Metabolic priming by multiple enzyme systems supports glycolysis, HIF1α stabilisation, and human cancer cell survival in early hypoxia.

Adaptation to chronic hypoxia occurs through changes in protein expression, which are controlled by hypoxia-inducible factor 1α (HIF1α) and are necessary for cancer cell survival. However, the mechanisms that enable cancer cells to adapt in early hypoxia, before the HIF1α-mediated transcription programme is fully established, remain poorly understood. Here we show in human breast cancer cells, that within 3 h of hypoxia exposure, glycolytic flux increases in a HIF1α-independent manner but is limited by NAD+ availability. Glycolytic ATP maintenance and cell survival in early hypoxia rely on reserve lactate dehydrogenase A capacity as well as the activity of glutamate-oxoglutarate transaminase 1 (GOT1), an enzyme that fuels malate dehydrogenase 1 (MDH1)-derived NAD+. In addition, GOT1 maintains low α-ketoglutarate levels, thereby limiting prolyl hydroxylase activity to promote HIF1α stabilisation in early hypoxia and enable robust HIF1α target gene expression in later hypoxia. Our findings reveal that, in normoxia, multiple enzyme systems maintain cells in a primed state ready to support increased glycolysis and HIF1α stabilisation upon oxygen limitation, until other adaptive processes that require more time are fully established.

Indirect effect of the COVID-19 pandemic on hospital mortality in patients with hip fracture: a competing risk survival analysis using linked administrative data.

BACKGROUND: Hip fracture is a leading cause of disability and mortality among older people. During the COVID-19 pandemic, orthopaedic care pathways in the National Health Service in England were restructured to manage pressures on hospital capacity. We examined the indirect consequences of the pandemic for hospital mortality among older patients with hip fracture, admitted from care homes or the community. METHODS: Retrospective analysis of linked care home and hospital inpatient data for patients with hip fracture aged 65 years and over admitted to hospitals in England during the first year of the pandemic (1 March 2020 to 28 February 2021) or during the previous year. We performed survival analysis, adjusting for case mix and COVID-19 infection, and considered live discharge as a competing risk. We present cause-specific hazard ratios (HRCS) for the effect of admission year on hospital mortality risk. RESULTS: During the first year of the pandemic, there were 55 648 hip fracture admissions: a 5.2% decrease on the previous year. 9.5% of patients had confirmed or suspected COVID-19. Hospital stays were substantially shorter (p<0.05), and there was a higher daily chance of discharge (HRCS 1.40, 95% CI 1.38 to 1.41). Overall hip fracture inpatient mortality increased (7.2% in 2020/2021 vs 6.4% in 2019/2020), but patients without concomitant COVID-19 infection had lower mortality rates compared with the year before (5.3%). Admission during the pandemic was associated with a 11% increase in the daily risk of hospital death for patients with hip fracture (HRCS 1.11, 95% CI 1.05 to 1.16). CONCLUSIONS: Although COVID-19 infections led to increases in hospital mortality, overall hospital mortality risk for older patients with hip fracture remained largely stable during the first year of the pandemic.

Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic.

In response to unprecedented surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized patients with COVID-19 to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc policies, we develop a linear programming framework to optimally schedule elective procedures and allocate hospital beds among all planned and emergency patients to minimize years of life lost. Leveraging a large dataset of administrative patient medical records, we apply our framework to the National Health Service in England and show that an extra 50,750-5,891,608 years of life can be gained compared with prioritization policies that reflect those implemented during the pandemic. Notable health gains are observed for neoplasms, diseases of the digestive system, and injuries and poisoning. Our open-source framework provides a computationally efficient approximation of a large-scale discrete optimization problem that can be applied globally to support national-level care prioritization policies.

Hypoxia-induced switch in SNAT2/SLC38A2 regulation generates endocrine resistance in breast cancer.

Tumor hypoxia is associated with poor patient outcomes in estrogen receptor-α-positive (ERα+) breast cancer. Hypoxia is known to affect tumor growth by reprogramming metabolism and regulating amino acid (AA) uptake. Here, we show that the glutamine transporter, SNAT2, is the AA transporter most frequently induced by hypoxia in breast cancer, and is regulated by hypoxia both in vitro and in vivo in xenografts. SNAT2 induction in MCF7 cells was also regulated by ERα, but it became predominantly a hypoxia-inducible factor 1α (HIF-1α)-dependent gene under hypoxia. Relevant to this, binding sites for both HIF-1α and ERα overlap in SNAT2's cis-regulatory elements. In addition, the down-regulation of SNAT2 by the ER antagonist fulvestrant was reverted in hypoxia. Overexpression of SNAT2 in vitro to recapitulate the levels induced by hypoxia caused enhanced growth, particularly after ERα inhibition, in hypoxia, or when glutamine levels were low. SNAT2 up-regulation in vivo caused complete resistance to antiestrogen and, partially, anti-VEGF therapies. Finally, high SNAT2 expression levels correlated with hypoxia profiles and worse outcome in patients given antiestrogen therapies. Our findings show a switch in the regulation of SNAT2 between ERα and HIF-1α, leading to endocrine resistance in hypoxia. Development of drugs targeting SNAT2 may be of value for a subset of hormone-resistant breast cancer.

MCT2 mediates concentration-dependent inhibition of glutamine metabolism by MOG.

α-Ketoglutarate (αKG) is a key node in many important metabolic pathways. The αKG analog N-oxalylglycine (NOG) and its cell-permeable prodrug dimethyloxalylglycine (DMOG) are extensively used to inhibit αKG-dependent dioxygenases. However, whether NOG interference with other αKG-dependent processes contributes to its mode of action remains poorly understood. Here we show that, in aqueous solutions, DMOG is rapidly hydrolyzed, yielding methyloxalylglycine (MOG). MOG elicits cytotoxicity in a manner that depends on its transport by monocarboxylate transporter 2 (MCT2) and is associated with decreased glutamine-derived tricarboxylic acid-cycle flux, suppressed mitochondrial respiration and decreased ATP production. MCT2-facilitated entry of MOG into cells leads to sufficiently high concentrations of NOG to inhibit multiple enzymes in glutamine metabolism, including glutamate dehydrogenase. These findings reveal that MCT2 dictates the mode of action of NOG by determining its intracellular concentration and have important implications for the use of (D)MOG in studying αKG-dependent signaling and metabolism.

Gas Chromatography Coupled to Mass Spectrometry (GC-MS) to Study Metabolism in Cultured Cells.

Cancer cells exhibit characteristic patterns of metabolic behaviour that can be exploited for therapeutic purposes. Conditions found within the tumour microenvironment, such as hypoxia and selective nutrient availability, are known to influence the metabolism of cancer and stromal cells. Understanding cancer metabolism requires the use of analytical methods that allow detection and quantification of many metabolites simultaneously. Gas chromatography-mass spectrometry (GC-MS) is a versatile method to quantify metabolite abundance and, in combination with stable isotope labelled compounds, can yield important insights into the activity of metabolic pathways in cancer cells. This chapter provides an overview of the use of GC-MS for metabolic analysis of adherent cancer cells with an emphasis on the technical background that should be taken into consideration when designing and executing GC-MS-based metabolomics experiments.