A functional analysis of 180 cancer cell lines reveals conserved intrinsic metabolic programs.

Cancer cells reprogram their metabolism to support growth and invasion. While previous work has highlighted how single altered reactions and pathways can drive tumorigenesis, it remains unclear how individual changes propagate at the network level and eventually determine global metabolic activity. To characterize the metabolic lifestyle of cancer cells across pathways and genotypes, we profiled the intracellular metabolome of 180 pan-cancer cell lines grown in identical conditions. For each cell line, we estimated activity for 49 pathways spanning the entirety of the metabolic network. Upon clustering, we discovered a convergence into only two major metabolic types. These were functionally confirmed by 13 C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. They revealed that the major differences in cancers are associated with lipid, TCA cycle, and carbohydrate metabolism. Thorough integration of these types with multiomics highlighted little association with genetic alterations but a strong association with markers of epithelial-mesenchymal transition. Our analysis indicates that in absence of variations imposed by the microenvironment, cancer cells adopt distinct metabolic programs which serve as vulnerabilities for therapy.

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

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.

A framework for large-scale metabolome drug profiling links coenzyme A metabolism to the toxicity of anti-cancer drug dichloroacetate.

Metabolic profiling of cell line collections has become an invaluable tool to study disease etiology, drug modes of action and to select personalized treatments. However, large-scale in vitro dynamic metabolic profiling is limited by time-consuming sampling and complex measurement procedures. By adapting a mass spectrometry-based metabolomics workflow for high-throughput profiling of diverse adherent mammalian cells, we establish a framework for the rapid measurement and analysis of drug-induced dynamic changes in intracellular metabolites. This methodology is scalable to large compound libraries and is here applied to study the mechanism underlying the toxic effect of dichloroacetate in ovarian cancer cell lines. System-level analysis of the metabolic responses revealed a key and unexpected role of CoA biosynthesis in dichloroacetate toxicity and the more general importance of CoA homeostasis across diverse human cell lines. The herein-proposed strategy for high-content drug metabolic profiling is complementary to other molecular profiling techniques, opening new scientific and drug-discovery opportunities.

Metabotypes of breast cancer cell lines revealed by non-targeted metabolomics.

We present an analysis of intracellular metabolism by non-targeted, high-throughput metabolomics profiling of 18 breast cell lines. We profiled >900 putatively annotated metabolite ions for >100 samples collected under both normoxic and hypoxic conditions and revealed extensive heterogeneity across all metabolic pathways and cell lines. Cell line-specific metabolome profiles dominated over patterns associated with malignancy or with the clinical nomenclature of breast cancer cells. Such characteristic metabolome profiles were reproducible across different laboratories and experiments and exhibited mild to robust changes with change in experimental conditions. To extract a functional overview of cell line heterogeneity, we devised an unsupervised metabotyping procedure that for each pathway automatically recognized metabolic types from metabolome data and assigned cell lines. Our procedure provided a condensed yet global representation of cell line metabolism, revealing the fine structure of metabolic heterogeneity across all tested pathways and cell lines. In follow-up experiments on selected pathways, we confirmed that different metabolic types correlated to differences in the underlying fluxes and difference sensitivity to gene knockdown or pharmacological inhibition. Thus, the identified metabotypes recapitulated functional differences at the pathway level. Metabotyping provides a powerful compression of multi-dimensional data that preserves functional information and serves as a resource for reconciling or understanding heterogeneous metabolic phenotypes or response to inhibition of metabolic pathways.

Systems proteomics of liver mitochondria function.

Recent improvements in quantitative proteomics approaches, including Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS), permit reproducible large-scale protein measurements across diverse cohorts. Together with genomics, transcriptomics, and other technologies, transomic data sets can be generated that permit detailed analyses across broad molecular interaction networks. Here, we examine mitochondrial links to liver metabolism through the genome, transcriptome, proteome, and metabolome of 386 individuals in the BXD mouse reference population. Several links were validated between genetic variants toward transcripts, proteins, metabolites, and phenotypes. Among these, sequence variants in Cox7a2l alter its protein's activity, which in turn leads to downstream differences in mitochondrial supercomplex formation. This data set demonstrates that the proteome can now be quantified comprehensively, serving as a key complement to transcriptomics, genomics, and metabolomics--a combination moving us forward in complex trait analysis.

Functional screening identifies MCT4 as a key regulator of breast cancer cell metabolism and survival.

