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.

State of the Interactomes: an evaluation of molecular networks for generating biological insights.

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

Evaluation of large language models for discovery of gene set function.

Gene set analysis is a mainstay of functional genomics, but it relies on curated databases of gene functions that are incomplete. Here we evaluate five Large Language Models (LLMs) for their ability to discover the common biological functions represented by a gene set, substantiated by supporting rationale, citations and a confidence assessment. Benchmarking against canonical gene sets from the Gene Ontology, GPT-4 confidently recovered the curated name or a more general concept (73% of cases), while benchmarking against random gene sets correctly yielded zero confidence. Gemini-Pro and Mixtral-Instruct showed ability in naming but were falsely confident for random sets, whereas Llama2-70b had poor performance overall. In gene sets derived from 'omics data, GPT-4 identified novel functions not reported by classical functional enrichment (32% of cases), which independent review indicated were largely verifiable and not hallucinations. The ability to rapidly synthesize common gene functions positions LLMs as valuable 'omics assistants.

Management of patients suspected for non-ST elevation-acute coronary syndrome in the prehospital phase.

The management of patients with suspected acute coronary syndrome, especially in prehospital settings, is challenging. This Special Report focuses on studies in emergency medical services concerning chest pain patients' triage and risk stratification. In addition, it emphasizes advancements in point-of-care cardiac troponin testing. These developments are compared with in-hospital guidelines, proposing an initial framework for a new acute care pathway. This pathway integrates a risk stratification tool with high-sensitivity cardiac troponin testing, aiming to deliver optimal care and collaboration within the acute care chain. It has the potential to contribute to a significant reduction in hospital referrals, reduce observation time and overcrowding at emergency departments and hospital admissions.

Evaluation of large language models for discovery of gene set function.

Gene set analysis is a mainstay of functional genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of biological context. Here we evaluate the ability of OpenAI's GPT-4, a Large Language Model (LLM), to develop hypotheses about common gene functions from its embedded biomedical knowledge. We created a GPT-4 pipeline to label gene sets with names that summarize their consensus functions, substantiated by analysis text and citations. Benchmarking against named gene sets in the Gene Ontology, GPT-4 generated very similar names in 50% of cases, while in most remaining cases it recovered the name of a more general concept. In gene sets discovered in 'omics data, GPT-4 names were more informative than gene set enrichment, with supporting statements and citations that largely verified in human review. The ability to rapidly synthesize common gene functions positions LLMs as valuable functional genomics assistants.

Consequences of different cut-off values for high-sensitivity cardiac troponin for risk stratification of patients suspected for NSTE-ACS with a modified HEART score.

Aim: This study aims to enhance prehospital risk assessment for suspected non-ST-elevation acute coronary syndrome (NSTE-ACS) patients using the HEART-score. By incorporating novel point-of-care high-sensitivity cardiac troponin devices, a modified HEART-score was developed and compared with the conventional approach. Patients & methods: Troponin points within the modified HEART-score are based on values below the limit of quantitation (LoQ), between the LoQ and 99th percentile and above the 99th percentile of the used device. A total HEART-score of three or lower is considered low-risk for major adverse cardiac events. Results & conclusion: The number of low-risk patients decreased based on the modified HEART-score. The sensitivity and negative predictive value increased which suggests increasing safety in ruling out patients with suspected NSTE-ACS.

Development of a Generic PBK Model for Human Biomonitoring with an Application to Deoxynivalenol.

Toxicokinetic modelling provides a powerful tool in relating internal human exposure (i.e., assessed through urinary biomarker levels) to external exposure. Chemical specific toxicokinetic models are available; however, this specificity prevents their application to similar contaminants or to other routes of exposure. For this reason, we investigated whether a generic physiological-based kinetic (PBK) model might be a suitable alternative for a biokinetic model of deoxynivalenol (DON). IndusChemFate (ICF) was selected as a generic PBK model, which could be fit for purpose. Being suited for simulating multiple routes of exposure, ICF has particularly been used to relate the inhalation and dermal exposure of industrial chemicals to their urinary excretion. For the first time, the ICF model was adapted as a generic model for the human biomonitoring of mycotoxins, thereby extending its applicability domain. For this purpose, chemical-specific data for DON and its metabolites were collected directly from the literature (distribution and metabolism) or indirectly (absorption and excretion) by fitting the ICF model to previously described urinary excretion data. The obtained results indicate that this generic model can be used to model the urinary excretion of DON and its glucuronidated metabolites following dietary exposure to DON. Additionally, the present study establishes the basis for further development of the model to include an inhalation exposure route alongside the oral exposure route.

