Combination of low glucose and SCD1 inhibition impairs cancer metabolic plasticity and growth in MCF-7 cancer cells: a comprehensive metabolomic and lipidomic analysis.

BACKGROUND: Cancer cells exhibit remarkable metabolic plasticity, enabling them to adapt to fluctuating nutrient conditions. This study investigates the impact of a combination of low glucose levels and inhibition of stearoyl-CoA desaturase 1 (SCD1) using A939572 on cancer metabolic plasticity and growth. METHODS: A comprehensive metabolomic and lipidomic analysis was conducted to unravel the intricate changes in cellular metabolites and lipids. MCF-7 cells were subjected to low glucose conditions, and SCD1 was inhibited using A939572. The resulting alterations in metabolic pathways and lipid profiles were explored to elucidate the synergistic effects on cancer cell physiology. RESULTS: The combination of low glucose and A939572-induced SCD1 inhibition significantly impaired cancer cell metabolic plasticity. Metabolomic analysis highlighted shifts in key glycolytic and amino acid pathways, indicating the cells' struggle to adapt to restricted glucose availability. Lipidomic profiling revealed alterations in lipid composition, implying disruptions in membrane integrity and signaling cascades. CONCLUSION: Our findings underscore the critical roles of glucose availability and SCD1 activity in sustaining cancer metabolic plasticity and growth. Simultaneously targeting these pathways emerges as a promising strategy to impede cancer progression. The comprehensive metabolomic and lipidomic analysis provides a detailed roadmap of molecular alterations induced by this combination treatment, that may help identify potential therapeutic targets.

Natural variation in age-related dopamine neuron degeneration is glutathione dependent and linked to life span.

Aging is the biggest risk factor for Parkinson's disease (PD), suggesting that age-related changes in the brain promote dopamine neuron vulnerability. It is unclear, however, whether aging alone is sufficient to cause significant dopamine neuron loss, and if so, how this intersects with PD-related neurodegeneration. Here, through examining a large collection of naturally varying Drosophila strains, we find a strong relationship between life span and age-related dopamine neuron loss. Strains with naturally short-lived animals exhibit a loss of dopamine neurons without generalized neurodegeneration, while animals from long-lived strains retain dopamine neurons across age. Metabolomic profiling reveals lower glutathione levels in short-lived strains which is associated with elevated levels of reactive oxygen species (ROS), sensitivity to oxidative stress, and vulnerability to silencing the familial PD gene parkin. Strikingly, boosting neuronal glutathione levels via glutamate-cysteine ligase (Gcl) overexpression is sufficient to normalize ROS levels, extend life span, and block dopamine neurons loss in short-lived backgrounds, demonstrating that glutathione deficiencies are central to neurodegenerative phenotypes associated with short longevity. These findings may be relevant to human PD pathogenesis, where glutathione depletion is reported to occur in the idiopathic PD patient brain through unknown mechanisms. Building on this, we find reduced expression of the Gcl catalytic subunit in both Drosophila strains vulnerable to age-related dopamine neuron loss and in the human brain from familial PD patients harboring the common LRRK2 G2019S mutation. Our study across Drosophila and human PD systems suggests that glutathione synthesis and levels play a conserved role in regulating age-related dopamine neuron health.

Discovery of Hypoxanthine and Inosine as Robust Biomarkers for Predicting the Preanalytical Quality of Human Plasma and Serum for Metabolomics.

In cold human blood, the anomalous dynamics of adenosine triphosphate (ATP) result in the progressive accumulation of adenosine diphosphate (ADP), adenosine monophosphate (AMP), inosine monophosphate (IMP), inosine, and hypoxanthine. While the ATP, ADP, AMP, and IMP are confined to red blood cells (RBCs), inosine and hypoxanthine are excreted into plasma/serum. The plasma/serum levels of inosine and hypoxanthine depend on the temperature of blood and the plasma/serum contact time with the RBCs, and hence they represent robust biomarkers for evaluating the preanalytical quality of plasma/serum. These biomarkers are highly specific since they are generally absent or at very low levels in fresh plasma/serum and are highly sensitive since they are derived from ATP, one of the most abundant metabolites in blood. Further, whether blood was kept at room temperature or on ice could be predicted based on inosine levels. An analysis of >2000 plasma/serum samples processed for metabolomics-centric analyses showed alarmingly high levels of inosine and hypoxanthine. The results highlight the gravity of sample quality challenges with high risk of grossly inaccurate measurements and incorrect study outcomes. The discovery of these robust biomarkers provides new ways to address the longstanding and underappreciated preanalytical sample quality challenges in the blood metabolomics field.

