz 1 , E. N. Taboada 1 , V. P. 

J. Gannon 1 

1 

CANADA - Laboratory for Foodborne Zoonoses, 

Public Health Agency of Canada 

Introduction: Predictive genomics is the 

translation of raw genome sequence data into 

an assessment of the phenotypes exhibited by 

the organism. For bacterial pathogens, these 

phenotypes can range from environmental 

survivability, to the severity of human disease 

associated with them. Significant progress has 

been made in the development of generic tools 

for genomic analyses that are broadly applicable 

to all microorganisms; however, a fundamental 

missing component is the ability to 

analyze genomic data in the context of organism-specific 

phenotypic knowledge, which has 

been accumulated from decades of research 

and can provide a meaningful interpretation 

of genome sequence data. Implementation: 

In this study, we present SuperPhy, an online 

predictive genomics platform (http://lfz.corefacility.ca/superphy/) 

for Escherichia coli. 

The platform integrates the analyses tools and 

genome sequence data for all publicly available 

E. coli genomes and facilitates the upload 

of new genome sequences from users under 

public or private settings. SuperPhy provides 

real-time analyses of thousands of genome 

sequences with results that are understandable 

and useful to a wide community, including 

those in the fields of clinical medicine, epidemiology, 

ecology, and evolution. SuperPhy 

includes identification of: 1) virulence and 

antimicrobial resistance determinants 2) statistical 

associations between genotypes, biomarkers, 

geospatial distribution, host, source, 

and phylogenetic clade; 3) the identification 

of biomarkers for groups of genomes on the 

based presence / absence of specific genomic 

regions and single-nucleotide polymorphisms 

and 4) in silico Shiga-toxin subtype. Conclusions: 

SuperPhy is a predictive genomics platform 

that attempts to provide an essential link 

between the vast amounts of genome information 

currently being generated and phenotypic 

knowledge in an organism-specific context. 

n 104 

MOLECULAR SURVEILLANCE OF A. 

BAUMANNII IN A REGIONAL WASTE 

STABILISATION POND IN AUSTRALIA 

1, 2 

Maxim Sheludchenko, 2 Mohammad Katouli 

and 1 Helen Stratton 

1 

Smart Water Research Centre, Griffith University, 

Southport, Queensland and 2 Genecology 

Research Centre, University of the Sunshine 

Coast, Sippy Downs, Queensland, Australia 

A.baumannii is an increasingly important 

pathogen responsible for many nosocomial 

infections. Survival of these bacteria in the 

environment, especially in waste stabilization 

ponds (WSPs) have not been investigated before 

although some reports indicate isolation 

of these A. baumannii from filaments of active 

sludge of wastewaters and paleosol contaminated 

by waste leakage. Identification of A. 

baumannii from the environmental is normally 

done by growing samples on selective culture 

media but this requires the use of molecular 

techniques for confirmation. Furthermore, 

the ability of these bacteria to grow on some 

selective media may also cause misinterpretations 

of the data. In this study we report such 

an event and show that the use of molecular 

techniques especially 16S rRNA sequencing 

helped identification and surveillance of these 

bacteria in a WSP. Between October 2013 and 

September 2014 we surveyed the die off of a 

number of pathogens in the maturation pond of 

a WSP in a regional area of Australia. Samples 

cultivated on mCCDA agar containing selec- 

110 

ASM Conferences


tive and recommended supplements for growth 

of Campylobacter, yielded a high number of 

colonies with characteristic of Campylobacter. 

However, subsequent PCR tests using speciesspecific 

primers proved that these bacteria 

were not Campylobacter. Using 16s rRNA, followed 

by additional confirmatory tests such as 

VS1, ompA, bla OXA-51-like 

, bla OXA-23-like 

and Class 

1 integrase gene confirmed that these colonies 

were in fact A. baumannii. Using this methodology 

an intensive sampling was carried out 

from the inlet and the outlet of this maturation 

pond and tested for A.baumannii. The results 

indicated the number of these bacteria in most 

sampling rounds did not differ significantly 

between the inlet and outlet of the maturation 

pond suggesting the survival of these bacteria 

in such waste treatment system. The implication 

of genomic technics for identification of 

bacteria in relation to public health studies will 

be discussed. 



