Sheets 1–4: Enriched Reactome pathways. Lists of mouse gene names of significantly differentially expressed genes (S2 Table) were used for Reactome pathway enrichment [11], in which they were converted to their human orthologues and analysed to determine which pathways are significantly over-represented. Input gene lists are indicated in the sheet name, for example ‘Reactome_TS_up’ shows the Reactome enrichment results generated using the ‘TS_up’ list from S2 Table as input. Sheets 5–8: Enriched GO terms. The same differentially expressed mouse gene lists were used for GO term enrichment analysis by PANTHER [114], against a background list of all the genes which passed the threshold for inclusion in that expression analysis. Column labels are as described in both Reactome and PANTHER user guides. All results with significant p values (≤ 0.05) are shown. (XLSX)

Reactome pathway analysis of down-regulated proteins.

Reactome Knowledgebase version 68

Pathways with an entities p value

The most interesting pathways extracted via Reactome are shown. Ratio refers to the proportion of Reactome pathway molecules represented by this pathway. The p-value is the result of the statistical test for over-representation, and the False Discovery rate (FDR) is the corrected probability of over-representation. (XLSX)

pathway analysis with reactome

Reactome pathway over-representation analysis of nodes within the CD8 or CD11b subnetworks. P-values are adjusted to control for multiple comparisons.

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.

The result of pathway gene enrichment analysis was performed using the Reactome Pathway Database.

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.

Enriched pathways derived from Reactome Pathway Analysis.

Humans are exposed to millions of potential pathogens daily, through contact, ingestion, and inhalation. Our ability to avoid infection depends on the adaptive immune system and during the first critical hours and days of exposure to a new pathogen, our innate immune system.

Eukaryotic genes are transcribed to yield pre-mRNAs that are processed to add methyl guanosine cap structures and polyadenylate tails and to splice together segments of a pre-mRNA termed exons, thereby removing segments termed introns. More than 90% of human genes contain introns, with an average of 4.0 introns per gene and 3413 nucleotides per intron compared with 5.0 exons per gene and 50.9 nucleotides per exon (Deutsch and Long 1999). (Notable exceptions are the histone genes, which are intronless.)
Pre-mRNA splicing is performed by a large ribonucleoprotein complex, the spliceosome, which contains 5 small nuclear RNAs (snRNAs) and more than 150 proteins (reviewed in Will and Luhrmann 2011, Kastner et al. 2019, Yan et al. 2019, Fica et al. 2020, Wan et al. 2020, Wilkinson et al. 2020). The catalyst in the spliceosome comprises magnesium ions coordinated by the U6 snRNA that catalyze transesterification reactions between hydroxyl groups and phosphate groups from the pre-mRNA. The role of the U6 snRNA demonstrates that the spliceosome is a ribozyme hints at the origin of the spliceosome as a self-splicing group II intron.
The spliceosome is initially assembled cotranscriptionally on the pre-mRNA as the Spliceosomal E (Early) complex and then remodelled sequentially by association and dissociation of proteins and snRNAs to catalyze of the two reactions of splicing. First, a nucleophilic attack by the 2' hydroxyl group of a conserved adenine residue, the branch point, within the intron on the phosphate group of the 5' residue of the intron yields a lariat (looped) structure in the intron joined to the downstream (3') exon and a free upstream exon with a 3' hydroxyl group. Second, a nucleophilic attack by the 3' hydroxyl group of the upstream exon on the phosphate of the 5' residue of the downstream exon yields a spliced mRNA containing the upstream exon ligated to the downstream exon and a free intron containing a lariat structure.
The Spliceosomal E complex contains the U1 snRNP bound to the 5' splice site, SF1 bound to the branch point, and the U2AF complex bound to the polypyrimidine tract of the intron and the 3' splice site of the pre-mRNA (Zhuang and Weiner 1986, Hong et al. 1997, Das et al. 2000, Hartmuth et al. 2002, Rappsilber et al. 2002, Hegele et al. 2012, Makarov et al. 2012, Crisci et al. 2015, Kondo et al. 2015, Tan et al. 2016). SF1 and U2AF are displaced on the pre-mRNA and the U2 snRNP binds the branch region to yield the Spliceosomal A complex (Wu and Manley 1989, Fleckner et al. 1997, Neubauer et al. 1998, Hartmuth et al. 2002, Rappsilber et al. 2002, Xu et al. 2004, Behzadnia et al. 2007, Shen et al. 2008, Chen et al. 2017, Zhang et al. 2020). The U4/U6.U5 tri-snRNP, containing the U4 snRNA base-paired with the U6 snRNA plus the U5 snRNP and accessory proteins, binds the Spliceosomal A complex to form the Spliceosomal Pre-B complex (Hausner et al. 1990, Kataoka and Dreyfuss 2004, Chi et al. 2013, Mohlmann et al. 2014, Boesler et al. 2016, Zhan et al. 2018, Charenton et al. 2019, Kastner et al. 2019, Townsend et al. 2020). The U1 snRNP is replaced at the 5' splice site by the U6 snRNA and the spliceosome is remodelled to yield the Spliceosomal B complex (Ismaïli et al. 2001, Deckert et al. 2006, Bessonov et al. 2008, Wolf et al. 2009, Bessonov et al. 2010, Schmidt et al. 2014, Boesler et al. 2016, Bertram et al. 2017, Zhang et al. 2018, Kastner et al. 2019). The Spliceosomal B complex is activated to form the Spliceosomal Bact complex by dissociation of the U4 snRNP and Lsm proteins from the U6 snRNA, freeing the U6 snRNA to form the active site of the spliceosome (Lamond et al. 1988, Laggerbauer et al. 1998, Ajuh et al. 2000, Bessonov et al. 2010, Agafonov et al. 2011, Haselbach et al. 2018, Zhang et al. 2018, Kastner et al. 2019, Busetto et al. 2020). Dissociation of the SF3A and SF3B subcomplexes of the U2 snRNP allows the intron branch point to dock near the 5' splice site, forming the B* Spliceosomal complex. Reaction of the branch point at the 5' splice site, yields the Spliceosomal C complex (Jurica et al. 2002, Makarov et al. 2002, Rappsilber et al. 2002, Reichert et al. 2002, Kataoka and Dreyfuss 2004, Bessonov et al. 2010, Gencheva et al. 2010, Agafonov et al. 2011, Alexandrov et al. 2012, Barbosa et al. 2012, Steckelberg et al. 2012, Schmidt et al. 2014, Zhan et al. 2018, Kastner et al. 2019, Busetto et al. 2020). The branch point is rotated to allow the 3' splice site to enter the active site, yielding the Spliceosomal C* complex (Ortlepp et al. 1998, Zhou and Reed 1998, Jurica et al. 2002, Makarov et al. 2002, Rappsilber et al. 2002, Ilagan et al. 2013, Bertram et al. 2017, Zhang et al. 2017, Kastner et al. 2019). Reaction of the 3' hydroxyl group of the upstream exon at the 3' splice site yields the Spliceosomal P (postcatalytic) complex (Zhou et al. 2000, Kataoka and Dreyfuss 2004, Tange et al. 2005, Zhang and Krainer 2007, Chi et al. 2013, Ilagan et al. 2013, Bertram et al. 2017, Zhang et al. 2017, Fica et al. 2019, Zhang et al. 2019). The Spliceosomal P complex then dissociates to yield an mRNP containing the spliced mRNA and associated proteins, including the exon junction complex (EJC) (Ohno and Shimura 1996, Merz et al. 2007, Yoshimoto et al. 2009, Zanini et al. 2017, Felisberto-Rodrigues et al. 2019, Zhang et al. 2019, EJC reviewed in Schlautmann and Gehring 2020), and the Intron Lariat Spliceosome (ILS), which contains the intron lariat. The ILS is then disassembled to free its components for further splicing reactions and the intron lariat is degraded (Wen et al. 2008, Yoshimoto et al. 2009, Yoshimoto et al. 2014, Zhang et al. 2019, Studer et al. 2020).

Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of MECP2, especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results.

This dataset contains transcriptomics data for breast cancer samples.

It includes differential gene expression analysis results for various breast cancer subtypes.

The data is derived from high-throughput RNA sequencing studies.

It integrates functional enrichment analyses including KEGG and Reactome pathways.

Both gene-level and pathway-level insights are provided for breast cancer research.

Researchers can use this dataset to explore molecular mechanisms underlying breast cancer.

The dataset supports biomarker discovery and therapeutic target identification.

It provides interactive visualizations to facilitate exploration of gene expression patterns.

The dataset is suitable for bioinformatics, computational biology, and translational research.

Data files are processed and structured for immediate use in downstream analyses.

It enables reproducible research by providing both raw and analyzed results.

The dataset can be used for machine learning applications in cancer classification and prognosis.

Cytokines are small proteins that regulate and mediate immunity, inflammation, and hematopoiesis. They are secreted in response to immune stimuli, and usually act briefly, locally, at very low concentrations. Cytokines bind to specific membrane receptors, which then signal the cell via second messengers, to regulate cellular activity.

Reactome pathways data set: Pathway information and the genes involved in them, inferred through orthologues from Human curated pathways

Reactome pathways data set

For retroviral DNA to direct production of progeny virions it must become covalently integrated into the host cell chromosome (reviewed in Coffin et al. 1997; Hansen et al. 1998). Analyses of mutants have identified the viral integrase coding region (part of the retroviral pol gene) as essential for the integration process (Donehower 1988; Donehower and Varmus 1984; Panganiban and Temin 1984; Quinn and Grandgenett 1988; Schwartzberg et al. 1984). Also essential are regions at the ends of the viral long terminal repeats (LTRs) that serve as recognition sites for integrase protein (Colicelli and Goff 1985, 1988; Panganiban and Temin 1983).

