Fitness costs are key determinants of whether drug resistance alleles establish and how fast they spread within populations. More than 125 different kelch13 alleles, each containing a different amino acid substitution, have arisen in Southeast Asian malaria parasite (Plasmodium falciparum) populations under artemisinin selection over the past 15 years in a dramatic example of a soft selective event.

Animals respond to chemical stress with an array of gene families and pathways termed “chemical defensome”. In arthropods, despite many defensome genes have been detected, how their activation is arranged during toxic exposure remains poorly understood. Here, we sequenced the transcriptome of Anopheles stephensi larvae exposed for six, 24 and 48 hours to the LD50 dose of the insecticide permethrin to monitor transcriptional changes of defensome genes across time. A total of 177 genes involved in insecticide defense were differentially expressed (DE) in at least one time-point, including genes encoding for Phase 0, I, II, III and antioxidant enzymes and for Heat Shock and Cuticular Proteins. Three major patterns emerged throughout time. First, most of DE genes were down-regulated at all time-points, suggesting a reallocation of energetic resources during insecticide stress. Second, single genes and clusters of genes turn off and on from six to 48 hours of treatment, showing a modulated response across time. Third, the number of up-regulated genes peaked at six hours and then decreased during exposure. Our results give a first picture of how defensome gene families respond against toxicants and provide a valuable resource for understanding how defensome genes work together during insecticide stress.

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BackgroundMachine learning (ML) is powerful tool that can identify and classify patterns from large quantities of cancer genomic data that may lead to the discovery of new biomarkers, new drug targets, and a better understanding of important cancer genes. The aim of this systematic review was to evaluate the existing literature and assess the application of machine learning of genomic data in head and neck cancer (HNC).Materials and methodsThe addressed focused question was “Does machine learning of genomic data play a role in prognostic prediction of HNC?” PubMed, EMBASE, Scopus, Web of Science, and gray literature from January 1990 up to and including May 2018 were searched. Two independent reviewers performed the study selection according to eligibility criteria.ResultsA total of seven studies that met the eligibility criteria were included. The majority of studies were cohort studies, one a case‐control study and one a randomized controlled trial. Two studies each evaluated oral cancer and laryngeal cancer, while other one study each evaluated nasopharyngeal cancer and oropharyngeal cancer. The majority of studies employed support vector machine (SVM) as a ML technique. Among the included studies, the accuracy rates for ML techniques ranged from 56.7% to 99.4%.ConclusionOur findings showed that ML techniques for the analysis of genomic data can play a role in the prognostic prediction of HNC.

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