To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the non-essential genome. Using these signatures, we generated a catalogue of 297 functional modules, and assigned function to 144 previously uncharacterised genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional cross-talk between biological processes, suggesting general design principles of genetic interactomes.

SUMMARY A population of CD4 T lymphocytes harboring latent HIV genomes can persist in patients on antiretroviral therapy, posing a barrier to HIV eradication. To examine cellular complexes controlling HIV latency, we conducted a genome-wide screen with a pooled ultracomplex shRNA library and in vitro system modeling HIV latency and identified the mTOR complex as a modulator of HIV latency. Knockdown of mTOR complex subunits or pharmacological inhibition of mTOR activity suppresses reversal of latency in various HIV-1 latency models and HIV-infected patient cells. mTOR inhibitors suppress HIV transcription both through the viral transactivator Tat as well as via Tat-independent mechanisms. This inhibition occurs at least in part via blocking the phosphorylation of, CDK9, a p-TEFb complex member that serves as a cofactor for Tat-mediated transcription. The control of HIV latency by mTOR signaling identifies a pathway that may have significant therapeutic opportunities.

The long-term health outcome of prenatal exposure to arsenic has been associated with increased mortality in human populations. In this study, the extent to which maternal arsenic exposure impacts gene expression in the newborn was addressed. We monitored gene expression profiles in a population of newborns whose mothers experienced varying levels of arsenic exposure during pregnancy. Through the application of machine learning–based two-class prediction algorithms, we identified expression signatures from babies born to arsenic-unexposed and -exposed mothers that were highly predictive of prenatal arsenic exposure in a subsequent test population. Furthermore, 11 transcripts were identified that captured the maximal predictive capacity to classify prenatal arsenic exposure. Network analysis of the arsenic-modulated transcripts identified the activation of extensive molecular networks that are indicative of stress, inflammation, metal exposure, and apoptosis in the newborn. Exposure to arsenic is an important health hazard both in the United States and around the world, and is associated with increased risk for several types of cancer and other chronic diseases. These studies clearly demonstrate the robust impact of a mother's arsenic consumption on fetal gene expression as evidenced by transcript levels in newborn cord blood.

Human immunodeficiency virus (HIV) infection is currently incurable, due to the persistence of latently infected cells. The ‘shock and kill’ approach to a cure proposes to eliminate this reservoir via transcriptional activation of latent proviruses, enabling direct or indirect killing of infected cells. Currently available latency-reversing agents (LRAs) have however proven ineffective. To understand why, we used a novel HIV reporter strain in primary CD4+ T cells and determined which latently infected cells are reactivatable by current candidate LRAs. Remarkably, none of these agents reactivated more than 5% of cells carrying a latent provirus. Sequencing analysis of reactivatable vs. non-reactivatable populations revealed that the integration sites were distinguishable in terms of chromatin functional states. Our findings challenge the feasibility of ‘shock and kill’, and suggest the need to explore other strategies to control the latent HIV reservoir.