WikiPathways 2024: next generation pathway database.

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

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.

A systematically derived overview of the non-ubiquitous pathways and genes that define the molecular and genetic signature of the healthy trabecular meshwork.

PURPOSE: The trabecular meshwork (TM) is situated in the most frontal part of the eye and is thought to play an important role in the regulation of the eye pressure. However, this tissue is rather difficult to harvest for research. The purpose of this study is therefore to integrate the existing gene expression data of the healthy TM to increase sample size and identify its signature genes and pathways. This provides a robust reference for the study of molecular disease processes and supports the selection of candidate target genes for new treatments. METHODS: A systematic search identified microarray data of healthy TM tissue. After quality control, datasets of low quality and deviating samples were excluded. Remaining individuals were jointly normalized and integrated into one database. The average gene expression of each tested gene over all individuals was calculated. The 25% genes with the highest average expression were identified as the most active genes in the healthy TM and used as input for pathway and network analysis. Additionally, ubiquitous pathways and genes were identified and excluded from the results. Lastly, we identified genes which are likely to be TM-specific. RESULTS: The gene expression data of 44 individuals, obtained from 18 datasets, were jointly normalized. Ubiquitous genes (n = 688) and ubiquitous pathways (n = 73) were identified and excluded. Following, 1882 genes and 211 pathways were identified as the signature genes and pathways of the healthy TM. Pathway analysis revealed multiple molecular processes of which some were already known to be active in the TM, for example extracellular matrix and elastic fiber formation. Forty-six candidate TM-specific genes were identified. These consist mainly of pseudogenes or novel transcripts of which the function is unknown. CONCLUSIONS: In this comprehensive meta-analysis we identified non-ubiquitous genes and pathways that form the signature of the functioning of the healthy TM. Additionally, 46 candidate TM-specific genes were identified. This method can also be used for other tissues that are difficult to obtain for study.

Comparison of metabolic states using genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.

Investigating the Molecular Processes behind the Cell-Specific Toxicity Response to Titanium Dioxide Nanobelts.

Some engineered nanomaterials incite toxicological effects, but the underlying molecular processes are understudied. The varied physicochemical properties cause different initial molecular interactions, complicating toxicological predictions. Gene expression data allow us to study the responses of genes and biological processes. Overrepresentation analysis identifies enriched biological processes using the experimental data but prompts broad results instead of detailed toxicological processes. We demonstrate a targeted filtering approach to compare public gene expression data for low and high exposure on three cell lines to titanium dioxide nanobelts. Our workflow finds cell and concentration-specific changes in affected pathways linked to four Gene Ontology terms (apoptosis, inflammation, DNA damage, and oxidative stress) to select pathways with a clear toxicity focus. We saw more differentially expressed genes at higher exposure, but our analysis identifies clear differences between the cell lines in affected processes. Colorectal adenocarcinoma cells showed resilience to both concentrations. Small airway epithelial cells displayed a cytotoxic response to the high concentration, but not as strongly as monocytic-like cells. The pathway-gene networks highlighted the gene overlap between altered toxicity-related pathways. The automated workflow is flexible and can focus on other biological processes by selecting other GO terms.

Review and gap analysis: molecular pathways leading to fetal alcohol spectrum disorders.

Alcohol exposure during pregnancy affects the development of the fetus in various ways and may lead to Fetal Alcohol Spectrum Disorders (FASD). FASD is one of the leading preventable forms of neurodevelopmental disorders. In the light of prevention and early intervention, knowledge on how ethanol exposure induces fetal damage is urgently needed. Besides direct ethanol and acetaldehyde toxicity, alcohol increases oxidative stress, and subsequent general effects (e.g., epigenetic imprinting, gene expression, and metabolite levels). The current review provides an overview of the existing knowledge about specific downstream pathways for FASD that affects e.g., the SHH pathway, cholesterol homeostasis, neurotransmitter signaling, and effects on the cytoskeleton. Available human data vary greatly, while animal studies with controlled ethanol exposition are only to a certain limit transferable to humans. The main deficits in knowledge about FASD are the lack of pathophysiological understanding and dose-response relationships, together with the lack of reliable biomarkers for either FASD detection or estimation of susceptibility. In addition to single outcome experiments, omics data should be generated to overcome this problem. Therefore, for future studies we recommend holistic data driven analysis, which allows integrative analyses over multiple levels of genetic variation, transcriptomics and metabolomics data to investigate the whole image of FASD development and to provide insight in potential drug targets for intervention.

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.

MECP2 variation in Rett syndrome-An overview of current coverage of genetic and phenotype data within existing databases.

