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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P26. PASSENGER MUTATIONS CONFOUND INTERPRETATION OF ALL GENETICALLY MODIFIED CONGENIC MICE Paco Hulpiau 1,2,3 *, Liesbet Martens 1,2,3 *, Yvan Saeys 1,2,3 , Peter Vandenabeele 1,2,4 & Tom Vanden Berghe 1,2 . Inflammation Research Center, VIB, Ghent, Belgium 1 ; Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium 2 ; Data Mining and Modelling for Biomedicine (DaMBi), Ghent, Belgium 3 ; Methusalem Program, Ghent University, Belgium 4 . *paco.hulpiau@irc.vib-ugent.be, liesbet.martens@irc.vib-ugent.be Targeted mutagenesis in mice is a powerful tool for functional analysis of genes. However, genetic variation between embryonic stem cells (ESCs) used for targeting (previously almost exclusively 129-derived) and recipient strains (often C57BL/6J) typically results in congenic mice in which the targeted gene is flanked by ESC-derived passenger DNA potentially containing mutations. Comparative genomic analysis of 129 and C57BL/6J mouse strains revealed indels and single nucleotide polymorphisms resulting in alternative or aberrant amino acid sequences in 1,084 genes in the 129- strain genome. INTRODUCTION Annotating the passenger mutations to the reported genetically modified congenic mice that were generated using 129-strain ESCs revealed that nearly all these mice possess multiple passenger mutations potentially influencing the phenotypic outcome. We illustrated this phenotypic interference of 129-derived passenger mutations with several case studies and developed a Me- PaMuFind-It web tool to estimate the number and possible effect of passenger mutations in transgenic mice of interest. METHODS We analyzed the SNP data release v3 from the Mouse Genome Project available at Sanger Institute (Keane et al., 2011). The data in the indel vcf file and SNP vcf file were filtered to retrieve indels and SNPs present in at least one of the three 129 strains (129P2/OlaH, 129S1/SvIm and 129S5SvEvB) and affecting the protein coding sequence of the genes. These so-called protein coding variants are based on the following sequence ontology (SO) terms: stop gained, stop lost, inframe insertion, inframe deletion, frameshift variant, splice donor variant, splice acceptor variant, and coding sequence variant. In total, 949 indels and 446 SNPs affecting 1,084 mouse genes were retained. We gathered chromosome and gene start and end positions for 1,084 genes covering 1,395 variations. The Ensembl gene ID was used to find the most upstream and downstream start and stop in all Ensembl transcripts for that gene. Next these genome coordinates were used to search for flanking genes within 2, 10, and 20 Mbps upstream and downstream. We then downloaded all mouse phenotypic allele data from the MGI resource and extracted the data of genetically modified mouse lines. Information on 5,322 genes (corresponding to 7,979 129- derived genetically modified mouse lines) was connected to genes with passenger mutations and affected genes. Additionally we filtered the data to identify putative regulatory variants. All data were stored in a MySQL database and can be queried using the publicly available web tool Me-PaMuFind-It: http://me-pamufind-it.org/ Passenger genome mutations in gene-targeted mice (Nechanitzky and Mak, 2015) RESULTS & DISCUSSION The vast majority of existing and well-characterized genetically engineered congenic mice have been created using 129 ESCs. 99.5% of these mouse lines are affected by a median number of 20 passenger mutations within a 10 cM flanking region. This implies that nearly all genetically modified congenic mice contain multiple passenger mutations despite intensive backcrossing. Consequently, the phenotypes observed in these mice might be due to flanking passenger mutations rather than a defect in the targeted gene (Vanden Berghe et al, 2015). REFERENCES Keane, T.M., Goodstadt, L., Danecek, P., White, M.A., Wong, K., Yalcin, B., Heger, A., Agam, A., Slater, G., Goodson, M., et al. (2011). Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294. Nechanitzky R, Mak TW (2015). Passenger Mutations Identified in the Blink of an Eye. Immunity 43(1), 9-11. Vanden Berghe, T., Hulpiau, P., Martens, L. et al (2015). Passenger Mutations Confound Interpretation of All Genetically Modified Congenic Mice. Immunity 43(1), 200-9. 70
