Defining blood-induced microglia functions in neurodegeneration through multiomic profiling.

Blood protein extravasation through a disrupted blood-brain barrier and innate immune activation are hallmarks of neurological diseases and emerging therapeutic targets. However, how blood proteins polarize innate immune cells remains largely unknown. Here, we established an unbiased blood-innate immunity multiomic and genetic loss-of-function pipeline to define the transcriptome and global phosphoproteome of blood-induced innate immune polarization and its role in microglia neurotoxicity. Blood induced widespread microglial transcriptional changes, including changes involving oxidative stress and neurodegenerative genes. Comparative functional multiomics showed that blood proteins induce distinct receptor-mediated transcriptional programs in microglia and macrophages, such as redox, type I interferon and lymphocyte recruitment. Deletion of the blood coagulation factor fibrinogen largely reversed blood-induced microglia neurodegenerative signatures. Genetic elimination of the fibrinogen-binding motif to CD11b in Alzheimer's disease mice reduced microglial lipid metabolism and neurodegenerative signatures that were shared with autoimmune-driven neuroinflammation in multiple sclerosis mice. Our data provide an interactive resource for investigation of the immunology of blood proteins that could support therapeutic targeting of microglia activation by immune and vascular signals.

Proteogenomic Characterization of Endometrial Carcinoma.

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/ß-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.

Author Correction: Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation.

Oxidative stress is a central part of innate immune-induced neurodegeneration. However, the transcriptomic landscape of central nervous system (CNS) innate immune cells contributing to oxidative stress is unknown, and therapies to target their neurotoxic functions are not widely available. Here, we provide the oxidative stress innate immune cell atlas in neuroinflammatory disease and report the discovery of new druggable pathways. Transcriptional profiling of oxidative stress-producing CNS innate immune cells identified a core oxidative stress gene signature coupled to coagulation and glutathione-pathway genes shared between a microglia cluster and infiltrating macrophages. Tox-seq followed by a microglia high-throughput screen and oxidative stress gene network analysis identified the glutathione-regulating compound acivicin, with potent therapeutic effects that decrease oxidative stress and axonal damage in chronic and relapsing multiple sclerosis models. Thus, oxidative stress transcriptomics identified neurotoxic CNS innate immune populations and may enable discovery of selective neuroprotective strategies.

Fibrin-targeting immunotherapy protects against neuroinflammation and neurodegeneration.

Activation of innate immunity and deposition of blood-derived fibrin in the central nervous system (CNS) occur in autoimmune and neurodegenerative diseases, including multiple sclerosis (MS) and Alzheimer's disease (AD). However, the mechanisms that link disruption of the blood-brain barrier (BBB) to neurodegeneration are poorly understood, and exploration of fibrin as a therapeutic target has been limited by its beneficial clotting functions. Here we report the generation of monoclonal antibody 5B8, targeted against the cryptic fibrin epitope γ377-395, to selectively inhibit fibrin-induced inflammation and oxidative stress without interfering with clotting. 5B8 suppressed fibrin-induced nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activation and the expression of proinflammatory genes. In animal models of MS and AD, 5B8 entered the CNS and bound to parenchymal fibrin, and its therapeutic administration reduced the activation of innate immunity and neurodegeneration. Thus, fibrin-targeting immunotherapy inhibited autoimmunity- and amyloid-driven neurotoxicity and might have clinical benefit without globally suppressing innate immunity or interfering with coagulation in diverse neurological diseases.

From scientific discovery to cures: bright stars within a galaxy.

We propose that data mining and network analysis utilizing public databases can identify and quantify relationships between scientific discoveries and major advances in medicine (cures). Further development of such approaches could help to increase public understanding and governmental support for life science research and could enhance decision making in the quest for cures.

Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage.

Heart development is exquisitely sensitive to the precise temporal regulation of thousands of genes that govern developmental decisions during differentiation. However, we currently lack a detailed understanding of how chromatin and gene expression patterns are coordinated during developmental transitions in the cardiac lineage. Here, we interrogated the transcriptome and several histone modifications across the genome during defined stages of cardiac differentiation. We find distinct chromatin patterns that are coordinated with stage-specific expression of functionally related genes, including many human disease-associated genes. Moreover, we discover a novel preactivation chromatin pattern at the promoters of genes associated with heart development and cardiac function. We further identify stage-specific distal enhancer elements and find enriched DNA binding motifs within these regions that predict sets of transcription factors that orchestrate cardiac differentiation. Together, these findings form a basis for understanding developmentally regulated chromatin transitions during lineage commitment and the molecular etiology of congenital heart disease.

WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics.

Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org.

WikiPathways 2024: next generation pathway database.

