DOKI: Domain knowledge-driven inference method for reverse-engineering transcriptional regulatory relationships among genes in cancer

Adabor, Emmanuel S.; Acquaah‐Mensah, George

doi:10.1016/j.compbiomed.2020.104017

Cited by 4 publications

(1 citation statement)

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“…However simultaneous cooperative recruitment of more than two transcriptional partners may occur and is difficult to demonstrate experimentally. Various in silico methods have been proposed to infer either gene regulatory networks using dynamic or static mRNA gene expression 9 – 12 or transcriptional regulatory networks using mRNA and protein data to suggest direct relationships between regulators and target genes 13 – 15 . Such methods were developed in various biological contexts including breast cancer (BC) for subtypes identification 16 , 17 .…”

Section: Introductionmentioning

confidence: 99%

Computational identification of new potential transcriptional partners of ERRα in breast cancer cells: specific partners for specific targets

Cerutti

Zhang

Tribollet

et al. 2022

Sci Rep

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Estrogen related receptors are orphan members of the nuclear receptor superfamily acting as transcription factors (TFs). In contrast to classical nuclear receptors, the activities of the ERRs are not controlled by a natural ligand. Regulation of their activities thus relies on availability of transcriptional co-regulators. In this paper, we focus on ERRα, whose involvement in cancer progression has been broadly demonstrated. We propose a new approach to identify potential co-activators, starting from previously identified ERRα-activated genes in a breast cancer (BC) cell line. Considering mRNA gene expression from two sets of human BC cells as major endpoint, we used sparse partial least squares modeling to uncover new transcriptional regulators associated with ERRα. Among them, DDX21, MYBBP1A, NFKB1, and SETD7 are functionally relevant in MDA-MB-231 cells, specifically activating the expression of subsets of ERRα-activated genes. We studied SET7 in more details and showed its co-localization with ERRα and its ERRα-dependent transcriptional and phenotypic effects. Our results thus demonstrate the ability of a modeling approach to identify new transcriptional partners from gene expression. Finally, experimental results show that ERRα cooperates with distinct co-regulators to control the expression of distinct sets of target genes, thus reinforcing the combinatorial specificity of transcription.

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