Multi-Omics Profiles of Chronic Low Back Pain and Fibromyalgia - Study Protocol.

Background: Chronic low back pain (CLBP) and fibromyalgia (FM) are leading causes of suffering, disability, and social costs. Current pharmacological treatments do not target molecular mechanisms driving CLBP and FM, and no validated biomarkers are available, hampering the development of effective therapeutics. Omics research has the potential to substantially advance our ability to develop mechanism-specific therapeutics by identifying pathways involved in the pathophysiology of CLBP and FM, and facilitate the development of diagnostic, predictive, and prognostic biomarkers. We will conduct a blood and urine multi-omics study in comprehensively phenotyped and clinically characterized patients with CLBP and FM. Our aims are to identify molecular pathways potentially involved in the pathophysiology of CLBP and FM that would shift the focus of research to the development of target-specific therapeutics, and identify candidate diagnostic, predictive, and prognostic biomarkers. Methods: We are conducting a prospective cohort study of adults ≥18 years of age with CLBP (n=100) and FM (n=100), and pain-free controls (n=200). Phenotyping measures include demographics, medication use, pain-related clinical characteristics, physical function, neuropathiccomponents (quantitative sensory tests and DN4 questionnaire), pain facilitation (temporal summation), and psychosocial function as moderator. Blood and urine samples are collected to analyze metabolomics, lipidomics and proteomics. We will integrate the overall omics data to identify common mechanisms and pathways, and associate multi-omics profiles to pain-related clinical characteristics, physical function, indicators of neuropathic pain, and pain facilitation, with psychosocial variables as moderators. Discussion: Our study addresses the need for a better understanding of the molecular mechanisms underlying chronic low back pain and fibromyalgia. Using a multi-omics approach, we hope to identify converging evidence for potential targets of future therapeutic developments, as well as promising candidate biomarkers for further investigation by biomarker validation studies. We believe that accurate patient phenotyping will be essential for the discovery process, as both conditions are characterized by high heterogeneity and complexity, likely rendering molecular mechanisms phenotype specific.

Metabolite Predictors of Breast and Colorectal Cancer Risk in the Women's Health Initiative.

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women's Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Natural Variation in Age-Related Dopamine Neuron Degeneration is Glutathione-Dependent and Linked to Life Span.

Aging is the biggest risk factor for Parkinson's disease (PD), suggesting that age-related changes in the brain promote dopamine neuron vulnerability. It is unclear, however, whether aging alone is sufficient to cause significant dopamine neuron loss and if so, how this intersects with PD-related neurodegeneration. Here, through examining a large collection of naturally varying Drosophila strains, we find a strong relationship between life span and age-related dopamine neuron loss. Naturally short-lived strains exhibit a loss of dopamine neurons but not generalized neurodegeneration, while long-lived strains retain dopamine neurons across age. Metabolomic profiling reveals lower glutathione levels in short-lived strains which is associated with elevated levels of reactive oxygen species (ROS), sensitivity to oxidative stress and vulnerability to silencing the familial PD gene parkin . Strikingly, boosting neuronal glutathione levels via glutamate-cysteine ligase (GCL) overexpression is sufficient to normalize ROS levels, extend life span and block dopamine neurons loss in short-lived backgrounds, demonstrating that glutathione deficiencies are central to neurodegenerative phenotypes associated with short longevity. These findings may be relevant to human PD pathogenesis, where glutathione depletion is frequently reported in idiopathic PD patient brain. Building on this evidence, we detect reduced levels of GCL catalytic and modulatory subunits in brain from PD patients harboring the LRRK2 G2019S mutation, implicating possible glutathione deficits in familial LRRK2-linked PD. Our study across Drosophila and human PD systems suggests that glutathione plays an important role in the influence of aging on PD neurodegeneration.

Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease.

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

Metabolomics Biomarkers for Fatty Acid Intake and Biomarker-Calibrated Fatty Acid Associations with Chronic Disease Risk in Postmenopausal Women.

BACKGROUND: A substantial observational literature relating specific fatty acid classes to chronic disease risk may be limited by its reliance on self-reported dietary data. OBJECTIVES: We aimed to develop biomarkers for saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acid densities, and to study their associations with cardiovascular disease (CVD), cancer, and type 2 diabetes (T2D) in Women's Health Initiative (WHI) cohorts. METHODS: Biomarker equations were based primarily on serum and urine metabolomics profiles from an embedded WHI human feeding study (n = 153). Calibration equations were based on biomarker values in a WHI nutritional biomarker study (n = 436). Calibrated intakes were assessed in relation to disease incidence in larger WHI cohorts (n = 81,894). Participants were postmenopausal women, aged 50-79 when enrolled at 40 United States Clinical Centers (1993-1998), with a follow-up period of ∼20 y. RESULTS: Biomarker equations meeting criteria were developed for SFA, MUFA, and PUFA densities. That for SFA density depended somewhat weakly on metabolite profiles. On the basis of our metabolomics platforms, biomarkers were insensitive to trans fatty acid intake. Calibration equations meeting criteria were developed for SFA and PUFA density, but not for MUFA density. With or without biomarker calibration, SFA density was associated positively with risk of CVD, cancer, and T2D, but with small hazard ratios, and CVD associations were not statistically significant after controlling for other dietary variables, including trans fatty acid and fiber intake. Following this same control, PUFA density was not significantly associated with CVD risk, but there were positive associations for some cancers and T2D, with or without biomarker calibration. CONCLUSIONS: Higher SFA and PUFA diets were associated with null or somewhat higher risk for clinical outcomes considered in this population of postmenopausal United States women. Further research is needed to develop even stronger biomarkers for these fatty acid densities and their major components. This study is registered with clinicaltrials.gov identifier: NCT00000611.

Metabolomics-Based Biomarker for Dietary Fat and Associations with Chronic Disease Risk in Postmenopausal Women.

BACKGROUND: The Women's Health Initiative (WHI) randomized, controlled Dietary Modification (DM) trial of a low-fat dietary pattern suggested intervention benefits related to breast cancer, coronary heart disease (CHD), and diabetes. Here, we use WHI observational data for further insight into the chronic disease implications of adopting this type of low-fat dietary pattern. OBJECTIVES: We aimed to use our earlier work on metabolomics-based biomarkers of carbohydrate and protein to develop a fat intake biomarker by subtraction, to use the resulting biomarker to develop calibration equations that adjusts self-reported fat intake for measurement error, and to study associations of biomarker-calibrated fat intake with chronic disease risk in WHI cohorts. Corresponding studies for specific fatty acids will follow separately. METHODS: Prospective disease association results are presented using WHI cohorts of postmenopausal women, aged 50-79 y when enrolled at 40 United States clinical centers. Biomarker equations were developed using an embedded human feeding study (n = 153). Calibration equations were developed using a WHI nutritional biomarker study (n = 436). Calibrated intakes were associated with cancer, cardiovascular diseases, and diabetes incidence in WHI cohorts (n = 81,954) over an approximate 20-y follow-up period. RESULTS: A biomarker for fat density was developed by subtracting protein, carbohydrate, and alcohol densities from one. A calibration equation was developed for fat density. Hazard ratios (95% confidence intervals) for 20% higher fat density were 1.16 (1.06, 1.27) for breast cancer, 1.13 (1.02, 1.26) for CHD, and 1.19 (1.13, 1.26) for diabetes, in substantial agreement with findings from the DM trial. With control for additional dietary variables, especially fiber, fat density was no longer associated with CHD, with hazard ratio (95% confidence interval) of 1.00 (0.88, 1.13), whereas that for breast cancer was 1.11 (1.00, 1.24). CONCLUSIONS: WHI observational data support prior DM trial findings of low-fat dietary pattern benefits in this population of postmenopausal United States women. TRIAL REGISTRATION NUMBER: This study is registered with clinicaltrials.gov identifier: NCT00000611.

