BackgroundRecent advances in next-generation DNA sequencing enable rapid high-throughput quantitation of microbial community composition in human samples, opening up a new field of microbiomics. One of the promises of this field is linking abundances of microbial taxa to phenotypic and physiological states, which can inform development of new diagnostic, personalized medicine, and forensic modalities. Prior research has demonstrated the feasibility of applying machine learning methods to perform body site and subject classification with microbiomic data. However, it is currently unknown which classifiers perform best among the many available alternatives for classification with microbiomic data.ResultsIn this work, we performed a systematic comparison of 18 major classification methods, 5 feature selection methods, and 2 accuracy metrics using 8 datasets spanning 1,802 human samples and various classification tasks: body site and subject classification and diagnosis.ConclusionsWe found that random forests, support vector machines, kernel ridge regression, and Bayesian logistic regression with Laplace priors are the most effective machine learning techniques for performing accurate classification from these microbiomic data.

Summary In seeds and other parts of cultivated, tetraploid cotton ( Gossypium hirsutum L.), multicellular groups of cells lysigenously form dark glands containing toxic terpenoids such as gossypol that defend the plant against pests and pathogens. Using RNA ‐seq analysis of embryos from near‐isogenic glanded ( Gl 2 Gl 2 Gl 3 Gl 3 ) versus glandless ( gl 2 gl 2 gl 3 gl 3 ) plants, we identified 33 genes that expressed exclusively or at higher levels in embryos just prior to gland formation in glanded plants. Virus‐induced gene silencing against three gene pairs led to significant reductions in the number of glands in the leaves, and significantly lower levels of gossypol and related terpenoids. These genes encode transcription factors and have been designated the ‘Cotton Gland Formation’ ( CGF ) genes. No sequence differences were found between glanded and glandless cotton for CGF 1 and CGF 2 gene pairs. The glandless cotton has a transposon insertion within the coding sequence of the Go PGF (synonym CGF 3 ) gene of the A subgenome and extensive mutations in the promoter of D subgenome homeolog. Overexpression of Go PGF (synonym CGF 3 ) led to a dramatic increase in gossypol and related terpenoids in cultured cells, whereas CRISPR /Cas9 knockout of Go PGF (synonym CGF 3 ) genes resulted in glandless phenotype. Taken collectively, the results show that the Go PGF (synonym CGF 3 ) gene plays a critical role in the formation of glands in the cotton plant. Seed‐specific silencing of CGF genes, either individually or in combination, could eliminate glands, thus gossypol, from the cottonseed to render it safe as food or feed for monogastrics.

Antecedent viral infection may contribute to increased susceptibility to several neurological diseases, such as multiple sclerosis and Parkinson’s disease. Variation in clinical presentations of these diseases is often associated with gender, genetic background, or a combination of these and other factors. The complicated etiologies of these virally influenced diseases are difficult to study in conventional laboratory mouse models, which display a very limited number of phenotypes. We have used the genetically and phenotypically diverse Collaborative Cross mouse panel to examine complex neurological phenotypes after viral infection. Female and male mice from 18 CC strains were evaluated using a multifaceted phenotyping pipeline to define their unique disease profiles following infection with Theiler’s Murine Encephalomyelitis Virus, a neurotropic virus. We identified 4 distinct disease progression profiles based on limb-specific paresis and paralysis, tremors and seizures, and other clinical signs, along with separate gait profiles. We found that mice of the same strain had more similar profiles compared to those of different strains, and also identified strains and phenotypic parameters in which sex played a significant role in profile differences. These results demonstrate the value of using CC mice for studying complex disease subtypes influenced by sex and genetic background. Our findings will be useful for developing novel mouse models of virally induced neurological diseases with heterogenous presentation, an important step for designing personalized, precise treatments.

Lifestyle factors and chronic pathologic states are important contributors to interindividual variability in susceptibility to xenobiotic-induced toxicity. Nonalcoholic fatty liver disease (NAFLD) is an increasingly prevalent condition that can dramatically affect chemical metabolism. We examined the effect of NAFLD on toxicokinetics of tetrachloroethylene (PERC), a ubiquitous environmental contaminant that requires metabolic activation to induce adverse health effects. Mice (C57Bl/6J, male) were fed a low-fat diet (LFD), high-fat diet (HFD), or methionine/folate/choline-deficient diet (MCD) to model a healthy liver, steatosis, or nonalcoholic steatohepatitis (NASH), respectively. After 8 weeks, mice were orally administered a single dose of PERC (300 mg/kg) or vehicle (aqueous Alkamuls-EL620) and euthanized at various time points (1-36 hours). Levels of PERC and its metabolites were measured in blood/serum, liver, and fat. Effects of diets on liver gene expression and tissue: air partition coefficients were evaluated. We found that hepatic levels of PERC were 6-and 7.6-fold higher in HFD-and MCD-fed mice compared with LFD-fed mice; this was associated with an increased PERC liver:blood partition coefficient. Liver and serum C max for trichloroacetate (TCA) was lower in MCD-fed mice; however, hepatic clearance of TCA was profoundly reduced by HFD or MCD feeding, leading to TCA accumulation. Hepatic mRNA/protein expression and ex vivo activity assays revealed decreased xenobiotic metabolism in HFD-and MCD-, compared with LFD-fed, groups. In conclusion, experimental NAFLD was associated with modulation of xenobiotic disposition and metabolism and increased hepatic exposure to PERC and TCA. Underlying NAFLD may be an important susceptibility factor for PERC-associated hepatotoxicity.