111

Index 

Bold number indicates presenter. 

Aanensen, D. M. S7:5 

Aarestrup, F. M. 41 



Aarestrup, F. M. S10:6 

Abrams, A. 70 

Achtman, M. S7:8 

Adam, J. 7, 8, S7:10 

Adams, M. D. S9:4 

Addy, N. 50 

Agarwala, R. S3:3 

Akange, N. 47 

Albayrak, L. 27 

Ali, F. 45 

Alikhan, N.-F. S7:8 

Allard, M. W. 39 

Allard, M. W. 54, S3:5 

Al-Shahib, A. S7:9 

Alyanak, E. 64 

Alzate, J. F. 97 

Anand, M. 74 

Ancora, M. 40 

Andersen, V. D. 46 

Anderson, M. S9:3 

Andrusch, A. 9 

Appalla, L. 13, 14, 30, 

S9:5 

Argimon, S. S7:5 

Ashton, P. 

83, S2:5, 

S7:9 

Au-Young, J. 29 

Avillan, J. J. 64 

AW, T. 94 

Ayers, S. L. 54 

Baert, L. S2:2 

Baker, D. J. 74 

Baker, D. J. 79 

Barrette, R. W. S2:3 

Barretto, C. S2:2 

Baugher, J. S4:6 

Beatson, S. A. 16 

Beiko, R. G. 8, S7:10 

Bell, R. L. 39 

Bennett, C. 72 

Ben Zakour, N. L. 16 

Bergmark, L. S10:6 

Berry, C. S7:10 

Besser, J. 87, 88 

Besser, T. 44 

Blais, B. W. 55 

Blosser, S. J. 12 

Boisvert, S. S4:5 

Bonomo, R. A. S9:4 

Bonten, M. 33 

Bopp, D. 74 

Bowden, K. E. 82 

Bowden, K. 86 

Bradshaw, J. 15 

Brettin, T. S4:5 

Brinkman, F. 7, S7:10 

Brinkman, F. S. 8 

Bristow, F. 7, 8, S7:10 

Bronstein, P. S3:3 

Brouwer, E. 33 

Brown, E. 39 

Brown, E. W. 54, S3:5 

Bulach, D. M. S7:7 

Burrows, E. 39 

Burton, J. N. 100, S4:7 

Busch, J. 57 

Cabral, J. S7:10 

Calistri, P. 40 

Cammà, C. 40 

Camp, P. 36 

Campbell, E. M. 12 

Carleton, H. 71, 72 

Carleton, H. 65, 84, 

S3:3, S3:6 

Carleton, H. A. 87, 88 

Carleton-Romer, 

H. A. 81 

Carrico, J. A. 7, 8 

Carriço, J. A. S7:10 

Carrillo, C. D. 55 

Carroll, L. 44 

Catalyurek, U. V. S3:5 

Catanzaro, A. S9:6 

Catanzaro, D. S9:6 

Chain, P. 90 

Chang, W. 34, 91, 92 

Chapman, E. L. 12 

Chase, H. 50 

Chase, K. 59 

Chen, Y. 72 

Chilton, C. S2:2 

Christopher- 

Hennings, J. 47, 52 

Chumakov, S. M. 27 

Chung, H. S9:3 

Chung, T. 50 

Clifford, R. 13, 14, 

S9:5 

Cohen, T. S9:6 

Colman, R. 57 

Colman, R. E. S9:6 

Comandatore, F. 32 

Conrad, C. 12 

Conrad, N. R. S4:5 

Cooper, A. 1 

Corander, J. 37 

Courtot, M. 7, 8 

Courtot, M. S7:10 

Cowan, L. 85 

Croughs, P. D. 24 

Crudu, V. S9:6 

Cui, H. 90 

Curk, T. 96 

Currie, B. 57 

Dabrowski, P. S10:4 

Dabrowski, P. 10, 21, 9, 

95 

Dallman, T. 

