Reactome pathway analysis of up-regulated proteins.

Reactome Knowledgebase version 68

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.

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.

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

ExpressionData.csv

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A small subset of transcriptomics data (30 genes) curated for learning Gene Regulatory Networks (GRNs) pertaining to signaling by the ALK pathway. Genes were selected by referencing the "signaling by ALK" pathway from Reactome (https://reactome.org/content/detail/R-HSA-201556). This subset of data belongs the TARGET-NBL project (https://portal.gdc.cancer.gov/projects/TARGET-NBL), hosted via the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/). Please refer to GDCs data access policies (https://gdc.cancer.gov/about-gdc/gdc-policies) if planning to use the data.

refNetwork.csv

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Contains a reference network of known pairwise regulatory relationships among the genes of which we have transcriptomics data available in "ExpressionData.csv." These relationships were again determined by referencing the "signaling by ALK" pathway from Reactome (https://reactome.org/content/detail/R-HSA-201556).

To determine the consequences of Brg1 expression in ALCL, cells were transfected with shRNA to Brg1 leading to a decrease in Brg1 transcript and protein levels. RNA was isolated 72 hours later from the shRNA5 (TRCN0000015551) expressing cells and sequencing conducted to examine changes to the transcriptome mediated by Brg1. Following differential gene expression analysis of the scrambled shRNA versus shRNA5, the transcriptome was identified as being dramatically altered showing both downregulation (2,527 mRNA transcripts: of a cumulative 26,833 genes, adj. p ≤ 0.05) and upregulation (2,900 mRNA transcripts, of a cumulative 26,833 genes, adj. p ≤ 0.05) of genes. When analysing the the 15,285 protein-coding genes alone, 1,934 (13%) were downregulated while 2,797 (18%) were upregulated (adj. p ≤ 0.05). To establish which physiological processes are most affected by the activity of BRG1, pathway enrichment analyses were undertaken. The input comprised of the significant (adj. p ≤ 0.05) protein-coding targets, where the absolute log2 fold change of expression was ± 1.5. Transcripts whose expression was either induced (n=33) or repressed (n=243) by BRG1 were investigated. Reactome analysis suggests that BRG1 plays roles in upregulating genes associated with the cell cycle, while suppressing genes associated with epigenetic regulation.

Table showing the top ten pathways obtained performing over-representation analysis in PathVisio.

The human pathway database which contains different biological entities and reactions and software tools for analysis. PATIKA Database integrates data from several sources, including Entrez Gene, UniProt, PubChem, GO, IntAct, HPRD, and Reactome. Users can query and access this data using the PATIKAweb query interface. Users can also save their results in XML or export to common picture formats. The BioPAX and SBML exporters can be used as part of this Web service.

The global molecular biology online tool market is projected to grow from an estimated value of USD 215.1 million in 2025 to USD 383.4 million by 2033, exhibiting a CAGR of 6.4% during the forecast period. This growth is attributed to factors such as the increasing adoption of cloud-based and on-premises solutions, the rising prevalence of chronic diseases, and the growing research and development activities in the field of molecular biology. Additionally, government initiatives and funding for research projects are expected to further drive market growth. The molecular biology online tool market is segmented into various types and applications. The cloud-based segment is expected to dominate the market during the forecast period due to its scalability, cost-effectiveness, and ease of deployment. The on-premises segment is also anticipated to have a significant market share as it offers greater security and control over data. In terms of applications, the market is segmented into SMEs and large enterprises. Large enterprises are expected to account for a larger share of the market due to their higher spending on research and development activities and their need for robust and comprehensive tools. The key players in the market include CapitalBio Technology, GSL Biotech LLC, Integrated DNA Technologies, Inc. (IDT), clontech, SoftGenetics, LLC., Lynnon Biosoft, Thermo Fisher Scientific, PREMIER Biosoft, Biomatters, Benchling, BioVision, BLAST, Ensembl, UniProt, Ingenuity Pathway Analysis (IPA), Reactome, GeneMANIA, MOLBIOTOOLS, and others. These companies offer a range of molecular biology online tools and services, including gene editing, sequencing, and analysis software.

