Biology subjects

Clish, C.

Publications and source records attributed to Clish, C..

14 recordsLinked to original sources

bioRxiv2025

ODC1 restricts meningeal B cell age-associated-like phenotype and function in multiple sclerosis: a human and experimental study

Meningeal inflammation, as a clinical feature of multiple sclerosis (MS), is associated with worse clinical disease outcomes. In both relapsing and secondary progressive MS and the experimental autoimmune encephalomyelitis (EAE) MS model, the meninges have been found to contain ectopic lymphoid follicles enriched with B cells. The metabolic requirement of meningeal B cell function in MS or EAE is not well elucidated. Using 7-Tesla MRI brain scans of MS patients and leptomeningeal enhancement (LME) as a marker, we found a correlation between meningeal inflammation and metabolites of the arginine/polyamine pathway, a finding recapitulated in the EAE model. Ornithine Decarboxylase (ODC1), the rate limiting enzyme for polyamine biosynthesis, as well as polyamine metabolism was diminished in the dura meningeal B cells from mice with MOG35-55 induced EAE mice as compared to naive controls. Pharmacological inhibition of ODC1 restricted meningeal T cells but promoted meningeal B cell proliferation. B cell-specific deletion of ODC1 resulted in expansion of B cells with age-associated B cell-like phenotype (CD11c+CD21/35-CD23-IgD-) and exacerbated disease in the MOG1-125 EAE model. Together, these findings demonstrate a divergent role of polyamines in regulating B and T cell responses in the meninges during autoimmunity. Significance StatementThis study identified the polyamine pathway to be associated with meningeal inflammation in multiple sclerosis, a clinical phenotype associated with worse disease outcomes and without targeted therapy. Using a mouse experimental model, we found that ODC1, the rate limiting enzyme of the polyamine biosynthesis pathway, was suppressed in meningeal B cells, restricted the development of age-associated B cells in the meninges and limited disease severity. This study elucidated a metabolic pathway regulating meningeal B cell function, informing its therapeutic applications in autoimmune diseases.

immunology↗

bioRxiv2025

Distinct Classes of Gut Bacterial Molybdenum-Dependent Enzymes Produce Urolithins

Urolithin A is an anti-aging and anti-inflammatory gut bacterial metabolite derived from ellagic acid (EA), a polyphenol abundant in berries and nuts. The conversion of EA to urolithin A involves multiple chemically challenging phenol dehydroxylation steps that produce urolithins with varying bioactivities. Despite their biological and chemical significance, the bacterial enzymes responsible for urolithin production remain unidentified. Here, we use differential gene expression analysis, anaerobic protein expression, and enzyme assays to identify members of two distinct molybdenum enzyme families (the DMSO reductase family and the xanthine oxidase family) capable of regioselective dehydroxylation and urolithin generation. These two enzyme families have distinct substrate requirements, suggesting they employ different catalytic mechanisms for phenol dehydroxylation. Multi-omics analysis of a human cohort uncovers decreased levels of urolithin A and genes encoding urolithin A-producing enzymes in patients with inflammatory bowel disease (IBD), implying reduced health effects of EA consumption in this setting. Together, this study elucidates the molecular basis of urolithin production, expands the known enzymatic repertoire of the human gut microbiome, and suggests a potential link between gut bacterial urolithin production and host inflammation. Significance statementThe human gut microbiome modulates the health effects of dietary compounds by modifying their chemical structures. For example, gut microbes extensively metabolize polyphenols, a group of diverse plant-derived compounds associated with positive health outcomes. Urolithin A, a gut bacterial metabolite derived from a polyphenol abundant in berries and nuts, exhibits potent anti-inflammatory activity. However, the gut bacterial enzymes involved in its production remain largely unknown. Here, we report urolithin-producing gut bacterial enzymes, including four phenol dehydroxylases from two distinct molybdenum-dependent enzyme families. Analyzing human gut microbiomes suggests a potential link between genes encoding these enzymes and host inflammation. Together, our findings fully map urolithin A production to enzymes, increasing our understanding of how the gut microbiome can alter the impacts of diet on human health.

biochemistry↗

bioRxiv2024

The glycolytic reaction PGAM unexpectedly restrains Th17 pathogenicity and Th17-dependent autoimmunity

