Faculty Bibliography
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193
Biocuration of a Transcription Factors Network Involved in Submergence Tolerance during Seed Germination and Coleoptile Elongation in Rice (Oryza sativa)
Modeling biological processes and genetic-regulatory networks using in silico approaches provides a valuable framework for understanding how genes and associated allelic and genotypic differences result in specific traits. Submergence tolerance is a significant agronomic trait in rice; however, the gene-gene interactions linked with this polygenic trait remain largely unknown. In this study, we constructed a network of 57 transcription factors involved in seed germination and coleoptile elongation under submergence. The gene-gene interactions were based on the co-expression profiles of genes and the presence of transcription factor binding sites in the promoter region of target genes. We also incorporated published experimental evidence, wherever available, to support gene-gene, gene-protein, and protein-protein interactions. The co-expression data were obtained by re-analyzing publicly available transcriptome data from rice. Notably, this network includes OSH1, OSH15, OSH71, Sub1B, ERFs, WRKYs, NACs, ZFP36, TCPs, etc., which play key regulatory roles in seed germination, coleoptile elongation and submergence response, and mediate gravitropic signaling by regulating OsLAZY1 and/or IL2. The network of transcription factors was manually biocurated and submitted to the Plant Reactome Knowledgebase to make it publicly accessible. We expect this work will facilitate the re-analysis/re-use of OMICs data and aid genomics research to accelerate crop improvement.
PMCID:10255735
PMID: 37299125
ISSN: 2223-7747
CID: 5609822
Illuminate the Functions of Dark Proteins Using the Reactome-IDG Web Portal
Understudied or dark proteins have the potential to shed light on as-yet undiscovered molecular mechanisms that underlie phenotypes and suggest innovative therapeutic approaches for many diseases. The Reactome-IDG (Illuminating the Druggable Genome) project aims to place dark proteins in the context of manually curated, highly reliable pathways in Reactome, the most comprehensive, open-source biological pathway knowledgebase, facilitating the understanding functions and predicting therapeutic potentials of dark proteins. The Reactome-IDG web portal, deployed at https://idg.reactome.org, provides a simple, interactive web page for users to search pathways that may functionally interact with dark proteins, enabling the prediction of functions of dark proteins in the context of Reactome pathways. Enhanced visualization features implemented at the portal allow users to investigate the functional contexts for dark proteins based on tissue-specific gene or protein expression, drug-target interactions, or protein or gene pairwise relationships in the original Reactome's systems biology graph notation (SBGN) diagrams or the new simplified functional interaction (FI) network view of pathways. The protocols in this chapter describe step-by-step procedures to use the web portal to learn biological functions of dark proteins in the context of Reactome pathways. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Search for interacting pathways of a protein Support Protocol: Interacting pathway results for an annotated protein Alternate Protocol: Use individual pairwise relationships to predict interacting pathways of a protein Basic Protocol 2: Using the IDG pathway browser to study interacting pathways Basic Protocol 3: Overlaying tissue-specific expression data Basic Protocol 4: Overlaying protein/gene pairwise relationships in the pathway context Basic Protocol 5: Visualizing drug/target interactions.
PMID: 37467006
ISSN: 2691-1299
CID: 5535802
Using the Reactome Database
Pathway databases provide descriptions of the roles of proteins, nucleic acids, lipids, carbohydrates, and other molecular entities within their biological cellular contexts. Pathway-centric views of these roles may allow for the discovery of unexpected functional relationships in data such as gene expression profiles and somatic mutation catalogues from tumor cells. For this reason, there is a high demand for high-quality pathway databases and their associated tools. The Reactome project (a collaboration between the Ontario Institute for Cancer Research, New York University Langone Health, the European Bioinformatics Institute, and Oregon Health & Science University) is one such pathway database. Reactome collects detailed information on biological pathways and processes in humans from the primary literature. Reactome content is manually curated, expert-authored, and peer-reviewed and spans the gamut from simple intermediate metabolism to signaling pathways and complex cellular events. This information is supplemented with likely orthologous molecular reactions in mouse, rat, zebrafish, worm, and other model organisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Browsing a Reactome pathway Basic Protocol 2: Exploring Reactome annotations of disease and drugs Basic Protocol 3: Finding the pathways involving a gene or protein Alternate Protocol 1: Finding the pathways involving a gene or protein using UniProtKB (SwissProt), Ensembl, or Entrez gene identifier Alternate Protocol 2: Using advanced search Basic Protocol 4: Using the Reactome pathway analysis tool to identify statistically overrepresented pathways Basic Protocol 5: Using the Reactome pathway analysis tool to overlay expression data onto Reactome pathway diagrams Basic Protocol 6: Comparing inferred model organism and human pathways using the Species Comparison tool Basic Protocol 7: Comparing tissue-specific expression using the Tissue Distribution tool.
