Epostatin

Mycobacterium tuberculosis is an acid-fast bacterium that causes tuberculosis worldwide. The role of epistatic interactions among different loci of the M. tuberculosis genome under selective pressure may be crucial for understanding the disease and the molecular basis of antibiotic resistance acquisition. Here, we analyzed polymorphic loci interactions by applying a model-free method for epistasis detection, SpydrPick, on a pan-genome-wide alignment created from a set of 254 complete reference genomes. By means of the analysis of an epistatic network created with the detected epistatic interactions, we found that glgB (α-1,4-glucan branching enzyme) and oppA (oligopeptide-binding protein) are putative targets of co-selection in M. tuberculosis as they were associated in the network with M. tuberculosis genes related to virulence, pathogenesis, transport system modulators of the immune response, and antibiotic resistance. In addition, our work unveiled potential pharmacological applications for genotypic antibiotic resistance inherent to the mutations of glgB and oppA as they epistatically interact with fprA and embC, two genes recently included as antibiotic-resistant genes in the catalog of the World Health Organization. Our findings showed that this approach allows the identification of relevant epistatic interactions that may lead to a better understanding of M. tuberculosis by deciphering the complex interactions of molecules involved in its metabolism, virulence, and pathogenesis and that may be applied to different bacterial populations.

Detecting single nucleotide polymorphisms (SNPs) interactions is crucial to identify susceptibility genes associated with complex human diseases in genome-wide association studies. Clustering-based approaches are widely used in reducing search space and exploring potential relationships between SNPs in epistasis analysis. However, these approaches all only use a single measure to filter out nonsignificant SNP combinations, which may be significant ones from another perspective. In this paper, we propose a two-stage approach named EpiMC (Epistatic Interactions detection based on Multiple Clusterings) that employs multiple clusterings to obtain more precise candidate sets and more comprehensively detect high-order interactions based on these sets. In the first stage, EpiMC proposes a matrix factorization based multiple clusterings algorithm to generate multiple diverse clusterings, each of which divide all SNPs into different clusters. This stage aims to reduce the chance of filtering out potential candidates overlooked by a single clustering and groups associated SNPs together from different clustering perspectives. In the next stage, EpiMC considers both the single-locus effects and interaction effects to select high-quality disease associated SNPs, and then uses Jaccard similarity to get candidate sets. Finally, EpiMC uses exhaustive search on the obtained small candidate sets to precisely detect epsitatic interactions. Extensive simulation experiments show that EpiMC has a better performance in detecting high-order interactions than state-of-the-art solutions. On the Wellcome Trust Case Control Consortium (WTCCC) dataset, EpiMC detects several significant epistatic interactions associated with breast cancer (BC) and age-related macular degeneration (AMD), which again corroborate the effectiveness of EpiMC.

Epistatic interactions can make the outcomes of evolution unpredictable, but no comprehensive data are available on the extent and temporal dynamics of changes in the effects of mutations as protein sequences evolve. Here, we use phylogenetic deep mutational scanning to measure the functional effect of every possible amino acid mutation in a series of ancestral and extant steroid receptor DNA binding domains. Across 700 million years of evolution, epistatic interactions caused the effects of most mutations to become decorrelated from their initial effects and their windows of evolutionary accessibility to open and close transiently. Most effects changed gradually and without bias at rates that were largely constant across time, indicating a neutral process caused by many weak epistatic interactions. Our findings show that protein sequences drift inexorably into contingency and unpredictability, but that the process is statistically predictable, given sufficient phylogenetic and experimental data.