WikiPathways (wikipathways.org) is an open-source biological pathway database. Collaboration and open science are pivotal to the success of WikiPathways. Here we highlight the continuing efforts supporting WikiPathways, content growth and collaboration among pathway researchers. As an evolving database, there is a growing need for WikiPathways to address and overcome technical challenges. In this direction, WikiPathways has undergone major restructuring, enabling a renewed approach for sharing and curating pathway knowledge, thus providing stability for the future of community pathway curation. The website has been redesigned to improve and enhance user experience. This next generation of WikiPathways continues to support existing features while improving maintainability of the database and facilitating community input by providing new functionality and leveraging automation.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Incident heart failure in chronic kidney disease: proteomics informs biology and risk stratification.

BACKGROUND AND AIMS: Incident heart failure (HF) among individuals with chronic kidney disease (CKD) incurs hospitalizations that burden patients and health care systems. There are few preventative therapies, and the Pooled Cohort equations to Prevent Heart Failure (PCP-HF) perform poorly in the setting of CKD. New drug targets and better risk stratification are urgently needed. METHODS: In this analysis of incident HF, SomaScan V4.0 (4638 proteins) was analysed in 2906 participants of the Chronic Renal Insufficiency Cohort (CRIC) with validation in the Atherosclerosis Risk in Communities (ARIC) study. The primary outcome was 14-year incident HF (390 events); secondary outcomes included 4-year HF (183 events), HF with reduced ejection fraction (137 events), and HF with preserved ejection fraction (165 events). Mendelian randomization and Gene Ontology were applied to examine causality and pathways. The performance of novel multi-protein risk models was compared to the PCP-HF risk score. RESULTS: Over 200 proteins were associated with incident HF after adjustment for estimated glomerular filtration rate at P < 1 × 10-5. After adjustment for covariates including N-terminal pro-B-type natriuretic peptide, 17 proteins remained associated at P < 1 × 10-5. Mendelian randomization associations were found for six proteins, of which four are druggable targets: FCG2B, IGFBP3, CAH6, and ASGR1. For the primary outcome, the C-statistic (95% confidence interval [CI]) for the 48-protein model in CRIC was 0.790 (0.735, 0.844) vs. 0.703 (0.644, 0.762) for the PCP-HF model (P = .001). C-statistic (95% CI) for the protein model in ARIC was 0.747 (0.707, 0.787). CONCLUSIONS: Large-scale proteomics reveal novel circulating protein biomarkers and potential mediators of HF in CKD. Proteomic risk models improve upon the PCP-HF risk score in this population.

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.

SUMMARY: Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time. AVAILABILITY AND IMPLEMENTATION: More information can be found at https://explorer.biothings.io and code is available at https://github.com/biothings/biothings_explorer.

The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information.

MOTIVATION: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. RESULTS: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts. AVAILABILITY AND IMPLEMENTATION: The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Using published pathway figures in enrichment analysis and machine learning.

Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in comparison with popular pathway databases through an assessment of disease coverage and analytical applications. In addition to common pathway analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses.

Mir-30d Regulates Cardiac Remodeling by Intracellular and Paracrine Signaling.

RATIONALE: Previous translational studies implicate plasma extracellular microRNA-30d (miR-30d) as a biomarker in left ventricular remodeling and clinical outcome in heart failure (HF) patients, although precise mechanisms remain obscure. OBJECTIVE: To investigate the mechanism of miR-30d-mediated cardioprotection in HF. METHODS AND RESULTS: In rat and mouse models of ischemic HF, we show that miR-30d gain of function (genetic, lentivirus, or agomiR-mediated) improves cardiac function, decreases myocardial fibrosis, and attenuates cardiomyocyte (CM) apoptosis. Genetic or locked nucleic acid-based knock-down of miR-30d expression potentiates pathological left ventricular remodeling, with increased dysfunction, fibrosis, and cardiomyocyte death. RNA sequencing of in vitro miR-30d gain and loss of function, together with bioinformatic prediction and experimental validation in cardiac myocytes and fibroblasts, were used to identify and validate direct targets of miR-30d. miR-30d expression is selectively enriched in cardiomyocytes, induced by hypoxic stress and is acutely protective, targeting MAP4K4 (mitogen-associate protein kinase 4) to ameliorate apoptosis. Moreover, miR-30d is secreted primarily in extracellular vesicles by cardiomyocytes and inhibits fibroblast proliferation and activation by directly targeting integrin α5 in the acute phase via paracrine signaling to cardiac fibroblasts. In the chronic phase of ischemic remodeling, lower expression of miR-30d in the heart and plasma extracellular vesicles is associated with adverse remodeling in rodent models and human subjects and is linked to whole-blood expression of genes implicated in fibrosis and inflammation, consistent with observations in model systems. CONCLUSIONS: These findings provide the mechanistic underpinning for the cardioprotective association of miR-30d in human HF. More broadly, our findings support an emerging paradigm involving intercellular communication of extracellular vesicle-contained miRNAs (microRNAs) to transregulate distinct signaling pathways across cell types. Functionally validated RNA biomarkers and their signaling networks may warrant further investigation as novel therapeutic targets in HF.