Rett syndrome (RTT) is a monogenic rare disorder that causes severe neurological problems. In most cases, it results from a loss-of-function mutation in the gene encoding methyl-CPG-binding protein 2 (MECP2). Currently, about 900 unique MECP2 variations (benign and pathogenic) have been identified and it is suspected that the different mutations contribute to different levels of disease severity. For researchers and clinicians, it is important that genotype-phenotype information is available to identify disease-causing mutations for diagnosis, to aid in clinical management of the disorder, and to provide counseling for parents. In this study, 13 genotype-phenotype databases were surveyed for their general functionality and availability of RTT-specific MECP2 variation data. For each database, we investigated findability and interoperability alongside practical user functionality, and type and amount of genetic and phenotype data. The main conclusions are that, as well as being challenging to find these databases and specific MECP2 variants held within, interoperability is as yet poorly developed and requires effort to search across databases. Nevertheless, we found several thousand online database entries for MECP2 variations and their associated phenotypes, diagnosis, or predicted variant effects, which is a good starting point for researchers and clinicians who want to provide, annotate, and use the data.

Role of 1α,25-Dihydroxyvitamin D3 in Adipogenesis of SGBS Cells: New Insights into Human Preadipocyte Proliferation.

BACKGROUND/AIMS: Compared with non-obese individuals, obese individuals commonly store more vitamin D in adipose tissue. VDR expression in adipose tissue can influence adipogenesis and is therefore a target pathway deserving further study. This study aims to assess the role of 1,25(OH)2D3 in human preadipocyte proliferation and differentiation. METHODS: RTCA, MTT, and trypan blue assays were used to assess the effects of 1,25(OH)2D3 on the viability, proliferation, and adipogenic differentiation of SGBS cells. Cell cycle and apoptosis analyses were performed with flow cytometry, triglycerides were quantified, and RT-qPCR was used to assess gene expression. RESULTS: We confirmed that the SGBS cell model is suitable for studying adipogenesis and demonstrated that the differentiation protocol induces cell maturation, thereby increasing the lipid content of cells independently of treatment. 1,25(OH)2D3 treatment had different effects according to the cell stage, indicating different modes of action driving proliferation and differentiation. In preadipocytes, 1,25(OH)2D3 induced G1 growth arrest at both tested concentrations without altering CDKN1A gene expression. Treatment with 100 nM 1,25(OH)2D3 also decreased MTT absorbance and the lipid concentration. Moreover, increased normalized cell index values and decreased metabolic activity were not induced by proliferation or apoptosis. Exposure to 100 nM 1,25(OH)2D3 induced VDR, CEBPA, and CEBPB expression, even in the preadipocyte stage. During adipogenesis, 1,25(OH)2D3 had limited effects on processes such as VDR and PPARG gene expression, but it upregulated CEBPA expression. CONCLUSIONS: We demonstrated for the first time that 1,25(OH)2D3 induces changes in preadipocytes, including VDR expression and growth arrest, and increases the lipid content in adipocytes treated for 16 days. Preadipocytes are important cells in adipose tissue homeostasis, and understanding the role of 1,25(OH)2D3 in adipogenesis is a crucial step in ensuring adequate vitamin D supplementation, especially for obese individuals.

WikiPathways: capturing the full diversity of pathway knowledge.

WikiPathways (http://www.wikipathways.org) is an open, collaborative platform for capturing and disseminating models of biological pathways for data visualization and analysis. Since our last NAR update, 4 years ago, WikiPathways has experienced massive growth in content, which continues to be contributed by hundreds of individuals each year. New aspects of the diversity and depth of the collected pathways are described from the perspective of researchers interested in using pathway information in their studies. We provide updates on extensions and services to support pathway analysis and visualization via popular standalone tools, i.e. PathVisio and Cytoscape, web applications and common programming environments. We introduce the Quick Edit feature for pathway authors and curators, in addition to new means of publishing pathways and maintaining custom pathway collections to serve specific research topics and communities. In addition to the latest milestones in our pathway collection and curation effort, we also highlight the latest means to access the content as publishable figures, as standard data files, and as linked data, including bulk and programmatic access.

Visualizing the regulatory role of Angiopoietin-like protein 8 (ANGPTL8) in glucose and lipid metabolic pathways.

ANGPTL8 (Angiopoietin-like protein 8) is a newly identified hormone emerging as a novel drug target for treatment of diabetes mellitus and dyslipidemia due to its unique metabolic nature. With increasing number of studies targeting the regulation of ANGPTL8, integration of their findings becomes indispensable. This study has been conducted with the aim to collect, analyze, integrate and visualize the available knowledge in the literature about ANGPTL8 and its regulation. We utilized this knowledge to construct a regulatory pathway of ANGPTL8 which is available at WikiPathways, an open source pathways database. It allows us to visualize ANGPTL8's regulation with respect to other genes/proteins in different pathways helping us to understand the complex interplay of novel hormones/genes/proteins in metabolic disorders. To the best of our knowledge, this is the first attempt to present an integrated pathway view of ANGPTL8's regulation and its associated pathways and is important resource for future omics-based studies.