The viral genomic RNA is reverse transcribed to form a linear double-stranded DNA molecule, the precursor to the integrated provirus (Brown et al. 1987, 1989; Fujiwara and Mizuuchi 1988). The provirus is colinear with unintegrated linear viral DNA (Dhar et al. 1980; Hughes et al. 1978) but differs from the reverse transcription product in that it is missing two bases from each end (Hughes et al. 1981). Flanking the integrated HIV provirus are direct repeats of the cellular DNA that are 5 base pairs in length (Vincent et al. 1990). This duplication of cellular sequences flanking the viral DNA is generated as a consequence of the integration mechanism (Coffin et al., 1997).

Linear viral DNA is found in a complex with proteins in the cytoplasm of infected cells. These complexes (termed "preintegration complexes", PICs) can be isolated and have been shown to mediate integration of viral DNA into target DNA in vitro (Bowerman et al. 1989; Brown et al. 1987; Ellison et al. 1990; Farnet and Haseltine 1990, 1991).

The development of in vitro assays with purified integrase has allowed its enzymatic functions to be elucidated. The provirus is formed by two reactions catalyzed by the viral integrase: terminal cleavage and strand transfer. Studies with purified integrase have shown that it is sufficient for both 3' end cleavage (Bushman and Craigie 1991; Craigie et al. 1990; Katzman et al. 1989; Sherman and Fyfe 1990) and joining of the viral DNA to the cellular chromosome or naked target DNA (Bushman et al. 1990; Craigie et al. 1990; Katz et al. 1990). HIV integrase catalyze the removal of two bases from the 3' end of each viral DNA strand, leaving recessed 3' hydroxyl groups (Brown et al. 1989; Fujiwara and Mizuuchi 1988; Roth et al. 1989; Sherman and Fyfe 1990). This terminal cleavage reaction is required for proper integration. It may allow the virus to create a standard end from viral DNA termini that can be heterogeneous due to the terminal transferase activity of reverse transcriptase (Miller et al. 1997; Patel and Preston 1994). In addition, the terminal cleavage step is coupled to the formation of a stable integrase-DNA complex (Ellison and Brown 1994; Vink et al. 1994). Following terminal cleavage, a recessed hydroxyl is exposed that immediately follows a CA dinucleotide. More internal LTR sites are also important for integration (Balakrishnan and Jonsson 1997; Bushman and Craigie 1990; Leavitt et al. 1992). After end processing, integrase catalyzes the covalent attachment of hydroxyl groups at the viral DNA termini to protruding 5' phosphoryl ends of the host cell DNA (Brown et al. 1987; Brown et al. 1989; Fujiwara and Mizuuchi 1988). The DNA cleavage and joining reactions involved in integration are shown in the figure below. Both the viral DNA 3' end cleavage and strand transfer reactions are mediated by single-step transesterification chemistry as shown by stereochemical analysis of reaction products (Engelman et al. 1991). Biochemical analysis of purified integrase revealed that it requires a divalent metal - either Mg2+ or Mn2+ - to carry out reactions with model substrates, that probably mediate the reaction chemistry (Bushman and Craigie 1991; Craigie et al. 1990; Katzman et al. 1989; Sherman and Fyfe 1990; Gao et al. 2004).

Gene Ontology, KEGG, Reactome and WIKI pathway analysis of unfiltered RNA-Seq DESeq2 data in response to pulsatile or constant CORT infusion compared to pulsatile or constant VEH respectively. Results for pulsatile and constant infusion are on separate sheets, and each spreadsheet details process ID, brief description of pathway and rank (BD) as well further detailed information (E-J) for each analysis. (XLSX)

ObjectiveThis study aims to characterize differential expressed pathways (DEP) in subjects with preclinical systemic sclerosis (PreSSc) characterized uniquely by Raynaud phenomenon, specific autoantibodies, and/or capillaroscopy positive for scleroderma pattern.MethodsWhole-blood samples from 33 PreSSc with clinical prospective data (baseline and after 4 years of follow-up) and 16 matched healthy controls (HC) were analyzed for global gene expression transcriptome analysis via RNA sequencing. Functional Analysis of Individual Microarray Expression method annotated Reactome individualized pathways. ANOVA analysis identified DEP whose predictive capability were tested in logistic regression models after extensive internal validation.ResultsAt 4 years, 42.4% subjects progressed (evolving PreSSc), while the others kept stable PreSSc clinical features (stable PreSSc). At baseline, out of 831 pathways, 541 DEP were significant at a false discovery rate <0.05, differentiating PreSSc versus HC with an AUROC = 0.792 ± 0.242 in regression models. Four clinical groups were identified via unsupervised clustering (HC, HC and PreSSc with HC-like features, PreSSc and HC with PreSSc-like features, and PreSSc). Biological signatures changed with disease progression while remaining unchanged in stable subjects. The magnitude of change was related to the baseline cluster, yet no DEP at baseline was predictive of progression. Disease progression was mostly related to changes in signal transduction pathways especially linked to calcium-related events and inositol 1,4,5-triphosphate metabolism.ConclusionPreSSc had distinguished Reactome pathway signatures compared to HC. Progression to definite SSc was characterized by a shift in biological fingertips. Calcium-related events promoting endothelial damage and vasculopathy may be relevant to disease progression.