Transcriptional network predicts viral set point during acute HIV-1 infection

Hsun Hsien Chang, Kelly Soderberg, Jason A. Skinner, Jacques Banchereau, Damien J. Chaussabel, Barton F. Haynes, Marco Ramoni, Norman L. Letvin

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1103-1109
Number of pages7
JournalJournal of the American Medical Informatics Association
Volume19
Issue number6
DOIs
Publication statusPublished - Nov 2012
Externally publishedYes

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Gene Regulatory Networks
HIV Infections
HIV-1
Gene Expression Profiling
Blood Cells
HLA-DRB1 Chains
Malawi
Interferons
Longitudinal Studies
Acquired Immunodeficiency Syndrome
Membranes
Infection
Genes

ASJC Scopus subject areas

  • Health Informatics

Cite this

Transcriptional network predicts viral set point during acute HIV-1 infection. / Chang, Hsun Hsien; Soderberg, Kelly; Skinner, Jason A.; Banchereau, Jacques; Chaussabel, Damien J.; Haynes, Barton F.; Ramoni, Marco; Letvin, Norman L.

In: Journal of the American Medical Informatics Association, Vol. 19, No. 6, 11.2012, p. 1103-1109.

Research output: Contribution to journalArticle

Chang, HH, Soderberg, K, Skinner, JA, Banchereau, J, Chaussabel, DJ, Haynes, BF, Ramoni, M & Letvin, NL 2012, 'Transcriptional network predicts viral set point during acute HIV-1 infection', Journal of the American Medical Informatics Association, vol. 19, no. 6, pp. 1103-1109. https://doi.org/10.1136/amiajnl-2012-000867
Chang, Hsun Hsien ; Soderberg, Kelly ; Skinner, Jason A. ; Banchereau, Jacques ; Chaussabel, Damien J. ; Haynes, Barton F. ; Ramoni, Marco ; Letvin, Norman L. / Transcriptional network predicts viral set point during acute HIV-1 infection. In: Journal of the American Medical Informatics Association. 2012 ; Vol. 19, No. 6. pp. 1103-1109.
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abstract = "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.",
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