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Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics 1/2017

01.12.2017 | Original Article

Identification of potential transcription factor and protein kinases for regulation of differentially expressed genes for fluoride exposure in human using Expression2Kinases (X2K) approach

verfasst von: J. K. Choudhari, B. P. Sahariah, J. K. Choubey, A. Patel, M. K. Verma

Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics | Ausgabe 1/2017

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Abstract

Fluorosis due to longtime exposure of fluoride with deleterious public health problem is common in developed and developing countries. Researches demonstrated that fluoride induces the gene expression and causes apoptosis. The present study aims to predict potential transcription factor (TF) and associated protein kinase which are responsible for the regulation of gene expression exposure of fluoride. In this study, 60 genes classified into 16 gene families were shorted that show the differential expression (up/down) in human exposure to fluoride; where 47 genes show the decreased expression and other 13 show the increased expression. The TFs SOX2, GATA1 and MYC for down-regulation gene and MYC, GATA1 for up-regulation of genes expression were predicted using the X2K approach. The potential protein kinase MAPK1, MAPK3, CSNK2A1 and CSNK2A2 for down-regulation and MAPK1, MAPK3, MAPK8, IKBKB AKT1, GSK3B and CDK2 for up-regulation of the genes are identified which are connected to a maximum number of intermediate protein and TFs that can be applied to the prediction novel targeting the disease-suppressive potential target in human longtime fluoride exposure.

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Metadaten
Titel
Identification of potential transcription factor and protein kinases for regulation of differentially expressed genes for fluoride exposure in human using Expression2Kinases (X2K) approach
verfasst von
J. K. Choudhari
B. P. Sahariah
J. K. Choubey
A. Patel
M. K. Verma
Publikationsdatum
01.12.2017
Verlag
Springer Vienna
Erschienen in
Network Modeling Analysis in Health Informatics and Bioinformatics / Ausgabe 1/2017
Print ISSN: 2192-6662
Elektronische ISSN: 2192-6670
DOI
https://doi.org/10.1007/s13721-017-0148-7

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