83, S2:5, 

S7:9 

Daquigan, N. 43, 48 

Das, S. 50, 52 

David, S. S3:3 

Davis, J. J. S4:5 

Davis, M. 44 

Davis, S. 2, S4:6 

Deatherage 

Kaiser, B. S4:4 

de Boer, R. F. 24 

De Bruyne, K. 59, S7:3 

Defibaugh- 

Chavez, S. S3:3 

Delisle, J. 57 

De Massis, F. 40 

den Bakker, H. 44 

112 ASM Conferences

Deng, X. S4:4 

de Pinna, E. 83, S2:5 

Dermott, P. F. 54 

Dessai, U. 88 

Deurenberg, R. H. 22 

Dhand, A. 20 

Dhere, T. 61 

Dhillon, B. 7, 8 

Dickinson, M. 74 

Di Giannatale, E. 40 

Dimitrova, N. 18, 19, 20, 

28 

Dinsmore, B. 71 

Dinsmore, B. S4:4 

Disz, T. S4:5 

Dooley, D. 7, 8, S7:10 

Drees, K. 40 

Dunham, M. J. 100, S4:7 

Dunn, S. 1 

Duwve, J. W. 12 

Dykes, J. K. 89 

Edirisinghe, J. S4:5 

Edwards, R. A. S4:5 

Einer-Jensen, K. 6, S7:4 

Eldegail, M. 95 

Elson, R. S2:5 

Engelthaler, D. 57 

Engelthaler, D. M. S9:6 

Eppinger, M. 11 

Erkkila, T. 90 

Escuyer, V. 25 

Evans, P. 88 

Evans, P. S. S3:5 

Ezeoke, I. S3:4 

Fallon, J. 20 

Fallon, J. T. 18, 19, 28 

Fasulo, D. 87 

Federhen, S. S6:4 

Fedosejev, A. S7:5 

Felton, A. 29 

Ferdous, M. 23, 24 

Ferreira, C. 39 

Fields, P. 71 

Fields, P. S4:4 

Fitzgerald, C. 71, 72 

Fitzgerald, C. S4:4 

Fitzpatrick, M. A. 31 

Flett, K. B. S9:3 

Fofanov, Y. 27 

Foley, A. 47, 52 

Foster, J. T. 40 

Fournier, C. S2:2 

Fredricks, D. N. 100, S4:7 

Friedrich, A. W. 22 

Friedrich, A. W. 23, 24 

Friesema, I. H. 24 

Fu, S. 67 

Galac, M. R. S3:4 

Galang, R. R. 12 

Ganova-Raeva, L. 12 

Garcia, D. S7:5 

Garcia-Toledo, L. 71 

Garcia-Toledo, L. 65 

Garofolo, G. 40 

Gauthier, M. 55 

Gehr, E. 80 

Gentry, J. 12 

Gerner-Smidt, P. 71, 72 

Gerner-Smidt, P. 65, 88, 

S3:6 

Gibbons, H. S. S3:4 

Gillece, J. 57 

Gimonet, J. S2:2 

Gladney, L. 71, 72 

Gladney, L. 65 

Gladney, L. M. 87 

Glaser, A. 50 

Glasner, C. S7:5 

Goater, R. S7:5 

Golovko, G. 27 

Gonzalez- 

Escalona, N. 54 

Gopinath, G. 50 

Graham, M. 7, 8, S7:10 

Graham, R. 75 

Grant, K. 83, S2:5 

Grau, F. R. S2:3 

Green, K. Y. S10:3 

Griffiths, E. 8, S7:10 

Griffiths, E. J. 7 

Grim, C. 48 

Grim, C. J. 43 

Griswold, T. 65 

Grundmann, H. 22 

Gulvik, C. A. 64 

Guo, X. 58 

Gupta, A. 18, 20 

Gutiérrez, P. A. 17, 97 

Gutierrez- 

Aguirre, I. 96 

Index 

Gymoese, P. S1:3 

Habibi, N. S6:3 

Haley, B. J. 49 

Hall, C. 57 

Halpin, J. L. 89 

Halse, T. 25, 79 

Hanes, D. 48, 50 

Hanes, D. E. 43 

Hänninen, M.-L. 37 

Hauser, A. R. 31 

Haydek, J. P. 61 

