Immunoediting instructs tumor metabolic reprogramming to support immune evasion.

Immunoediting sculpts immunogenicity and thwarts host anti-tumor responses in tumor cells during tumorigenesis; however, it remains unknown whether metabolic programming of tumor cells can be guided by immunosurveillance. Here, we report that T cell-mediated immunosurveillance in early-stage tumorigenesis instructs c-Myc upregulation and metabolic reprogramming in tumor cells. This previously unexplored tumor-immune interaction is controlled by non-canonical interferon gamma (IFNγ)-STAT3 signaling and supports tumor immune evasion. Our findings uncover that immunoediting instructs deregulated bioenergetic programs in tumor cells to empower them to disarm the T cell-mediated immunosurveillance by imposing metabolic tug-of-war between tumor and infiltrating T cells and forming the suppressive tumor microenvironment.

PHGDH heterogeneity potentiates cancer cell dissemination and metastasis.

Cancer metastasis requires the transient activation of cellular programs enabling dissemination and seeding in distant organs1. Genetic, transcriptional and translational heterogeneity contributes to this dynamic process2,3. Metabolic heterogeneity has also been observed4, yet its role in cancer progression is less explored. Here we find that the loss of phosphoglycerate dehydrogenase (PHGDH) potentiates metastatic dissemination. Specifically, we find that heterogeneous or low PHGDH expression in primary tumours of patients with breast cancer is associated with decreased metastasis-free survival time. In mice, circulating tumour cells and early metastatic lesions are enriched with Phgdhlow cancer cells, and silencing Phgdh in primary tumours increases metastasis formation. Mechanistically, Phgdh interacts with the glycolytic enzyme phosphofructokinase, and the loss of this interaction activates the hexosamine-sialic acid pathway, which provides precursors for protein glycosylation. As a consequence, aberrant protein glycosylation occurs, including increased sialylation of integrin αvß3, which potentiates cell migration and invasion. Inhibition of sialylation counteracts the metastatic ability of Phgdhlow cancer cells. In conclusion, although the catalytic activity of PHGDH supports cancer cell proliferation, low PHGDH protein expression non-catalytically potentiates cancer dissemination and metastasis formation. Thus, the presence of PHDGH heterogeneity in primary tumours could be considered a sign of tumour aggressiveness.

The mitochondrial pyruvate carrier regulates memory T cell differentiation and antitumor function.

Glycolysis, including both lactate fermentation and pyruvate oxidation, orchestrates CD8+ T cell differentiation. However, how mitochondrial pyruvate metabolism and uptake controlled by the mitochondrial pyruvate carrier (MPC) impact T cell function and fate remains elusive. We found that genetic deletion of MPC drives CD8+ T cell differentiation toward a memory phenotype. Metabolic flexibility induced by MPC inhibition facilitated acetyl-coenzyme-A production by glutamine and fatty acid oxidation that results in enhanced histone acetylation and chromatin accessibility on pro-memory genes. However, in the tumor microenvironment, MPC is essential for sustaining lactate oxidation to support CD8+ T cell antitumor function. We further revealed that chimeric antigen receptor (CAR) T cell manufacturing with an MPC inhibitor imprinted a memory phenotype and demonstrated that infusing MPC inhibitor-conditioned CAR T cells resulted in superior and long-lasting antitumor activity. Altogether, we uncover that mitochondrial pyruvate uptake instructs metabolic flexibility for guiding T cell differentiation and antitumor responses.

Combined Plasma and Urinary Metabolomics Uncover Metabolic Perturbations Associated with Severe Respiratory Syncytial Viral Infection and Future Development of Asthma in Infant Patients.

A large percentage of infants develop viral bronchiolitis needing medical intervention and often develop further airway disease such as asthma. To characterize metabolic perturbations in acute respiratory syncytial viral (RSV) bronchiolitis, we compared metabolomic profiles of moderate and severe RSV patients versus sedation controls. RSV patients were classified as moderate or severe based on the need for invasive mechanical ventilation. Whole blood and urine samples were collected at two time points (baseline and 72 h). Plasma and urinary metabolites were extracted in cold methanol and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), and data from the two biofluids were combined for multivariate data analysis. Metabolite profiles were clustered according to severity, characterized by unique metabolic changes in both plasma and urine. Plasma metabolites that correlated with severity included intermediates in the sialic acid biosynthesis, while urinary metabolites included citrate as well as multiple nucleotides. Furthermore, metabolomic profiles were predictive of future development of asthma, with urinary metabolites exhibiting higher predictive power than plasma. These metabolites may offer unique insights into the pathology of RSV bronchiolitis and may be useful in identifying patients at risk for developing asthma.

