Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARγ) in modulating plasticity between Th17 and iTreg cells. PPARγ regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARγ activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARγ. Deletion of PPARγ in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARγ in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.

Abscisic acid (ABA) has shown efficacy in the treatment of diabetes and inflammation; however, its molecular targets and the mechanisms of action underlying its immunomodulatory effects remain unclear. This study investigates the role of peroxisome proliferator-activated receptor ␥ (PPAR ␥) and lanthionine synthetase C-like 2 (LANCL2) as molecular targets for ABA. We demonstrate that ABA increases PPAR ␥ reporter activity in RAW 264.7 macrophages and increases ppar ␥ expression in vivo, although it does not bind to the ligand-binding domain of PPAR ␥. LANCL2 knockdown studies provide evidence that ABAmediated activation of macrophage PPAR ␥ is dependent on lancl2 expression. Consistent with the association of LANCL2 with G proteins, we provide evidence that ABA increases cAMP accumulation in immune cells. ABA suppresses LPS-induced prostaglandin E 2 and MCP-1 production via a PPAR ␥-dependent mechanism possibly involving activation of PPAR ␥ and suppression of NF-B and nuclear factor of activated T cells. LPS challenge studies in PPAR ␥-expressing and immune cell-specific PPAR ␥ null mice demonstrate that ABA down-regulates toll-like receptor 4 expression in macrophages and T cells in vivo through a PPAR ␥-dependent mechanism. Global transcriptomic profiling and confirmatory quantitative RT-PCR suggest novel candidate targets and demonstrate that ABA treatment mitigates the effect of LPS on the expression of genes involved in inflammation, metabolism, and cell signaling, in part, through PPAR ␥. In conclusion, ABA decreases LPS-mediated inflammation and regulates innate immune responses through a bifurcating pathway involving LANCL2 and an alternative, ligand-binding domain-independent mechanism of PPAR ␥ activation.

Pomegranate fruit presents strong anti-inflammatory, antioxidant, antiobesity, and antitumoral properties, thus leading to an increased popularity as a functional food and nutraceutical source since ancient times. It can be divided into three parts: seeds, peel, and juice, all of which seem to have medicinal benefits. Several studies investigate its bioactive components as a means to associate them with a specific beneficial effect and develop future products and therapeutic applications. Many beneficial effects are related to the presence of ellagic acid, ellagitannins (including punicalagins), punicic acid and other fatty acids, flavonoids, anthocyanidins, anthocyanins, estrogenic flavonols, and flavones, which seem to be its most therapeutically beneficial components. However, the synergistic action of the pomegranate constituents appears to be superior when compared to individual constituents. Promising results have been obtained for the treatment of certain diseases including obesity, insulin resistance, intestinal inflammation, and cancer. Although moderate consumption of pomegranate does not result in adverse effects, future studies are needed to assess safety and potential interactions with drugs that may alter the bioavailability of bioactive constituents of pomegranate as well as drugs. The aim of this review is to summarize the health effects and mechanisms of action of pomegranate extracts in chronic inflammatory diseases.

Inflammatory bowel disease (IBD) is a debilitating and widespread immune-mediated illness characterized by excessive inflammatory and effector mucosal responses leading to tissue destruction at the gastrointestinal tract. Interactions among the immune system, the commensal microbiota and the host genotype are thought to underlie the pathogenesis of IBD. However, the precise etiology of IBD remains unknown. Diet-induced changes in the composition of the gut microbiome can modulate the induction of regulatory versus effector immune responses at the gut mucosa and improve health outcomes. Therefore, manipulation of gut microbiota composition and the local production of microbial-derived metabolites by using prebiotics, probiotics and dietary fibers is being explored as a promising avenue of prophylactic and therapeutic intervention against gut inflammation. Prebiotics and fiber carbohydrates are fermented by resident microflora into short chain fatty acids (SCFAs) in the colon. SCFAs then activate peroxisome proliferator-activated receptor (PPAR)γ, a nuclear transcription factor with widely demonstrated anti-inflammatory efficacy in experimental IBD. The activation of PPARγ by naturally ocurring compounds such as conjugated linoleic acid, pomegranate seed oil-derived punicic acid, eleostearic acid and abscisic acid has been explored as nutritional interventions that suppress colitis by directly modulating the host immune response. The aim of this review is to summarize the status of innovative nutritional interventions against gastrointestinal inflammation, their proposed mechanisms of action, preclinical and clinical efficacy as well as bioinformatics and computational modeling approaches that accelerate discovery in nutritional and mucosal immunology research.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.