Background: An assessment of biomarkers from an analysis of human peripheral blood mononuclear cell gene-expression profiles was made, to acquire an understanding of transcriptional changes associated with human immunodeficiency virus type 1 (HIV-1) infection in vivo.
Methods: Supervised learning algorithms were used to create signature gene sets that could be used to distinguish seropositive from seronegative samples and delineate changes in disease status during the early stages of infection. Bioinformatic tools were used to classify persons and to functionally characterize groups of differentially expressed genes, to elucidate the impact of viral infection on host cell gene-expression patterns.
Results: A 10-gene signature set that could be used to accurately determine the HIV-1 serostatus was identified. A 6-gene signature set was used to distinguish seropositive persons exhibiting differential changes in CD4(+) T cell counts, with 93% accuracy. Functional classification of differentially expressed genes in HIV-1 indicated a preponderance of down-regulated genes with functions related to the immune response and apoptosis. Hierarchical cluster analysis in persons whose CD4(+) T cell counts increased, compared with that in persons whose CD4(+) T cell counts decreased, was characterized by the down-regulation of genes associated with apoptosis, mitochondrial function, protein biosynthesis, and RNA binding.
Conclusions: Gene-expression profile analysis of a complex infectious virus, such as HIV-1, may be useful to elucidate the functional genomic relationships associated with viral infection.