PMID: 37053306
ISSN: 2691-1299
CID: 5464262
Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases
Plant Reactome (https://plantreactome.gramene.org) and PubChem ( https://pubchem.ncbi.nlm.nih.gov ) are two reference data portals and resources for curated plant pathways, small molecules, metabolites, gene products, and macromolecular interactions. Plant Reactome knowledgebase, a conceptual plant pathway network, is built by biocuration and integrating (bio)chemical entities, gene products, and macromolecular interactions. It provides manually curated pathways for the reference species Oryza sativa (rice) and gene orthology-based projections that extend pathway knowledge to 106 plant species. Currently, it hosts 320 reference pathways for plant metabolism, hormone signaling, transport, genetic regulation, plant organ development and differentiation, and biotic and abiotic stress responses. In addition to the pathway browsing and search functions, the Plant Reactome provides the analysis tools for pathway comparison between reference and projected species, pathway enrichment in gene expression data, and overlay of gene-gene interaction data on pathways. PubChem, a popular reference database of (bio)chemical entities, provides information on small molecules and other types of chemical entities, such as siRNAs, miRNAs, lipids, carbohydrates, and chemically modified nucleotides. The data in PubChem is collected from hundreds of data sources, including Plant Reactome. This chapter provides a brief overview of the Plant Reactome and the PubChem knowledgebases, their association to other public resources providing accessory information, and how users can readily access the contents.
PMID: 35037224
ISSN: 1940-6029
CID: 5131362
The reactome pathway knowledgebase 2022
The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.
PMID: 34788843
ISSN: 1362-4962
CID: 5049232
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.
PMCID:8524328
PMID: 34664389
ISSN: 1744-4292
CID: 5037672
The Gene Ontology resource: enriching a GOld mine
ISI:000608437800042
ISSN: 0305-1048
CID: 4774022
Gramene 2021: harnessing the power of comparative genomics and pathways for plant research
Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabulary Extraction (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes-over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene-gene interactions. Gramene integrates ontology-based protein structure-function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.
PMCID:7779000
PMID: 33170273
ISSN: 1362-4962
CID: 4770752
Using Reactome to build an autophagy mechanism knowledgebase
The 21st century has revealed much about the fundamental cellular process of autophagy. Autophagy controls the catabolism and recycling of various cellular components both as a constitutive process and as a response to stress and foreign material invasion. There is considerable knowledge of the molecular mechanisms of autophagy, and this is still growing as new modalities emerge. There is a need to investigate autophagy mechanisms reliably, comprehensively and conveniently. Reactome is a freely available knowledgebase that consists of manually curated molecular events (reactions) organized into cellular pathways (https://reactome.org/). Pathways/reactions in Reactome are hierarchically structured, graphically presented and extensively annotated. Data analysis tools, such as pathway enrichment, expression data overlay and species comparison, are also available. For customized analysis, information can also be programmatically queried. Here, we discuss the curation and annotation of the molecular mechanisms of autophagy in Reactome. We also demonstrate the value that Reactome adds to research by reanalyzing a previously published work on genome-wide CRISPR screening of autophagy components.
PMID: 32486891
ISSN: 1554-8635
CID: 4480972
The reactome pathway knowledgebase
The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations in a single consistent data model, an extended version of a classic metabolic map. Reactome functions both as an archive of biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. To extend our ability to annotate human disease processes, we have implemented a new drug class and have used it initially to annotate drugs relevant to cardiovascular disease. Our annotation model depends on external domain experts to identify new areas for annotation and to review new content. New web pages facilitate recruitment of community experts and allow those who have contributed to Reactome to identify their contributions and link them to their ORCID records. To improve visualization of our content, we have implemented a new tool to automatically lay out the components of individual reactions with multiple options for downloading the reaction diagrams and associated data, and a new display of our event hierarchy that will facilitate visual interpretation of pathway analysis results.
PMID: 31691815
ISSN: 1362-4962
CID: 4179402
