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.

In vivo protein turnover rates in varying oxygen tensions nominate MYBBP1A as a mediator of the hyperoxia response

Oxygen deprivation and excess are both toxic. Thus, the body's ability to adapt to varying oxygen tensions is critical for survival. While the hypoxia transcriptional response has been well studied, the post-translational effects of oxygen have been underexplored. In this study, we systematically investigate protein turnover rates in mouse heart, lung, and brain under different inhaled oxygen tensions. We find that the lung proteome is the most responsive to varying oxygen tensions. In particular, several extracellular matrix (ECM) proteins are stabilized in the lung under both hypoxia and hyperoxia. Furthermore, we show that complex 1 of the electron transport chain is destabilized in hyperoxia, in accordance with the exacerbation of associated disease models by hyperoxia and rescue by hypoxia. Moreover, we nominate MYBBP1A as a hyperoxia transcriptional regulator, particularly in the context of rRNA homeostasis. Overall, our study highlights the importance of varying oxygen tensions on protein turnover rates and identifies tissue-specific mediators of oxygen-dependent responses.

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.

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.

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.

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.

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

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, BioThing Explorer is distributed as a lightweight application that dynamically retrieves information at query time. 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.

Introducing R as a smart version of calculators enables beginners to explore it on their own

Rapid technological advances in the past decades have enabled molecular biologists to generate large-scale and complex data with affordable resource investments, or obtain such data from public repositories. Yet, many graduate students, postdoctoral scholars, and senior researchers in the biosciences find themselves ill-equipped to analyze large-scale data. Global surveys have revealed that active researchers prefer short training workshops to fill their skill gaps. In this article, we focus on the challenge of delivering a short data analysis workshop to absolute beginners in computer programming. We propose that introducing R or other programming languages for data analysis as smart versions of calculators can help lower the communication barrier with absolute beginners. We describe this comparison with a few analogies and hope that other instructors will find them useful. We utilized these in our four-hour long training workshops involving participatory live coding, which we delivered in person and via videoconferencing. Anecdotal evidence suggests that our exposition made R programming seem easy and enabled beginners to explore it on their own.

SARS-CoV-2 spike protein induces abnormal inflammatory blood clots neutralized by fibrin immunotherapy

Blood clots are a central feature of coronavirus disease-2019 (COVID-19) and can culminate in pulmonary embolism, stroke, and sudden death. However, it is not known how abnormal blood clots form in COVID-19 or why they occur even in asymptomatic and convalescent patients. Here we report that the Spike protein from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to the blood coagulation factor fibrinogen and induces structurally abnormal blood clots with heightened proinflammatory activity. SARS-CoV-2 Spike virions enhanced fibrin-mediated microglia activation and induced fibrinogen-dependent lung pathology. COVID-19 patients had fibrin autoantibodies that persisted long after acute infection. Monoclonal antibody 5B8, targeting the cryptic inflammatory fibrin epitope, inhibited thromboinflammation. Our results reveal a procoagulant role for the SARS-CoV-2 Spike and propose fibrin-targeting interventions as a treatment for thromboinflammation in COVID-19.

One-Sentence Summary

SARS-CoV-2 spike induces structurally abnormal blood clots and thromboinflammation neutralized by a fibrin-targeting antibody.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

scNetViz: from single cells to networks using Cytoscape

Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz-  a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

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.

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.

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 β (TGF-β), 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-β 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.

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.

Pathway information extracted from 25 years of pathway figures

Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figures published between 1995 and 2019 and extracted 1,112,551 instances of human genes, comprising 13,464 unique NCBI genes, participating in a wide variety of biological processes. This collection represents an order of magnitude more genes than found in the text of the same papers, and thousands of genes missing from other pathway databases, thus presenting new opportunities for discovery and research.

PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data

The authors present pathwayPCA, an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic-net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple covariates and/or interaction effects. It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets. In addition, several case studies are provided to illustrate pathwayPCA analysis with gene selection, estimating, and visualizing sample-specific pathway activities, identifying sex-specific pathway effects in kidney cancer, and building integrative models for predicting patient prognosis. pathwayPCA is an open-source R package, freely available through the Bioconductor repository. pathwayPCA is expected to be a useful tool for empowering the wider scientific community to analyze and interpret the wealth of available proteomics data, along with other types of molecular data recently made available by Clinical Proteomic Tumor Analysis Consortium and other large consortiums.

Molecular Signature of Multisystem Cardiometabolic Stress and Its Association With Prognosis

Importance

Cardiometabolic disease is responsible for decreased longevity and poorer cardiovascular outcomes in the modern era. Metabolite profiling provides a specific measure of global metabolic function to examine specific metabolic mechanisms and pathways of cardiometabolic disease beyond its clinical definitions.

