Third generation long-read sequencing of HIV-1 transcripts discloses cell type specific and

temporal regulation of RNA splicing

Frederic Bushman

International AIDS MeetingWashington DC, 2012

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

Why Study HIV Splicing?

Approach

• Amplification:18 primer pairs

• Canonical splicing• Rare splicing

• New splicing

cDNATemplate Mix

Break Emulsion

Sequence

RainDance Technologies: Single Molecule Droplet PCRTewhey et al., Nature Biotechnology, 2009RainDance Technologies

b cDNA prep from infected cells

a Primer Library

Overlapping primer pairs amplify cDNA maintaining ratios

PrimerLibrary

PCR

Pacific Biosciences: Single molecule sequencing

Fixed polymerase

Phosphate-labeled nucleotides

High throughput single molecule real-time sequencing provides long reads, maintaining linkage between exons

Error mitigated by

1. Alignment to 10kb HIV genome

2. SMRTbell approach…

www.pacificbiosciences.com

930,294 HIV sequences of up to 2629 bp

Pacific Biosciences: Sequence Output

Cell Type Mappable Reads

Median Raw Read-length

Longest HIV Sequence

HOS (18,24,48hpi) 88,975 3678 bp 2105 bp

Primary CD4T (7 donors triplicate,

48hpi)841,319 2595 bp 2629 bp

2 Novel Splice Donors

Scott Sherrill-Mix

11 Novel Splice Acceptors

Scott Sherrill-Mix

Novel Splice Sites

Genetic Map

Exons

SD Splice DonorSA Splice Acceptor* site does not adhere to consensus

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

Novel Acceptor A8c

Novel splice acceptor A8c creates new ORFs in HIV-189.6

Dynamic Transcript PopulationsMutually exclusive acceptors :

Temporal, cell-type and intra-human variability

Dynamic Transcript Populations

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

CreditsBushman laboratory Former Bushman Lab CollaboratorsTroy Brady Gary WangCharles BerryKyle Bittinger Brett BeitzelSumit ChandaRohini Sinha Mary LewinskiJohn YoungScott Sherrill-Mix Astrid Schroder Renate KoenigFrances Male Angela Ciuffi Joe EckerChristian Hoffmann Heather Marshall Rose Craig HydeNirav Malani Jeremy Leipzig Mark YeagerBrendan Kelly Matt CulybaKushol GuptaYoung Hwang Rick MitchellGreg Van DuyneStephanie Grunberg Tracy Diamond Masahiro YamashitaSerena Dollive Emily CharlsonMike EmermanAlexandra Bryson Shannah Roth Francis CollinsSam Minot Karen OcwiejaPhilippe LeboulchSpencer Barton Keshet Ronen Alain FischerAubrey Bailey Greg Peterfreund Marina Cavazzana-Calvo

Rithun MukherjeeSalima Hacien-Bey-Abina

Jennifer HwangRik Gijsbers

Kristine YoderZeger Debyser

Rebecca Custers-Allen

RNA in infected cells is 14% viral.

Ratios among HIV message forms

HIV infection associated with intron retention in cellular genes

Solexa/Illumina Hi Seq

100 base paired end reads

2 uninfected samples3 infected samples HIV89.6 in human T-cells

~ 1 Billion sequence readsBoth human and HIV