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions.

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

Monitoring live mitochondrial metabolism in real-time using NMR spectroscopy.

Investigation of mitochondrial metabolism is gaining increased interest owing to the growing recognition of the role of mitochondria in health and numerous diseases. Studies of isolated mitochondria promise novel insights into the metabolism devoid of confounding effects from other cellular organelles such as cytoplasm. This study describes the isolation of mitochondria from mouse skeletal myoblast cells (C2C12) and the investigation of live mitochondrial metabolism in real-time using isotope tracer-based NMR spectroscopy. [3-13 C1 ]pyruvate was used as the substrate to monitor the dynamic changes of the downstream metabolites in mitochondria. The results demonstrate an intriguing phenomenon, in which lactate is produced from pyruvate inside the mitochondria and the results were confirmed by treating mitochondria with an inhibitor of mitochondrial pyruvate carrier (UK5099). Lactate is associated with health and numerous diseases including cancer and, to date, it is known to occur only in the cytoplasm. The insight that lactate is also produced inside mitochondria opens avenues for exploring new pathways of lactate metabolism. Further, experiments performed using inhibitors of the mitochondrial respiratory chain, FCCP and rotenone, show that [2-13 C1 ]acetyl coenzyme A, which is produced from [3-13 C1 ]pyruvate and acts as a primary substrate for the tricarboxylic acid cycle in mitochondria, exhibits a remarkable sensitivity to the inhibitors. These results offer a direct approach to visualize mitochondrial respiration through altered levels of the associated metabolites.

The neuroendocrine stress response compensates for suppression of insulin secretion by volatile anesthetic agents: An observational study.

Alterations in perioperative metabolic function, particularly hyperglycemia, are associated with increased post-operative complications, even in patients without preexisting metabolic abnormalities. Anesthetic medications and the neuroendocrine stress response to surgery may both contribute to altered energy metabolism through impaired glucose and insulin homeostasis but the discrete pathways involved are unclear. Prior human studies, though informative, have been limited by analytic sensitivity or technique, preventing resolution of underlying mechanisms. We hypothesized that general anesthesia with a volatile agent would suppress basal insulin secretion without altering hepatic insulin extraction, and that surgical stress would promote hyperglycemia through gluconeogenesis, lipid oxidation, and insulin resistance. In order to address these hypotheses, we conducted an observational study of subjects undergoing multi-level lumbar surgery with an inhaled anesthetic agent. We measured circulating glucose, insulin, c-peptide, and cortisol frequently throughout the perioperative period and analyzed the circulating metabolome in a subset of these samples. We found volatile anesthetic agents suppress basal insulin secretion and uncouple glucose-stimulated insulin secretion. Following surgical stimulus, this inhibition disappeared and there was gluconeogenesis with selective amino acid metabolism. No robust evidence of lipid metabolism or insulin resistance was observed. These results show that volatile anesthetic agents suppress basal insulin secretion, which results in reduced glucose metabolism. The neuroendocrine stress response to surgery ameliorates the inhibitory effect of the volatile agent on insulin secretion and glucose metabolism, promoting catabolic gluconeogenesis. A better understanding of the complex metabolic interaction between anesthetic medications and surgical stress is needed to inform design of clinical pathways aimed at improving perioperative metabolic function.

dATP elevation induces myocardial metabolic remodeling to support improved cardiac function.

Hallmark features of systolic heart failure are reduced contractility and impaired metabolic flexibility of the myocardium. Cardiomyocytes (CMs) with elevated deoxy ATP (dATP) via overexpression of ribonucleotide reductase (RNR) enzyme robustly improve contractility. However, the effect of dATP elevation on cardiac metabolism is unknown. Here, we developed proteolysis-resistant versions of RNR and demonstrate that elevation of dATP/ATP to ∼1% in CMs in a transgenic mouse (TgRRB) resulted in robust improvement of cardiac function. Pharmacological approaches showed that CMs with elevated dATP have greater basal respiratory rates by shifting myosin states to more active forms, independent of its isoform, in relaxed CMs. Targeted metabolomic profiling revealed a significant reprogramming towards oxidative phosphorylation in TgRRB-CMs. Higher cristae density and activity in the mitochondria of TgRRB-CMs improved respiratory capacity. Our results revealed a critical property of dATP to modulate myosin states to enhance contractility and induce metabolic flexibility to support improved function in CMs.

