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Aim: To investigate cardiac signalling pathways connecting substrate utilization with left ventricular remodelling in a murine pressure overload model. Methods: Cardiac hypertrophy was induced by transverse aortic constriction surgery in 20‐week‐old C57BL/6J mice treated with or without the sodium‐glucose co‐transporter 2 (SGLT2) inhibitor ertugliflozin (225 mg kg−1 chow diet) for 10 weeks. Results: Ertugliflozin improved left ventricular function and reduced myocardial fibrosis. This occurred simultaneously with a fasting‐like response characterized by improved glucose tolerance and increased ketone body concentrations. While cardiac insulin signalling was reduced in response to SGLT2 inhibition, AMP‐activated protein kinase (AMPK) signalling was increased with induction of the fatty acid transporter cluster of differentiation 36 and phosphorylation of acetyl‐CoA carboxylase (ACC). Further, enzymes responsible for ketone body catabolism (β‐hydroxybutyrate dehydrogenase, succinyl‐CoA:3‐oxoacid‐CoA transferase and acetyl‐CoA acetyltransferase 1) were induced by SGLT2 inhibition. Ertugliflozin led to more cardiac abundance of fatty acids, tricarboxylic acid cycle metabolites and ATP. Downstream mechanistic target of rapamycin (mTOR) pathway, relevant for protein synthesis, cardiac hypertrophy and adverse cardiac remodelling, was reduced by SGLT2 inhibition, with alleviation of endoplasmic reticulum (ER) stress and unfolded protein response (UPR) providing a potential mechanism for abundant reduced left ventricular apoptosis and fibrosis. Conclusion: SGLT2 inhibition reduced left ventricular fibrosis in a murine model of cardiac hypertrophy. Mechanistically, this was associated with reduced cardiac insulin and increased AMPK signalling as a potential mechanism for less cardiac mTOR activation with alleviation of downstream ER stress, UPR and apoptosis. [ABSTRACT FROM AUTHOR]

• AML-surviving iFLT3 relies on mitochondrial metabolism, mainly glutamine oxidation. • iFLT3 and asparaginase synergize, targeting resistant AML cells. • iFLT3 and asparaginase promise better treatment outcomes by tackling resistance. Acute myeloid leukemias are a group of hematological malignancies characterized by a poor prognosis for survival. The discovery of oncogenic mutations in the FMS-like tyrosine kinase 3 (FLT3) gene has led to the development of tyrosine kinase inhibitors such as quizartinib. However, achieving complete remission in patients remains challenging because these new tyrosine kinase inhibitors (TKIs) are unable to completely eradicate all leukemic cells. Residual leukemic cells persist during quizartinib treatment, leading to the rapid emergence of drug-resistant leukemia. Given that mitochondrial oxidative metabolism promotes the survival of leukemic cells after exposure to multiple anticancer drugs, we characterized the metabolism of leukemic cells that persisted during quizartinib treatment and developed metabolic strategies to eradicate them. In our study, employing biochemical and metabolomics approaches, we confirmed that the survival of leukemic cells treated with FLT3 inhibitors critically depends on maintaining mitochondrial metabolism, specifically through glutamine oxidation. We uncovered a synergistic interaction between the FLT3 inhibitor quizartinib and L-asparaginase, operating through antimetabolic mechanisms. Utilizing various models of persistent leukemia, we demonstrated that leukemic cells resistant to quizartinib are susceptible to L-asparaginase. This combined therapeutic strategy shows promise in reducing the development of resistance to FLT3 inhibitors, offering a potential strategy to enhance treatment outcomes. [Display omitted] [ABSTRACT FROM AUTHOR]

(D) Top 10 metabolites by two-sided t-test corrected for multiple testing by false discovery rate (FDR < 0.3) of human MI cohort serum metabolomics (left) and mice serum metabolomics (right). Human and mouse non-targeted metabolomics identify 1,5-anhydroglucitol as SGLT2-dependent glycemic marker Keywords: biomarker; metabolomics; SGLT2; type 2 diabetes EN biomarker metabolomics SGLT2 type 2 diabetes 1 7 7 07/01/21 20210601 NES 210601 Abbreviations 1,5-AG 1,5-anhydroglucitol AUC area under the curve BCAA branched-chain amino acid EMPA empagliflozin treatment HbA1c glycated hemoglobin MI myocardial infarction ROC receiver operating characteristic SGLT sodium-glucose cotransporter Metabolomics provide a powerful tool in the search for biomarkers and pathways related to a disease or in response to a drug therapy. In this study, we combined three different non-targeted serum metabolomics datasets in human and mice to explore pathways and biomarkers related to diabetes. [Extracted from the article]

Limiting dysfunctional neutrophilic inflammation while preserving effective immunity requires a better understanding of the processes that dictate neutrophil function in the tissues. Quantitative mass-spectrometry identified how inflammatory murine neutrophils regulated expression of cell surface receptors, signal transduction networks, and metabolic machinery to shape neutrophil phenotypes in response to hypoxia. Through the tracing of labeled amino acids into metabolic enzymes, proinflammatory mediators, and granule proteins, we demonstrated that ongoing protein synthesis shapes the neutrophil proteome. To maintain energy supplies in the tissues, neutrophils consumed extracellular proteins to fuel central carbon metabolism. The physiological stresses of hypoxia and hypoglycemia, characteristic of inflamed tissues, promoted this extracellular protein scavenging with activation of the lysosomal compartment, further driving exploitation of the protein-rich inflammatory milieu. This study provides a comprehensive map of neutrophil proteomes, analysis of which has led to the identification of active catabolic and anabolic pathways that enable neutrophils to sustain synthetic and effector functions in the tissues. [ABSTRACT FROM AUTHOR]

Triple negative breast cancer (TNBC) is an aggressive malignancy for which chemotherapy remains the standard treatment. However, between 3 and 5 years after chemotherapy, about half patients will relapse and it is essential to identify vulnerabilities of cancer cells surviving neoadujuvant therapy. In this study, we established persistent TNBC cell models after treating MDA-MB-231 and SUM159-PT TNBC cell lines with epirubicin and cyclophosphamide, and then with paclitaxel, for a total of 18 weeks. The resulting chemo-persistent cell lines were more proliferative, both in vitro and in xenografted mice. Interestingly, MDA-MB-231 persistent cells became less sensitive to chemotherapeutic drugs, whereas SUM159-PT persistent cells kept similar sensitivity compared to control cells. The reduced sensitivity to chemotherapy in MDA-MB-231 persistent cells was found to be associated with an increased oxidative phosphorylation (OXPHOS) and modified levels of tricarboxylic acid cycle (TCA) intermediates. Integration of data from proteomics and metabolomics demonstrated TCA cycle among the most upregulated pathways in MDA-MB-231 persistent cells. The absence of glucose and pyruvate impeded OXPHOS in persistent cells, while the absence of glutamine did not. In contrast, OXPHOS was not modified in control cells independently of TCA substrates, indicating that MDA-MB-231 persistent cells evolved towards a more pyruvate dependent profile. Finally, the inhibition of pyruvate entry into mitochondria with UK-5099 reduced OXPHOS and re-sensitized persistent cells to therapeutic agents. Together, these findings suggest that targeting mitochondrial pyruvate metabolism may help to overcome mitochondrial adaptation of chemo-persistent TNBC. [ABSTRACT FROM AUTHOR]

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. 13 C tracer analysis, although less information-rich than quantitative 13 C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting 13 C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments. [ABSTRACT FROM AUTHOR]