Background:Patients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients. Methods:We analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide. Results:In CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8×10 -6 , proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log 2 1.8 (p=2.1×10 -12 ) and HR per annual doubling 1.6 (p=6.8×10 -6 ). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004). Conclusions:SVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings. Key Points:Patients with kidney failure undergoing hemodialysis have 20-fold higher cardiovascular mortality compared to the general population, and conventional risk factors have low prognostic utility for these patients.By applying large-scale circulating proteomics in two independent hemodialysis cohorts, we have discovered >20 novel proteins that predict major adverse cardiovascular events(MACE).Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1) surpassed >6000 individual proteins and clinical factors for predicting MACE.

Since its introduction in 2003, Cytoscape has been a de facto standard for visualizing and analyzing biological networks. We now introduce Cytoscape Web (https://web.cytoscape.org), an online implementation that captures the interface and key visualization functionality of the desktop while providing integration with web tools and databases. Cytoscape Web enhances accessibility, simplifying collaboration through online data sharing. It integrates with Cytoscape desktop via the CX2 network exchange format and with the Network Data Exchange for storing and sharing networks. The platform supports extensibility through an App framework for UI components and Service Apps for algorithm integration, fostering community-driven development of new analysis tools. Overall, Cytoscape Web enhances network biology by providing a versatile, accessible, and collaborative online platform that adapts to evolving computational challenges, laying a foundation for future incorporation of advanced network analysis capabilities by the community.

More than 50% of patients with kidney failure undergoing maintenance hemodialysis die within 5 years, a fate unexplained by traditional risk factors. To identify biological risk factors, we analyze 6287 circulating proteins and mortality in 893 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort (CRIC) and Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease (PACE) studies. Proteins are measured shortly after (incident period) and one year after (prevalent period) dialysis initiation. In CRIC prevalent period, Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), R-spondin 4, tetranectin and 24 other proteins attain Bonferroni significance (p < 7 × 10-6). At false discovery rate<0.05, 184 proteins are significant in CRIC; 123/184 remain significant after adjustment for covariates including those linked to inflammation. Pathways related to insulin-like growth factor are prominent. In the pooled CRIC + PACE cohort, prevalent time period, AUC(95%CI) for a 3-protein model of 5-year mortality is 0.826 (0.742, 0.896), compared to 0.629 (0.528, 0.722) for a Cohort Clinical model (p < 0.001). Adding the 3 proteins (SVEP1, R-spondin 4 and tetranectin) to the Cohort Clinical model significantly improves the AUC (p < 0.001). These biomarkers should be validated in future studies and their roles as potential disease mediators elucidated.

Cellular energy metabolism and oxygen availability shape neuronal function and vulnerability, yet the genetic regulators of these metabolic processes in human neurons remain incompletely understood. Here, we performed CRISPR interference (CRISPRi) screens in human induced pluripotent stem cell (iPSC)-derived neurons across four distinct metabolic conditions and at three physiologically relevant oxygen tensions. This combinatorial approach enabled systematic interrogation of gene-environment interactions that govern neuronal metabolic adaptation. We identified genes-including genes associated with Leigh syndrome and autism spectrum disorder-whose importance for cell survival is highly sensitive to environmental context, revealing potential mechanisms underlying metabolic specification and selective neuronal vulnerability in neurological disorders. Our screens also uncovered regulators of neuronal glycolysis, including KIAA1429 and MAPT among others, which are previously uncharacterized modulators of neuronal glucose utilization and metabolic flexibility. Our work nominates candidate metabolic interventions and gene targets for enhancing neuronal resilience under hypoxic or nutrient-limited conditions.

