Transcriptional network predicts viral set point during acute HIV-1 infection.
BACKGROUND
HIV-1-infected individuals with higher viral set points progress to AIDS more rapidly than those with lower set points. Predicting viral set point early following infection can contribute to our understanding of early control of HIV-1 replication, to predicting long-term clinical outcomes, and to the choice of optimal therapeutic regimens.


METHODS
In a longitudinal study of 10 untreated HIV-1-infected patients, we used gene expression profiling of peripheral blood mononuclear cells to identify transcriptional networks for viral set point prediction. At each sampling time, a statistical analysis inferred the optimal transcriptional network that best predicted viral set point. We then assessed the accuracy of this transcriptional model by predicting viral set point in an independent cohort of 10 untreated HIV-1-infected patients from Malawi.


RESULTS
The gene network inferred at time of enrollment predicted viral set point 24 weeks later in the independent Malawian cohort with an accuracy of 87.5%. As expected, the predictive accuracy of the networks inferred at later time points was even greater, exceeding 90% after week 4. The composition of the inferred networks was largely conserved between time points. The 12 genes comprising this dynamic signature of viral set point implicated the involvement of two major canonical pathways: interferon signaling (p<0.0003) and membrane fraction (p<0.02). A silico knockout study showed that HLA-DRB1 and C4BPA may contribute to restricting HIV-1 replication.


CONCLUSIONS
Longitudinal gene expression profiling of peripheral blood mononuclear cells from patients with acute HIV-1 infection can be used to create transcriptional network models to early predict viral set point with a high degree of accuracy.
Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu
Hsun-Hsien
HH
Chang
Kelly
K
Soderberg
Jason A
JA
Skinner
Jacques
J
Banchereau
Damien
D
Chaussabel
Barton F
BF
Haynes
Marco
M
Ramoni
Norman L
NL
Letvin
D003198:Computer Simulation; D018450:Disease Progression; D020869:Gene Expression Profiling; D055786:Gene Knockout Techniques; D053263:Gene Regulatory Networks; D015658:HIV Infections / Q000175:diagnosis*; D015497:HIV-1; D006801:Humans; D007963:Leukocytes, Mononuclear; D008137:Longitudinal Studies; D008295:Malawi; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D011379:Prognosis; D012367:RNA, Viral; D012044:Regression Analysis; D014481:United States; D019562:Viral Load / Q000235:genetics*
D012367:RNA, Viral