Sexual Dimorphism, Age, and Fat Mass Are Key Phenotypic Drivers of Proteomic Signatures.

Validated protein biomarkers are needed for assessing health trajectories, predicting and subclassifying disease, and optimizing diagnostic and therapeutic clinical decision-making. The sensitivity, specificity, accuracy, and precision of single or combinations of protein biomarkers may be altered by differences in physiological states limiting the ability to translate research results to clinically useful diagnostic tests. Aptamer based affinity assays were used to test whether low abundant serum proteins differed based on age, sex, and fat mass in a healthy population of 94 males and 102 females from the MECHE cohort. The findings were replicated in 217 healthy male and 377 healthy female participants in the DiOGenes consortium. Of the 1129 proteins in the panel, 141, 51, and 112 proteins (adjusted p < 0.1) were identified in the MECHE cohort and significantly replicated in DiOGenes for sexual dimorphism, age, and fat mass, respectively. Pathway analysis classified a subset of proteins from the 3 phenotypes to the complement and coagulation cascades pathways and to immune and coagulation processes. These results demonstrated that specific proteins were statistically associated with dichotomous (male vs female) and continuous phenotypes (age, fat mass), which may influence the identification and use of biomarkers of clinical utility for health diagnosis and therapeutic strategies.

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources.

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.

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.

The systems biology format converter.

BACKGROUND: Interoperability between formats is a recurring problem in systems biology research. Many tools have been developed to convert computational models from one format to another. However, they have been developed independently, resulting in redundancy of efforts and lack of synergy. RESULTS: Here we present the System Biology Format Converter (SBFC), which provide a generic framework to potentially convert any format into another. The framework currently includes several converters translating between the following formats: SBML, BioPAX, SBGN-ML, Matlab, Octave, XPP, GPML, Dot, MDL and APM. This software is written in Java and can be used as a standalone executable or web service. CONCLUSIONS: The SBFC framework is an evolving software project. Existing converters can be used and improved, and new converters can be easily added, making SBFC useful to both modellers and developers. The source code and documentation of the framework are freely available from the project web site.

diXa: a data infrastructure for chemical safety assessment.

MOTIVATION: The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. RESULTS: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. AVAILABILITY AND IMPLEMENTATION: http://www.dixa-fp7.eu CONTACT: d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PathVisio 3: an extendable pathway analysis toolbox.

PathVisio is a commonly used pathway editor, visualization and analysis software. Biological pathways have been used by biologists for many years to describe the detailed steps in biological processes. Those powerful, visual representations help researchers to better understand, share and discuss knowledge. Since the first publication of PathVisio in 2008, the original paper was cited more than 170 times and PathVisio was used in many different biological studies. As an online editor PathVisio is also integrated in the community curated pathway database WikiPathways. Here we present the third version of PathVisio with the newest additions and improvements of the application. The core features of PathVisio are pathway drawing, advanced data visualization and pathway statistics. Additionally, PathVisio 3 introduces a new powerful extension systems that allows other developers to contribute additional functionality in form of plugins without changing the core application. PathVisio can be downloaded from http://www.pathvisio.org and in 2014 PathVisio 3 has been downloaded over 5,500 times. There are already more than 15 plugins available in the central plugin repository. PathVisio is a freely available, open-source tool published under the Apache 2.0 license (http://www.apache.org/licenses/LICENSE-2.0). It is implemented in Java and thus runs on all major operating systems. The code repository is available at http://svn.bigcat.unimaas.nl/pathvisio. The support mailing list for users is available on https://groups.google.com/forum/#!forum/wikipathways-discuss and for developers on https://groups.google.com/forum/#!forum/wikipathways-devel.

New insights in Rett syndrome using pathway analysis for transcriptomics data.

The analysis of transcriptomics data is able to give an overview of cellular processes, but requires sophisticated bioinformatics tools and methods to identify the changes. Pathway analysis software, like PathVisio, captures the information about biological pathways from databases and brings this together with the experimental data to enable visualization and understanding of the underlying processes. Rett syndrome is a rare disease, but still one of the most abundant causes of intellectual disability in females. Cause of this neurological disorder is mutation of one single gene, the methyl-CpG-binding protein 2 (MECP2) gene. This gene is responsible for many steps in neuronal development and function. Although the genetic mutation and the clinical phenotype are well described, the molecular pathways linking them are not yet fully elucidated. In this study we demonstrate a workflow for the analysis of transcriptomics data to identify biological pathways and processes which are changed in a Mecp2 (-/y) mouse model.