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

Michael ImbeaultLaboratory of Dr. Michel J. TremblayUniversité Laval, Québec CanadaAIDS 2010 Vienna

Goal

•Describe the transcriptomic profile of primary CD4+ T cells exposed to HIV-1 in vitro

•Compare infected cells and bystander cells

Infection of primary lymphocytes in vitro

0 1 2 3 40123456789

% of infected cells

Time (days)

% i

nfe

cte

d c

ell

s (G

FP

)

Problem

Mock Control Infected (10%)

Quantification of RNA - 10 copies vs 12 copies = 1.2 foldBut in infected cells, its a 3 fold induction

NL4-3-IRES-HSA

• Express all viral genes

• Allow for separation of infected cells using magnetic beads

• More sensitive than the parental GFP virus (Levy & al)

• Details published in Virology. 2009 Oct 10;393(1):160-7

NL4-3-IRES-HSA

NL4-3-IRES-HSA

Human Exon 1.0 ST array• Latest offering from

Affymetrix

• 1.4 millions probesets

• 1 million exons

• Covers▫ All known human genes▫ in silico predicted genes▫ ESTs

• Allow for quantification of expression and determination of alternative splicing events

Human Exon 1.0 ST array

Protocol

Separation of infected cells

•Isolate a mean of 500 000 infected cells starting from▫50 million cells at day 1▫25 million cells at day 2 and 3

•Extraction of RNA using a dual Trizol – Qiagen custom protocol

•Quantification of purity by Taqman qRT-PCR against Tat-spliced

Tat-spliced PCR

Mock 24h

HIV Neg 24h

HIV Pos 24h

Mock 48h

HIV Neg 48h

HIV Pos 48h

Mock 72h

HIV Neg 72h

HIV Pos 72h

0

0.5

1

1.5

2

2.5

3

ABC

Rela

tive

Tat s

plic

ed m

RNA

leve

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norm

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n 18

S

Analysis

•Strict statististical analysis using Bioconductor’s LIMMA

•FDR 1%

•1.7 fold minimum

Results

Results

Automated litterature analysis

•Automated analysis of literature using Genomatix Bibliosphere

•Citation of 2 genes in the same sentence in at least 3 different abstracts

•Exported to graph management software Gephi▫Gephi.org

Automated analysis of literature

Main features

•AP-1 (FOS and JUN, some other related genes)

•A group of genes related to activated / effector T cells

▫Many cytokines associated to Th1, Th2, Th17

▫Th17 related genes have higher surexpression values, perhaps indicating a higher susceptibility

•p53 related genes

AP-1

FOS JUN

Promoter analysis

Cytokine related

Th1 (IFNG, TNF-a, TNF-b, IL1A, IL3)

Th2 (IL4, IL5, IL-10, IL13)

Th17 (IL17A, IL17F, IL21, IL22, IL23R, IRF4)

Th17 related genes

IL17A IL17F

Th17 related genes

IL22 IL23R

p53 related genes

Automated analysis of literature

Alternative splicing analysis• Used the combination of two of the best

algorithms▫MADS and PECA-SI

• P < 0.01

• Splicing index of at least 0.6

• Exon can be detected in at least 1/3 of arrays

• Filter out exons not currently associated with genes

Alternative splicing results

•547 probes in 372 transcripts

•52% of these are in non-coding UTRs

•48% in coding exons

PTPRC

Alternative splicing - PTPRC

Mock A

Mock B

Mock C

Bystander A

Bystander B

Bystander C

Infected A

Infected B

Infected C

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

24h48h72h

Rel

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4 o

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D45

Conclusion

• Infected cells have a transcriptomic profile of highly activated / effector / memory T cells

•No effect at all in bystander cells

•Confirmed a lot of things▫Role of p53 in HIV-1 pathogenenis

•Many interesting candidates▫Potential susceptibility and restriction factors

Acknowledgements

• Michel J. Tremblay

• Project Leaders• Corinne Barat, Ph.D.• Réjean Cantin, Ph.D.• Robert Lodge, Ph.D.• Michel Ouellet, Ph.D.

• Postdoctoral Fellows• Ravendra Garg, Ph.D.• Pranav Kumar, Ph.D.• Guadalupe Andreani,

Ph.D.• Masayuki Fujino, Ph.D.• Sandra Côté, Ph.D.

• Ph.D. Students• Rémi Fromentin, M.Sc.• Alexandra Lambert, M.Sc.• Lise-Andrée Gobeil, M.Sc.• Jonathan Bertin, M.Sc.• Pascal Jalaguier, M.Sc.• Anissa Cheikh, M.Sc.• Alejandro Martin Gomez

Lic.

• M.Sc. Students• Alexis Danylo, B.Sc.• Katia Giguère, B.Sc.• Audrey Plante, B.Sc.• Jean-François Bolduc, B.Sc.• Véronique Veillette, B.Sc.

Questions