Objectives

To define a molecular basis for cardiometabolic stress and assess its association with cardiovascular prognosis.

Design, Setting, and Participants

A prospective observational cohort study was conducted in a population-based setting across 2 geographically distinct centers (Boston Puerto Rican Health Study [BPRHS], an ongoing study of individuals enrolled between June 1, 2004, and October 31, 2009; and Atherosclerosis Risk in Communities [ARIC] study, whose participants were originally sampled between November 24, 1986, and February 10, 1990, and followed up through December 31, 2017). Participants in the BPRHS were 668 Puerto Rican individuals with metabolite profiling living in Massachusetts, and participants in the ARIC study were 2152 individuals with metabolite profiling and long-term follow-up for mortality and cardiovascular outcomes. Statistical analysis was performed from October 1, 2018, to March 13, 2020.

Exposure

The primary exposure was metabolite profiles across both cohorts.

Main Outcomes and Measures

Outcomes included associations with multisystem cardiometabolic stress and all-cause mortality and incident coronary heart disease (in the ARIC study).

Results

Participants in the BPRHS (N = 668; 491 women; mean [SD] age, 57.0 [7.4] years; mean [SD] body mass index [calculated as weight in kilograms divided by height in meters squared], 32.0 [6.5]) had higher prevalent cardiometabolic risk relative to those in the ARIC study (N = 2152; 599 African American individuals; 1213 women; mean [SD] age, 54.3 [5.7] years; mean [SD] body mass index, 28.0 [5.5]). Multisystem cardiometabolic stress was defined for 668 Puerto Rican individuals in the BPRHS as a multidimensional composite of hypothalamic-adrenal axis activity, sympathetic activation, blood pressure, proatherogenic dyslipidemia, insulin resistance, visceral adiposity, and inflammation. A total of 260 metabolites associated with cardiometabolic stress were identified in the BPRHS, involving known and novel pathways of cardiometabolic disease (eg, amino acid metabolism, oxidative stress, and inflammation). A parsimonious metabolite-based score associated with cardiometabolic stress in the BPRHS was subsequently created; this score was applied to shared metabolites in the ARIC study, demonstrating significant associations with coronary heart disease and all-cause mortality after multivariable adjustment at a 30-year horizon (per SD increase in metabolomic score: hazard ratio, 1.14; 95% CI, 1.00-1.31; P = .045 for coronary heart disease; and hazard ratio, 1.15; 95% CI, 1.07-1.24; P < .001 for all-cause mortality).

Conclusions and Relevance

Metabolites associated with cardiometabolic stress identified known and novel pathways of cardiometabolic disease in high-risk, community-based cohorts and were associated with coronary heart disease and survival at a 30-year time horizon. These results underscore the shared molecular pathophysiology of metabolic dysfunction, cardiovascular disease, and longevity and suggest pathways for modification to improve prognosis across all linked conditions.

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.

COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

Researchers around the world join forces to reconstruct the molecular processes of the virus-host interactions aiming to combat the cause of the ongoing pandemic.

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 the 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 (Tox-seq) 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 (HTS) and oxidative stress gene network analysis, identified the glutathione regulating compound acivicin with potent therapeutic effects decreasing oxidative stress and axonal damage in chronic and relapsing multiple sclerosis (MS) models. Thus, oxidative stress transcriptomics identified neurotoxic CNS innate immune populations and may enable the discovery of selective neuroprotective strategies.

Wikidata as a knowledge graph for the life sciences

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.

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/β-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.

Proteins Altered by Surgical Weight Loss Highlight Biomarkers of Insulin Resistance in the Community

Objective- Mechanisms of early and late improvements in cardiovascular risk after bariatric surgery and applicability to larger, at-risk populations remain unclear. We aimed to identify proteins altered after bariatric surgery and their relations to metabolic syndrome and diabetes mellitus. Approach and Results- We identified 19 proteins altered in 32 nonfasting plasma samples from a study of patients undergoing bariatric surgery who were evaluated preoperatively (visit 1) versus both early (visit 2; ≈3 months) and late (visit 3; ≈12 months) postoperative follow-up using predefined protein panels (Olink). Using in silico methods and publicly available gene expression repositories, we found that genes encoding 8 out of 19 proteins had highest expression in liver relative to other assayed tissues, with the top biological and disease processes, including major obesity-related vascular diseases. Of 19 candidate proteins in the surgical cohort, 6 were previously measured in >3000 FHS (Framingham Heart Study) participants (IGFBP [insulin-like growth factor binding protein]-1, IGFBP-2, P-selectin, CD163, LDL (low-density lipoprotein)-receptor, and PAI [plasminogen activator inhibitor]-1). A higher concentration of IGFBP-2 at baseline was associated with a lower risk of incident metabolic syndrome (odds ratio per log-normal unit, 0.45; 95% CI, 0.32-0.64; P=7.7×10^-6) and diabetes mellitus (odds ratio, 0.63; 95% CI, 0.49-0.79; P=0.0001) after multivariable adjustment. Conclusions- Using a directed protein quantification platform (Olink), we identified known and novel proteins altered after surgical weight loss, including IGFBP-2. Future efforts in well-defined obesity intervention settings may further define and validate novel targets for the prevention of vascular disease in obesity.

