Post on 18-Jan-2016
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
ls
norm
aliz
ed o
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
ativ
e n
orm
aliz
ed le
vels
of
exon
4 o
f C
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