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Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism.

ABSTRACT: Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs.

SUBMITTER: Ortmayr K

PROVIDER: S-EPMC6478870 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism.

Ortmayr Karin K Dubuis Sébastien S Zampieri Mattia M

Nature communications 20190423 1

Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic ...[more]

PMID: 31015463

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Project description:The human commensal and opportunistic fungal pathogen Candida albicans displays extensive genetic and phenotypic variation across clinical isolates. Here, we performed RNA sequencing on 21 well-characterized isolates to examine how genetic variation contributes to gene expression differences and to link these differences to phenotypic traits. C. albicans adapts primarily through clonal evolution, and yet hierarchical clustering of gene expression profiles in this set of isolates did not reproduce their phylogenetic relationship. Strikingly, strain-specific gene expression was prevalent in some strain backgrounds. Association of gene expression with phenotypic data by differential analysis, linear correlation, and assembly of gene networks connected both previously characterized and novel genes with 23 C. albicans traits. Construction of de novo gene modules produced a gene atlas incorporating 67% of C. albicans genes and revealed correlations between expression modules and important phenotypes such as systemic virulence. Furthermore, targeted investigation of two modules that have novel roles in growth and filamentation supported our bioinformatic predictions. Together, these studies reveal widespread transcriptional variation across C. albicans isolates and identify genetic and epigenetic links to phenotypic variation based on coexpression network analysis.IMPORTANCE Infectious fungal species are often treated uniformly despite clear evidence of genotypic and phenotypic heterogeneity being widespread across strains. Identifying the genetic basis for this phenotypic diversity is extremely challenging because of the tens or hundreds of thousands of variants that may distinguish two strains. Here, we use transcriptional profiling to determine differences in gene expression that can be linked to phenotypic variation among a set of 21 Candida albicans isolates. Analysis of this transcriptional data set uncovered clear trends in gene expression characteristics for this species and new genes and pathways that were associated with variation in pathogenic processes. Direct investigation confirmed functional predictions for a number of new regulators associated with growth and filamentation, demonstrating the utility of these approaches in linking genes to important phenotypes.

Dataset Information

Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism.

Publications

Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism.

Similar Datasets

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