Circulating miRNAs and Risk of Sudden Death in Patients With Coronary Heart Disease

OBJECTIVES

This study evaluated whether plasma miRNAs were specifically associated with sudden cardiac and/or arrhythmic death (SCD) in a cohort of patients with coronary heart disease (CHD), most of whom were without primary prevention implantable cardioverter-defibrillators.

BACKGROUND

Novel biomarkers for sudden death risk stratification are needed in patients with CHD to more precisely target preventive therapies, such as implantable cardioverter-defibrillators. miRNAs have been implicated in regulating inflammation and cardiac fibrosis in cells, and plasma miRNAs have been shown to predict cardiovascular death in patients with CHD.

METHODS

We performed a nested case control study within a multicenter cohort of 5,956 patients with CHD followed prospectively for SCD. Plasma levels of 18 candidate miRNAs previously associated with cardiac remodeling were measured in 129 SCD cases and 258 control subjects matched on age, sex, race, and left ventricular ejection fraction.

RESULTS

miR-150-5p, miR-29a-3p, and miR-30a-5p were associated with increased SCD risk (odds ratios and 95% confidence intervals: 2.03 [1.12 to 3.67]; p = 0.02; 1.93 [1.07 to 3.50]; p = 0.02; 0.55 [0.31 to 0.97]; p = 0.04, respectively, for third vs. first tertile miRNA level). Unfavorable levels of all 3 miRNAs was associated with a 4.8-fold increased SCD risk (1.59 to 14.51; p = 0.006). A bioinformatics-based approach predicted miR-150-5p, miR-29a-3p, and miR-30a-5p to be involved in apoptosis, fibrosis, and inflammation.

CONCLUSIONS

These findings suggest that plasma miRNAs may regulate pathways important for remodeling and may be useful in identifying patients with CHD at increased risk of SCD.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

Associations of Circulating Extracellular RNAs With Myocardial Remodeling and Heart Failure

Importance

Mortality is high among patients heart failure (HF) who are receiving treatment, and therefore identifying new pathways rooted in preclinical cardiac remodeling phenotypes may afford novel biomarkers and therapeutic avenues. Circulating extracellular RNAs (ex-RNAs) are an emerging class of biomarkers with target-organ epigenetic effects relevant to myocardial biology, although large human investigations remain limited.

Objective

To measure the association of highly expressed circulating ex-RNAs with left ventricular remodeling and incident HF in a community-based cohort.

Design, Setting, and Participants

This is a prospective observational cohort study of individuals who were included in the eighth examination of the Framingham Offspring Cohort (2005-2008). Collected data include measurements of the left ventricle via electrocardiography, determination of circulating ex-RNAs in plasma, and incidence of heart failure. Data analysis was completed from December 2016 to June 2018.

Exposures

A total of 398 circulating ex-RNA molecules in plasma were measured by reverse transcription polymerase chain reaction; disease ontology analysis was also performed.

Main Outcomes and Measures

Echocardiographic indices of left ventricular (LV) remodeling and incident heart failure.

Results

A total of 2763 participants of the Framingham Heart Study with measured ex-RNAs (mean [SD] age, 66.3 [9.0] years; 1499 [54.3%] female) were included in this study. Of this sample, 2429 to 2432 individuals had echocardiographic measures recorded (depending on the measurement). A total of 2681 individuals had HF status determined, of whom 116 (4.3%) experienced HF (median [interquartile range] follow-up, 7.7 [6.6-8.6] years). We identified 12 ex-RNAs associated with LV mass and at least 1 other echocardiographic phenotype (LV end-diastolic volume or left atrial dimension). Of these 12 ex-RNAs, 3 micro RNAs (miR-17, miR-20a, and miR-106b) were associated with a 15% reduction in long-term incident HF per 2-fold increase in circulating level during the follow-up period, after adjustments for age, sex, established HF risk factors, and prevalent or interim myocardial infarction. These 3 RNAs shared sequence homology and targeted a shared group of messenger RNAs that specified pathways relevant to HF (eg, transforming growth factor-β signaling, growth/cell cycle, and apoptosis), and shared a disease association with hypertension in disease ontology analysis.