Network biology, an interdisciplinary field at the intersection of computational and biological sciences, is critical for deepening understanding of cellular functioning and disease. While the field has existed for about two decades now, it is still relatively young. There have been rapid changes to it and new computational challenges have arisen. This is caused by many factors, including increasing data complexity, such as multiple types of data becoming available at different levels of biological organization, as well as growing data size. This means that the research directions in the field need to evolve as well. Hence, a workshop on Future Directions in Network Biology was organized and held at the University of Notre Dame in 2022, which brought together active researchers in various computational and in particular algorithmic aspects of network biology to identify pressing challenges in this field. Topics that were discussed during the workshop include: inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Video recordings of the workshop presentations are publicly available on YouTube. For even broader impact of the workshop, this paper, co-authored mostly by the workshop participants, summarizes the discussion from the workshop. As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.

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.

Life-threatening thrombotic events and neurological symptoms are prevalent in COVID-19 and are persistent in patients with long COVID experiencing post-acute sequelae of SARS-CoV-2 infection1-4. Despite the clinical evidence1,5-7, the underlying mechanisms of coagulopathy in COVID-19 and its consequences in inflammation and neuropathology remain poorly understood and treatment options are insufficient. Fibrinogen, the central structural component of blood clots, is abundantly deposited in the lungs and brains of patients with COVID-19, correlates with disease severity and is a predictive biomarker for post-COVID-19 cognitive deficits1,5,8-10. Here we show that fibrin binds to the SARS-CoV-2 spike protein, forming proinflammatory blood clots that drive systemic thromboinflammation and neuropathology in COVID-19. Fibrin, acting through its inflammatory domain, is required for oxidative stress and macrophage activation in the lungs, whereas it suppresses natural killer cells, after SARS-CoV-2 infection. Fibrin promotes neuroinflammation and neuronal loss after infection, as well as innate immune activation in the brain and lungs independently of active infection. A monoclonal antibody targeting the inflammatory fibrin domain provides protection from microglial activation and neuronal injury, as well as from thromboinflammation in the lung after infection. Thus, fibrin drives inflammation and neuropathology in SARS-CoV-2 infection, and fibrin-targeting immunotherapy may represent a therapeutic intervention for patients with acute COVID-19 and long COVID.

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.

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.

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.

Computer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbiased, and sensitive methods to assess mouse behavior. Here, we used the ML-based variational animal motion embedding (VAME) segmentation platform to assess spontaneous behavior in humanized App knockin and transgenic APP models of Alzheimer's disease (AD) and to test the role of AD-related neuroinflammation in these behavioral manifestations. We found marked alterations in spontaneous behavior in App NL-G-F and 5xFAD mice, including age-dependent changes in motif utilization, disorganized behavioral sequences, increased transitions, and randomness. Notably, blocking fibrinogen-microglia interactions in 5xFAD-Fggg390-396A mice largely prevented spontaneous behavioral alterations, indicating a key role for neuroinflammation. Thus, AD-related spontaneous behavioral alterations are prominent in knockin and transgenic models and sensitive to therapeutic interventions. VAME outcomes had higher specificity and sensitivity than conventional behavioral outcomes. We conclude that spontaneous behavior effectively captures age- and sex-dependent disease manifestations and treatment efficacy in AD models.

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology.

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.

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.

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.

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.

Motivation The investigation of sets of genes using biological pathways is a common task for researchers and is supported by a wide variety of software tools. This type of analysis generates hypotheses about the biological processes active or modulated in a specific experimental context. Results The NDEx Integrated Query (IQuery) is a new tool for network and pathway-based gene set interpretation that complements or extends existing resources. It combines novel sources of pathways, integration with Cytoscape, and the ability to store and share analysis results. The IQuery web application performs multiple gene set analyses based on diverse pathways and networks stored in NDEx. These include curated pathways from WikiPathways and SIGNOR, published pathway figures from the last 27 years, machine-assembled networks using the INDRA system, and the new NCI-PID v2.0, an updated version of the popular NCI Pathway Interaction Database. IQuery’s integration with MSigDB and cBioPortal now provides pathway analysis in the context of these two resources. Availability and Implementation IQuery is available at https://www.ndexbio.org/iquery and is implemented in Javascript and Java. Contact Dexter Pratt ( depratt@health.ucsd.edu )

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.

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).

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.