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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., (© 2023 Wiley Periodicals LLC.)

Host responses to vaccines are complex but important to investigate. To facilitate the study, we have developed a tool called Vaccine Induced Gene Expression Analysis Tool (VIGET), with the aim to provide an interactive online tool for users to efficiently and robustly analyze the host immune response gene expression data collected in the ImmPort/GEO databases. VIGET allows users to select vaccines, choose ImmPort studies, set up analysis models by choosing confounding variables and two groups of samples having different vaccination times, and then perform differential expression analysis to select genes for pathway enrichment analysis and functional interaction network construction using the Reactome's web services. VIGET provides features for users to compare results from two analyses, facilitating comparative response analysis across different demographic groups. VIGET uses the Vaccine Ontology (VO) to classify various types of vaccines such as live or inactivated flu vaccines, yellow fever vaccines, etc. To showcase the utilities of VIGET, we conducted a longitudinal analysis of immune responses to yellow fever vaccines and found an intriguing complex activity response pattern of pathways in the immune system annotated in Reactome, demonstrating that VIGET is a valuable web portal that supports effective vaccine response studies using Reactome pathways and ImmPort data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Brunson, Sanati, Huffman, Masci, Zheng, Cooke, Conley, He and Wu.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: With COVID-19 still in its pandemic stage, extensive research has generated increasing amounts of data and knowledge. As many studies are published within a short span of time, we often lose an integrative and comprehensive picture of host-coronavirus interaction (HCI) mechanisms. As of early April 2021, the ImmPort database has stored 7 studies (with 6 having details) that cover topics including molecular immune signatures, epitopes, and sex differences in terms of mortality in COVID-19 patients. The Coronavirus Infectious Disease Ontology (CIDO) represents basic HCI information. We hypothesize that the CIDO can be used as the platform to represent newly recorded information from ImmPort leading the reinforcement of CIDO., Methods: The CIDO was used as the semantic platform for logically modeling and representing newly identified knowledge reported in the 6 ImmPort studies. A recursive eXtensible Ontology Development (XOD) strategy was established to support the CIDO representation and enhancement. Secondary data analysis was also performed to analyze different aspects of the HCI from these ImmPort studies and other related literature reports., Results: The topics covered by the 6 ImmPort papers were identified to overlap with existing CIDO representation. SARS-CoV-2 viral S protein related HCI knowledge was emphasized for CIDO modeling, including its binding with ACE2, mutations causing different variants, and epitope homology by comparison with other coronavirus S proteins. Different types of cytokine signatures were also identified and added to CIDO. Our secondary analysis of two cohort COVID-19 studies with cytokine panel detection found that a total of 11 cytokines were up-regulated in female patients after infection and 8 cytokines in male patients. These sex-specific gene responses were newly modeled and represented in CIDO. A new DL query was generated to demonstrate the benefits of such integrative ontology representation. Furthermore, IL-10 signaling pathway was found to be statistically significant for both male patients and female patients., Conclusion: Using the recursive XOD strategy, six new ImmPort COVID-19 studies were systematically reviewed, the results were modeled and represented in CIDO, leading to the enhancement of CIDO. The enhanced ontology and further seconary analysis supported more comprehensive understanding of the molecular mechanism of host responses to COVID-19 infection., (© 2021. The Author(s).)

Vaccines stimulate various immune factors critical to protective immune responses. However, a comprehensive picture of vaccine-induced immune factors and pathways have not been systematically collected and analyzed. To address this issue, we developed VaximmutorDB, a web-based database system of vaccine immune factors (abbreviated as "vaximmutors") manually curated from peer-reviewed articles. VaximmutorDB currently stores 1,740 vaccine immune factors from 13 host species (e.g., human, mouse, and pig). These vaximmutors were induced by 154 vaccines for 46 pathogens. Top 10 vaximmutors include three antibodies (IgG, IgG2a and IgG1), Th1 immune factors (IFN-γ and IL-2), Th2 immune factors (IL-4 and IL-6), TNF-α, CASP-1, and TLR8. Many enriched host processes (e.g., stimulatory C-type lectin receptor signaling pathway, SRP-dependent cotranslational protein targeting to membrane) and cellular components (e.g., extracellular exosome, nucleoplasm) by all the vaximmutors were identified. Using influenza as a model, live attenuated and killed inactivated influenza vaccines stimulate many shared pathways such as signaling of many interleukins (including IL-1, IL-4, IL-6, IL-13, IL-20, and IL-27), interferon signaling, MARK1 activation, and neutrophil degranulation. However, they also present their unique response patterns. While live attenuated influenza vaccine FluMist induced significant signal transduction responses, killed inactivated influenza vaccine Fluarix induced significant metabolism of protein responses. Two different Yellow Fever vaccine (YF-Vax) studies resulted in overlapping gene lists; however, they shared more portions of pathways than gene lists. Interestingly, live attenuated YF-Vax simulates significant metabolism of protein responses, which was similar to the pattern induced by killed inactivated Fluarix. A user-friendly web interface was generated to access, browse and search the VaximmutorDB database information. As the first web-based database of vaccine immune factors, VaximmutorDB provides systematical collection, standardization, storage, and analysis of experimentally verified vaccine immune factors, supporting better understanding of protective vaccine immunity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Berke, Sun, Ong, Sanati, Huffman, Brunson, Loney, Ostrow, Racz, Zhao, Xiang, Masci, Zheng, Wu and He.)

The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22 ; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.