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: 000 Category: Abstract template 10th Benelux Bioinformatics Conference bbc 2015 P27. DETECTING MIXED MYCOBACTERIUM TUBERCULOSIS INFECTION AND DIFFERENCES IN DRUG SUSCEPTIBILITY WITH WGS DATA Arlin Keo 1 & Thomas Abeel 1,2,* . Delft Bioinformatics Lab, Delft University of Technology , Delft, the Netherlands 1 ; Broad Institute of MIT and Harvard, Cambridge, MA, USA 2 . * t.abeel@ tudelft.nl Mycobacterium tuberculosis is a bacterial pathogen that causes tuberculosis and infects millions of people. When a person is infected with more than one distinct strain type of tuberculosis (TB), referred to as mixed infection, diagnosis and treatment is complicated. Due to difficulty of diagnosis the prevalence of mixed infections among TB patients remain uncertain. Whole genome sequencing (WGS) yields a great number of single nucleotide polymorphisms (SNPs) and offers increased resolution to distinguish distinct strains. Here, we present a tool that maps sample reads against 21 bp cluster specific SNP markers to detect putative mixed infections and estimate the frequencies of the present subpopulations. INTRODUCTION Mycobacterium tuberculosis is a clonal, bacterial pathogen that causes the pulmonary disease tuberculosis (TB), and it infects and kills millions of people worldwide [1]. The study of genetic diversity within the M. tuberculosis complex (MTBC) is complicated by mixed TB infections, which happens when a person is infected with more than one distinct strain type of MTBC. This often results in poor diagnosis and treatment of patients as the bacterial subpopulation may have undetected differences in drug susceptibility [2]. A strain typing method should be able to distinguish closely related strains, to also allow the detection of a mixed infection at finer resolutions [3]. This study aims to detect a possible mixed TB infection at different levels in MTBC and to determine the frequencies of the present strains based on established tree paths in the MTBC phylogenetic tree. METHODS A global comprehensive dataset of 5992 MTBC strains was used for analysis, and 226570 SNPs were extracted from this set to construct a SNP-based phylogenetic tree with RAxML. In this bifurcating tree, each branch represents a cluster of strains and splits into two new monophyletic subclusters of genetically more closely related strain. These ¨splits¨ were used to define clusters and subclusters that contain more than 10. Global SNP association was done for each cluster to get clusterspecific SNPs, those for which the true positive rate, true negative rate, positive predictive value, and negative predictive value were >0.95. Markers were generated from these SNPs by extending them with 10 bp sequence on each side based on reference genome H37Rv. Each hierarchical cluster now has a set of specific SNP markers. By mapping sample reads against these 21 bp clusterspecific SNP markers the tool determines the presence of paths in the phylogenetic tree that start at the MTBC root node. Paths that split indicate the presence of multiple strains and thus a mixed infection. The read depth at the root node represents a frequency of 1 of the present MTBC species. If the path splits further in the tree, the total read depth is divided over the two subpaths and determines the frequencies of those present subclusters (Figure 1). FIGURE 1. Detection of mixed TB infection with hierarchical clusters. The detected strains are combined with detected drug susceptibility profiles. A minimized reference genome consisting of drug resistance genes and 1000 bp flanking regions is used to map sample reads with BWA, and call variants with Pilon. Ambiguous variation calls may indicate that present strains in a mixed infection sample also have differences in drug susceptibility. RESULTS & DISCUSSION In the phylogenetic tree 308 clusters (MTBC root excluded) were defined and there are 14823 SNP markers in total that are specific to a cluster and unique within the cluster. The known MTBC lineages 1 to 6 have between 355-614 markers. 7661 TB samples were tested, present strain(s) and frequencies could be predicted for 7495 samples of which 914 (~12%) are mixed infections (Table 1). # of subpopulations 1 2 3 >3 # of samples 6581 798 95 21 TABLE 1. 914 Out of 7495 samples is a mixed infection. REFERENCES 1. World Health Organization. Global Tuberculosis Report. World Health Organization, Geneva, Switzerland, 2014. 2. Zetola et al. Mixed Mycobacterium tuberculosis complex infections and false-negative results for rifampicin resistance by GeneXpert MTB/RIF are associated with poor clinical outcomes. Journal of Clin. Microb., 52:2422-2429, 2014. 3. G. Plazzotta, T. Cohen, and C. Colijn. Magnitude and sources of bias in the detection of mixed strain M. tuberculosis infection. Journal of theoretical biology, 368:67–73, 2015. 71
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