eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks

Clarke, Daniel J B; Kuleshov, Maxim V.; Schilder, Brian M; Torre, Denis; Duffy, Mary E.; Keenan, Alexandra B.; Lachmann, Alexander; Feldmann, Axel S.; Gundersen, Gregory W.; Silverstein, Moshe C.; Wang, Zichen; Ma’ayan, Avi

doi:10.1093/nar/gky458

Cited by 157 publications

(119 citation statements)

References 28 publications

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“…However, considering gene expression to be a downstream effect of pathway activity instead leads to more accurate estimations (Cantini et al 2018;Schubert et al 2018). Furthermore, TF activities can be used to infer the activity of upstream proteins using knowledge of pathways and how they affect TFs (Melas et al 2015;Tuncbag et al 2016;Clarke et al 2018). The resources and confidence estimates that we propose will support the development of such methods.…”

Section: Benchmark Of Transcription Factor Regulonsmentioning

confidence: 99%

Benchmark and integration of resources for the estimation of human transcription factor activities

et al. 2019

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Section: Benchmark Of Transcription Factor Regulonsmentioning

confidence: 99%

Benchmark and integration of resources for the estimation of human transcription factor activities

et al. 2019

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“…However, considering gene expression as a downstream effect of pathway activity instead leads to more accurate estimations 72,73 . Further on, TF activities can be used to infer the activity of upstream proteins using knowledge on pathways and how they impinge on TFs [74][75][76] . The resources and confidence estimates we propose will support the development of such methods.…”

Section: Discussionmentioning

confidence: 99%

Benchmark and integration of resources for the estimation of human transcription factor activities

García-Alonso

Ibrahim

Türei

et al. 2018

Preprint

132

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Prediction of transcription factor (TF) activities from the gene expression of their targets (i.e. TF regulon) is becoming a widely-used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and datasets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are: (i) manually curated repositories, (ii) interactions derived from ChIP-seq binding data, (iii) in silico prediction of TF binding on gene promoters, and (iv) reverse-engineered regulons from large gene expression datasets. However, it is not known how these different sources of regulons affect the TF activity estimations, and thereby downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark datasets. We assembled a collection of TF-target interactions among 1,541 TFs, and evaluated how the different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities or mode of interaction with the chromatin, affect the predictions of TF activity changes. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies with a confidence score, as a resource for enhanced prediction of TF activities.

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“…The upstream regulatory networks from signatures of differentially expressed genes obtained from toxicants target prediction, were determined by transcription factor enrichment analysis, proteinprotein interaction network expansion and kinase enrichment analysis, using the 50 predicted target genes on eXpression2Kinases (X2K) webserver (Clarke et al 2018).…”

Section: Target Gene Expression Analysesmentioning

confidence: 99%

In silico toxicological analyzes of selected toxic compounds from dumpsite or contaminated soils on human health

Ibraheem

Fatoki

Enibukun

et al. 2019

Nova Biotechnologica Et Chimica

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The soil is a key component of natural ecosystems because environmental sustainability depends largely on a sustainable soil ecosystem. The objective of this study was to predict the impact of selected toxic compounds from dumpsite or contaminated soils on human health at the molecular level of biological processes. The in silico methods that were used include toxicokinetics and target gene prediction, molecular docking, and gene expressing network analysis. The result showed bisphenol A (BPA), 2,20-bis(p-chlorophenyl)-1,1-dichloroethane (DDD), 2,20-bis(p-chlorophenyl)-1,1-trichloroethane (DDT), diethylhexyl phthalate (DEHP), nonylphenol (NP) and tetrachlorodibenzodioxin (TCDD) as the active toxic compounds that can modulate biological system and are considered as potential cause of several diseases including cancer. The principal target genes include substance-P receptor (also known as Neurokinin 1 receptor), 5-hydroxytryptamine receptor, human serotonin transporter; estrogen receptor alpha; and aryl hydrocarbon receptor. These genes implicated SUZ12, STAT3, and TRIM28 as the major transcription factors while mitogen-activated protein kinases and cyclin-dependent kinases were the major kinases from the protein-protein interaction. All the six toxicants investigated showed good free binding energies (ΔG) which were below - 5.0 kcal.mol−1. These toxic compounds showed ligand efficiency greater than 0.25 kcal.mol−1. HA and would possibly cause fatal damage on human health. The order of in silico predicted toxicity of these compounds were BPA > DDD = DDT > TCDD > NP > DEHP. Our results identified potential threats, which the selected toxicants can pose to public health. More importantly, it provides basis for investigation of super bugs (microorganisms) that can remediate these toxicants in our environment. Environmental monitoring and modern wastes management system should be implemented and enforced in the affected countries in order to safeguard the health of the citizenry.

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eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks

Cited by 157 publications

References 28 publications

Benchmark and integration of resources for the estimation of human transcription factor activities

Benchmark and integration of resources for the estimation of human transcription factor activities

Benchmark and integration of resources for the estimation of human transcription factor activities

In silico toxicological analyzes of selected toxic compounds from dumpsite or contaminated soils on human health

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