Uncovering functional metabolic pathways using metabolomics: case studies of mammalian nucleus and dormant cancer cells

Beyond its fundamental role in fulfilling the nutritional and energetic needs of the cell, metabolism has emerged as an important component of cellular regulatory processes which are central to diverse biological phenomena, ranging from cell differentiation to cancer and longevity. These metabolic pleiotropic roles often converge on the crosstalk with gene expression regulation which is sensitive to the availability of specific metabolites utilized for chromatin and RNA chemical modifications. These required metabolites are often assumed to freely diffuse into the nuclear space, with their key biosynthetic pathways mainly assigned to function elsewhere. However, considering that the intracellular environment is a rather viscous space where the free diffusion between the different compartments could be restricted, a significant question arises of how the nucleus ensures a reliable supply of these essential metabolites, especially as it is often a reaction-diffusion scenario and not only diffusion. Along these lines, the aim of the current PhD thesis was to explore the hypothesis that the nucleus could harbor extended metabolic networks, and not only individual enzymatic steps, for local production of nuclear-relevant metabolites. To examine this, firstly, nuclear proteomics data and nuclear localization signal motif analysis were utilized to assess the potentiality of a nuclear presence of the corresponding metabolic enzymes. Next, by employing stable isotope [U-13C]-based metabolomics analysis in isolated nuclei, we tracked an operational activity. Proximity ligation mass spectrometry for selected enzymatic players allowed us to examine their proximity interactome further corroborating a nuclear subcellular topology. Cumulatively, our data provided multi-level evidence for a functional metabolic pathway operating in a mammalian nucleus. The identified pathway is made of parts of the TCA cycle with intermediates having key roles in chromatin and RNA modifications, reflecting thus the presence of a metabolic nuclear niche ensuring a stable supply of essential metabolites with nucleus-relevant functionalities. The aforementioned crosstalk between metabolism and gene expression regulation highlights the importance of considering metabolic deregulations in pathophysiological conditions. Cancer metabolic alterations are a well-studied phenomenon. Yet, little is known for the metabolic physiology of residual cancer cells that survive treatment and contribute to cancer relapse. The current PhD thesis contributed to the characterization of the metabolic particularities of residual cancer cells derived from a mouse model of breast cancer. The analysis indicated that the residual cells, although phenotypically similar to their normal counterparts and despite the absence of oncogenes expression, preserved a tumorous metabolic memory with main characteristics of an enhanced glycolysis, deregulated TCA and urea cycle. Considering glycolysis’ central role, we next aimed at investigating the network-wide metabolic responses upon inhibition of two important facilitators of the pathway, namely lactate dehydrogenase A and the monocarboxylate transporters 1 and 2, involved in lactate generation and transportation, respectively, in cancer cell lines. The results revealed opposite changes in metabolite concentration pools in glycolysis and TCA cycle intermediates between the two inhibitors treatment, and an overall lower biosynthetic flux. Interesting metabolic nodes were identified that could potentially be therapeutically exploited. Uncovering and understanding metabolic network activities in previously overlooked places, like the existence of a nuclear multistep metabolic network, or the perseverance in cancer regressed cells of a metabolic phenotype mnemonic to the tumorous state, can shed light on the hitherto unknown mechanisms of gene regulation and its interplay with the metabolic state of a cell

Yeast chassis design for dicarboxylic acids production

info:eu-repo/semantics/publishedVersio

Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion.

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness-costly metabolites through natural selection. In this strategy, metabolic cross-feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co-evolved wild-type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B-group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange

Model-guided development of an evolutionarily stable yeast chassis.

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories

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

Unravelling metabolic cross‐feeding in a yeast–bacteria community using 13C‐based proteomics

Abstract Cross‐feeding is fundamental to the diversity and function of microbial communities. However, identification of cross‐fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use 13C isotope tracing in peptides to elucidate cross‐fed metabolites in co‐cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with 13C. Data analysis allowing for the possible mass‐shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross‐fed metabolites was further supported by metabolite labelling in the co‐culture supernatant, and by diminished fitness of a galactose‐negative yeast mutant in the community. Together, our results demonstrate the utility of 13C‐based proteomics for uncovering microbial interactions

Unravelling metabolic cross-feeding in a yeast-bacteria community using 13 C-based proteomics.

Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use 13 C isotope tracing in peptides to elucidate cross-fed metabolites in co-cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with 13 C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of 13 C-based proteomics for uncovering microbial interactions

Glycolytic flux-signaling controls mouse embryo mesoderm development.

How cellular metabolic state impacts cellular programs is a fundamental, unresolved question. Here, we investigated how glycolytic flux impacts embryonic development, using presomitic mesoderm (PSM) patterning as the experimental model. First, we identified fructose 1,6-bisphosphate (FBP) as an in vivo sentinel metabolite that mirrors glycolytic flux within PSM cells of post-implantation mouse embryos. We found that medium-supplementation with FBP, but not with other glycolytic metabolites, such as fructose 6-phosphate and 3-phosphoglycerate, impaired mesoderm segmentation. To genetically manipulate glycolytic flux and FBP levels, we generated a mouse model enabling the conditional overexpression of dominant active, cytoplasmic PFKFB3 (cytoPFKFB3). Overexpression of cytoPFKFB3 indeed led to increased glycolytic flux/FBP levels and caused an impairment of mesoderm segmentation, paralleled by the downregulation of Wnt-signaling, reminiscent of the effects seen upon FBP-supplementation. To probe for mechanisms underlying glycolytic flux-signaling, we performed subcellular proteome analysis and revealed that cytoPFKFB3 overexpression altered subcellular localization of certain proteins, including glycolytic enzymes, in PSM cells. Specifically, we revealed that FBP supplementation caused depletion of Pfkl and Aldoa from the nuclear-soluble fraction. Combined, we propose that FBP functions as a flux-signaling metabolite connecting glycolysis and PSM patterning, potentially through modulating subcellular protein localization

Operation of a TCA cycle subnetwork in the mammalian nucleus

Nucleic acid and histone modifications critically depend on central metabolism for substrates and co-factors. Although a few enzymes related to the formation of these required metabolites have been reported in the nucleus, the corresponding metabolic pathways are considered to function elsewhere in the cell. Here we show that a substantial part of the mitochondrial tricarboxylic acid (TCA) cycle, the biosynthetic hub of epigenetic modification factors, is operational also in the nucleus. Using 13C-tracer analysis, we identified activity of glutamine-to-fumarate, citrate-to-succinate, and glutamine-to-aspartate routes in the nuclei of HeLa cells. Proximity labeling mass-spectrometry revealed a spatial vicinity of the involved enzymes with core nuclear proteins, supporting their nuclear location. We further show nuclear localization of aconitase 2 and 2-oxoglutarate dehydrogenase in mouse embryonic stem cells. Together, our results demonstrate operation of an extended metabolic pathway in the nucleus warranting a revision of the canonical view on metabolic compartmentalization and gene expression regulation