Heaton, H. 57 

Hendriksen, R. S. 41, S10:6 

Heneine, W. 12 

Henry, C. S4:5 

Herrick, J. 80 

Highlander, S. 35 

Hillman, D. 12 

Hinkle, M. 13, 14 

Hinkle, M. K. S9:5 

Hjelmsø, M. H. S10:6 

Hoffmann, M. 54, S3:5 

Holler, L. 52 

Howden, B. P. S7:7 

Hsiao, W. 7, 8, S7:10 

Huang, A. 71, 72 

Huang, A. 87 

Huang, A. D. 73, 81 

Huang, W. 18, 19, 28 

Hueftle, Y. 57 

Im, S. 71 

Im, S. 65 

Im, S. B. 81 

Isaac-Renton, J. 8 

Ismail, A. 5 

Iwamoto, T. 3 

Jang, G. 38 

Janies, D. S3:5 

Janies, D. A. S3:4 

Janssens, K. S7:3 

Jaramillo Mesa, H. 17, 97 

Jarvis, K. 48 

Jarvis, K. G. 43 

Jayaram, A. 50 

Jean-Gilles 

Beaubrun, J. 50 

Jennison, A. 75 

Jia, H. 12 

Jironkin, A. S7:9 

Joensen, K. 65 



113

Index 

Joensen, K. G. 78 

Johansen, J. 56, 6, S7:4 

Jones, M. 35, S4:4 

Joseph, L. 72 

Julius, M. 14 

Kajon, A. 99 

Kapsak, C. 80 

Karangwa, C. K. S10:3 

Karns, J. S. 49 

Kasam, M. S2:2 

Kato, S. 3 

Katz, L. S. 71, 72 

Katz, L. S. 65 

Katz, L. S. 81, 88, 

S3:3, 

S7:10 

Kaur, S. 66 

Kearse, M. 1, S7:2 

Keddy, A. 8, S7:10 

Keddy, K. H. 5 

Keena, M. 47, 52 

Keim, P. 57, S9:6 

Kenyon, R. W. S4:5 

Kersey, R. 91, 92 

Kersey, R. K. 34 

Khan, M. W. S6:3 

Khanipov, K. 27 

Khudyakov, Y. 12 

Kiil, K. 76 

Kim, Y. 94 

Kishony, R. S9:3 

Kjeldsen, M. S1:3 

Klenner, J. 21 

Klimke, W. S3:3 

Knox, N. S7:10 

Koenig, S. S. 11 

Kohl, C. 21, 95 

Konstantinidis, 

K. T. 73 

Kooistra-Smid, 

A. M. 23, 24 

Koren, M. 30 

Kornblum, J. S3:4 

Koziol, A. 55 

Kraft, C. S. 61 

Krepps, M. D. S3:4 

Kreuze, J. F. 96 

Kruczkiewicz, P. 8, S2:4, 

S7:10 

Ku, Y.-C. 29 

Kubota, K. 88 

Kucerova, Z. 71 

Kucerova, Z. 88 

Kuhn, J. 1 

Kuroda, M. 3 

Kurth, A. S10:4 

Kutnjak, D. 96 

Kwak, Y. 14 

Kwak, Y. I. 30 

Kwong, J. S7:7 

Laird, M. 7, 8 

Laird, M. R. S7:10 

Lambert, D. 55 

Lamson, D. 29 

Lamson, D. M. 99 

Lan, R. 66, 67 

Lane, C. 83 

Lapierre, P. 25, 74, 79 

Laplante, J. 99 

Lasker, B. 77 

Lauer, A. C. 77 

Lee, H. 38 

Leekitcharoenphon, 

P. 41 

Lesho, E. 13, 14 

Lesho, E. P. 30, S9:5 

Levinson, K. J. 74 

Li, A.-D. 63 

Li, Y. 58 

Liachko, I. 100, S4:7 

Liboriussen, P. 56, 6, S7:4 

Libuit, K. 80 

Lim, S. 38 

Limbago, B. M. 64 

Lin, H. 19, 20, 28 

Lin, H. C. 18 

Lin, Y. S3:4 

Lindsey, R. L. 71 

Lindsey, R. L. 65 

Lindsey, R. L. 81, 87 

Litrup, E. 76 

Liu, P. 58 

Lokate, M. 22 

Lovchik, J. 12 

Lui, L. 12 

Lukjancenko, O. 78, S10:6 

Luo, Y. 