WikiPathways: connecting communities.

WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

The Dynamic Platelet Transcriptome in Obesity and Weight Loss.

OBJECTIVE: Adiposity is associated with oxidative stress, inflammation, and glucose intolerance. Previous data suggest that platelet gene expression is associated with key cardiometabolic phenotypes, including body mass index but stable in healthy individuals over time. However, modulation of gene expression in platelets in response to metabolic shifts (eg, weight reduction) is unknown and may be important to defining mechanism. Approach and Results: Platelet RNA sequencing and aggregation were performed from 21 individuals with massive weight loss (>45 kg) following bariatric surgery. Based on RNA sequencing data, we measured the expression of 67 genes from isolated platelet RNA using high-throughput quantitative reverse transcription quantitative PCR in 1864 FHS (Framingham Heart Study) participants. Many transcripts not previously studied in platelets were differentially expressed with bariatric surgical weight loss, appeared specific to platelets (eg, not differentially expressed in leukocytes), and were enriched for a nonalcoholic fatty liver disease pathway. Platelet aggregation studies did not detect alteration in platelet function after significant weight loss. Linear regression models demonstrated several platelet genes modestly associated with cross-sectional cardiometabolic phenotypes, including body mass index. There were no associations between studied transcripts and incident diabetes or cardiovascular end points. CONCLUSIONS: In summary, while there is no change in platelet aggregation function after significant weight loss, the human platelet experiences a dramatic transcriptional shift that implicates pathways potentially relevant to improved cardiometabolic risk postweight loss (eg, nonalcoholic fatty liver disease). Further studies are needed to determine the mechanistic importance of these observations.

Comprehensive Metabolic Phenotyping Refines Cardiovascular Risk in Young Adults.

BACKGROUND: Whereas cardiovascular disease (CVD) metrics define risk in individuals >40 years of age, the earliest lesions of CVD appear well before this age. Despite the role of metabolism in CVD antecedents, studies in younger, biracial populations to define precise metabolic risk phenotypes are lacking. METHODS: We studied 2330 White and Black young adults (mean age, 32 years; 45% Black) in the CARDIA study (Coronary Artery Risk Development in Young Adults) to identify metabolite profiles associated with an adverse CVD phenome (myocardial structure/function, fitness, vascular calcification), mechanisms, and outcomes over 2 decades. Statistical learning methods (elastic nets/principal components analysis) and Cox regression generated parsimonious, metabolite-based risk scores validated in >1800 individuals in the Framingham Heart Study. RESULTS: In the CARDIA study, metabolite profiles quantified in early adulthood were associated with subclinical CVD development over 20 years, specifying known and novel pathways of CVD (eg, transcriptional regulation, brain-derived neurotrophic factor, nitric oxide, renin-angiotensin). We found 2 multiparametric, metabolite-based scores linked independently to vascular and myocardial health, with metabolites included in each score specifying microbial metabolism, hepatic steatosis, oxidative stress, nitric oxide modulation, and collagen metabolism. The metabolite-based vascular scores were lower in men, and myocardial scores were lower in Black participants. Over a nearly 25-year median follow-up in CARDIA, the metabolite-based vascular score (hazard ratio, 0.68 per SD [95% CI, 0.50-0.92]; P=0.01) and myocardial score (hazard ratio, 0.60 per SD [95% CI, 0.45-0.80]; P=0.0005) in the third and fourth decades of life were associated with clinical CVD with a synergistic association with outcome (Pinteraction=0.009). We replicated these findings in 1898 individuals in the Framingham Heart Study over 2 decades, with a similar association with outcome (including interaction), reclassification, and discrimination. In the Framingham Heart Study, the metabolite scores exhibited an age interaction (P=0.0004 for a combined myocardial-vascular score with incident CVD), such that young adults with poorer metabolite-based health scores had highest hazard of future CVD. CONCLUSIONS: Metabolic signatures of myocardial and vascular health in young adulthood specify known/novel pathways of metabolic dysfunction relevant to CVD, associated with outcome in 2 independent cohorts. Efforts to include precision measures of metabolic health in risk stratification to interrupt CVD at its earliest stage are warranted.

Metabolic Architecture of Acute Exercise Response in Middle-Aged Adults in the Community.

