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

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exon level transcriptome analysis of hiv 1 infected and bystander primary cd4 t lymphocytes

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

slide2
Goal
  • Describe the transcriptomic profile of primary CD4+ T cellsexposed to HIV-1 in vitro
  • Compare infectedcells and bystandercells
problem
Infected (10%)Problem

Mock Control

  • Quantification of RNA - 10 copies vs 12 copies = 1.2 fold
  • But in infectedcells, its a 3 fold induction
nl4 3 ires hsa
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
human exon 1 0 st array
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
separation of infected cells
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
analysis
Analysis
  • Strict statististicalanalysisusingBioconductor’s LIMMA
  • FDR 1%
  • 1.7 fold minimum
automated litterature analysis
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
main features
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
cytokine related
Cytokine related
  • Th1 (IFNG, TNF-a, TNF-b, IL1A, IL3)
  • Th2 (IL4, IL5, IL-10, IL13)
  • Th17 (IL17A, IL17F, IL21, IL22, IL23R, IRF4)
alternative splicing analysis
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
alternative splicing results
Alternative splicing results
  • 547 probes in 372 transcripts
  • 52% of these are in non-codingUTRs
  • 48% in coding exons
conclusion
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
acknowledgements
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.