Metabolic reprogramming in cancer enhances macromolecule biosynthesis and supports cell survival. Oncogenic drivers affect metabolism by altering distinct metabolic processes and render cancer cells sensitive to perturbations of the metabolic network. This study aimed to identify selective metabolic dependencies in breast cancer by investigating 17 breast cancer cells lines representative of the genetic diversity of the disease. Using a functional screen, we demonstrate here that monocarboxylate transporter 4 (MCT4) is an important regulator of breast cancer cell survival. MCT4 supports pH maintenance, lactate secretion and non-oxidative glucose metabolism in breast cancer cells. Moreover, MCT4 depletion caused an increased dependence of cancer cells on mitochondrial respiration and glutamine metabolism. MCT4 depletion reduced the ability of breast cancer cells to grow in a three-dimensional (3D) matrix or as multilayered spheroids. Moreover, MCT4 expression is regulated by the PI3K-Akt signalling pathway and highly expressed in HER2-positive breast cancers. These results suggest that MCT4 is a potential therapeutic target in defined breast cancer subtypes and reveal novel avenues for combination treatment.

Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population.

The manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study, we quantified the transcriptome--a subset of the metabolome--and, using targeted proteomics, quantified a subset of the liver proteome from 40 strains of the BXD mouse genetic reference population on two diverse diets. We discovered dozens of transcript, protein, and metabolite QTLs, several of which linked to metabolic phenotypes. Most prominently, Dhtkd1 was identified as a primary regulator of 2-aminoadipate, explaining variance in fasted glucose and diabetes status in both mice and humans. These integrated molecular profiles also allowed further characterization of complex pathways, particularly the mitochondrial unfolded protein response (UPR(mt)). UPR(mt) shows strikingly variant responses at the transcript and protein level that are remarkably conserved among C. elegans, mice, and humans. Overall, these examples demonstrate the value of an integrated multilayered omics approach to characterize complex metabolic phenotypes.

D-Glucosamine supplementation extends life span of nematodes and of ageing mice.

D-Glucosamine (GlcN) is a freely available and commonly used dietary supplement potentially promoting cartilage health in humans, which also acts as an inhibitor of glycolysis. Here we show that GlcN, independent of the hexosamine pathway, extends Caenorhabditis elegans life span by impairing glucose metabolism that activates AMP-activated protein kinase (AMPK/AAK-2) and increases mitochondrial biogenesis. Consistent with the concept of mitohormesis, GlcN promotes increased formation of mitochondrial reactive oxygen species (ROS) culminating in increased expression of the nematodal amino acid-transporter 1 (aat-1) gene. Ameliorating mitochondrial ROS formation or impairment of aat-1-expression abolishes GlcN-mediated life span extension in an NRF2/SKN-1-dependent fashion. Unlike other calorie restriction mimetics, such as 2-deoxyglucose, GlcN extends life span of ageing C57BL/6 mice, which show an induction of mitochondrial biogenesis, lowered blood glucose levels, enhanced expression of several murine amino-acid transporters, as well as increased amino-acid catabolism. Taken together, we provide evidence that GlcN extends life span in evolutionary distinct species by mimicking a low-carbohydrate diet.

Evolution of nucleosome occupancy: conservation of global properties and divergence of gene-specific patterns.

To examine the role of nucleosome occupancy in the evolution of gene expression, we measured the genome-wide nucleosome profiles of four yeast species, three belonging to the Saccharomyces sensu stricto lineage and the more distantly related Candida glabrata. Nucleosomes and associated promoter elements at C. glabrata genes are typically shifted upstream by ∼20 bp, compared to their orthologs from sensu stricto species. Nonetheless, all species display the same global organization features first described for Saccharomyces cerevisiae: a stereotypical nucleosome organization along genes and a division of promoters into those that contain or lack a pronounced nucleosome-depleted region (NDR), with the latter displaying a more dynamic pattern of gene expression. Despite this global similarity, however, nucleosome occupancy at specific genes diverged extensively between sensu stricto and C. glabrata orthologs (∼50 million years). Orthologs with dynamic expression patterns tend to maintain their lack of NDR, but apart from that, sensu stricto and C. glabrata orthologs are nearly as similar in nucleosome occupancy patterns as nonorthologous genes. This extensive divergence in nucleosome occupancy contrasts with a conserved pattern of gene expression. Thus, while some evolutionary changes in nucleosome occupancy contribute to gene expression divergence, nucleosome occupancy often diverges extensively with apparently little impact on gene expression.