Exon-level transcriptome analysis of HIV-1 infected and bystander primary CD4+ T lymphocytes

1. Exon-level transcriptome analysis of HIV-1 infected and bystander primary CD4+ T lymphocytes Michael Imbeault Laboratory of Dr. Michel J. Tremblay Université Laval, Québec Canada AIDS 2010 Vienna

2. Goal • Describe the transcriptomic profile of primary CD4+ T cellsexposed to HIV-1 in vitro • Compare infectedcells and bystandercells

3. Infection of primary lymphocytes in vitro

4. Infected (10%) Problem Mock Control • Quantification of RNA - 10 copies vs 12 copies = 1.2 fold • But in infectedcells, its a 3 fold induction

5. NL4-3-IRES-HSA • Express all viral genes • Allow for separation of infectedcellsusingmagneticbeads • More sensitive than the parental GFP virus (Levy & al) • Detailspublished in Virology. 2009 Oct 10;393(1):160-7

6. NL4-3-IRES-HSA

7. NL4-3-IRES-HSA

8. Human Exon 1.0 ST array • Latest offering from Affymetrix • 1.4 millions probesets • 1 million exons • Covers • All knownhumangenes • in silicopredictedgenes • ESTs • Allow for quantification of expression and determination of alternative splicingevents

9. Human Exon 1.0 ST array

10. Protocol

11. Separation of infectedcells • Isolate a mean of 500 000 infectedcellsstartingfrom • 50 million cellsatday 1 • 25 million cellsatday 2 and 3 • Extraction of RNA using a dual Trizol – Qiagen custom protocol • Quantification of purity by Taqman qRT-PCR against Tat-spliced

12. Tat-spliced PCR

13. Analysis • Strict statististicalanalysisusingBioconductor’s LIMMA • FDR 1% • 1.7 fold minimum

14. Results

15. Results

16. Automatedlitteratureanalysis • Automatedanalysis of literatureusingGenomatixBibliosphere • Citation of 2 genes in the same sentence in at least 3 different abstracts • Exported to graph management software Gephi • Gephi.org

17. Automatedanalysis of literature

18. Main features • AP-1 (FOS and JUN, someotherrelatedgenes) • A group of genesrelated to activated / effector T cells • Many cytokines associated to Th1, Th2, Th17 • Th17 relatedgenes have higher surexpression values, perhapsindicating a highersusceptibility • p53 relatedgenes

19. FOS JUN AP-1

20. Promoter analysis

21. Cytokine related • Th1 (IFNG, TNF-a, TNF-b, IL1A, IL3) • Th2 (IL4, IL5, IL-10, IL13) • Th17 (IL17A, IL17F, IL21, IL22, IL23R, IRF4)

22. IL17A IL17F Th17 related genes

23. IL22 IL23R Th17 related genes

24. p53 relatedgenes

25. Automatedanalysis of literature

26. Alternative splicinganalysis • Used the combination of two of the best algorithms • MADS and PECA-SI • P < 0.01 • Splicing index of at least 0.6 • Exon canbedetected in at least 1/3 of arrays • Filter out exons not currentlyassociatedwithgenes

27. Alternative splicing results • 547 probes in 372 transcripts • 52% of these are in non-codingUTRs • 48% in coding exons

28. PTPRC

29. Alternative splicing - PTPRC

30. Conclusion • Infectedcells have a transcriptomic profile of highlyactivated / effector / memory T cells • No effectat all in bystandercells • Confirmed a lot of things • Role of p53 in HIV-1 pathogenenis • Manyinteresting candidates • Potentialsusceptibility and restriction factors

31. Acknowledgements • Michel J. Tremblay • Project Leaders • Corinne Barat, Ph.D. • RéjeanCantin, Ph.D. • Robert Lodge, Ph.D. • Michel Ouellet, Ph.D. • Postdoctoral Fellows • RavendraGarg, Ph.D. • Pranav Kumar, Ph.D. • Guadalupe Andreani, Ph.D. • Masayuki Fujino, Ph.D. • Sandra Côté, Ph.D. • Ph.D. Students • RémiFromentin, M.Sc. • Alexandra Lambert, M.Sc. • Lise-AndréeGobeil, M.Sc. • Jonathan Bertin, M.Sc. • Pascal Jalaguier, M.Sc. • AnissaCheikh, M.Sc. • Alejandro Martin Gomez Lic. • M.Sc. Students • Alexis Danylo, B.Sc. • KatiaGiguère, B.Sc. • Audrey Plante, B.Sc. • Jean-François Bolduc, B.Sc. • VéroniqueVeillette, B.Sc.

32. Questions