Conclusions and Relevance

This study identifies a group of circulating, noncoding RNAs associated with echocardiographic phenotypes, long-term incident HF, and pathways relevant to myocardial remodeling in a large community-based sample. Further investigations into the functional biology of these ex-RNAs are warranted for surveillance for HF prevention.

Cytoscape Automation: empowering workflow-based network analysis

Cytoscape is one of the most successful network biology analysis and visualization tools, but because of its interactive nature, its role in creating reproducible, scalable, and novel workflows has been limited. We describe Cytoscape Automation (CA), which marries Cytoscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio. We expose over 270 Cytoscape core functions and 34 Cytoscape apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation. Independent projects to create and publish Python/R native CA interface libraries have reached an advanced stage, and a number of automation workflows are already published.

WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.

MicroRNAs Associated With Reverse Left Ventricular Remodeling in Humans Identify Pathways of Heart Failure Progression

BACKGROUND

Plasma extracellular RNAs have recently garnered interest as biomarkers in heart failure (HF). Most studies in HF focus on single extracellular RNAs related to phenotypes and outcomes, and few describe their functional roles. We hypothesized that clusters of plasma microRNAs (miRNAs) associated with left ventricular (LV) remodeling in human HF would identify novel subsets of genes involved in HF in animal models.

METHODS AND RESULTS

We prospectively measured circulating miRNAs in 64 patients with systolic HF (mean age, 64.8 years; 91% men; median LV ejection fraction, 26%) with serial echocardiography (10 months apart) during medical therapy. We defined LV reverse remodeling as a 15% reduction in LV end-systolic volume index. Using principal components analysis, we identified a component associated with LV reverse remodeling (odds ratio=3.99; P=0.01) that provided risk discrimination for LV reverse remodeling superior to a clinical model (C statistic, 0.58 for a clinical model versus 0.71 for RNA-based model). Using network bioinformatics, we uncovered genes not previously widely described in HF regulated simultaneously by >2 miRNAs. We observed increased myocardial expression of these miRNAs during HF development in animals, with downregulation of target gene expression, suggesting coordinate miRNA-mRNA regulation. Target mRNAs were involved in autophagy, metabolism, and inflammation.

CONCLUSIONS

Plasma miRNAs associated with LV reverse remodeling in humans are dysregulated in animal HF and target clusters of genes involved in mechanisms implicated in HF. A translational approach integrating human HF, bioinformatics, and model systems may uncover novel pathways involved in HF.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov. Unique identifier: NCT00351390.

exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and the fact that some dedicated methods are available only as web servers with limited cell type gene expression signatures. Methods: In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Results: Our results show that, in general, all four methods show a high performance in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.94, sd = 0.036), whereas precision-recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). Conclusions: CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

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, mechanisms linking blood-brain barrier (BBB) disruption with 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 γ_377–395 to selectively inhibit fibrin-induced inflammation and oxidative stress without interfering with clotting. 5B8 suppressed fibrin-induced nicotinamide adenine dinucleotide phosphate (NADPH) oxidase and proinflammatory gene expression. In animal models of MS and AD, 5B8 entered the CNS and bound to parenchymal fibrin, and its therapeutic administration reduced innate immune activation and neurodegeneration. Thus, fibrin-targeting immunotherapy inhibits autoimmune- and amyloid-driven neurotoxicity and may have clinical benefit without globally suppressing innate immunity or interfering with coagulation in diverse neurological diseases.

The Cytoscape Automation app article collection

Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape's CyREST feature has allowed researchers to write workflows that call basic Cytoscape functions, but provides no access to its high value app-based functions. With Cytoscape Automation, workflows can now call apps that have been upgraded to expose their functionality. This article collection is a resource to assist readers in quickly and economically leveraging such apps in reproducible workflows that scale independently to large data sets and production runs.

Identifier Mapping in Cytoscape

Identifier Mapping, the association of terms across disparate taxonomies and databases, is a common hurdle in bioinformatics workflows. The idmapper app for Cytoscape simplifies identifier mapping for genes and proteins in the context of common biological networks. This app provides a unified interface to different identifier resources accessible through a right-click on the table's column header. It also provides an OSGi programming interface via Cytoscape Commands and CyREST that can be utilized for identifier mapping in scripts and other Cytoscape apps, and supports integrated Swagger documentation.

