SPEED2: inferring upstream pathway activity from differential gene expression

Rydenfelt, Mattias; Klinger, Bertram; Klünemann, Martina; Blüthgen, Nils

doi:10.1093/nar/gkaa236

Cited by 30 publications

(23 citation statements)

References 18 publications

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“…Extracting the activity of signal transduction pathways from transcriptome data is important for inferring the mechanism origin of abnormal transcriptome regulation. Therefore, we used SPEED2 ( https://speed2.sys-bio.net/ ) [ 19 ] to infer upstream pathway activity from common genes between quercetin and COVID-19/DENGUE. When providing a list of genes, the Web server allows inferences about signaling pathways that may cause these genes to be dysregulated.…”

Section: Methodsmentioning

confidence: 99%

Quercetin for COVID-19 and DENGUE co-infection: a potential therapeutic strategy of targeting critical host signal pathways triggered by SARS-CoV-2 and DENV

Zheng

Wang

et al. 2021

Briefings in Bioinformatics

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Background The clinical consequences of SARS-CoV-2 and DENGUE virus co-infection are not promising. However, their treatment options are currently unavailable. Current studies have shown that quercetin is both resistant to COVID-19 and DENGUE; this study aimed to evaluate the possible functional roles and underlying mechanisms of action of quercetin as a potential molecular candidate against COVID-19 and DENGUE co-infection. Methods We used a series of bioinformatics analyses to understand and characterize the biological functions, pharmacological targets and therapeutic mechanisms of quercetin in COVID-19 and DENGUE co-infection. Results We revealed the clinical characteristics of COVID-19 and DENGUE, including pathological mechanisms, key inflammatory pathways and possible methods of intervention, 60 overlapping targets related to the co-infection and the drug were identified, the protein–protein interaction (PPI) was constructed and TNFα, CCL-2 and CXCL8 could become potential drug targets. Furthermore, we disclosed the signaling pathways, biological functions and upstream pathway activity of quercetin in COVID-19 and DENGUE. The analysis indicated that quercetin could inhibit cytokines release, alleviate excessive immune responses and eliminate inflammation, through NF-κB, IL-17 and Toll-like receptor signaling pathway. Conclusions This study is the first to reveal quercetin as a pharmacological drug for COVID-19 and DENGUE co-infection. COVID-19 and DENGUE co-infection remain a potential threat to the world’s public health system. Therefore, we need innovative thinking to provide admissible evidence for quercetin as a potential molecule drug for the treatment of COVID-19 and DENGUE, but the findings have not been verified in actual patients, so further clinical drug trials are needed.

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Section: Methodsmentioning

confidence: 99%

Quercetin for COVID-19 and DENGUE co-infection: a potential therapeutic strategy of targeting critical host signal pathways triggered by SARS-CoV-2 and DENV

Zheng

Wang

et al. 2021

Briefings in Bioinformatics

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“…Functional Enrichment analysis for the original MetaIntegrator signature was performed using the Enrich R package against the following databases: GO Biological Processes (GO BPs), GO Molecular Functions (GO MFs), GO Cellular Components (GO CCs), and KEGG. Upstream signaling pathways were extracted using the Signaling Pathway Enrichment using Experimental Datasets (SPEED) web-tool ( 43 ). Enrichment for upstream pathways using a list of either upregulated or downregulated genes was tested using the Bates distribution test.…”

Section: Methodsmentioning

confidence: 99%

“…In comparison with pathway membership based methods such as Reactome ( 44 ) and gene ontology, SPEED offers some advantage due to its ability to infer causative upstream signals. Its overall performance is compatible with GSEA when using the Bates test ( 43 ).…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Integration of Genome-Wide Association and Gene Expression Studies Reveals Novel Gene Signatures and Potential Therapeutic Targets for Helicobacter pylori-Induced Gastric Disease

