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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. SYNOPSIS: Despite their normal morphology and non‐proliferative phenotype, treatment‐resistant breast cancer cells retain epigenetic imprinting and metabolic characteristics of their prior tumour state. Multi‐omics analysis and flux modelling reveals elevated glycolysis and abnormal urea cycle pathway activity in residual cells (Minimal Residual Disease, MRD).Transcriptome data from human samples also indicate elevated glycolysis of MRD.In contrast to normal epithelial cells and cancer, MRD is dependent on glycolysis for survival and can be selectively targeted using a small‐molecule inhibitor.Epigenetic similarity between MRD‐ and tumour‐cells suggests a mechanistic basis for metabolic memory. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade‐offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient‐rich conditions, i.e., multiple available nitrogen sources, such as Lactococcus lactis. Here, we present a proteome‐constrained genome‐scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose‐limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model‐based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient‐rich environments and thus form targets of fitness improvement.

Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade‐offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient‐rich conditions, i.e., multiple available nitrogen sources, such as Lactococcus lactis. Here, we present a proteome‐constrained genome‐scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose‐limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model‐based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient‐rich environments and thus form targets of fitness improvement. Synopsis: A proteome‐constrained genome‐scale metabolic model of Lactococcus lactis (pcLactis) is presented. The model is used to interpret microbial behaviour in nutrient‐rich conditions and predicts testable targets for fitness improvement. A proteome‐constrained genome‐scale metabolic model of L. lactis is built.Arginine catabolism in L. lactis can be explained by the resource allocation paradigm.Glucose and arginine uptake are predicted to have great impact on growth when total proteome is not constrained.Improved fitness is related with upregulation of glucose and arginine metabolism. [ABSTRACT FROM AUTHOR]

Abstract Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.

Situations of extremely low substrate availability, resulting in slow growth, are common in natural environments. To mimic these conditions, Lactobacillus plantarum was grown in a carbon-limited retentostat with complete biomass retention. The physiology of extremely slow-growing L. plantarum--as studied by genome-scale modeling and transcriptomics--was fundamentally different from that of stationary-phase cells. Stress resistance mechanisms were not massively induced during transition to extremely slow growth. The energy-generating metabolism was remarkably stable and remained largely based on the conversion of glucose to lactate. The combination of metabolic and transcriptomic analyses revealed behaviors involved in interactions with the environment, more particularly with plants: production of plant hormones or precursors thereof, and preparedness for the utilization of plant-derived substrates. Accordingly, the production of compounds interfering with plant root development was demonstrated in slow-growing L. plantarum. Thus, conditions of slow growth and limited substrate availability seem to trigger a plant environment-like response, even in the absence of plant-derived material, suggesting that this might constitute an intrinsic behavior in L. plantarum.

Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans. Synopsis: Sex‐specific, whole‐body human metabolic models were developed and constrained with physiological, dietary, and metabolomic data. They recapitulate known whole‐body metabolic functions and enable mechanistic exploration of host‐microbiome co‐metabolism. Sex‐specific whole‐body metabolic reconstructions represent the integrated function of 26 organs and six blood cell types.Stoichiometric reconstructions of metabolism can be constrained with whole‐body physiological and metabolomic data to generate personalized models.Whole‐body metabolic models recapitulate known inter‐organ metabolic cycles and energy use, successfully predict known biomarkers of inherited metabolic diseases, and explore potential host‐microbiome co‐metabolism. [ABSTRACT FROM AUTHOR]

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field. [ABSTRACT FROM AUTHOR]

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint‐based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time‐dependent changes, albeit using a static model. By performing an in silico knock‐out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.

Situations of extremely low substrate availability, resulting in slow growth, are common in natural environments. To mimic these conditions, Lactobacillus plantarum was grown in a carbon‐limited retentostat with complete biomass retention. The physiology of extremely slow‐growing L. plantarum—as studied by genome‐scale modeling and transcriptomics—was fundamentally different from that of stationary‐phase cells. Stress resistance mechanisms were not massively induced during transition to extremely slow growth. The energy‐generating metabolism was remarkably stable and remained largely based on the conversion of glucose to lactate. The combination of metabolic and transcriptomic analyses revealed behaviors involved in interactions with the environment, more particularly with plants: production of plant hormones or precursors thereof, and preparedness for the utilization of plant‐derived substrates. Accordingly, the production of compounds interfering with plant root development was demonstrated in slow‐growing L. plantarum. Thus, conditions of slow growth and limited substrate availability seem to trigger a plant environment‐like response, even in the absence of plant‐derived material, suggesting that this might constitute an intrinsic behavior in L. plantarum.

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology forwhich substantial experimental information is available.With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized themetabolic network of M. pneumoniae in great detail, integrating data fromdifferent omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth.We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of timedependent changes, albeit using a staticmodel. By performing an in silico knock-out study as well as analyzing flux distributions in single and doublemutant phenotypes,we demonstrated that themodel accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms. [ABSTRACT FROM AUTHOR]

We present a genome-scale metabolic model for the archaeal methanogen Methanosarcina barkeri. We characterize the metabolic network and compare it to reconstructions from the prokaryotic, eukaryotic and archaeal domains. Using the model in conjunction with constraint-based methods, we simulate the metabolic fluxes and resulting phenotypes induced by different environmental and genetic conditions. This represents the first large-scale simulation of either a methanogen or an archaeal species. Model predictions are validated by comparison to experimental growth measurements and phenotypes of M. barkeri on different substrates. The predicted growth phenotypes for wild type and mutants of the methanogenic pathway have a high level of agreement with experimental findings. We further examine the efficiency of the energy-conserving reactions in the methanogenic pathway, specifically the Ech hydrogenase reaction, and determine a stoichiometry for the nitrogenase reaction. This work demonstrates that a reconstructed metabolic network can serve as an analysis platform to predict cellular phenotypes, characterize methanogenic growth, improve the genome annotation and further uncover the metabolic characteristics of methanogenesis. [ABSTRACT FROM AUTHOR]