Research synergy and drug development: Bright stars in neighboring constellations

Drug discovery and subsequent availability of a new breakthrough therapeutic or 'cure' is a compelling example of societal benefit from research advances. These advances are invariably collaborative, involving the contributions of many scientists to a discovery network in which theory and experiment are built upon. To document and understand such scientific advances, data mining of public and commercial data sources coupled with network analysis can be used as a digital methodology to assemble and analyze component events in the history of a therapeutic. This methodology is extensible beyond the history of therapeutics and its use more generally supports (i) efficiency in exploring the scientific history of a research advance (ii) documenting and understanding collaboration (iii) portfolio analysis, planning and optimization (iv) communication of the societal value of research. Building upon prior art, we have conducted a case study of five anti-cancer therapeutics to identify the collaborations that resulted in the successful development of these therapeutics both within and across their respective networks. We have linked the work of over 235,000 authors in roughly 106,000 scientific publications that capture the research crucial for the development of these five therapeutics. Applying retrospective citation discovery, we have identified a core set of publications cited in the networks of all five therapeutics and additional intersections in combinations of networks. We have enriched the content of these networks by annotating them with information on research awards from the US National Institutes of Health (NIH). Lastly, we have mapped these awards to their cognate peer review panels, identifying another layer of collaborative scientific activity that influenced the research represented in these networks.

Reactome from a WikiPathways Perspective

Reactome and WikiPathways are two of the most popular freely available databases for biological pathways. Reactome pathways are centrally curated with periodic input from selected domain experts. WikiPathways is a community-based platform where pathways are created and continually curated by any interested party. The nascent collaboration between WikiPathways and Reactome illustrates the mutual benefits of combining these two approaches. We created a format converter that converts Reactome pathways to the GPML format used in WikiPathways. In addition, we developed the ComplexViz plugin for PathVisio which simplifies looking up complex components. The plugin can also score the complexes on a pathway based on a user defined criterion. This score can then be visualized on the complex nodes using the visualization options provided by the plugin. Using the merged collection of curated and converted Reactome pathways, we demonstrate improved pathway coverage of relevant biological processes for the analysis of a previously described polycystic ovary syndrome gene expression dataset. Additionally, this conversion allows researchers to visualize their data on Reactome pathways using PathVisio's advanced data visualization functionalities. WikiPathways benefits from the dedicated focus and attention provided to the content converted from Reactome and the wealth of semantic information about interactions. Reactome in turn benefits from the continuous community curation available on WikiPathways. The research community at large benefits from the availability of a larger set of pathways for analysis in PathVisio and Cytoscape. The pathway statistics results obtained from PathVisio are significantly better when using a larger set of candidate pathways for analysis. The conversion serves as a general model for integration of multiple pathway resources developed using different approaches.

Longer genotypically-estimated leukocyte telomere length is associated with increased adult glioma risk

Telomere maintenance has emerged as an important molecular feature with impacts on adult glioma susceptibility and prognosis. Whether longer or shorter leukocyte telomere length (LTL) is associated with glioma risk remains elusive and is often confounded by the effects of age and patient treatment. We sought to determine if genotypically-estimated LTL is associated with glioma risk and if inherited single nucleotide polymorphisms (SNPs) that are associated with LTL are glioma risk factors. Using a Mendelian randomization approach, we assessed differences in genotypically-estimated relative LTL in two independent glioma case-control datasets from the UCSF Adult Glioma Study (652 patients and 3735 controls) and The Cancer Genome Atlas (478 non-overlapping patients and 2559 controls). LTL estimates were based on a weighted linear combination of subject genotype at eight SNPs, previously associated with LTL in the ENGAGE Consortium Telomere Project. Mean estimated LTL was 31bp (5.7%) longer in glioma patients than controls in discovery analyses (P = 7.82x10-8) and 27bp (5.0%) longer in glioma patients than controls in replication analyses (1.48x10-3). Glioma risk increased monotonically with each increasing septile of LTL (O.R.=1.12; P = 3.83x10-12). Four LTL-associated SNPs were significantly associated with glioma risk in pooled analyses, including those in the telomerase component genes TERC (O.R.=1.14; 95% C.I.=1.03-1.28) and TERT (O.R.=1.39; 95% C.I.=1.27-1.52), and those in the CST complex genes OBFC1 (O.R.=1.18; 95% C.I.=1.05-1.33) and CTC1 (O.R.=1.14; 95% C.I.=1.02-1.28). Future work is needed to characterize the role of the CST complex in gliomagenesis and further elucidate the complex balance between ageing, telomere length, and molecular carcinogenesis.