Luquette, A. 59 

Lúquez, C. 89 

2, S3:5, 

S4:6 

Mabon, P. S7:10 

Machi, D. S4:5 

Magana, L. 98 

Mahmoud, I. 95 

Maiden, M. C. 72 

Mangone, I. 40 

Manninger, P. 55 

Mao, C. S4:5 

Mao, Y. 62 

Marcacci, M. 40 

Marín Montoya, 

M. A. 17, 97 

Markowitz, S. 1 

Martin, H. 71 

Martin, H. 65 

Maslanka, S. M. 89 

Materna, A. 56, S7:4 

Materna, A. C. 6, S7:4 

Matthews, T. 7, 8, S7:10 

Maybank, R. 13, S9:5 

Mayigowda, P. 18, 20 

Mayo, M. 57 

McDermott, P. 72 

Mc Gann, P. 30, S9:5 

McGann, P. 13 

McGinnis, J. 99 

McIntosh, M. T. S2:3 

McQuiston, J. R. 77 

Meng, J. 54, S3:5 

Mi, T. 18, 20 

Michot, L. S2:2 

Millard, A. S7:8 

Miller, J. 54 

Miller, W. 72 

Mitarai, S. 3 

Moine, D. S2:2 

Moir, R. 1, S7:2 

Molina, M. 15, 60 

Monday, S. R. 54, S3:5 

Montasser, M. S. 93 

Montmayeur, A. 98 

Moon, D. 38 

Munk, P. 46 

Muñoz, L. 97 

Muñoz Baena, L. 17 

Muñoz Escudero, D. 17 

Murase, Y. 3 

Murugesan, K. 18, 20 

Muruvanda, T. 39, S3:5 

Musser, K. 25 

114 ASM Conferences

Musser, K. A. 74 

Musser, K. A. 79 

Mustafa, A. S. S6:3 

Myers, R. S3:5 

Nair, S. 83, S2:5 

Nash, J. H. S2:4 

Neish, E. 61 

Nelson, E. 47, 52 

Ng, T. 98 

Ngeno, E. S10:6 

Ngom-Bru, C. S2:2 

Nitsche, A. 10, 21, 9, 

95, S10:4 

Nolan, S. M. 19, 28 

Norman, K. N. 51 

NT, J. S7:5 

Oberste, S. 98 

Octavia, S. 66, 67 

Ogunremi, D. 53 

Ojo, O. O. 68 

Olsen, C. 1, S7:2 

Olsen, G. J. S4:5 

Olson, R. S4:5 

Ong, A. 13 

Ong, A. C. 30, S9:5 

Onmus-Leone, F. 13, 14, 30, 

S9:5 

Osman, A. 95 

Overbeek, R. S4:5 

Ozer, E. A. 31 

Padilla, J. 14 

Pallen, M. J. S7:8 

Parikh, C. 29 

Parra, G. I. S10:3 

Parrello, B. S4:5 

Partridge, S. 4 

Payne, J. 54 

Pena-Gonzalez, A. 73 

Peng, Y. 82, 86 

Pereira, R. 44 

Perez, A. 12 

Peters, P. J. 12 

Peters, T. 83, S2:5 

Petkau, A. 7, 8, S7:10 

Pettengill, J. 39, 54, 

S4:6 

Pettengill, J. B. 2 

Peyrani, P. 12 

Pillatzki, A. 52 

Pimenova, M. 27 

Platone, I. 40 

Pontones, P. 12 

Posey, J. 85 

Pot, B. S7:3 

Pouseele, H. 71, 72 

Pouseele, H. 59, 65, 88, 

S7:3 

Prarat, M. 36 

Prentice, M. B. 68 

Priebe, G. P. S9:3 

Pruckler, J. 71, 72 

Purucker, T. 15 

Pusch, G. D. S4:5 

Qaadri, K. 1, S7:2 

Quinlan, T. 79 

Raangs, G. C. 22 

Radonic, A. 95, S10:4 

Ramachandran, S. 12 

Ramos, E. 98 

Rand, H. 