A member of the PP2A family of phosphatases dephosphorylates both cytosolic and nuclear forms of ChREBP (Carbohydrate Response Element Binding Protein). In the nucleus, dephosphorylated ChREBP complexes with MLX protein and binds to ChRE sequence elements in chromosomal DNA, activating transcription of genes involved in glycolysis and lipogenesis. The phosphatase is activated by Xylulose-5-phosphate, an intermediate of the pentose phosphate pathway (Kabashima et al. 2003). The rat enzyme has been purified to homogeneity and shown by partial amino acid sequence analysis to differ from previously described PP2A phosphatases (Nishimura and Uyeda 1995) - the human enzyme has not been characterized.

Structural analysis of MyD88:IRAK4 and MyD88:IRAK4:IRAK2 suggested that upon MyD88 recruitment to an activated dimerized TLR the MyD88 death domains clustering induces the formation of Mydosome, a large oligomeric signaling platform (Motshwene PG et al 2009, Lin SC et al 2010). Assembly of these Myddosome complexes brings the kinase domains of IRAKs into proximity for phosphorylation and activation. The oligomer complex stoichiometry was reported as 7:4 and 8:4 for MyD88:IRAK4 (Motshwene PG et al 2009), and 6:4:4 in the complex of MyD88:IRAK4:IRAK2(Lin SC et al 2010).

Phenylacetyl CoA and glutamine react to form phenylacetyl glutamine and Coenzyme A. The enzyme that catalyzes this reaction has been purified from human liver mitochondria and shown to be a distinct polypeptide species from glycine-N-acyltransferase (Webster et al. 1976). This human glutamine-N-acyltransferase activity has not been characterized by sequence analysis at the protein or DNA level, however, and thus cannot be associated with a known human protein in the annotation of phenylacetate conjugation.

MSH2 is homologous to the E. coli MutS gene and is involved in DNA mismatch repair (MMR) (Fishel et al., 1994).
Evidence to support a role for the mismatch repair genes human mutS homolog 2 (hMSH2) in the etiology of colorectal cancer has come from linkage analysis, segregation studies, and molecular biologic analysis. More recently, carriers of potentially pathogenic mutations in the hMSH2 genes have consistently been shown to be at a greatly increased risk of developing colorectal cancer compared with the general population.
Two variants are described here MSH2 ARG406TER and MSH2 GLN601TER (Leach et al., 1993, Kolodner et al., 1994). Both variants disrupt the formation of the MSH2:MSH3 complex. The MSH2 ARG406TER occurred in a kindred with hereditary nonpolyposis colorectal cancer. The variant contains a CGA-to-TGA transition in codon 406, resulting in change of arginine to a stop.
The MSH2 GLN601TER variant occurs in a kindred with characteristics of the Muir-Torre syndrome (Kolodner et al., 1994).

In mammals activated IRAK4 phosphorylates both IRAK1 and IRAK2. Activated IRAK1 and IRAK2 in turn induce NFkB and AP-1 intracellular signaling cascades mediated by TRAF6.

Bioinformatic analysis reveals no evidence of IRAK1 in the chicken genome. It is possible that the chicken TLR pathway utilizes only IRAK-2, which shares 47% amino acid sequence identity with human IRAK2.

adipose RNA seq data

https://doi.org/10.5061/dryad.h44j0zpsj

RNAseq data from epidydimal white adipose tissue isolated from 2 mouse treatment groups: group1-mice consuming water containing 0.5 micron polystyrene beads and injected with DMSO group2- mice consuming water containing 0.5 micron polystyrene beads and injected with delphinidin

Description of the data and file structure

Two Excel files listing the differentially expressed genes (DEGs) are supplied. One file lists those genes which are up-regulated in the delphinidin group versus the DMSO group (TO-DELPvsDMSO_up.xlsx). The second file lists those genes which are down-regulated in the delphinidin group versus the DMSO group (TO-DELPvsDMSO_down.xlsx). In each Excel file, there are two tabs listing the DEGs. One tab lists in order of fold change, while the other lists in order of p-value. As columns in each tab and in each file, there are: A: level in the delphinidin group; B: level in the DMSO group; C:log2 fold change...