BACKGROUND: Whereas regular exercise is associated with lower risk of cardiovascular disease and mortality, mechanisms of exercise-mediated health benefits remain less clear. We used metabolite profiling before and after acute exercise to delineate the metabolic architecture of exercise response patterns in humans. METHODS: Cardiopulmonary exercise testing and metabolite profiling was performed on Framingham Heart Study participants (age 53±8 years, 63% women) with blood drawn at rest (n=471) and at peak exercise (n=411). RESULTS: We observed changes in circulating levels for 502 of 588 measured metabolites from rest to peak exercise (exercise duration 11.9±2.1 minutes) at a 5% false discovery rate. Changes included reductions in metabolites implicated in insulin resistance (glutamate, -29%; P=1.5×10-55; dimethylguanidino valeric acid [DMGV], -18%; P=5.8×10-18) and increases in metabolites associated with lipolysis (1-methylnicotinamide, +33%; P=6.1×10-67), nitric oxide bioavailability (arginine/ornithine + citrulline, +29%; P=2.8×10-169), and adipose browning (12,13-dihydroxy-9Z-octadecenoic acid +26%; P=7.4×10-38), among other pathways relevant to cardiometabolic risk. We assayed 177 metabolites in a separate Framingham Heart Study replication sample (n=783, age 54±8 years, 51% women) and observed concordant changes in 164 metabolites (92.6%) at 5% false discovery rate. Exercise-induced metabolite changes were variably related to the amount of exercise performed (peak workload), sex, and body mass index. There was attenuation of favorable excursions in some metabolites in individuals with higher body mass index and greater excursions in select cardioprotective metabolites in women despite less exercise performed. Distinct preexercise metabolite levels were associated with different physiologic dimensions of fitness (eg, ventilatory efficiency, exercise blood pressure, peak Vo2). We identified 4 metabolite signatures of exercise response patterns that were then analyzed in a separate cohort (Framingham Offspring Study; n=2045, age 55±10 years, 51% women), 2 of which were associated with overall mortality over median follow-up of 23.1 years (P≤0.003 for both). CONCLUSIONS: In a large sample of community-dwelling individuals, acute exercise elicits widespread changes in the circulating metabolome. Metabolic changes identify pathways central to cardiometabolic health, cardiovascular disease, and long-term outcome. These findings provide a detailed map of the metabolic response to acute exercise in humans and identify potential mechanisms responsible for the beneficial cardiometabolic effects of exercise for future study.

Shared Mechanisms Govern HIV Transcriptional Suppression in Circulating CD103+ and Gut CD4+ T Cells.

Latent HIV infection is the main barrier to cure, and most HIV-infected cells reside in the gut, where distinct but unknown mechanisms may promote viral latency. Transforming growth factor ß (TGF-ß), which induces the expression of CD103 on tissue-resident memory T cells, has been implicated in HIV latency. Using CD103 as a surrogate marker to identify cells that have undergone TGF-ß signaling, we compared the HIV RNA/DNA contents and cellular transcriptomes of CD103+ and CD103- CD4 T cells from the blood and rectum of HIV-negative (HIV-) and antiretroviral therapy (ART)-suppressed HIV-positive (HIV+) individuals. Like gut CD4+ T cells, circulating CD103+ cells harbored more HIV DNA than did CD103- cells but transcribed less HIV RNA per provirus. Circulating CD103+ cells also shared a gene expression profile that is closer to that of gut CD4 T cells than to that of circulating CD103- cells, with significantly lower expression levels of ribosomal proteins and transcriptional and translational pathways associated with HIV expression but higher expression levels of a subset of genes implicated in suppressing HIV transcription. These findings suggest that blood CD103+ CD4 T cells can serve as a model to study the molecular mechanisms of HIV latency in the gut and reveal new cellular factors that may contribute to HIV latency.IMPORTANCE The ability of HIV to establish a reversibly silent, "latent" infection is widely regarded as the main barrier to curing HIV. Most HIV-infected cells reside in tissues such as the gut, but it is unclear what mechanisms maintain HIV latency in the blood or gut. We found that circulating CD103+ CD4+ T cells are enriched for HIV-infected cells in a latent-like state. Using RNA sequencing (RNA-seq), we found that CD103+ T cells share a cellular transcriptome that more closely resembles that of CD4+ T cells from the gut, suggesting that they are homing to or from the gut. We also identified the cellular genes whose expression distinguishes gut CD4+ or circulating CD103+ T cells from circulating CD103- T cells, including some genes that have been implicated in HIV expression. These genes may contribute to latent HIV infection in the gut and may serve as new targets for therapies aimed at curing HIV.