STITCH 4: integration of protein–chemical interactions with user data

Kuhn, Michael; Szklarczyk, Damian; Pletscher-Frankild, Sune; Blicher, Thomas; Mering, Christian von; Jensen, Lars Juhl; Bork, Peer

doi:10.1093/nar/gkt1207

Cited by 405 publications

(333 citation statements)

References 43 publications

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“…We confirmed our finding in longitudinal KORA data and replicated them in two independent studies: the Erasmus Rucphen Family study (ERF) (28) and the Netherlands Twin Register (NTR) (29). The biologically relevant pathways for the identified metabolites and their associated genes were further analyzed in organ-specific proteinmetabolite interaction networks (30,31). Additionally, we assessed the effects of metformin treatment on LDL-C levels.…”

supporting

confidence: 76%

“…Information on proteinprotein interactions was extracted from STITCH (30). Known metformin target genes were retrieved from the DrugBank (4).…”

Section: Pathway Analysismentioning

confidence: 99%

“…To explore potentially related pathways, we used a bioinformatics approach, integrating the 17 identified genes with 6 known metformin target genes (4) into a protein-protein interaction network (9,30,31). For 3 of the 17 genes, there was no record for Homo sapiens in the STITCH (30); therefore, we investigated the interaction of the remaining 14 genes with the 6 metformin targets (Supplementary Table 11).…”

Section: Seventeen Genes Are Linked To Metformin-associated Metabolitmentioning

confidence: 99%

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Effects of Metformin on Metabolite Profiles and LDL Cholesterol in Patients With Type 2 Diabetes

et al. 2015

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OBJECTIVEMetformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin. RESEARCH DESIGN AND METHODSWe analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metforminassociated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways. RESULTSWe found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target. CONCLUSIONSOur results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease.Type 2 diabetes (T2D) is a chronic disease with diminished response to insulin and relative insulin deficiency (1). Patients with T2D mostly take metformin as first-line oral treatment to lower their glucose levels and to improve insulin sensitivity (2). Despite metformin's use as an antihyperglycemic agent for more than 50 years, its

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supporting

confidence: 76%

“…Information on proteinprotein interactions was extracted from STITCH (30). Known metformin target genes were retrieved from the DrugBank (4).…”

Section: Pathway Analysismentioning

confidence: 99%

Section: Seventeen Genes Are Linked To Metformin-associated Metabolitmentioning

confidence: 99%

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Effects of Metformin on Metabolite Profiles and LDL Cholesterol in Patients With Type 2 Diabetes

et al. 2015

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“…Protein-protein interactions were indicated as solid lines, chemical-protein interactions as dashed lines and interactions between chemicals as dotted lines. High confidence interactions were identified if they possessed a score >0.7 (15).…”

Section: Lc-ms/ms Analysis Lc-ms/ms Analyses Of the Digested Peptidementioning

confidence: 99%

High expression of CCDC25 in cholangiocarcinoma tissue samples

et al. 2017

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Abstract. Cholangiocarcinoma (CCA) is a malignant transformation of biliary epithelial cells. It is a slow growing tumor, but is also highly metastatic with a poor prognosis. Bile acids are known to transactivate the epidermal growth factor receptor (EGFR) in cholangiocytes and induce cyclooxygenase-2 expression. The protein expression profiles of bile acid-treated CCA cells were studied using a proteomic approach. To elucidate the possible mechanisms involved in the bile acid-mediated enhancement of CCA cell migration, the effects of six bile acids, including cholic, deoxycholic, taurocholic, taurodeoxycholic, glycocholic and glycodeoxycholic acid, on the migration of CCA cells were examined in vitro using wound healing assays. Subsequently, the possible proteins involved in enhanced CCA cell migration were investigated using a proteomic approach. Changes to the protein expression profiles of CCA cells following bile acid treatment was examined using two-dimensional electrophoresis and mass spectrometry. The results demonstrated that cholic and deoxycholic acid significantly enhanced the migration of CCA cells, compared with the treated MMNK-1 control cells. CCA cells had 77 overexpressed protein spots following cholic acid treatment, and 50 protein spots following deoxycholic acid treatment, compared with the treated MMNK-1 control cells. Liquid chromatography tandem-mass spectrometry analysis revealed that coiled-coil domain containing 25 (CCDC25) was significantly overexpressed in cholic acid-treated CCA cells compared with in cholic acid-treated control cells. When the expression levels of CCDC25 were investigated using western blot analysis, CCDC25 was demonstrated to be highly expressed in CCA tissues, but not in the adjacent non-cancerous tissue samples. The identified proteins were further analyzed for protein-chemical interactions using STITCH version 3.1 software. CCDC25 protein was identified to be associated with Son of sevenless homolog 1 and growth factor receptor-bound protein 2, which are involved in EGFR signaling. The results of the present study demonstrated that following cholic acid treatment, CCDC25 is overexpressed in CCA cells, which is associated with significantly enhanced cell migration. This suggests that CCDC25 is a potential therapeutic target for the treatment of patients with CCA. IntroductionCholangiocarcinoma (CCA) is a malignant transformation of biliary epithelial cells. It is a slow growing tumor, but it is also highly metastatic with a poor prognosis (1). The highest incidence of this cancer has been identified in Asia, particularly in Northeast Thailand (2). In the majority of cases of CCA, the etiology is unknown. Chronic inflammation and cellular injury within bile ducts, together with the partial obstruction of bile flow result in high-risk conditions for CCA development (3). The tumor not only may develop mechanisms to survive in the toxic environment of bile, but may also utilize bile components to promote cell growth, survival and invasion (4-6). Bile acids are known...

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“…1. First, pharmacological targets were identified from the STITCH database, 14 and differentially expressed genes upon perturbation with the interrogated drugs were identified from the Connectivity Map. 3 Subsequently, an algorithm based on the Integer Linear Programming (ILP) formulation published in Melas et al 12 was used to identify functional interactions that model signal transduction from the drug targets to the differentially expressed genes.…”

Section: Workflowmentioning

confidence: 99%

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

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Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury. Insight, innovation, integrationIn this manuscript we introduce a novel approach for the identification of signaling pathways that are functional in a specific biological context, by leveraging gene expression data and prior knowledge of protein connectivity. In more detail, we introduce a linear programming formulation to model signal transduction from one node to the next in a Prior Knowledge Network (PKN), and by minimizing the mismatch between model predictions and experimental data, we are able to identify subsets of the PKN that are most probably functional in the specific biological context. More specifically, we address the problem of identifying the modes of action of drugs that have been reported to induce respiratory side effects, based on their gene expression profiles, and subsequently, merge the drug specific pathways together to construct a signaling network that captures the signaling mechanisms underlying Drug Induced Lung Disease (DILD). Moreover, to demonstrate the predictive power and biological relevance of the DILD network, we use it to suggest potential drug repositioning for treating lung injury.

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STITCH 4: integration of protein–chemical interactions with user data

Cited by 405 publications

References 43 publications

Effects of Metformin on Metabolite Profiles and LDL Cholesterol in Patients With Type 2 Diabetes

Effects of Metformin on Metabolite Profiles and LDL Cholesterol in Patients With Type 2 Diabetes

High expression of CCDC25 in cholangiocarcinoma tissue samples

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

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