An expanding multi-omics resource that enables rapid browsing of gene and protein expression information from publicly available studies on humans and model organisms. MOPED also serves the greater research community by enabling users to visualize their own expression data, compare it with existing studies, and share it with others via private accounts. MOPED uniquely provides gene and protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis utilizing SPIRE (Systematic Protein Investigative Research Environment). Data can be queried for specific genes and proteins; browsed based on organism, tissue, localization and condition; and sorted by false discovery rate and expression. MOPED links to various gene, protein, and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED (MOPED 2.5) The current version of MOPED (MOPED 2.5, 2014) contains approximately 5 million total records including ~260 experiments and ~390 conditions.

A fluorescence-activated cell sorting (FACS)-based, genome-wide CRISPR-Cas9 screen on a HEK293T NF-κB reporter cell line identified alpha protein kinase 1 (ALPK1), tumor necrosis factor (TNF-α) receptor-associated factor (TRAF)-interacting protein with the forkhead-associated domain (TIFA) and TRAF6 as mediators of NF-kB activation induced by bacterial ADP L-glycero-β-d-manno-heptose (ADP-heptose) or by Yersinia pseudotuberculosis (Y. pseudotuberculosis) (Zhou P et al. 2018). ADP-heptose is metabolic intermediate in the lipopolysaccharide (LPS) biosynthesis, which is present in all Gram-negative and some Gram-positive bacteria (Tang W et a. 2018). ADP-heptose stimulated coimmunoprecipitation of TIFA with ALPK1 and TRAF6 in HEK293T cells (Zhou P et al. 2018). The co-localization of both proteins was visualized in Shigella flexneri (S. flexneri)-infected HeLa cells co-transfected with TIFA-myc and TRAF6-Flag cDNA constructs (Milivojevic M et al. 2017). The same result was obtained upon infection of human epithelial colorectal adenocarcinoma Caco-2 cells. The interaction between TIFA and TRAF6 was further confirmed by co-immunoprecipitation assay in HeLa cells co-transfected with TIFA-myc and TRAF6-Flag cDNA constructs (Milivojevic M et al. 2017). The E178A TIFA mutant was unable to bind TRAF6 suggesting that TRAF6 activation was dependent on the TRAF6 binding motif of TIFA (Milivojevic M et al. 2017). Finally, structural studies further support the interaction between TIFA and TRAF6 (Huang WC et al. 2019). Small interfering RNA (siRNA) oligonucleotides targeting Ubc13, TRAF6, or TRAF2 strongly inhibited TIFA-mediated NF-κB activation upon the expression of these genes in HEK293 cells transfected with TIFA expression vector and a luciferase reporter gene thus suggesting that Ubc13, TRAF2, and TRAF6 are required for TIFA-mediated NF-κB activation in living cells (Ea CK et al. 2004). Further, analysis of the molecular sizes by glycerol-gradient ultracentrifugation showed that only the high-molecular-weight forms of TIFA co-sedimented with TRAF6, suggesting that oligomerization of TIFA greatly enhances its ability to bind to TRAF6 (Ea CK et al. 2004). The TIFA mutant that did not bind to TRAF6 was also unable to induce TRAF6 oligomerization (Ea CK et al. 2004). In vitro ubiquitination assay in the presence of E1, Ubc13-Uev1A, purified endogenous TRAF6, Ub, and ATP showed that TIFA enhanced the Ub ligase activity of TRAF6 (Ea CK et al. 2004). ALPK1 kinase activity was found to control TIFA oligomerization and TRAF6 activation in response to the invasive bacteria Y. pseudotuberculosis, S. flexneri and Salmonella typhimurium as well as to the extracellular pathogen Neisseria meningitidis (Milivojevic M et al. 2017; Zhou P et al. 2018). Thus, ALPK1 induces TIFA oligomerization upon bacterial infection (Zhou P et al. 2018; Milivojevic M et al. 2017). The oligomerized forms of TIFA bind to TRAF6 and promote TRAF6 oligomerization (Ea CK et al. 2004). As a result, the TRAF6 Ub ligase is activated to catalyze K63-linked polyubiquitination in conjunction with the Ubc13-Uev1A E2 complex (Ea CK et al. 2004). Activated TRAF6 promotes polyubiquitin-mediated activation of the protein kinase TAK1 (MAP3K7) complex (Ea CK et al. 2004). TAK1 is then phosphorylates IkB kinase (IKK) at key serine residues within the activation loop, thereby activating IKK complex (Israël A 2010).