Glucose metabolism is a critical regulator of T cell function, largely thought to support their activation and effector differentiation. Here, we investigate the relevance of individual glycolytic reactions in determining the pathogenicity of T helper 17 (Th17) cells using single-cell RNA-seq and Compass, an algorithm we previously developed for estimating metabolic flux from single-cell transcriptomes. Surprisingly, Compass predicted that the metabolic shunt between 3-phosphoglycerate (3PG) and 2-phosphoglycerate (2PG) is inversely correlated with pathogenicity in these cells, whereas both its upstream and downstream reactions were positively correlated. Perturbation of phosphoglycerate mutase (PGAM), an enzyme required for 3PG to 2PG conversion, resulted in an increase in protein expression of IL2, IL17, and TNFa, as well as induction of a pathogenic gene expression program. Consistent with PGAM playing a pro-regulatory role, inhibiting PGAM in Th17 cells resulted in exacerbated autoimmune responses in the adoptive transfer model of experimental autoimmune encephalomyelitis (EAE). Finally, we further investigated the effects of modulating glucose concentration on Th17 cells in culture. Th17 cells differentiated under high- and low-glucose conditions substantially differed in their metabolic and effector transcriptomic programs, both central to Th17 function. Importantly, the PGAM-dependent gene module marks the least pathogenic state of Th17 cells irrespective of glucose concentration. Overall, our study identifies PGAM, contrary to other glycolytic enzymes, as a negative regulator of Th17 pathogenicity.

immunology↗

bioRxiv2024

Genetic screening and metabolomics identify glial adenosine metabolism as a therapeutic target in Parkinson's disease

Neuronal alpha-synucleinopathies, including Parkinson's disease (PD) and dementia with Lewy bodies (DLB), lack disease-modifying therapies. We developed a Drosophila model for identifying novel glial-based therapeutic targets for alpha-synucleinopathies. In this model, human alpha-synuclein is expressed in neurons and individual genes are independently knocked down in glia. We performed a forward genetic screen, knocking down the entire Drosophila kinome in glia in alpha-synuclein expressing flies. Among the top hits were five genes (Ak1, Ak6, Adk1, Adk2, and awd) involved in adenosine metabolism. Knockdown of each gene increased brain adenosine levels, improved locomotor dysfunction, reduced alpha-synuclein oligomer levels, and rescued neurodegeneration. We determined that the mechanism of neuroprotection requires adenosine signaling through the neuronal adenosine receptor (AdoR) in Drosophila. These experiments support glial adenosine as a novel therapeutic target in PD.

neuroscience↗

bioRxiv2023

Engineered probiotics limit CNS autoimmunity by stabilizing HIF-1α in dendritic cells

Dendritic cells (DCs) control the generation of self-reactive pathogenic T cells. Thus, DCs are considered attractive therapeutic targets for autoimmune diseases. Using single-cell and bulk transcriptional and metabolic analyses in combination with cell-specific gene perturbation studies we identified a negative feedback regulatory pathway that operates in DCs to limit immunopathology. Specifically, we found that lactate, produced by activated DCs and other immune cells, boosts NDUFA4L2 expression through a mechanism mediated by HIF-1. NDUFA4L2 limits the production of mitochondrial reactive oxygen species that activate XBP1-driven transcriptional modules in DCs involved in the control of pathogenic autoimmune T cells. Moreover, we engineered a probiotic that produces lactate and suppresses T-cell autoimmunity in the central nervous system via the activation of HIF-1/NDUFA4L2 signaling in DCs. In summary, we identified an immunometabolic pathway that regulates DC function, and developed a synthetic probiotic for its therapeutic activation.

immunology↗

bioRxiv2022

Temporal dynamics of the multi-omic response to endurance exercise training across tissues

