Bridging HIV-1 cellular latency and clinical long-term non-progressor: an interactomic view

• Published in: PloS one Vol. 8; no. 2; p. e55791
• Main Authors: Yang, Jin, Yang, Zongxing, Lv, Hangjun, Lou, Yi, Wang, Juan, Wu, Nanping
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25-02-2013; Public Library of Science (PLoS)
• Subjects: Acquired immune deficiency syndrome; AIDS; Bayesian analysis; Biology; Cluster analysis; Clustering; Data processing; Development and progression; DNA microarrays; Gene expression; Gene Expression Regulation, Viral - genetics; Gene Expression Regulation, Viral - physiology; Gene regulation; Genes; Genomics; HIV; HIV Infections - genetics; HIV Infections - metabolism; HIV Long-Term Survivors; HIV-1 - genetics; HIV-1 - metabolism; HIV-1 - pathogenicity; Hospitals; Human immunodeficiency virus; Humans; Infections; Infectious diseases; Kinases; Laboratories; Latency; Lymphocytes; Mathematics; Medical diagnosis; Medicine; Network latency; Nuclear transport; Ribonucleic acid; RNA; RNA processing; RNA transport; Statistical inference; Transcription; Transcription (Genetics); Variance analysis; Viral Proteins - genetics; Viral Proteins - metabolism; Virology; Virus Latency - genetics; Virus Latency - physiology; Virus replication; Virus Replication - genetics; Virus Replication - physiology; Viruses; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0055791

Development of an effective HIV management is enticed by the fact that long-term non-progressors (LTNP) restrict viral replication spontaneously, but is hindered by HIV-1 latency. Given that the most overlapping characteristics found between HIV-1 LTNP and latency, detailed analysis of the difference would disclose the essentials of latency. In this study, microarray data from our previous study was combined with HIV-1 latency and LTNP data obtained from NCBI GEO database. Principal variance component analysis and hierarchical clustering verified the removal of batch effect across platform. The analysis revealed a total of 456 differential expressed genes with >2-fold change and B-statistic >0. Bayesian inference was used to reconstitute the transcriptional network of HIV-1 latency or LTNP, respectively. Gene regulation was reprogrammed under different disease condition. By network interference, KPNA2 and ATP5G3 were identified as the hubs in latency network which mediate nuclear export and RNA processing. These data offer comparative insights into HIV-1 latency, which will facilitate the understanding of the genetic basis of HIV-1 latency in vivo and serve as a clue for future treatment dealing with key targets in HIV-1 latency.