Transcription profiling of CD4⁺ T cells in rhesus macaques that infected with simian-human immunodeficiency virus and re-challenged with SIVmac251.

BACKGROUND: Insights into the host factors that contribute to an effective antiviral immune response may be obtained by examining global gene expression in simian-human immunodeficiency virus (SHIV)-infected non-human primates that exhibit different virological outcomes. METHODS: Six chronically SHIV-infected macaques were rectally challenged with SIVmac251. Viral RNA and proviral DNA load in blood were measured. Gene expression profiles in CD4+ T cells were examined and compared between animals with different levels of infection following challenge. RESULTS AND CONCLUSIONS: Viral RNA was markedly controlled in four challenged animals, whereas two animals had persistent high viremia. Analysis of the gene expression profiles at early infection revealed gene expression signatures between protectors and non-protectors and identified potential protective biomarkers. Pathway analyses revealed that IFN pathway genes are down-regulated in protectors compared to unprotectors. This study suggests that high levels of expression of type 1 IFN-related genes may paradoxically promote virus replication.

Biologically anchored knowledge expansion approach uncovers KLF4 as a novel insulin signaling regulator.

One of the biggest challenges in analyzing high throughput omics data in biological studies is extracting information that is relevant to specific biological mechanisms of interest while simultaneously restricting the number of false positive findings. Due to random chances with numerous candidate targets and mechanisms, computational approaches often yield a large number of false positives that cannot easily be discerned from relevant biological findings without costly, and often infeasible, biological experiments. We here introduce and apply an integrative bioinformatics approach, Biologically Anchored Knowledge Expansion (BAKE), which uses sequential statistical analysis and literature mining to identify highly relevant network genes and effectively removes false positive findings. Applying BAKE to genomic expression data collected from mouse (Mus musculus) adipocytes during insulin resistance progression, we uncovered the transcription factor Krueppel-like Factor 4 (KLF4) as a regulator of early insulin signaling. We experimentally confirmed that KLF4 controls the expression of two key insulin signaling molecules, the Insulin Receptor Substrate 2 (IRS2) and Tuberous Sclerosis Complex 2 (TSC2).

Correction: Biologically anchored knowledge expansion approach uncovers KLF4 as a novel insulin signaling regulator.

[This corrects the article DOI: 10.1371/journal.pone.0204100.].

Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism.

Different computational approaches have been examined and compared for inferring network relationships from time-series genomic data on human disease mechanisms under the recent Dialogue on Reverse Engineering Assessment and Methods (DREAM) challenge. Many of these approaches infer all possible relationships among all candidate genes, often resulting in extremely crowded candidate network relationships with many more False Positives than True Positives. To overcome this limitation, we introduce a novel approach, Module Anchored Network Inference (MANI), that constructs networks by analyzing sequentially small adjacent building blocks (modules). Using MANI, we inferred a 7-gene adipogenesis network based on time-series gene expression data during adipocyte differentiation. MANI was also applied to infer two 10-gene networks based on time-course perturbation datasets from DREAM3 and DREAM4 challenges. MANI well inferred and distinguished serial, parallel, and time-dependent gene interactions and network cascades in these applications showing a superior performance to other in silico network inference techniques for discovering and reconstructing gene network relationships.