Regular exercise promotes whole-body health and prevents disease, yet the underlying molecular mechanisms throughout a whole organism are incompletely understood. Here, the Molecular Transducers of Physical Activity Consortium (MoTrPAC) profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome, and immunome in whole blood, plasma, and 18 solid tissues in Rattus norvegicus over 8 weeks of endurance exercise training. The resulting data compendium encompasses 9466 assays across 19 tissues, 25 molecular platforms, and 4 training time points in young adult male and female rats. We identified thousands of shared and tissue- and sex-specific molecular alterations. Temporal multi-omic and multi-tissue analyses demonstrated distinct patterns of tissue remodeling, with widespread regulation of immune, metabolism, heat shock stress response, and mitochondrial pathways. These patterns provide biological insights into the adaptive responses to endurance training over time. For example, exercise training induced heart remodeling via altered activity of the Mef2 family of transcription factors and tyrosine kinases. Translational analyses revealed changes that are consistent with human endurance training data and negatively correlated with disease, including increased phospholipids and decreased triacylglycerols in the liver. Sex differences in training adaptation were widespread, including those in the brain, adrenal gland, lung, and adipose tissue. Integrative analyses generated novel hypotheses of disease relevance, including candidate mechanisms that link training adaptation to non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health, and tissue injury and recovery. The data and analysis results presented in this study will serve as valuable resources for the broader community and are provided in an easily accessible public repository (https://motrpac-data.org/). HighlightsO_LIMulti-tissue resource identifies 35,439 analytes regulated by endurance exercise training at 5% FDR across 211 combinations of tissues and molecular platforms. C_LIO_LIInterpretation of systemic and tissue-specific molecular adaptations produced hypotheses to help describe the health benefits induced by exercise. C_LIO_LIRobust sex-specific responses to endurance exercise training are observed across multiple organs at the molecular level. C_LIO_LIDeep multi-omic profiling of six tissues defines regulatory signals for tissue adaptation to endurance exercise training. C_LIO_LIAll data are available in a public repository, and processed data, analysis results, and code to reproduce major analyses are additionally available in convenient R packages. C_LI

molecular biology↗

bioRxiv2022

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotypes ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimers disease.

genomics↗

bioRxiv2022

Multiset correlation and factor analysis enables exploration of multi-omic data

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce Multi-set Correlation and Factor Analysis, an unsupervised integration method that enables fast inference of shared and private factors in multi-modal data. Applied to 614 ancestry-diverse participant samples across five omics types, MCFA infers a shared space that captures clinically relevant molecular processes.

genomics↗

bioRxiv2021

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n=183), Chinese (n=71), European (n=416), and Hispanic/Latino (n=301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ~50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837328. Author summaryGene regulation is a critical mechanism underlying complex traits. Transcriptome-wide association studies (TWAS) have helped elucidate potential mechanisms because each association connects a gene rather than a variant to the complex trait. Like genome-wide association studies (GWAS), most TWAS are still conducted exclusively in populations of European ancestry, which misses the opportunity to test the full spectrum of human genetic variation for associations with complex traits. Here, move beyond the transcriptome and because protein measurement assays are growing to allow interrogation of the proteome, we use data from TOPMed MESA to develop genetic predictors of protein abundance in diverse ancestry populations. We compare model-building strategies with the goal of providing the best resource for protein association discovery with available data. We demonstrate how these prediction models can be used to perform proteome-wide association studies (PWAS) in diverse populations. We show the most protein-trait associations were discovered, colocalized, and replicated in independent cohorts using proteome prediction model training populations with similar ancestries to individuals in the GWAS. We shared our protein prediction models and performance statistics publicly to facilitate future proteome mapping studies in diverse populations.

genomics↗

bioRxiv2020

In Silico Modeling of Metabolic State in Single Th17 Cells Reveals Novel Regulators of Inflammation and Autoimmunity

Cellular metabolism, a key regulator of immune responses, is difficult to study with current technologies in individual cells Here, we present Compass, an algorithm to characterize the metabolic state of cells based on single-cell RNA-Seq and flux balance analysis. We applied Compass to associate metabolic states with functional variability (pathogenic potential) amongst Th17 cells and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known differences between Th17 and Treg cells, as well as novel targets in amino-acid pathways, which we tested through targeted metabolic assays. Compass further predicted a particular glycolytic reaction (phosphoglycerate mutase -- PGAM) that promotes an anti-inflammatory Th17 phenotype, contrary to the common understanding of glycolysis as pro-inflammatory. We demonstrate that PGAM inhibition leads non-pathogenic Th17 cells to adopt a pro-inflammatory transcriptome and induce autoimmunity in vivo. Compass is broadly applicable for characterizing metabolic states of cells and relating metabolic heterogeneity to other cellular phenotypes.