Loss of Health Promoting Bacteria in the Gastrointestinal Microbiome of PICU Infants with Bronchiolitis: A Single-Center Feasibility Study.

The feasibility of gastrointestinal (GI) microbiome work in a pediatric intensive care unit (PICU) to determine the GI microbiota composition of infants as compared to control infants from the same hospital was investigated. In a single-site observational study at an urban quaternary care children's hospital in Western Michigan, subjects less than 6 months of age, admitted to the PICU with severe respiratory syncytial virus (RSV) bronchiolitis, were compared to similarly aged control subjects undergoing procedural sedation in the outpatient department. GI microbiome samples were collected at admission (n = 20) and 72 h (n = 19) or at time of sedation (n = 10). GI bacteria were analyzed by sequencing the V4 region of the 16S rRNA gene. Alpha and beta diversity were calculated. Mechanical ventilation was required for the majority (n = 14) of study patients, and antibiotics were given at baseline (n = 8) and 72 h (n = 9). Control subjects' bacterial communities contained more Porphyromonas, and Prevotella (p = 0.004) than those of PICU infants. The ratio of Prevotella to Bacteroides was greater in the control than the RSV infants (mean ± SD-1.27 ± 0.85 vs. 0.61 ± 0.75: p = 0.03). Bacterial communities of PICU infants were less diverse than those of controls with a loss of potentially protective populations.

Targeting Subtype-Specific Metabolic Preferences in Nucleotide Biosynthesis Inhibits Tumor Growth in a Breast Cancer Model.

Investigating metabolic rewiring in cancer can lead to the discovery of new treatment strategies for breast cancer subtypes that currently lack targeted therapies. In this study, we used MMTV-Myc-driven tumors to model breast cancer heterogeneity, investigating the metabolic differences between two histologic subtypes, the epithelial-mesenchymal transition (EMT) and the papillary subtypes. A combination of genomic and metabolomic techniques identified differences in nucleotide metabolism between EMT and papillary subtypes. EMT tumors preferentially used the nucleotide salvage pathway, whereas papillary tumors preferred de novo nucleotide biosynthesis. CRISPR/Cas9 gene editing and mass spectrometry-based methods revealed that targeting the preferred pathway in each subtype resulted in greater metabolic impact than targeting the nonpreferred pathway. Knocking out the preferred nucleotide pathway in each subtype has a deleterious effect on in vivo tumor growth, whereas knocking out the nonpreferred pathway has a lesser effect or may even result in increased tumor growth. Collectively, these data suggest that significant differences in metabolic pathway utilization distinguish EMT and papillary subtypes of breast cancer and identify said pathways as a means to enhance subtype-specific diagnoses and treatment strategies. SIGNIFICANCE: These findings uncover differences in nucleotide salvage and de novo biosynthesis using a histologically heterogeneous breast cancer model, highlighting metabolic vulnerabilities in these pathways as promising targets for breast cancer subtypes.

In Vivo Evidence for Serine Biosynthesis-Defined Sensitivity of Lung Metastasis, but Not of Primary Breast Tumors, to mTORC1 Inhibition.

In tumors, nutrient availability and metabolism are known to be important modulators of growth signaling. However, it remains elusive whether cancer cells that are growing out in the metastatic niche rely on the same nutrients and metabolic pathways to activate growth signaling as cancer cells within the primary tumor. We discovered that breast-cancer-derived lung metastases, but not the corresponding primary breast tumors, use the serine biosynthesis pathway to support mTORC1 growth signaling. Mechanistically, pyruvate uptake through Mct2 supported mTORC1 signaling by fueling serine biosynthesis-derived α-ketoglutarate production in breast-cancer-derived lung metastases. Consequently, expression of the serine biosynthesis enzyme PHGDH was required for sensitivity to the mTORC1 inhibitor rapamycin in breast-cancer-derived lung tumors, but not in primary breast tumors. In summary, we provide in vivo evidence that the metabolic and nutrient requirements to activate growth signaling differ between the lung metastatic niche and the primary breast cancer site.

UDP-glucose 6-dehydrogenase knockout impairs migration and decreases in vivo metastatic ability of breast cancer cells.