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.

Patterns of SARS-CoV-2 circulation revealed by a nationwide sewage surveillance programme, the Netherlands, August 2020 to February 2022.

BackgroundSurveillance of SARS-CoV-2 in wastewater offers a near real-time tool to track circulation of SARS-CoV-2 at a local scale. However, individual measurements of SARS-CoV-2 in sewage are noisy, inherently variable and can be left-censored.AimWe aimed to infer latent virus loads in a comprehensive sewage surveillance programme that includes all sewage treatment plants (STPs) in the Netherlands and covers 99.6% of the Dutch population.MethodsWe applied a multilevel Bayesian penalised spline model to estimate time- and STP-specific virus loads based on water flow-adjusted SARS-CoV-2 qRT-PCR data for one to four sewage samples per week for each of the more than 300 STPs.ResultsThe model captured the epidemic upsurges and downturns in the Netherlands, despite substantial day-to-day variation in the measurements. Estimated STP virus loads varied by more than two orders of magnitude, from ca 1012 virus particles per 100,000 persons per day in the epidemic trough in August 2020 to almost 1015 per 100,000 in many STPs in January 2022. The timing of epidemics at the local level was slightly shifted between STPs and municipalities, which resulted in less pronounced peaks and troughs at the national level.ConclusionAlthough substantial day-to-day variation is observed in virus load measurements, wastewater-based surveillance of SARS-CoV-2 that is performed at high sampling frequency can track long-term progression of an epidemic at a local scale in near real time.

A multi-scale map of protein assemblies in the DNA damage response.

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Translating desktop success to the web in the cytoscape project.

Cytoscape is an open-source bioinformatics environment for the analysis, integration, visualization, and query of biological networks. In this perspective piece, we describe our project to bring the Cytoscape desktop application to the web while explaining our strategy in ways relevant to others in the bioinformatics community. We examine opportunities and challenges in developing bioinformatics software that spans both the desktop and web, and we describe our ongoing efforts to build a Cytoscape web application, highlighting the principles that guide our development.

NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange.

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 that are active or modulated in a specific experimental context. RESULTS: The Network Data Exchange Integrated Query (NDEx 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 NDEx 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. NDEx IQuery's integration with MSigDB and cBioPortal now provides pathway analysis in the context of these two resources. AVAILABILITY AND IMPLEMENTATION: NDEx IQuery is available at https://www.ndexbio.org/iquery and is implemented in Javascript and Java.

IL17A Blockade with Ixekizumab Suppresses MuvB Signaling in Clinical Psoriasis.

Unbiased informatics approaches have the potential to generate insights into uncharacterized signaling pathways in human disease. In this study, we generated longitudinal transcriptomic profiles of plaque psoriasis lesions from patients enrolled in a clinical trial of the anti-IL17A antibody ixekizumab (IXE). This dataset was then computed against a curated matrix of over 700 million data points derived from published psoriasis and signaling node perturbation transcriptomic and chromatin immunoprecipitation-sequencing datasets. We observed substantive enrichment within both psoriasis-induced and IXE-repressed gene sets of transcriptional targets of members of the MuvB complex, a master regulator of the mitotic cell cycle. These gene sets were similarly enriched for pathways involved in the regulation of the G2/M transition of the cell cycle. Moreover, transcriptional targets for MuvB nodes were strongly enriched within IXE-repressed genes whose expression levels correlated strongly with the extent and severity of the psoriatic disease. In models of human keratinocyte proliferation, genes encoding MuvB nodes were transcriptionally repressed by IXE, and depletion of MuvB nodes reduced cell proliferation. Finally, we made the expression and regulatory networks that supported this study available as a freely accessible, cloud-based hypothesis generation platform. Our study positions inhibition of MuvB signaling as an important determinant of the therapeutic impact of IXE in psoriasis.

Transcriptional regulatory networks of circulating immune cells in type 1 diabetes: A community knowledgebase.

