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Third generation long-read sequencing of HIV-1 transcripts discloses cell type specific and temporal regulation of RNA s

Third generation long-read sequencing of HIV-1 transcripts discloses cell type specific and temporal regulation of RNA splicing. Frederic Bushman International AIDS Meeting Washington DC, 2012. Why Study HIV Splicing?. Splicing factors prominent in genome-wide siRNA screens

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Third generation long-read sequencing of HIV-1 transcripts discloses cell type specific and temporal regulation of RNA s

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  1. Third generation long-read sequencing of HIV-1 transcripts discloses cell type specific and temporal regulation of RNA splicing Frederic Bushman International AIDS Meeting Washington DC, 2012

  2. Why Study HIV Splicing? Splicing factors prominent in genome-wide siRNA screens HIV RNAs spliced to yield at least 40 mRNAs Sensitivity suggests unexploited opportunity for intervention? Relevant ORFs remain to be discovered? Bushman et al. 2009 PLoS Path

  3. Approach • Amplification: 18 primer pairs • Canonical splicing • Rare splicing • New splicing

  4. RainDance Technologies: Single Molecule Droplet PCR Tewhey et al., Nature Biotechnology, 2009 RainDance Technologies a Primer Library Primer Library b cDNA prep from infected cells Break Emulsion Sequence cDNA Template Mix PCR Overlapping primer pairs amplify cDNA maintaining ratios

  5. Pacific Biosciences: Single molecule sequencing Phosphate-labeled nucleotides • Error mitigated by • Alignment to 10kb HIV genome • SMRTbell approach… Fixed polymerase www.pacificbiosciences.com High throughput single molecule real-time sequencing provides long reads, maintaining linkage between exons

  6. Pacific Biosciences: Sequence Output 930,294 HIV sequences of up to 2629 bp

  7. 2 Novel Splice Donors Scott Sherrill-Mix

  8. 11 Novel Splice Acceptors Scott Sherrill-Mix

  9. Novel Splice Sites Genetic Map Exons SD Splice Donor SA Splice Acceptor * site does not adhere to consensus

  10. Complete message population of HIV-189.6 in CD4+ T cells • 77 complete message structures • Evidence for 36 additional transcripts from partial reads • Total: 113 mRNAs • 19 novel transcripts including a new completely spliced class (~1kb) Scott Sherrill-Mix

  11. Novel Acceptor A8c Novel splice acceptor A8c creates new ORFs in HIV-189.6

  12. Dynamic Transcript Populations Mutually exclusive acceptors :

  13. Dynamic Transcript Populations Temporal, cell-type and intra-human variability

  14. Conclusions Long read single molecule sequencing works well to delineate HIV message populations At least 113 different HIV-1 transcripts 1 kb class of RNAs prominent in HIV89.6 Differential splicing by cell type, time after infection, and among cells from human subjects

  15. Credits Bushman laboratory Former Bushman LabCollaborators Troy Brady Gary Wang Charles Berry Kyle Bittinger Brett BeitzelSumitChanda RohiniSinha Mary Lewinski John Young Scott Sherrill-Mix Astrid Schroder Renate Koenig Frances Male Angela Ciuffi Joe Ecker Christian Hoffmann Heather Marshall Rose Craig Hyde NiravMalani Jeremy Leipzig Mark Yeager Brendan Kelly Matt Culyba Kushol Gupta Young Hwang Rick Mitchell Greg Van Duyne Stephanie Grunberg Tracy Diamond Masahiro Yamashita Serena Dollive Emily Charlson Mike Emerman Alexandra Bryson Shannah Roth Francis Collins Sam Minot Karen Ocwieja Philippe Leboulch Spencer Barton Keshet Ronen Alain Fischer Aubrey Bailey Greg Peterfreund Marina Cavazzana-Calvo RithunMukherjeeSalimaHacien-Bey-Abina Jennifer Hwang RikGijsbers Kristine Yoder ZegerDebyser Rebecca Custers-Allen

  16. Solexa/Illumina Hi Seq 100 base paired end reads 2 uninfected samples 3 infected samples HIV89.6 in human T-cells ~ 1 Billion sequence reads Both human and HIV RNA in infected cells is 14% viral. Ratios among HIV message forms HIV infection associated with intron retention in cellular genes

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