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.

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.

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field.

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.

BackgroundTumor-associated macrophages (TAMs) play a key immunosuppressive role that limits the ability of the immune system to fight cancer and hinder the antitumoral efficacy of most treatments currently applied in the clinic. Previous studies have evaluated the antitumoral immune response triggered by (TLR) agonists, such as poly(I:C), imiquimod (R837) or resiquimod (R848) as monotherapies; however, their combination for the treatment of cancer has not been explored. This study investigates the antitumoral efficacy and the macrophage reprogramming triggered by poly(I:C) combined with R848 or with R837, versus single treatments.MethodsTLR agonist treatments were evaluated in vitro for toxicity and immunostimulatory activity by Alamar Blue, ELISA and flow cytometry using primary human and murine M-CSF-differentiated macrophages. Cytotoxic activity of TLR-treated macrophages toward cancer cells was evaluated with an in vitro functional assay by flow cytometry. For in vivo experiments, the CMT167 lung cancer model and the MN/MCA1 fibrosarcoma model metastasizing to lungs were used; tumor-infiltrating leukocytes were evaluated by flow cytometry, RT-qPCR, multispectral immunophenotyping, quantitative proteomic experiments, and protein–protein interaction analysis.ResultsResults demonstrated the higher efficacy of poly(I:C) combined with R848 versus single treatments or combined with R837 to polarize macrophages toward M1-like antitumor effectors in vitro. In vivo, the intratumoral synergistic combination of poly(I:C)+R848 significantly prevented tumor growth and metastasis in lung cancer and fibrosarcoma immunocompetent murine models. Regressing tumors showed increased infiltration of macrophages with a higher M1:M2 ratio, recruitment of CD4+ and CD8+ T cells, accompanied by a reduction of immunosuppressive CD206+ TAMs and FOXP3+/CD4+ T cells. The depletion of both CD4+ and CD8+ T cells resulted in complete loss of treatment efficacy. Treated mice acquired systemic antitumoral response and resistance to tumor rechallenge mediated by boosted macrophage cytotoxic activity and T-cell proliferation. Proteomic experiments validate the superior activation of innate immunity by poly(I:C)+R848 combination versus single treatments or poly(I:C)+R837, and protein–protein-interaction network analysis reveal the key activation of the STAT1 pathway.DiscussionThese findings demonstrate the antitumor immune responses mediated by macrophage activation on local administration of poly(I:C)+R848 combination and support the intratumoral application of this therapy to patients with solid tumors in the clinic.