2, S3:3, 

S4:6 

Raphael, B. H. 89 

Ravnikar, M. 96 

Reed, E. 39 

Reimer, A. S7:10 

Renard, B. 10 

Ribot, E. M. 72 


Ribot, E. R. 87 


Rishishwar, L. 81 

Roache, K. 71 

Roache, K. 88 

Robbe-Austerman, 

S. 36 

Robinson, T. J. 16 

Rockweiler, T. 34 

Rodriguez, A. L. 11 

Rodwell, T. C. S9:6 

Rogers, M. 33 

Rojas, M. 27 

Rose, J. 94 

Roseberry, J. C. 12 

Rossen, J. W. 22 

Rossen, J. W. 23, 24 

Rossi, M. 37 

Rota, P. 98 

Rothgänger, J. S7:6 

Rupar, M. 96 

Rusconi, B. 11 

Index 

Sabol, A. 88 

Saeed, A. 91, 92 

Sahl, J. 57 

Saleem Haider, M. 45 

Sammons, S. 58 

Sandoval, M. 12 

Sapiro, V. 91, 92 

Scaria, J. 47, 50, 52 

Schauser, L. 56, 6, S7:4 

Scheutz, F. 65 

Schriml, L. 7, 8 

Schuenadel, L. 95, S10:4 

Schupp, J. 57 

Scott, H. M. 51 

Seemann, T. S7:7 

Sekizuka, T. 3 

Senturk, I. S3:5 

Sergeant, M. S7:8 

Shafiq, M. 45 

Shaheed, F. S6:3 

Shankar, A. 12 

Shaw, T. 60 

Shay, J. 7, 8 

Shea, J. 25 

Shearman, H. 1, S7:2 

Shendure, J. 100, S4:7 

Shudt, M. 25 

Shudt, M. 99 

Shukla, M. P. S4:5 

Shumway, M. S3:3 

Sieffert, C. S7:10 

Siler, J. 44 

Simmons, M. S3:3 

Sinnige, J. 33 

Sintchenko, V. 66, 67 

Sischo, W. 44 

Sjölund-Karlsson, 

M. 64 

Smith, A. M. 5 

Snesrud, E. 13, 14, 30, 

S9:5 

Sobral, B. W. S4:5 

Sosnovtsev, S. V. S10:3 

Sparks, M. 14 

Stanton-Cook, M. J. 16 

Stevens, E. L. 42 

Stevens, R. L. S4:5 

St. George, K. 29, 99 

Storey, D. B. S7:11 

Strain, E. 2, 39 



115

Index 

Strino, F. 56 

Stripling, D. 71 

Stripling, D. 65 

Strockbine, N. 71 

Strockbine, N. 65, 81, 87 

Stroika, S. 88 

Stuber, T. 36 

Sun, Y. 29 

Sutton, G. G. S9:4 

Suzuki, H. 47, 50, 52 

Switzer, W. H. 12 

Sycuro, L. 100, S4:7 

Taboada, E. 7, 8, S7:10 

Taboada, E. N. S2:4 

Tan, W. S10:5 

Tanaka, M. 66, 67 

Tang, P. 8 

Tarr, C. 71 

Tarr, C. 88 

Tarr, C. L. 73 

Tausch, S. 10 

Tauxe, W. M. 61 

Thachil, A. 50 

Thai, H. 12 

Thiessen, J. 8 

