Please use this identifier to cite or link to this item: https://doi.org/10.1038/srep23128
Title: Continuous time Bayesian networks identify Prdm1 as a negative regulator of TH17 cell differentiation in humans
Authors: Acerbi, E
Viganò, E
Poidinger, M 
Mortellaro, A
Zelante, T
Stella, F
Keywords: interleukin 17
PRDM1 protein, human
repressor protein
Bayes theorem
CD4+ T lymphocyte
cell culture
cell differentiation
cytology
fetus blood
gene expression regulation
gene regulatory network
genetics
human
metabolism
Th17 cell
Bayes Theorem
CD4-Positive T-Lymphocytes
Cell Differentiation
Cells, Cultured
Fetal Blood
Gene Expression Regulation
Gene Regulatory Networks
Humans
Interleukin-17
Repressor Proteins
Th17 Cells
Issue Date: 2016
Citation: Acerbi, E, Viganò, E, Poidinger, M, Mortellaro, A, Zelante, T, Stella, F (2016). Continuous time Bayesian networks identify Prdm1 as a negative regulator of TH17 cell differentiation in humans. Scientific Reports 6 : 23128. ScholarBank@NUS Repository. https://doi.org/10.1038/srep23128
Rights: Attribution 4.0 International
Abstract: T helper 17 (TH17) cells represent a pivotal adaptive cell subset involved in multiple immune disorders in mammalian species. Deciphering the molecular interactions regulating TH17 cell differentiation is particularly critical for novel drug target discovery designed to control maladaptive inflammatory conditions. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling TH17 differentiation. From the network, we identified the Prdm1 gene encoding the B lymphocyte-induced maturation protein 1 as a crucial negative regulator of human TH17 cell differentiation. The results have been validated by perturbing Prdm1 expression on freshly isolated CD4 + naïve T cells: reduction of Prdm1 expression leads to augmentation of IL-17 release. These data unravel a possible novel target to control TH17 polarization in inflammatory disorders. Furthermore, this study represents the first in vitro validation of continuous time Bayesian networks as gene network reconstruction method and as hypothesis generation tool for wet-lab biological experiments.
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/178934
ISSN: 20452322
DOI: 10.1038/srep23128
Rights: Attribution 4.0 International
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