Investigator-generated transcriptomic datasets interrogating circulating immune cell (CIC) gene expression in clinical type 1 diabetes (T1D) have underappreciated re-use value. Here, we repurposed these datasets to create an open science environment for the generation of hypotheses around CIC signaling pathways whose gain or loss of function contributes to T1D pathogenesis. We firstly computed sets of genes that were preferentially induced or repressed in T1D CICs and validated these against community benchmarks. We then inferred and validated signaling node networks regulating expression of these gene sets, as well as differentially expressed genes in the original underlying T1D case:control datasets. In a set of three use cases, we demonstrated how informed integration of these networks with complementary digital resources supports substantive, actionable hypotheses around signaling pathway dysfunction in T1D CICs. Finally, we developed a federated, cloud-based web resource that exposes the entire data matrix for unrestricted access and re-use by the research community.

Modelling patterns of SARS-CoV-2 circulation in the Netherlands, August 2020-February 2022, revealed by a nationwide sewage surveillance program

BackgroundSurveillance of SARS-CoV-2 in wastewater offers an unbiased and near real-time tool to track circulation of SARS-CoV-2 at a local scale, next to other epidemic indicators such as hospital admissions and test data. However, individual measurements of SARS-CoV-2 in sewage are noisy, inherently variable, and can be left-censored. AimWe aimed to infer latent virus loads in a comprehensive sewage surveillance program that includes all sewage treatment plants (STPs) in the Netherlands and covers 99.6% of the Dutch population. MethodsA multilevel Bayesian penalized spline model was developed and applied to estimate time- and STP-specific virus loads based on water flow adjusted SARS-CoV-2 qRT-PCR data from 1-4 sewage samples per week for each of the >300 STPs. ResultsThe model provided an adequate fit to the data and captured the epidemic upsurges and downturns in the Netherlands, despite substantial day-to-day measurement variation. Estimated STP virus loads varied by more than two orders of magnitude, from approximately 1012 (virus particles per 100,000 persons per day) in the epidemic trough in August 2020 to almost 1015 in many STPs in January 2022. Epidemics at the local levels were slightly shifted between STPs and municipalities, which resulted in less pronounced peaks and troughs at the national level. ConclusionAlthough substantial day-to-day variation is observed in virus load measurements, wastewater-based surveillance of SARS-CoV-2 can track long-term epidemic progression at a local scale in near real-time, especially at high sampling frequency.

Referral decisions based on a pre-hospital HEART score in suspected non-ST-elevation acute coronary syndrome: final results of the FamouS Triage study.

AIMS: Although pre-hospital risk stratification of patients with suspected non-ST-elevation acute coronary syndrome (NSTE-ACS) by ambulance paramedics is feasible, it has not been investigated in daily practice whether referral decisions based on this risk stratification is safe and does not increase major adverse cardiac events (MACE). In Phase III of the FamouS Triage study, it was investigated whether referral decisions by ambulance paramedics based on a pre-hospital HEART score, is non-inferior to routine management. METHODS AND RESULTS: FamouS Triage Phase III is a non-inferiority study, comparing the occurrence of MACE before (Phase II) and after (Phase III) implementation of referral decisions based on a pre-hospital HEART score. In Phase II, all patients were risk-stratified and referred to the hospital; in Phase III, low-risk patients (HEART score ≤ 3) were not referred. Primary endpoint was MACE (acute coronary syndrome, revascularization, or death) within 45 days. A total of 1236 patients were included. Mean age was 63 years, 43% were female, 700 patients were included in the second phase and 536 in the third phase in which 149 low-risk patients (28%) were not transferred to the hospital. Occurrence of 45 days MACE was 16.6% in Phase II and 15.7% in Phase III (P = 0.67). Percentage MACE in low-risk patients was 2.9% in Phase II and 1.3% in Phase III. After adjustments for differences in baseline variables, the hazard ratio of 45 days MACE in Phase III was 0.88 (95% confidence interval 0.63-1.25) as compared to Phase II. CONCLUSION: Pre-hospital risk stratification of patients with suspected NSTE-ACS, avoiding hospitalization of a substantial number of low-risk patients, seems feasible and non-inferior to transferring all patients to the hospital.

NDEx: Accessing Network Models and Streamlining Network Biology Workflows.

