Highlights d Screening of gut microbiota metabolomes against hundreds of G protein-coupled receptors d Phylogenetically diverse gut microbes produce agonists for many GPCRs including orphans d Bioactivity-based screening reveals diet-microbe-host and microbe-microbe-host axes d Microbiota-derived GPCR ligands impact local and systemic host physiology

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10−8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10−11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry.

Genome-wide association studies (GWASs) have identified hundreds of loci harboring genetic variation influencing inflammatory-disease susceptibility in humans. It has been hypothesized that present day inflammatory diseases may have arisen, in part, due to pleiotropic effects of host resistance to pathogens over the course of human history, with significant selective pressures acting to increase host resistance to pathogens. The extent to which genetic factors underlying inflammatory-disease susceptibility has been influenced by selective processes can now be quantified more comprehensively than previously possible. To understand the evolutionary forces that have shaped inflammatory-disease susceptibility and to elucidate functional pathways affected by selection, we performed a systems-based analysis to integrate (1) published GWASs for inflammatory diseases, (2) a genome-wide scan for signatures of positive selection in a population of European ancestry, (3) functional genomics data comprised of protein-protein interaction networks, and (4) a genome-wide expression quantitative trait locus (eQTL) mapping study in peripheral blood mononuclear cells (PBMCs). We demonstrate that loci for inflammatory-disease susceptibility are enriched for genomic signatures of recent positive natural selection, with selected loci forming a highly interconnected protein-protein interaction network. Further, we identify 21 loci for inflammatory-disease susceptibility that display signatures of recent positive selection, of which 13 also show evidence of cis-regulatory effects on genes within the associated locus. Thus, our integrated analyses highlight a set of susceptibility loci that might subserve a shared molecular function and has experienced selective pressure over the course of human history; today, these loci play a key role in influencing susceptibility to multiple different inflammatory diseases, in part through alterations of gene expression in immune cells.

Stem-like" TCF1 + CD8 + T cells (T SL ) are necessary for long-term maintenance of T cell responses and the efficacy of immunotherapy but, as tumors contain signals that should drive T-cell terminal-differentiation, how these cells are maintained in tumors remains unclear. In this study, we found that a small number of TCF1 + tumor-specific CD8 + T cells were present in lung tumors throughout their development. Yet, most intratumoral T cells differentiated as tumors progressed, corresponding with an immunologic shift in the tumor microenvironment (TME) from "hot" (T cell-inflamed) to "cold" (non-T cell-inflamed). By contrast, most tumor-specific CD8 + T cells in tumor-draining lymph nodes (dLNs) had functions and gene expression signatures similar to T SL from chronic LCMV infection, and this population was stable over time, despite the changes in the TME. dLN T cells were the developmental precursors of, and were clonally related to, their more differentiated intratumoral counterparts. Our data support the hypothesis that dLN T cells are the developmental precursors of the TCF1 + T cells in tumors which are maintained by continuous migration. Finally, CD8 + T cells similar to T SL were also present in LNs from lung adenocarcinoma patients, suggesting a similar model may be relevant in human disease. Thus, we propose that the dLN T SL reservoir has a critical function in sustaining antitumor T cells during tumor development and protecting them from the terminal differentiation that occurs in the TME.

Neuropil is a fundamental form of tissue organization within brains 1 . In neuropils, densely packed neurons synaptically interconnect into precise circuit architecture 2 , 3 , yet the structural and developmental principles governing this nanoscale precision remain largely unknown 4 , 5 . Here, we use diffusion condensation, an iterative data coarse-graining algorithm 6 , to identify nested circuit structures within the C. elegans neuropil (called the nerve ring). We show that the nerve ring neuropil is largely organized into four strata composed of related behavioral circuits. The stratified architecture of the neuropil is a geometrical representation of the functional segregation of sensory information and motor outputs, with specific sensory organs and muscle quadrants mapping onto particular neuropil strata. We identify groups of neurons with unique morphologies that integrate information across strata and that create neural structures that cage the strata within the nerve ring. We use high resolution light-sheet microscopy 7 , 8 , coupled with lineage-tracing and cell-tracking algorithms 9 , 10 , to resolve the developmental sequence and reveal principles of cell position, migration and outgrowth that guide stratified neuropil organization. Our results uncover conserved structural design principles underlying nerve ring neuropil architecture and function, and a pioneer-neuron-based, temporal progression of outgrowth that guides the hierarchical development of the layered neuropil. Our findings provide a systematic blueprint for using structural and developmental approaches to understand neuropil organization within brains.

As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16 hi CD66b lo neutrophil and IFN-γ + granzyme B + Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The multiple sclerosis (MS) patient population is highly heterogeneous in terms of disease course and treatment response. We used a transcriptional profile generated from peripheral blood mononuclear cells to define the structure of an MS patient population. Two subsets of MS subjects (MSA and MSB) were found among 141 untreated subjects. We replicated this structure in two additional groups of MS subjects treated with one of the two first-line disease-modifying treatments in MS: glatiramer acetate (GA) (n = 94) and interferon-β (IFN-β) (n = 128). One of the two subsets of subjects (MSA) was distinguished by higher expression of molecules involved in lymphocyte signaling pathways. Further, subjects in this MSA subset were more likely to have a new inflammatory event while on treatment with either GA or IFN-β (P = 0.0077). We thus report a transcriptional signature that differentiates subjects with MS into two classes with different levels of disease activity.

Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here, we consider multiple levels of abstraction via a multiresolution geometry of data points at different granularities. To construct this geometry we define a time-inhomogeneous diffusion process that effectively condenses data points together to uncover nested groupings at larger and larger granularities. This inhomogeneous process creates a deep cascade of intrinsic low pass filters on the data affinity graph that are applied in sequence to gradually eliminate local variability while adjusting the learned data geometry to increasingly coarser resolutions. We provide visualizations to exhibit our method as a "continuously-hierarchical" clustering with directions of eliminated variation highlighted at each step. The utility of our algorithm is demonstrated via neuronal data condensation, where the constructed multiresolution data geometry uncovers the organization, grouping, and connectivity between neurons.