Thobela, M. 5 

Thomas, M. 47, 50, 52 

Thompson, L. 74 

Tillman, G. S3:3 

Timme, R. E. S3:3 

Tondella, M. L. 82 

Tondella, M. L. 86 

Top, J. 33 

Torpdahl, M. S1:3 

Trees, D. 70 

Trees, E. 72 

Trees, E. 65, 81, 84, 

87, 88, 

S3:3 

Trees, E. K. S3:6 

Tsafnat, G. 4 

Turner, S. D. 80 

Turnsek, M. 71 

Underwood, A. S7:9 

Useh, N. 47 

Välimäki, N. 37 

Van Domselaar, G. 7, 8, S7:10 

van Duyne, S. 71 

Van Kessel, J. S. 49 

Van Roey, P. 25 

Vazquez, A. 57 

Vehkala, M. 37 

Vigre, H. 46 

Vinje, J. 98 

Vonstein, V. S4:5 

Vuyisich, M. 69 

Wagner, D. 72 

Wagner, D. D. 84 

Wagner, D. M. 57 

Waldram, A. S2:5 

Walker, G. T. 34 

Walker, T. 91, 92 

Wan, Q. 19 

Wang, C. 39 

Wang, G. 18, 19, 20, 

28 

Wang, L. 36 

Wang, Q. 66 

Wang, Y. S10:5 

Ward, A. 61 

Ward, G. 14 

Warnick, L. 44 

Warren, A. S4:5 

Waterman, P. 14 

Waterman, P. E. 30, S9:5 

Watt, J. 58 

Wattam, A. R. S4:5 

Weigand, M. R. 73, 82 

Weigand, M. R. 86 

Weimer, B. C. S7:11 

Weiss, D. S3:4 

Wengert, S. 94 

Whichard, J. 72 

White, J. 48 

White, J. R. 43 

Wiedmann, M. 44 

Will, R. S4:5 

Willems, R. 33 

Williams, G. 72 

Williams, G. 71 

Williams, M. M. 82, 86 

Winsor, G. 7, 8, S7:10 

Wirth, S. 74 

Wirth, S. E. 79 

Wiser, A. H. 100, S4:7 

Wolcott, M. 59 

Wolfgang, W. J. 74 

Wolfgang, W. J. 79 

Wolfgang, W. J. S3:5 

Wong, K. 15, 60 

Wright, M. S. S9:4 

Xia, F. S4:5 

Xia, G. 12 

Yamashita, A. 3 

Yeats, C. A. S7:5 

Yin, Y. S4:4 

Yoo, H. S4:5 

Yoo, Y. 50 

Yoshida, C. S2:4 

Yu, Q. 58 

Zhang, A. 62 

Zhang, J. 37 

Zhang, S. S4:4 

Zhang, T. 62, 63 

Zhang, Y. 36 

Zhang, Z. S4:4 

Zhao, S. 72 

Zhao, S. 54 

Zheng, J. 39 

Zhou, K. 22, 23 

Zhou, Z. S7:8 

Zhu, Q. 35 

Zhuge, J. 19, 28 

Zilli, K. 40 

Zong, Z. 26 

116 

ASM Conferences

American Society for Microbiology 

1752 N Street, N.W. 

Washington, DC 20036-2904

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