SUMMARY
Inflammatory bowel disease (IBD) results from a dysregulated interaction between the microbiota and a genetically susceptible host. Genetic studies have linked TNFSF15 polymorphisms and its protein TNF-like ligand 1A (TL1A) with IBD, but the functional role of TL1A is not known. Here, we found that adherent IBD-associated microbiota induced TL1A release from CX3CR1+ mononuclear phagocytes (MNPs). Using cell- specific genetic deletion models, we identified an essential role for CX3CR1+MNP- derived TL1A in driving group 3 innate lymphoid cell (ILC3) production of interleukin 22 and mucosal healing during acute colitis. In contrast to this protective role in acute colitis, TL1A-dependent expression of co-stimulatory molecule OX40L in MHCII+ ILC3s during colitis led to co-stimulation of antigen-specific T cells that was required for chronic T cell colitis. These results identify a role for ILC3s in activating intestinal T cells and reveal a central role for TL1A in promoting ILC3 barrier immunity during colitis.
Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.
Highlights d Multi-omic characterization of cancer biology in relation to aging d Identification of tumor types associated with divergent biology d Younger patients are linked to dysregulated aging and senescence d Age-group-specific targeted therapy identification
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