2021

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Helicobacter pylori is a gram-negative bacterium that colonizes the human gastric mucosa and can lead to gastric inflammation, ulcers, and stomach cancer. Due to the increase in H. pylori antimicrobial resistance new methods to identify the molecular mechanisms of H. pylori-induced pathology are urgently needed. Here we utilized a computational biology approach, harnessing genome-wide association and gene expression studies to identify genes and pathways determining disease development. We mined gene expression data related to H. pylori-infection and its complications from publicly available databases to identify four human datasets as discovery datasets and used two different multi-cohort analysis pipelines to define a H. pylori-induced gene signature. An initial Helicobacter-signature was curated using the MetaIntegrator pipeline and validated in cell line model datasets. With this approach we identified cell line models that best match gene regulation in human pathology. A second analysis pipeline through NetworkAnalyst was used to refine our initial signature. This approach defined a 55-gene signature that is stably deregulated in disease conditions. The 55-gene signature was validated in datasets from human gastric adenocarcinomas and could separate tumor from normal tissue. As only a small number of H. pylori patients develop cancer, this gene-signature must interact with other host and environmental factors to initiate tumorigenesis. We tested for possible interactions between our curated gene signature and host genomic background mutations and polymorphisms by integrating genome-wide association studies (GWAS) and known oncogenes. We analyzed public databases to identify genes harboring single nucleotide polymorphisms (SNPs) associated with gastric pathologies and driver genes in gastric cancers. Using this approach, we identified 37 genes from GWA studies and 61 oncogenes, which were used with our 55-gene signature to map gene-gene interaction networks. In conclusion, our analysis defines a unique gene signature driven by H. pylori-infection at early phases and that remains relevant through different stages of pathology up to gastric cancer, a stage where H. pylori itself is rarely detectable. Furthermore, this signature elucidates many factors of host gene and pathway regulation in infection and can be used as a target for drug repurposing and testing of infection models suitability to investigate human infection.

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“…Furthermore, tools to signal functional redundancies between signatures have been developed. One such tool is InfoSigMap, an interactive map that is composed of 962 informative signatures that have been singled out for their likelihood to be enriched in multiple comparative cancer studies and, as such, have been used to determine cross-relationships of such signatures as well as the visualization of omics data [ 72 ].…”

Section: Integration Of Multiple Datasets: Combining Forward and Rmentioning

confidence: 99%

“…The cluster on the left-hand side of the figure represents tools that are developed to test signature reproducibility across heterogenous and numerous datasets including those analyzing multiple independent studies of the same condition through gene signature using meta-analysis (GSMA) [ 68 ], inferring signaling pathways affected by TFs (SPEED2) [ 69 ], and determining signature validity across datasets (SigQC) [ 70 ]. The cluster on the right-hand side of the figure represents signature maps and repositories where one can access multiple signatures for validation or further experimentation including the Molecular Signatures Database (MSigDB) [ 71 ] and InfoSigMap [ 72 ]. Acronyms used in the figure: LOOCV (leave-one-out cross-validation); PCA (principal component analysis); GW (genome-wide); ARACNE (Algorithm for the Reconstruction of Accurate Cellular Network); CLR (context likelihood of relatedness); MILP (mixed integer linear programming); NMF (nonnegative matrix factorization); LPD (Latent Process Decomposition); GSEA (gene set enrichment analysis); and GSMA (gene signature using meta-analysis).…”

Section: Figurementioning

confidence: 99%

Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges

Doultsinos

Mills

2021

Cancers

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Prostate cancer is a high-incidence cancer that requires improved patient stratification to ensure accurate predictions of risk and treatment response. Due to the significant contributions of transcription factors and epigenetic regulators to prostate cancer progression, there has been considerable progress made in developing gene signatures that may achieve this. Some of these are aligned to activities of key drivers such as the androgen receptor, whilst others are more agnostic. In this review, we present an overview of these signatures, the strategies for their derivation, and future perspectives on their continued development and evolution.

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SPEED2: inferring upstream pathway activity from differential gene expression

Cited by 30 publications

References 18 publications

Quercetin for COVID-19 and DENGUE co-infection: a potential therapeutic strategy of targeting critical host signal pathways triggered by SARS-CoV-2 and DENV

Quercetin for COVID-19 and DENGUE co-infection: a potential therapeutic strategy of targeting critical host signal pathways triggered by SARS-CoV-2 and DENV

Comprehensive Integration of Genome-Wide Association and Gene Expression Studies Reveals Novel Gene Signatures and Potential Therapeutic Targets for Helicobacter pylori-Induced Gastric Disease

Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges

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