NDEx, the Network Data Exchange (https://www.ndexbio.org) is a web-based resource where users can find, store, share and publish network models of any type and size. NDEx is integrated with Cytoscape, the widely used desktop application for network analysis and visualization. NDEx and Cytoscape are the pillars of the Cytoscape Ecosystem, a diverse environment of resources, tools, applications and services for network biology workflows. In this article, we introduce researchers to NDEx and highlight how it can simplify common tasks in network biology workflows as well as streamline publication and access to). Finally, we show how NDEx can be used programmatically via Python with the 'ndex2' client library, and point readers to additional examples for other popular programming languages such as JavaScript and R. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Getting started with NDEx Basic Protocol 2: Using NDEx and Cytoscape in a publication-oriented workflow Basic Protocol 3: Manipulating networks in NDEx via Python.

Interpretation of cancer mutations using a multiscale map of protein systems.

A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges­how to comprehensively map such systems and how to identify which are under mutational selection­have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

Pre-hospital versus hospital acquired HEART score for risk classification of suspected non ST-elevation acute coronary syndrome.

INTRODUCTION: Although increasing evidence shows that in patients with suspected non-ST-elevation acute coronary syndrome (NSTE-ACS) both hospital and pre-hospital acquired HEART (History, ECG, Age, Risk factors, Troponin) scores have strong predictive value, pre-hospital and hospital acquired HEART scores have never been compared directly. METHODS: In patients with suspected NSTE-ACS, the HEART score was independently prospectively assessed in the pre-hospital setting by ambulance paramedics and in the hospital by physicians. The hospital HEART score was considered the gold standard. Low-risk (HEART score ≤3) was considered a negative test. Endpoint was occurrence of major adverse events within 45 days. RESULTS: A total of 699 patients were included in the analyses. In 516 (74%) patients pre-hospital and hospital risk classification was similar, in 50 (7%) pre-hospital risk classification was false negative (45 days mortality 0%) and in 133 (19%) false positive (45 days mortality 1.5%). False negative risk classifications were caused by differences in history (100%), risk factor assessment (66%) and troponin (18%) and were more common in older patients. Occurrence of major adverse events was comparable in pre-hospital and hospital low-risk patients (2.9% vs. 2.7%, p = 0.9). Incidence of major adverse events was 0% in the true negative group, 26% in the true positive group, 10% in the false negative group and 5% in the false positive group. Predictive value of both pre-hospital and hospital acquired HEART scores was high, although the 'area under the curve' of hospital acquired HEART score was higher (0.84 vs. 0.74, p < 0.001). CONCLUSION: In approximately 25% of patients hospital and pre-hospital HEART score risk classifications disagree, mainly by risk overestimation in the pre-hospital group. Since disagreement is primarily caused by different scoring of history and risk factors, additional training may improve pre-hospital scoring.

Lifetime cost-effectiveness and equity impacts of the Healthy Primary School of the Future initiative.

BACKGROUND: This study estimated the lifetime cost-effectiveness and equity impacts associated with two lifestyle interventions in the Dutch primary school setting (targeting 4-12 year olds). METHODS: The Healthy Primary School of the Future (HPSF; a healthy school lunch and structured physical activity) and the Physical Activity School (PAS; structured physical activity) were compared to the regular Dutch curriculum (N = 1676). An adolescence model, calculating weight development, and the RIVM Chronic Disease Model, calculating overweight-related chronic diseases, were linked to estimate the lifetime impact on chronic diseases, quality adjusted life years (QALYs), healthcare, and productivity costs. Cost-effectiveness was expressed as the additional costs/QALY gained and we used €20,000 as threshold. Scenario analyses accounted for alternative effect maintenance scenarios and equity analyses examined cost-effectiveness in different socioeconomic status (SES) groups. RESULTS: HPSF resulted in a lifetime costs of €773 (societal perspective) and a lifetime QALY gain of 0.039 per child versus control schools. HPSF led to lower costs and more QALYs as compared to PAS. From a societal perspective, HPSF had a cost/QALY gained of €19,734 versus control schools, 50% probability of being cost-effective, and beneficial equity impact (0.02 QALYs gained/child for low versus high SES). The cost-effectiveness threshold was surpassed when intervention effects decayed over time. CONCLUSIONS: HPSF may be a cost-effective and equitable strategy for combatting the lifetime burden of unhealthy lifestyles. The win-win situation will, however, only be realised if the intervention effect is sustained into adulthood for all SES groups. TRIAL REGISTRATION: Clinicaltrials.gov ( NCT02800616 ). Registered 15 June 2016 - Retrospectively registered.