Program Book - 27th Fungal Genetics Conference

FULL POSTER SESSION ABSTRACTSmore genes with annotated PFAM domains(~3% increase per genome). Analysis of few genomes with transcriptomics data shows that new annotation setsalso have a higher number of EST-supported splice sites at exon-intron boundaries.229. Using the phenotypic information in the PHI-base database to explore pathogen genomes, transcriptomes and proteomes. Martin Urban 1 , JohnAntoniw 2 , Natalia Martins 3 , Artem Lysenko 2 , Jacek Grzebyta 2 , Elzbieta Janowska-Sedja 2 , Mansoor Saqi 2 , Kim Hammond-Kosack 1 . 1) Plant Biology and CropScience, Rothamsted Research, Harpenden, Hertfordshire, United Kingdom; 2) Computational and Systems Biology, Rothamsted Research, Harpenden,Hertfordshire, United Kingdom; 3) Embrapa - Genetic Resources and Biotechnology, Brasília, Brazil.The Pathogen-Host Interactions database (www.phi-base.org), called PHI-base, stores expertly curated molecular and biological information on genes forwhich the effect on pathogen-host interactions has been tested experimentally. Fungal, oomycete and bacterial pathogens which infect animal, plant, fish,insect and/or fungal hosts are included. Information is also given on the target sites of some anti-infective chemistries. This database, available since 2005,is used to analyse effectively the growing number of verified genes that mediate an organism's ability to cause disease and/or to trigger host responses.PHI-base is also used as a valuable resource for the functional annotation of novel genomes (http://phytopathdb.org), in comparative genomics studiesand for the discovery of candidate targets in medically and agronomically important microbial pathogens for intervention with synthetic chemistries andnatural products (fungicides). Each curated entry in PHI-base is checked by individual species experts and is supported by strong experimental evidence(e.g. gene deletion, complementation experiments) and literature references. This extensive manual curation aims to position PHI-base as a ‘goldstandard’ for researchers in the pathogen-host biology community. Genes are annotated using controlled vocabularies (Gene Ontology terms, ECNumbers, etc.), and links to other external data sources (for example, NCBI taxonomy, EMBL and UniProt) are provided. Here we describe a significantupdate of PHI-base (Version 3.4) in which the data content has more than doubled. PHI-base now provides information on more than 2,200 genesdescribed in 3000 pathogen-host interactions, which are associated with more than 106 pathogenic species. A Fusarium species case study is presented,where the database content has been used in an integrated network analysis (combining information from gene co-expression, predicted protein-proteininteractions and sequence similarity) to predict proteins in Fusarium graminearum that may be involved in pathogenicity. This approach has identified 215candidates including 29 proteins currently annotated as ‘hypothetical’. As the content of PHI-base grows, we expect this database to be an importantresource for exploring conserved and species-specific themes in pathogenicity.230. RNA-Seq analysis reveals new gene models and alternative splicing in Fusarium graminearum. Chunzhao Zhao 1,2 , Cees Waalwijk 1 , Pierre Wit 1 ,Dingzhong Tang 2 , Theo vanderLee 1 . 1) Wageningen-UR, Wageningen, Gelderland, Netherlands; 2) 3State Key Laboratory of Plant Cell and ChromosomeEngineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.The genome of Fusarium graminearum has been sequenced and annotated, but correct gene annotation remains a challenge. In addition,posttranscriptional regulations, such as alternative splicing and RNA editing, are poorly understood in F. graminearum. Here we took advantage of RNA-Seq to improve gene annotations and to identify alternative splicing and RNA editing in F. graminearum. In total 25,720,650 reads were generated fromRNA-Seq. Transcripts were detected for 84% of the genes predicted by machine annotation in the BROAD database, Of these reads, 74.8% matched toexonic regions, 10.6% to untranslated regions (UTRs), 12.9% to intergenic regions and only 1.7% to intronic regions. We identified and revised 655incorrectly predicted gene models (10% of the gene models that could be tested), including revisions of intron predictions, intron splice sites andprediction of novel introns. In addition, we identified 231 genes with two or more alternative splice variants, mostly due to intron retention. In-frameanalysis showed that the majority of these alternatively spliced transcripts lead to premature termination codons, PTCs. Apart from PTC isoforms, somealternatively spliced transcripts encoding proteins with diverse lengths were identified. The effects of the diversity in the transcript length on the biologicalfunction of proteins are still unknown, but several functions including binding properties, intracellular localization, enzymatic activity or stability may beaffected. Interestingly, the expression ratios between different transcript isoforms appeared to be developmentally regulated. Surprisingly, no RNA editingwas identified in F. graminearum. Moreover, 2459 novel transcriptionally active regions (nTARs) were identified and our analysis indicates that many ofthese could be genes that were missed in the automated annotation. A number of representative novel gene models and alternatively spliced genes werevalidated by reverse transcription polymerase chain reaction and sequencing of the generated amplicons. Our results demonstrate that posttranscriptionalregulation can be studied efficiently using our developed RNA-Seq analysis pipeline and may be important in adaptation of F. graminearum to changingenvironmental conditions that occur during different growth stages.231. Comparison of transcriptome technologies in the MpkA deletion mutant of Aspergillus fumigatus. Clara Baldin 1,3 , Sebastian Mueller 2 , Marco Groth 4 ,Konrad Gruetzmann 5 , Reinhard Guthke 2 , Olaf Kniemeyer 1,3 , Axel Brakhage 1,3 , Vito Valiante 1 . 1) Department of Molecular and Applied Microbiology, LeibnizInstitute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany; 2) Department of SystemsBiology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena,Germany; 3) Department of Microbiology and Molecular Biology, Friedrich Schiller University Jena, Beutenbergstrasse 11a, 07745 Jena, Germany; 4)Genome Analysis, Leibniz Institute for Age Research - Fritz Lipmann Institute, Beutenbergstr. 11, 07745 Jena, Germany; 5) Department of Bioinformatics,Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.RNA deep sequencing techniques are rising as powerfull strategy to analyze the transcriptome profile of different organisms. Especially, this approachwill be very helpful whenever a microarray platform has not been established yet or when different platforms show low reproducibility of the generateddata. In the present study, the expression profile of Aspergillus fumigatus has been analysed via different transcriptome analysis approaches. A. fumigatusis a saprophytic fungus that is emerging as one of the most important airborne fungal pathogens. The adaptation of this fungus to different environmentsstimulated research on the regulation of the cell-wall integrity pathway, which is mediated by the Mitogen Activated Protein Kinase (MAPK) MpkA.Previuos microarray analyses showed that MpkA is involved not only in the regulation of genes responsible for cell wall maintenance, but also inprotection against reactive oxygen species, iron starvation response and secondary metabolites production (Jain et al., Mol. Microbiol. 2011). Using thesame strains and lab conditions, we performed a transcriptome study using RNA deep sequencing to directly compare different transcriptome analysistechniques. The RNA-seq technique was found to be more sensitive than microarray analyses giving us the possibility to gain new insight into the role ofMpkA. We were able to identify a substantial number of novel transcripts, to detect new exons, untranslated regions, thousands of new splice junctions,and found evidence for widespread alternative splicing events. We could also identify a large group of genes belonging to known and unknown geneclusters, which are normally involved in secondary metabolite production. They are differentially regulated in the DmpkA mutant strain. Moreover, thetranscriptome data were compared to proteome data. Comparison between these two biological levels contributes to a better understanding of transcriptstability and of post-transcriptional regulatory mechanisms, giving a more global overview about MpkA regulatory circuits (Müller, Baldin et al., BMC2012).27th Fungal Genetics Conference | 177