Mammalian CD59, the major inhibitor of the complement membrane attack complex (MAC), is an 18-20 kDa glycoprotein, linked to the membrane via a glycosylphosphatidylinositol (GPI)-anchor. It interacts with complement components C8 and C9 during assembly of the membrane attack complex (MAC) and inhibits C9 polymerization, thus preventing the formation of MAC (Lehto T and Meri S. 1993; Rollins SA et al. 1991).

RT-PCR analysis revealed that chicken CD59 mRNA is expressed in brain, heart, lung, spleen, liver, stomach and kidney. It is fully expressed in the following developmental stages: whole embryo 4- and 6-day old, embryo liver of 12- and 17-day old and neonate 2- and 5-day old (Mikrou A and Zarkadis IK 2010).

The module represents hypothetical chicken CD59, which may not have an identifier assigned in any of genome databases. The binding of chicken CD59 to MAC has not been verified experimentally but is inferred from properties of the human proteins.

The family of beta 4-galactosyltransferases (B4GALTs) is composed by at least six known members that mediate the transfer of galactose to N-glycan structures and either to begin or elongate keratan chains. Defective B4GALT1 is associated with congenital disorder of glycosylation type IId (B4GALT1-CDG, CDG-2d; MIM:607091), in which clinical symptoms are dominated by dysmorphic features, psychomotor and mental retardation, hypotonia, as well as blood coagulation abnormalities (Hansske et al. 2002). The mutant R345Kfs*6 results in a truncated, inactive polypeptide. Analysis of oligosaccharides from serum transferrin from these patients reveals loss of sialic acid and galactose residues (Hansske et al. 2002).

Accumulation of non-native or misfolded proteins upon cellular stress is believed to release monomeric HSF1 from chaperon regulatory proteins (Guo Y et al. 2001). The released HSF1 monomer is rapidly converted to a homotrimer (Baler R et al. 1993; Herbomel G et al 2013). Upon trimerization HSF1 undergoes significant conformational changes resulting in an assembly of a stable triple-stranded alpha-helical coiled-coil structure with the amino-terminal hydrophobic domains from individual monomeric units (Rabindran SK et al. 1993; Zuo J et al. 1994, 1995; Neef DW et al. 2013). Biochemical and structural analysis strongly suggest that the monomer-to-trimer transition is tightly regulated at several interdependent levels. Thus, HSPs and cofactors bind HSF1 monomers preventing trimerization (Zou J et al.1998; Guo Y et al. 2001). In addition, leucine zippers (LZ) in the trimerization domain (LZ1-LZ3) are thought to retain HSF1 in its inactive monomeric form by intramolecular coiled-coil interactions with LZ4 in the carboxyl-terminus of HSF1, while LZ interactions between trimerization domains of individual monomeric units facilitate homotrimerization (Rabindran SK et al. 1993; Zuo J et al. 1994, 1995; Neef DW et al. 2013). HSF1 flexible linker region between DNA binding domain and first LZ of the trimerization domain was also found to modulate the monomer-trimer equilibrium (Liu PCC and Thiele DJ 1999). Furthermore, intermolecular disulfide bonds between cysteine residues 36 and 103 were reported to stabilize HSF1 trimer, while intramolecular disulfide crosslink inhibited HSF1 oligomerization (Lu M et al. 2008, 2009). Moreover, redox regulatory mechanisms were shown to regulate thiol-disulfide exchange and the conformation and activity of mammalian HSF1 in response to stress (Manalo DJ et al. 2002; Ahn SG and Thiele DJ 2003).

A ribonucleoprotein complex containing translation elongation factor EEF1A1 (eEF1A) and a long non-coding RNA, HSR1 (heat shock RNA-1) was shown to mediate trimerization of HSF1 (Shamovsky I et al. 2006).