In PLOS Genetics, researchers from Australia's Monash University explore the process of genetic compensation in zebrafish model organisms with mutations affecting a highly conserved skeletal actin component gene implicated in a neuromuscular condition called nemaline myopathy in humans. Using PCR analyses and other approaches, the team tracked expression in zebrafish carrying actc1b gene mutations generated by chemical mutagenesis. The zebrafish displayed relatively mild muscle problems, they report, apparently due to a compensatory uptick in expression of a paralogous actin gene. Based on their findings, the authors argue that "genetic compensation may be more prevalent than previously anticipated and highlights phenotypic differences resulting from genetic mutations versus antisense knockdown approaches."
A team from Imperial College London and the Animal Health Trust takes a look at the role genetic diversity plays in tracking infectious disease transmission for a paper in PLOS Pathogens. The researchers tapped into transmission divergence clues — based on the number of mutations differentiating genome sequences for pairs of pathogens involved in an outbreak — to simulate outbreaks with 10 different pathogens. Their results suggest that while individual transmission events could be picked out in pathogens that evolve rapidly, leading to numerous genetic sequence differences, it appeared to be difficult or impossible to distinguish individual transmission in low divergence pathogens. "Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios," they write, "and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data."
Finally, researchers from Emory University and Rollins School of Public Health introduce an open-source web application for bringing together multiple 'omic data sets to characterize gene sets based on molecular similarities to given phenotypes in PLOS One. The so-called shinyGISPA tool builds on a prior "Gene Integrated Set Profile Analysis" (GISPA) method, the authors explain, an approach that "allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype." The team notes that the shinyGISPA tool is designed as a simpler, automated workflow to identify molecular changes — ranked by levels of support for these gene sets — in a single sample by comparing gene sets and molecular changes.