Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA.
J Am Med Inform Assoc. 2012 Nov-Dec;19(6):1103-9. doi: 10.1136/amiajnl-2012-000867. Epub 2012 Jun 14.
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.
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.
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.
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.
HIV-1 感染者的病毒设定点较高，比设定点较低的感染者更快进展为艾滋病。在感染后早期预测病毒设定点有助于我们了解 HIV-1 复制的早期控制，预测长期临床结局，并选择最佳治疗方案。
在一项对 10 例未经治疗的 HIV-1 感染者的纵向研究中，我们使用外周血单核细胞的基因表达谱来识别用于预测病毒设定点的转录网络。在每个采样时间，统计分析推断出最佳转录网络，该网络可以最佳地预测病毒设定点。然后，我们通过预测来自马拉维的 10 例未经治疗的 HIV-1 感染者的独立队列中的病毒设定点，来评估该转录模型的准确性。
在入组时推断出的基因网络可以在独立的马拉维队列中准确预测 24 周后的病毒设定点，准确率为 87.5%。正如预期的那样，在以后的时间点推断出的网络的预测准确性更高，在第 4 周后超过 90%。推断出的网络的组成在时间点之间基本保持一致。构成该病毒设定点动态特征的 12 个基因暗示了两个主要的经典途径的参与：干扰素信号（p<0.0003）和膜部分（p<0.02）。计算机模拟敲除研究表明 HLA-DRB1 和 C4BPA 可能有助于限制 HIV-1 的复制。
急性 HIV-1 感染患者外周血单核细胞的纵向基因表达谱分析可用于创建转录网络模型，以高度准确地早期预测病毒设定点。
