Metabolic memory underlying minimal residual disease in breast cancer.

Funder: European Molecular Biology LaboratoryFunder: European Molecular Biology Laboratory (EMBL)Tumor relapse from treatment-resistant cells (minimal residual disease, MRD) underlies most breast cancer-related deaths. Yet, the molecular characteristics defining their malignancy have largely remained elusive. Here, we integrated multi-omics data from a tractable organoid system with a metabolic modeling approach to uncover the metabolic and regulatory idiosyncrasies of the MRD. We find that the resistant cells, despite their non-proliferative phenotype and the absence of oncogenic signaling, feature increased glycolysis and activity of certain urea cycle enzyme reminiscent of the tumor. This metabolic distinctiveness was also evident in a mouse model and in transcriptomic data from patients following neo-adjuvant therapy. We further identified a marked similarity in DNA methylation profiles between tumor and residual cells. Taken together, our data reveal a metabolic and epigenetic memory of the treatment-resistant cells. We further demonstrate that the memorized elevated glycolysis in MRD is crucial for their survival and can be targeted using a small-molecule inhibitor without impacting normal cells. The metabolic aberrances of MRD thus offer new therapeutic opportunities for post-treatment care to prevent breast tumor recurrence

An unexpected link between fatty acid synthase and cholesterol synthesis in proinflammatory macrophage activation

Different immune activation states require distinct metabolic features and activities in immune cells. For instance, inhibition of fatty acid synthase (FASN), which catalyzes the synthesis of long-chain fatty acids, prevents the proinflammatory response in macrophages; however, the precise role of this enzyme in this response remains poorly defined. Consistent with previous studies, we found here that FASN is essential for lipopolysaccharide-induced, Toll-like receptor (TLR)-mediated macrophage activation. Interestingly, only agents that block FASN upstream of acetoacetyl-CoA synthesis, including the well-characterized FASN inhibitor C75, inhibited TLR4 signaling, while those acting downstream had no effect. We found that acetoacetyl-CoA could overcome C75's inhibitory effect, whereas other FASN metabolites, including palmitate, did not prevent C75-mediated inhibition. This suggested an unexpected role for acetoacetyl-CoA in inflammation that is independent of its role in palmitate synthesis. Our evidence further suggested that acetoacetyl-CoA arising from FASN activity promotes cholesterol production, indicating a surprising link between fatty acid synthesis and cholesterol synthesis. We further demonstrate that this process is required for TLR4 to enter lipid rafts and facilitate TLR4 signaling. In conclusion, we have uncovered an unexpected link between FASN and cholesterol synthesis that appears to be required for TLR signal transduction and proinflammatory macrophage activation

Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1.

The endogenous metabolite itaconate has recently emerged as a regulator of macrophage function, but its precise mechanism of action remains poorly understood. Here we show that itaconate is required for the activation of the anti-inflammatory transcription factor Nrf2 (also known as NFE2L2) by lipopolysaccharide in mouse and human macrophages. We find that itaconate directly modifies proteins via alkylation of cysteine residues. Itaconate alkylates cysteine residues 151, 257, 288, 273 and 297 on the protein KEAP1, enabling Nrf2 to increase the expression of downstream genes with anti-oxidant and anti-inflammatory capacities. The activation of Nrf2 is required for the anti-inflammatory action of itaconate. We describe the use of a new cell-permeable itaconate derivative, 4-octyl itaconate, which is protective against lipopolysaccharide-induced lethality in vivo and decreases cytokine production. We show that type I interferons boost the expression of Irg1 (also known as Acod1) and itaconate production. Furthermore, we find that itaconate production limits the type I interferon response, indicating a negative feedback loop that involves interferons and itaconate. Our findings demonstrate that itaconate is a crucial anti-inflammatory metabolite that acts via Nrf2 to limit inflammation and modulate type I interferons

Genomewide landscape of gene–metabolome associations in Escherichia coli

Abstract Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations

Genomewide landscape of gene–metabolome associations in Escherichia coli

Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.ISSN:1744-429

Global Metabolic Responses to Salt Stress in Fifteen Species

Cells constantly adapt to unpredictably changing extracellular solute concentrations. A cornerstone of the cellular osmotic stress response is the metabolic supply of energy and building blocks to mount appropriate defenses. Yet, the extent to which osmotic stress impinges on the metabolic network remains largely unknown. Moreover, it is mostly unclear which, if any, of the metabolic responses to osmotic stress are conserved among diverse organisms or confined to particular groups of species. Here we investigate the global metabolic responses of twelve bacteria, two yeasts and two human cell lines exposed to sustained hyperosmotic salt stress by measuring semiquantitative levels of hundreds of cellular metabolites using nontargeted metabolomics. Beyond the accumulation of osmoprotectants, we observed significant changes of numerous metabolites in all species. Global metabolic responses were predominantly species-specific, yet individual metabolites were characteristically affected depending on species’ taxonomy, natural habitat, envelope structure or salt tolerance. Exploiting the breadth of our dataset, the correlation of individual metabolite response magnitudes across all species implicated lower glycolysis, tricarboxylic acid cycle, branched-chain amino acid metabolism and heme biosynthesis to be generally important for salt tolerance. Thus, our findings place the global metabolic salt stress response into a phylogenetic context and provide insights into the cellular phenotype associated with salt tolerance.ISSN:1932-620

A dual control mechanism synchronizes riboflavin and sulphur metabolism in Bacillus subtilis.

Flavin mononucleotide (FMN) riboswitches are genetic elements, which in many bacteria control genes responsible for biosynthesis and/or transport of riboflavin (rib genes). Cytoplasmic riboflavin is rapidly and almost completely converted to FMN by flavokinases. When cytoplasmic levels of FMN are sufficient ("high levels"), FMN binding to FMN riboswitches leads to a reduction of rib gene expression. We report here that the protein RibR counteracts the FMN-induced "turn-off" activities of both FMN riboswitches in Bacillus subtilis, allowing rib gene expression even in the presence of high levels of FMN. The reason for this secondary metabolic control by RibR is to couple sulfur metabolism with riboflavin metabolism

Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data

<div><p>In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.</p></div

Correlation analysis of metabolite responses with salt tolerance.

<p>(A) Correlation of metabolite fold-changes in response to strong salt stress with IC₅₀ values of the different organisms was assessed by Pearson’s correlation coefficient R. For each metabolite, the upper quartile x_0.75 of absolute log₂ fold-changes across all species was plotted against R. Only metabolites detected in more than 10 species were considered. Metabolites with |R| > 0.5 and x_0.75 > 1 are highlighted in blue (anticorrelating metabolites) and pink (correlating metabolites), and the names of representative compounds are listed. The full correlation data is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s002" target="_blank">S2 Data</a>. (B) Visualization of metabolites correlating with salt tolerance on the KEGG metabolic pathway map using PathwayProjector [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.ref047" target="_blank">47</a>]. Color intensity of metabolites indicates strength of positive (pink) or negative (blue) correlation, and size indicates x_0.75. Key pathways are highlighted and labeled. (C) Correlation of fold-change with salt tolerance for selected compounds in lower glycolysis; (D) in cysteine and methionine metabolism; (E) in branched-chain amino acid metabolism; and (F) in heme biosynthesis. In panels C to F mean and standard deviation of four (microbes) or three (human cell lines) replicates are shown. Note that metabolite annotations are based on accurate mass and can be ambiguous; refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s001" target="_blank">S1 Data</a> for complete annotations.</p