Dysregulated metabolism is a hallmark of cancer that supports tumor growth and metastasis. One understudied aspect of cancer metabolism is altered nucleotide sugar biosynthesis, which drives aberrant cell surface glycosylation known to support various aspects of cancer cell behavior including migration and signaling. We examined clinical association of nucleotide sugar pathway gene expression and found that UGDH, encoding UDP-glucose 6-dehydrogenase which catalyzes production of UDP-glucuronate, is associated with worse breast cancer patient survival. Knocking out the mouse homolog Ugdh in highly-metastatic 6DT1 breast cancer cells impaired migration ability without affecting in vitro proliferation. Further, Ugdh-KO resulted in significantly decreased metastatic capacity in vivo when the cells were orthotopically injected in syngeneic mice. Our experiments show that UDP-glucuronate biosynthesis is critical for metastasis in a mouse model of breast cancer.

Metabolomic profiling of mouse mammary tumor-derived cell lines reveals targeted therapy options for cancer subtypes.

PURPOSE: Breast cancer is a heterogeneous disease with several subtypes that currently do not have targeted therapeutic options. Metabolomics has the potential to uncover novel targeted treatment strategies by identifying metabolic pathways required for cancer cells to survive and proliferate. METHODS: The metabolic profiles of two histologically distinct breast cancer subtypes from a MMTV-Myc mouse model, epithelial-mesenchymal-transition (EMT) and papillary, were investigated using mass spectrometry-based metabolomics methods. Based on metabolic profiles, drugs most likely to be effective against each subtype were selected and tested. RESULTS: We found that the EMT and papillary subtypes display different metabolic preferences. Compared to the papillary subtype, the EMT subtype exhibited increased glutathione and TCA cycle metabolism, while the papillary subtype exhibited increased nucleotide biosynthesis compared to the EMT subtype. Targeting these distinct metabolic pathways effectively inhibited cancer cell proliferation in a subtype-specific manner. CONCLUSIONS: Our results demonstrate the feasibility of metabolic profiling to develop novel personalized therapy strategies for different subtypes of breast cancer. Schematic overview of the experimental design for drug selection based on breast cancer subtype-specific metabolism. The epithelial mesenchymal transition (EMT) and papillary tumors are histologically distinct mouse mammary tumor subtypes from the MMTV-Myc mouse model. Cell lines derived from tumors can be used to determine metabolic pathways that can be used to select drug candidates for each subtype.

Mitochondrial Haplotype of the Host Stromal Microenvironment Alters Metastasis in a Non-cell Autonomous Manner.

Mitochondria contribute to tumor growth through multiple metabolic pathways, regulation of extracellular pH, calcium signaling, and apoptosis. Using the Mitochondrial Nuclear Exchange (MNX) mouse models, which pair nuclear genomes with different mitochondrial genomes, we previously showed that mitochondrial SNPs regulate mammary carcinoma tumorigenicity and metastatic potential in genetic crosses. Here, we tested the hypothesis that polymorphisms in stroma significantly affect tumorigenicity and experimental lung metastasis. Using syngeneic cancer cells (EO771 mammary carcinoma and B16-F10 melanoma cells) injected into wild-type and MNX mice (i.e., same nuclear DNA but different mitochondrial DNA), we showed mt-SNP-dependent increases (C3H/HeN) or decreases (C57BL/6J) in experimental metastasis. Superoxide scavenging reduced experimental metastasis. In addition, expression of lung nuclear-encoded genes changed specifically with mt-SNP. Thus, mitochondrial-nuclear cross-talk alters nuclear-encoded signaling pathways that mediate metastasis via both intrinsic and extrinsic mechanisms. SIGNIFICANCE: Stromal mitochondrial polymorphisms affect metastatic colonization through reactive oxygen species and mitochondrial-nuclear cross-talk.

Measuring the Nutrient Metabolism of Adherent Cells in Culture.

Metabolite extraction from cells cultured in vitro enables the comprehensive measurement of intracellular metabolites. These extracts can be analyzed using techniques such as liquid chromatography-mass spectrometry (LC-MS). This chapter describes in detail a method for metabolite extraction from cultured adherent mammalian cells to collect both polar and nonpolar intracellular metabolites. This chapter also describes experimental design considerations for performing stable isotope labeling experiments, and the use of chemical derivatization to increase the number of compounds that can be detected using one chromatography method.

