Thermoneutral housing exacerbates nonalcoholic fatty liver disease in mice and allows for sex-independent disease modeling

Nonalcoholic fatty liver disease (NAFLD) is recognized as the leading cause of chronic liver disease in adults and children. NAFLD encompasses a spectrum of liver injuries ranging from steatosis to steatohepatitis with or without fibrosis. Fibrosis may progress to cirrhosis and complications including hepatocellular carcinoma. Histologic findings represent the complexity of pathophysiology. NAFLD is closely associated with obesity and is most closely linked with insulin resistance; the current Western diet, high in saturated fats and fructose, plays a significant role. There are several mechanisms by which excess triglycerides are acquired and accumulate in hepatocytes. Formation of steatotic droplets may be disordered in NAFLD. Visceral adipose tissue dysfunction in obesity and insulin resistance results in aberrant cytokine expression; many cytokines have a role in liver injury in NAFLD. Cellular stress and immune reactions, as well as the endocannabinoid system, have been implicated in animal models and in some human studies.

RORγt(+) Th17 cells are important for mucosal defenses but also contribute to autoimmune disease. They accumulate in the intestine in response to microbiota and produce IL-17 cytokines. Segmented filamentous bacteria (SFB) are Th17-inducing commensals that potentiate autoimmunity in mice. RORγt(+) T cells were induced in mesenteric lymph nodes early after SFB colonization and distributed across different segments of the gastrointestinal tract. However, robust IL-17A production was restricted to the ileum, where SFB makes direct contact with the epithelium and induces serum amyloid A proteins 1 and 2 (SAA1/2), which promote local IL-17A expression in RORγt(+) T cells. We identified an SFB-dependent role of type 3 innate lymphoid cells (ILC3), which secreted IL-22 that induced epithelial SAA production in a Stat3-dependent manner. This highlights the critical role of tissue microenvironment in activating effector functions of committed Th17 cells, which may have important implications for how these cells contribute to inflammatory disease.

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.