Improving our understanding of Aspergillus fumigatus

1. Improving our understanding of Aspergillusfumigatus

2. Project Overview Aspergillusfumigatus • RNA-Seq • WGS • SNP calling • Statistical analysis • Network analysis

3. Why sequence Aspergillus fumigatus? • Allergic aspergillosis – associated with asthma • Invasive aspergillosis • > 40% hospital acquired ? • Very high mortality rate (50–90%) in treated patients • No effective drugs, vaccines or diagnostics • Emerging drug resistance: 10%-50% Bueid et al., 2010 Invasive aspergillosis

4. Two additional reference genomes Mitochodrial genetic diversity • AF10 and AF210 – chosen because they are different from Af293 and Af1163 • Unique MLST types • Af10: Clinical isolate from immunosuppressed patient in LA; fully susceptible to azoles and amphothericin B • Af210: Nosocomial infection in Manchester; MLST identical to ICU environmental strain and 5 other clinical samples • Sequencing completed; annotation pending • Mitochondria compared for 11 Aspergillusand Penicilliumgenomes Joardar, submitted

5. Novel drug resistance mechanisms:SNP calling, clustering, and association *sequential resistant isolates TBS SNP density along the 8 Afu293 chromosomes show a subtelomere bias SNP/10Kb

6. >50 loci show extreme allelic variability between strains • Greater than 15 SNPs/Kb • Several involved in vegetative incompatibility HET locus AFUB_017990 GBrowse2

7. Significant variability is also present in drug resistance genes • Cyp51A: 10-12 SNPs • Mdr1/AtrD ABC transporter: 2-8 SNPs cyp51A asn2 GBrowse2

8. Annotation improvement with RNAseq 630 updates so far • Use sequence reads to validate and/or modify existing gene models • Update categories • Gene extension • Gene mergers • UTRs • Alternative Isoforms • Novel genes • Exon boundary adjustments • Validation of hypotheticals

9. RNA-Seq reveals alternative isoforms in a transcription factor linked to hypoxia AFUB_018340 GBrowse2

10. RNA-Seq identifies novel genes Novel genes

11. RNA-Seqexpression profiling of host-pathogen interactions • Fungusexposed vs. non-exposed to NK cells • 171 genes • 63 genes • Human NK cells exposed to fungus • 503 genes • 518 genes • => Pathway enrichment and network analysis • Key drivers of the transcriptional changes • Targets for immuno-modulation therapy

12. Outbreak Investigation • Current methodology depends on analysis of short tandem repeats at 6 loci in the genome • However, there are no high-confidence global SNP markers for outbreak investigation • Sequenced “clonal” strains from two different outbreaks to understand variability • Identify novel SNPs to be used as markers • Technology is complementary to currently used typing schemes

13. Future directions • Integration of omics data for drug resistant isolates • => Drug resistance and diagnostic arrays • Additional genotyping markers • Ribosomal monomers • Mitochondrial genomes • HET genes • => Geographic variation and disease phenotypes • RNA-Seqsoftware evaluation tool • Sequencing of a highly virulent species A. tanneri • Development of a system for genetic analysis of A. fumigatus • Sequencing of “supermaters” • Testing drug resistance SNPs

14. Acknowledgements • J. Craig Venter Institute • Informatics • Natalie FedorovaAbrams • Suman Pakala • Vinita Joardar • NikhatZafar • Suchitra Pakala • PratapVenepally • VenkiMoktali • Culture & sample prep • Stephanie Maunoud • Yan Yu • Tatiana Slepushkina • Ashlee Dravis • IFX, IT, Sequencing Core • GSCID directors at JCVI • Bill Nierman • Karen Nelson • Funding: NIAID/NIH • Outside collaborators • U. of Manchester: David Denning • NIAID/NIH: June Kwon-Chung • CDC: Arun Balajee • U. of Montana: Robb Cramer • U. of Georgia: Michelle Momany • U. of Tuebingen: JuergenLoeffler • U. of Wisconsin: Nancy Keller

15. Questions