Dataset Information

Differential HDAC1/2 network analysis reveals a role for prefoldin/CCT in HDAC1/2 complex assembly.

ABSTRACT: HDAC1 and HDAC2 are components of several corepressor complexes (NuRD, Sin3, CoREST and MiDAC) that regulate transcription by deacetylating histones resulting in a more compact chromatin environment. This limits access of transcriptional machinery to genes and silences transcription. While using an AP-MS approach to map HDAC1/2 protein interaction networks, we noticed that N-terminally tagged versions of HDAC1 and HDAC2 did not assemble into HDAC corepressor complexes as expected, but instead appeared to be stalled with components of the prefoldin-CCT chaperonin pathway. These N-terminally tagged HDACs were also catalytically inactive. In contrast to the N-terminally tagged HDACs, C-terminally tagged HDAC1 and HDAC2 captured complete histone deacetylase complexes and the purified proteins had deacetylation activity that could be inhibited by SAHA (Vorinostat), a Class I/II HDAC inhibitor. This tag-mediated reprogramming of the HDAC1/2 protein interaction network suggests a mechanism whereby HDAC1 is first loaded into the CCT complex by prefoldin to complete folding, and then assembled into active, functional HDAC complexes. Imaging revealed that the prefoldin subunit VBP1 colocalises with nuclear HDAC1, suggesting that delivery of HDAC1 to the CCT complex happens in the nucleus.

SUBMITTER: Banks CAS

PROVIDER: S-EPMC6135828 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Differential HDAC1/2 network analysis reveals a role for prefoldin/CCT in HDAC1/2 complex assembly.

Banks Charles A S CAS Miah Sayem S Adams Mark K MK Eubanks Cassandra G CG Thornton Janet L JL Florens Laurence L Washburn Michael P MP

Scientific reports 20180912 1

HDAC1 and HDAC2 are components of several corepressor complexes (NuRD, Sin3, CoREST and MiDAC) that regulate transcription by deacetylating histones resulting in a more compact chromatin environment. This limits access of transcriptional machinery to genes and silences transcription. While using an AP-MS approach to map HDAC1/2 protein interaction networks, we noticed that N-terminally tagged versions of HDAC1 and HDAC2 did not assemble into HDAC corepressor complexes as expected, but instead ap ...[more]

PMID: 30209338

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Project description:Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analysis. We first applied DCEA to a gene expression dataset of gastric normal mucosa, adenoma and carcinoma samples to identify gene interconnection changes during cancer progression, based on which we inferred normal, adenoma, and carcinoma-specific gene regulation networks by using linear regression model. It was observed that cancer genes and drug targets were enriched in each network. To investigate the dynamic changes of gene regulation during carcinogenesis, we then designed two quantitative methods to prioritize differentially regulated genes (DRGs) and gene pairs or links (DRLs) between adjacent stages. It was found that known cancer genes and drug targets are significantly higher ranked. The top 4% normal vs. adenoma DRGs (36 genes) and top 6% adenoma vs. carcinoma DRGs (56 genes) proved to be worthy of further investigation to explore their association with gastric cancer. Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. adenoma and adenoma vs. carcinoma comparisons, 15 have been reported to be gastric cancer or cancer related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of GATA6, ESRRG and their signaling pathways in gastric carcinogenesis. Compared with established approaches which build genome-scale GRNs, or sub-networks around differentially expressed genes, the present one proved to be better at enriching cancer genes and drug targets, and prioritizing disease-related genes on the dataset we considered. We propose this extendable differential networking framework as a promising way to gain insights into gene regulatory mechanisms underlying cancer progression and other phenotypic changes.

Dataset Information

Differential HDAC1/2 network analysis reveals a role for prefoldin/CCT in HDAC1/2 complex assembly.

Publications

Differential HDAC1/2 network analysis reveals a role for prefoldin/CCT in HDAC1/2 complex assembly.

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