genomics↗

bioRxiv2020

Metabolic and Epigenomic Regulation of Th17/Treg Balance by the Polyamine Pathway

Cellular metabolism can orchestrate immune cell function. We previously demonstrated that lipid biosynthesis represents one such gatekeeper to Th17 cell functional state. Utilizing Compass, a transcriptome-based algorithm for prediction of metabolic flux, we constructed a comprehensive metabolic circuitry for Th17 cell function and identified the polyamine pathway as a candidate metabolic node, the flux of which regulates the inflammatory function of T cells. Testing this prediction, we found that expression and activities of enzymes of the polyamine pathway were enhanced in pathogenic Th17 cells and suppressed in regulatory T cells. Perturbation of the polyamine pathway in Th17 cells suppressed canonical Th17 cell cytokines and promoted the expression of Foxp3, accompanied by dramatic shift in transcriptome and epigenome, transitioning Th17 cells into a Treg-like state. Genetic and chemical perturbation of the polyamine pathway resulted in attenuation of tissue inflammation in an autoimmune disease model of central nervous system, with changes in T cell effector phenotype.

immunology↗

bioRxiv2019

Causal role of L-glutamine in sickle cell disease painful crises: a Mendelian randomization analysis

In a recent clinical trial, the metabolite L-glutamine was shown to reduce painful crises in sickle cell disease (SCD) patients. To confirm this observation and identify other metabolites implicated in SCD clinical heterogeneity, we profiled 129 metabolites in the plasma of 705 SCD patients. We tested correlations between metabolite levels and six SCD-related complications (painful crises, cholecystectomy, retinopathy, leg ulcer, priapism, aseptic necrosis) or estimated glomerular filtration rate (eGFR), and used Mendelian randomization (MR) to assess causality. We found a causal relationship between L-glutamine levels and painful crises (N=1,278, odds ratio (OR) [95% confidence interval] = 0.68 [0.52 - 0.89], P=0.0048). In two smaller SCD cohorts (N=299 and 406), the protective effect of L-glutamine was observed (OR=0.82 [0.50-1.34]), although the MR result was not significant (P=0.44). We identified 66 significant correlations between the levels of other metabolites and SCD-related complications or eGFR. We tested these correlations for causality using MR analyses and found no significant causal relationship. The baseline levels of quinolinic acid was associated with prospectively ascertained survival in SCD patients, and this effect was dependent on eGFR. Metabolomics provide a promising approach to prioritize small molecules that may serve as biomarkers or drug targets in SCD.

genetics↗

bioRxiv2019

genuMet: distinguish genuine untargeted metabolic features without quality control samples

MotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.\n\nResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.\n\nAvailability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.\n\nContactLiming Liang: lliang@hsph.harvard.edu\n\nSupplementary informationSupplementary data are available at ....

bioinformatics↗

bioRxiv2017

Endospores and other lysis-resistant bacteria comprise a widely shared core community within the human microbiota

Endospore-formers in the human microbiota are well adapted for host-to-host transmission, and an emerging consensus points to their role in determining health and disease states in the gut. The human gut, more than any other environment, encourages the maintenance of endospore formation, with recent culture-based work suggesting that over 50% of genera in the microbiome carry genes attributed to this trait. However, there has been limited work on the ecological role of endospores and other stress-resistant cellular states in the human gut. In fact, there is no data to indicate whether organisms with the genetic potential to form endospores actually form endospores in situ and how sporulation varies across individuals and over time. Here, we applied a culture-independent protocol to enrich for endospores and other stress-resistant cells in human feces to identify variation in these states across people and within an individual over time. We see that cells with resistant states are more likely than those without to be shared among multiple individuals, which suggests that these resistant states are particularly adapted for cross-host dissemination. Furthermore, we use untargeted fecal metabolomics in 24 individuals and within a person over time to show that these organisms respond to shared environmental signals, and in particular, dietary fatty acids, that likely mediate colonization of recently disturbed human guts.

microbiology↗