Cysteine catabolism and the serine biosynthesis pathway support pyruvate production during pyruvate kinase knockdown in pancreatic cancer cells.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with limited treatment options. Pyruvate kinase, especially the M2 isoform (PKM2), is highly expressed in PDAC cells, but its role in pancreatic cancer remains controversial. To investigate the role of pyruvate kinase in pancreatic cancer, we knocked down PKM2 individually as well as both PKM1 and PKM2 concurrently (PKM1/2) in cell lines derived from a Kras G12D/- ; p53 -/- pancreatic mouse model. METHODS: We used liquid chromatography tandem mass spectrometry (LC-MS/MS) to determine metabolic profiles of wildtype and PKM1/2 knockdown PDAC cells. We further used stable isotope-labeled metabolic precursors and LC-MS/MS to determine metabolic pathways upregulated in PKM1/2 knockdown cells. We then targeted metabolic pathways upregulated in PKM1/2 knockdown cells using CRISPR/Cas9 gene editing technology. RESULTS: PDAC cells are able to proliferate and continue to produce pyruvate despite PKM1/2 knockdown. The serine biosynthesis pathway partially contributed to pyruvate production during PKM1/2 knockdown: knockout of phosphoglycerate dehydrogenase in this pathway decreased pyruvate production from glucose. In addition, cysteine catabolism generated ~ 20% of intracellular pyruvate in PDAC cells. Other potential sources of pyruvate include the sialic acid pathway and catabolism of glutamine, serine, tryptophan, and threonine. However, these sources did not provide significant levels of pyruvate in PKM1/2 knockdown cells. CONCLUSION: PKM1/2 knockdown does not impact the proliferation of pancreatic cancer cells. The serine biosynthesis pathway supports conversion of glucose to pyruvate during pyruvate kinase knockdown. However, direct conversion of serine to pyruvate was not observed during PKM1/2 knockdown. Investigating several alternative sources of pyruvate identified cysteine catabolism for pyruvate production during PKM1/2 knockdown. Surprisingly, we find that a large percentage of intracellular pyruvate comes from cysteine. Our results highlight the ability of PDAC cells to adaptively rewire their metabolic pathways during knockdown of a key metabolic enzyme.

Metabolic repair through emergence of new pathways in Escherichia coli.

Escherichia coli can derive all essential metabolites and cofactors through a highly evolved metabolic system. Damage of pathways may affect cell growth and physiology, but the strategies by which damaged metabolic pathways can be circumvented remain intriguing. Here, we use a ΔpanD (encoding for aspartate 1-decarboxylase) strain of E. coli that is unable to produce the ß-alanine required for CoA biosynthesis to demonstrate that metabolic systems can overcome pathway damage by extensively rerouting metabolic pathways and modifying existing enzymes for unnatural functions. Using directed cell evolution, rewiring and repurposing of uracil metabolism allowed formation of an alternative ß-alanine biosynthetic pathway. After this pathway was deleted, a second was evolved that used a gain-of-function mutation on ornithine decarboxylase (SpeC) to alter reaction and substrate specificity toward an oxidative decarboxylation-deamination reaction. After deletion of both pathways, yet another independent pathway emerged using polyamine biosynthesis, demonstrating the vast capacity of metabolic repair.

Sialic Acid Metabolism: A Key Player in Breast Cancer Metastasis Revealed by Metabolomics.

Metastatic breast cancer is currently incurable. It has recently emerged that different metabolic pathways support metastatic breast cancer. To further uncover metabolic pathways enabling breast cancer metastasis, we investigated metabolic differences in mouse tumors of differing metastatic propensities using mass spectrometry-based metabolomics. We found that sialic acid metabolism is upregulated in highly metastatic breast tumors. Knocking out a key gene in sialic acid metabolism, Cmas, inhibits synthesis of the activated form of sialic acid, cytidine monophosphate-sialic acid and decreases the formation of lung metastases in vivo. Thus, the sialic acid pathway may be a new target against metastatic breast cancer.

Metabolism in cancer metastasis: bioenergetics, biosynthesis, and beyond.

Metabolic changes accompany tumor progression and metastatic dissemination of cancer cells. Yet, until recently, metabolism has received little attention in the study of cancer metastasis. Cancer cells undergo significant metabolic rewiring as they acquire metastatic traits and adapt to survive in multiple environments with varying nutrient availability, oxygen concentrations, and extracellular signals. Therefore, to effectively treat metastatic cancer, it is important to understand the metabolic strategies adopted by cancer cells during the metastatic process. Here, we focus on the metabolic pathways known to play a role in cancer metastasis, including glycolysis, the pentose phosphate pathway, tricarboxylic acid cycle, oxidative phosphorylation, amino acid metabolism, and fatty acid metabolism. Recent studies have uncovered roles for these pathways in cellular events that promote metastasis, including reactive oxygen species-mediated signaling, epigenetic regulation, and interaction with the extracellular matrix. We also discuss the metabolic interplay between immune cells and cancer cells supporting metastasis. Finally, we highlight the current limitations of our knowledge on this topic, and present future directions for the field. WIREs Syst Biol Med 2018, 10:e1406. doi: 10.1002/wsbm.1406 This article is categorized under: Biological Mechanisms > Metabolism.

