Dataset Information

Pan-Cancer Analysis of Mitochondria Chaperone-Client Co-Expression Reveals Chaperone Functional Partitioning.

ABSTRACT: Metabolic reprogramming is a hallmark of cancer. Such reprogramming entails the up-regulation of the expression of specific mitochondrial proteins, thus increasing the burden on the mitochondrial protein quality control. However, very little is known about the specificity of interactions between mitochondrial chaperones and their clients, or to what extent the mitochondrial chaperone-client co-expression is coordinated. We hypothesized that a physical interaction between a chaperone and its client in mitochondria ought to be manifested in the co-expression pattern of both transcripts. Using The Cancer Genome Atlas (TCGA) gene expression data from 13 tumor entities, we constructed the mitochondrial chaperone-client co-expression network. We determined that the network is comprised of three distinct modules, each populated with unique chaperone-clients co-expression pairs belonging to distinct functional groups. Surprisingly, chaperonins HSPD1 and HSPE1, which are known to comprise a functional complex, each occupied a different module: HSPD1 co-expressed with tricarboxylic acid cycle cycle enzymes, while HSPE1 co-expressed with proteins involved in oxidative phosphorylation. Importantly, we found that the genes in each module were enriched for discrete transcription factor binding sites, suggesting the mechanism for the coordinated co-expression. We propose that our mitochondrial chaperone-client interactome can facilitate the identification of chaperones supporting specific mitochondrial pathways and bring forth a fundamental principle in metabolic adaptation.

SUBMITTER: Galai G

PROVIDER: S-EPMC7226338 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pan-Cancer Analysis of Mitochondria Chaperone-Client Co-Expression Reveals Chaperone Functional Partitioning.

Galai Geut G Ben-David Hila H Levin Liron L Orth Martin F MF Grünewald Thomas G P TGP Pilosof Shai S Berstein Shimon S Rotblat Barak B

Cancers 20200330 4

Metabolic reprogramming is a hallmark of cancer. Such reprogramming entails the up-regulation of the expression of specific mitochondrial proteins, thus increasing the burden on the mitochondrial protein quality control. However, very little is known about the specificity of interactions between mitochondrial chaperones and their clients, or to what extent the mitochondrial chaperone-client co-expression is coordinated. We hypothesized that a physical interaction between a chaperone and its clie ...[more]

PMID: 32235444

Similar Datasets

Project description:BackgroundWith the advances in the high throughput next generation sequencing technologies, genome-wide association studies (GWAS) have identified a large set of variants associated with complex phenotypic traits at a very fine scale. Despite the progress in GWAS, identification of genotype-phenotype relationship remains challenging in maize due to its nature with dozens of variants controlling the same trait. As the causal variations results in the change in expression, gene expression analyses carry a pivotal role in unraveling the transcriptional regulatory mechanisms behind the phenotypes.ResultsTo address these challenges, we incorporated the gene expression and GWAS-driven traits to extend the knowledge of genotype-phenotype relationships and transcriptional regulatory mechanisms behind the phenotypes. We constructed a large collection of gene co-expression networks and identified more than 2 million co-expressing gene pairs in the GWAS-driven pan-network which contains all the gene-pairs in individual genomes of the nested association mapping (NAM) population. We defined four sub-categories for the pan-network: (1) core-network contains the highest represented ~ 1% of the gene-pairs, (2) near-core network contains the next highest represented 1-5% of the gene-pairs, (3) private-network contains ~ 50% of the gene pairs that are unique to individual genomes, and (4) the dispensable-network contains the remaining 50-95% of the gene-pairs in the maize pan-genome. Strikingly, the private-network contained almost all the genes in the pan-network but lacked half of the interactions. We performed gene ontology (GO) enrichment analysis for the pan-, core-, and private- networks and compared the contributions of variants overlapping with genes and promoters to the GWAS-driven pan-network.ConclusionsGene co-expression networks revealed meaningful information about groups of co-regulated genes that play a central role in regulatory processes. Pan-network approach enabled us to visualize the global view of the gene regulatory network for the studied system that could not be well inferred by the core-network alone.

Project description:Large-scale tumor genome sequencing projects have revealed a complex landscape of genomic mutations in multiple cancer types. A major goal of these projects is to characterize somatic mutations and discover cancer drivers, thereby providing important clues to uncover diagnostic or therapeutic targets for clinical treatment. However, distinguishing only a few somatic mutations from the majority of passenger mutations is still a major challenge facing the biological community. Fortunately, combining other functional features with mutations to predict cancer driver genes is an effective approach to solve the above problem. Protein lysine modifications are an important functional feature that regulates the development of cancer. Therefore, in this work, we have systematically analyzed somatic mutations on seven protein lysine modifications and identified several important drivers that are responsible for tumorigenesis. From published literature, we first collected more than 100,000 lysine modification sites for analysis. Another 1 million non-synonymous single nucleotide variants (SNVs) were then downloaded from TCGA and mapped to our collected lysine modification sites. To identify driver proteins that significantly altered lysine modifications, we further developed a hierarchical Bayesian model and applied the Markov Chain Monte Carlo (MCMC) method for testing. Strikingly, the coding sequences of 473 proteins were found to carry a higher mutation rate in lysine modification sites compared to other background regions. Hypergeometric tests also revealed that these gene products were enriched in known cancer drivers. Functional analysis suggested that mutations within the lysine modification regions possessed higher evolutionary conservation and deleteriousness. Furthermore, pathway enrichment showed that mutations on lysine modification sites mainly affected cancer related processes, such as cell cycle and RNA transport. Moreover, clinical studies also suggested that the driver proteins were significantly associated with patient survival, implying an opportunity to use lysine modifications as molecular markers in cancer diagnosis or treatment. By searching within protein-protein interaction networks using a random walk with restart (RWR) algorithm, we further identified a series of potential treatment agents and therapeutic targets for cancer related to lysine modifications. Collectively, this study reveals the functional importance of lysine modifications in cancer development and may benefit the discovery of novel mechanisms for cancer treatment.

Dataset Information

Pan-Cancer Analysis of Mitochondria Chaperone-Client Co-Expression Reveals Chaperone Functional Partitioning.

Publications

Pan-Cancer Analysis of Mitochondria Chaperone-Client Co-Expression Reveals Chaperone Functional Partitioning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets