Microbiota-Induced TNF-like Ligand 1A Drives Group 3 Innate Lymphoid Cell-Mediated Barrier Protection and Intestinal T Cell Activation during Colitis.

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.

A polygenic-score-based approach for identification of gene-drug interactions stratifying breast cancer risk.

An individual's genetics can dramatically influence breast cancer (BC) risk. Although clinical measures for prevention do exist, non-invasive personalized measures for reducing BC risk are limited. Commonly used medications are a promising set of modifiable factors, but no previous study has explored whether a range of widely taken approved drugs modulate BC genetics. In this study, we describe a quantitative framework for exploring the interaction between the genetic susceptibility of BC and medication usage among UK Biobank women. We computed BC polygenic scores (PGSs) that summarize BC genetic risk and find that the PGS explains nearly three-times greater variation in disease risk within corticosteroid users compared to non-users. We map 35 genes significantly interacting with corticosteroid use (FDR < 0.1), highlighting the transcription factor NRF2 as a common regulator of gene-corticosteroid interactions in BC. Finally, we discover a regulatory variant strongly stratifying BC risk according to corticosteroid use. Within risk allele carriers, 18.2% of women taking corticosteroids developed BC, compared to 5.1% of the non-users (with an HR = 3.41 per-allele within corticosteroid users). In comparison, there are no differences in BC risk within the reference allele homozygotes. Overall, this work highlights the clinical relevance of gene-drug interactions in disease risk and provides a roadmap for repurposing biobanks in drug repositioning and precision medicine.

Leveraging phenotypic variability to identify genetic interactions in human phenotypes.

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.

Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise.

Endurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus (sn) multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2max in four subjects, as well as in matched time of day samples from two supine resting circadian controls. High quality same-cell RNA-seq and ATAC-seq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Single-cell circuit analysis identified distinct adaptations in fast, slow and intermediate fibers as well as LUM-expressing FAP cells, involving a total of 328 transcription factors (TFs) acting at altered accessibility sites regulating 2,025 genes. These data and circuit mapping provide single-cell insight into the processes underlying tissue and metabolic remodeling responses to exercise.

Pan-cancer analysis reveals molecular patterns associated with age.

Older age is a strong risk factor for several diseases, including cancer. The etiology and biology of age-associated differences among cancers are poorly understood. To address this knowledge gap, we aim to delineate differences in tumor molecular characteristics between younger and older patients across a variety of tumor types from The Cancer Genome Atlas. We show that these groups exhibit widespread molecular differences in select tumor types. Our work shows that tumors in younger individuals exhibit a dysregulated molecular aging phenotype and are associated with hallmarks of premature senescence. Additionally, we find that these tumors are enriched for driver gene mutations, resulting in homologous recombination defects. Lastly, we observe a trend toward decreased immune infiltration and function in older patients and find that, immunologically, young tumor tissue resembles aged healthy tissue. Taken together, we find that tumors from young individuals possess unique characteristics that may be leveraged for therapy.

Demographic and genetic factors influence the abundance of infiltrating immune cells in human tissues.

Despite infiltrating immune cells having an essential function in human disease and patients' responses to treatments, mechanisms influencing variability in infiltration patterns remain unclear. Here, using bulk RNA-seq data from 46 tissues in the Genotype-Tissue Expression project, we apply cell-type deconvolution algorithms to evaluate the immune landscape across the healthy human body. We discover that 49 of 189 infiltration-related phenotypes are associated with either age or sex (FDR < 0.1). Genetic analyses further show that 31 infiltration-related phenotypes have genome-wide significant associations (iQTLs) (P < 5.0 × 10-8), with a significant enrichment of same-tissue expression quantitative trait loci in suggested iQTLs (P < 10-5). Furthermore, we find an association between helper T cell content in thyroid tissue and a COMMD3/DNAJC1 regulatory variant (P = 7.5 × 10-10), which is associated with thyroiditis in other cohorts. Together, our results identify key factors influencing inter-individual variability of immune infiltration, to provide insights on potential therapeutic targets.