Random sample consensus combined with partial least squares regression (RANSAC-PLS) for microbial metabolomics data mining and phenotype improvement.

In recent years, the advent of high-throughput omics technology has made possible a new class of strain engineering approaches, based on identification of possible gene targets for phenotype improvement from omic-level comparison of different strains or growth conditions. Metabolomics, with its focus on the omic level closest to the phenotype, lends itself naturally to this semi-rational methodology. When a quantitative phenotype such as growth rate under stress is considered, regression modeling using multivariate techniques such as partial least squares (PLS) is often used to identify metabolites correlated with the target phenotype. However, linear modeling techniques such as PLS require a consistent metabolite-phenotype trend across the samples, which may not be the case when outliers or multiple conflicting trends are present in the data. To address this, we proposed a data-mining strategy that utilizes random sample consensus (RANSAC) to select subsets of samples with consistent trends for construction of better regression models. By applying a combination of RANSAC and PLS (RANSAC-PLS) to a dataset from a previous study (gas chromatography/mass spectrometry metabolomics data and 1-butanol tolerance of 19 yeast mutant strains), new metabolites were indicated to be correlated with tolerance within certain subsets of the samples. The relevance of these metabolites to 1-butanol tolerance were then validated from single-deletion strains of corresponding metabolic genes. The results showed that RANSAC-PLS is a promising strategy to identify unique metabolites that provide additional hints for phenotype improvement, which could not be detected by traditional PLS modeling using the entire dataset.

A metabolomics-based strategy for identification of gene targets for phenotype improvement and its application to 1-butanol tolerance in Saccharomyces cerevisiae.

BACKGROUND: Traditional approaches to phenotype improvement include rational selection of genes for modification, and probability-driven processes such as laboratory evolution or random mutagenesis. A promising middle-ground approach is semi-rational engineering, where genetic modification targets are inferred from system-wide comparison of strains. Here, we have applied a metabolomics-based, semi-rational strategy of phenotype improvement to 1-butanol tolerance in Saccharomyces cerevisiae. RESULTS: Nineteen yeast single-deletion mutant strains with varying growth rates under 1-butanol stress were subjected to non-targeted metabolome analysis by GC/MS, and a regression model was constructed using metabolite peak intensities as predictors and stress growth rates as the response. From this model, metabolites positively and negatively correlated with growth rate were identified including threonine and citric acid. Based on the assumption that these metabolites were linked to 1-butanol tolerance, new deletion strains accumulating higher threonine or lower citric acid were selected and subjected to tolerance measurement and metabolome analysis. The new strains exhibiting the predicted changes in metabolite levels also displayed significantly higher growth rate under stress over the control strain, thus validating the link between these metabolites and 1-butanol tolerance. CONCLUSIONS: A strategy for semi-rational phenotype improvement using metabolomics was proposed and applied to the 1-butanol tolerance of S. cerevisiae. Metabolites correlated with growth rate under 1-butanol stress were identified, and new mutant strains showing higher growth rate under stress could be selected based on these metabolites. The results demonstrate the potential of metabolomics in semi-rational strain engineering.

Construction of a metabolome library for transcription factor-related single gene mutants of Saccharomyces cerevisiae.

Transcription factors (TFs) play an important role in gene regulation, providing control for cells to adapt to ever changing environments and different physiological states. Although great effort has been taken to study TFs through DNA-protein binding and microarray gene expression experiments, the understanding of transcriptional regulation is still lacking, due to lack of information that links TF regulatory events and final phenotypic change. Here, we focused on metabolites as the final readouts of gene transcription process. We performed metabolite profiling of 154 Saccharomyces cerevisiae's single gene knockouts each defective in a gene encoding transcription factor and built a metabolome library consists of 84 metabolites with good reproducibility. Using the metabolome dataset, we obtained significant correlations and identified differential strains that exhibit altered metabolism compared to control. This work presents a novel metabolome dataset library which will be invaluable for researchers working on transcriptional regulation and yeast biology in general.