Chronic Kidney Disease is Associated With Attenuated Plasma Metabolome Response to Oral Glucose Tolerance Testing.

OBJECTIVE: Chronic kidney disease (CKD) is associated with decreased anabolic response to insulin contributing to protein-energy wasting. Targeted metabolic profiling of oral glucose tolerance testing (OGTT) may help identify metabolic pathways contributing to disruptions to insulin response in CKD. METHODS: Using targeted metabolic profiling, we studied the plasma metabolome response in 41 moderate-to-severe nondiabetic CKD patients and 20 healthy controls at fasting and 2 hours after an oral glucose load. We used linear mixed modeling with random intercepts, adjusting for age, gender, race/ethnicity, body weight, and batch to assess heterogeneity in response to OGTT by CKD status. RESULTS: Mean estimated glomerular filtration rate among CKD participants was 38.9 ± 12.7 mL/min per 1.73 m2 compared to 87.2 ± 17.7 mL/min per 1.73 m2 among controls. Glucose ingestion induced an anabolic response resulting in increased glycolysis products and a reduction in a wide range of metabolites including amino acids, tricarboxylic acid cycle intermediates, and purine nucleotides compared to fasting. Participants with CKD demonstrated a blunted anabolic response to OGTT evidenced by significant changes in 13 metabolites compared to controls. The attenuated metabolome response predominant involved mitochondrial energy metabolism, vitamin B family, and purine nucleotides. Compared to controls, CKD participants had elevated lactate:pyruvate (L:P) ratio and decreased guanosine diphosphate:guanosine triphosphate ratio during OGTT. CONCLUSION: Metabolic profiling of OGTT response suggests a broad disruption of mitochondrial energy metabolism in CKD patients. These findings motivate further investigation into the impact of insulin sensitizers and mitochondrial targeted therapeutics on energy metabolism in patients with nondiabetic CKD.

Hydrogen-Deuterium Addition and Exchange in N-Ethylmaleimide Reaction with Glutathione Detected by NMR Spectroscopy.

Glutathione (GSH) is an important and ubiquitous thiol compound abundantly present in virtually every living cell. It is a powerful antioxidant critically required to protect cells from oxidative damage and free radical injury. Its quantification in ex vivo analysis remains a major challenge because it spontaneously oxidizes to form glutathione disulfide. N-Ethylmaleimide (NEM) is a well-known Michael acceptor, which reacts rapidly and irreversibly with thiol and prevents disulfide bond formation. Based on thiol conjugation to NEM, recently, the concentration of GSH was determined in human blood using NMR spectroscopy [Anal. Chem, 2021, 93(44): 14844-14850]. It was found that hydrogen-deuterium addition and exchange occur during the thiol-maleimide reaction as well as NMR analysis, generating a series of poorly explored diastereomers/isotopomers. Here, we establish a general NMR approach to identify the thiosuccinimide diastereomers/isotopomers derived from the thiol-maleimide reaction. The thiol-Michael addition reaction was conducted for GSH and another thiol compound, cysteine, separately, using D2O and H2O. The conjugates were characterized by 1H/13C 1D/2D NMR under different solvent, buffer, and pH conditions. The Michael addition combined with the H/D exchange formed twelve unique diastereomers/isotopomers. NMR measurements allowed the distinct assignment of all structures in solutions and quantification of H/D addition and exchange. Interestingly, the deuterium exchange rate was dependent on structure, pH, and buffer. The elucidation of the thiol-maleimide reaction and H/D exchange mechanism can potentially impact areas including metabolomics, small molecule synthesis, and bioconjugation chemistry.

Enzymatic Depletion of Mitochondrial Inorganic Polyphosphate (polyP) Increases the Generation of Reactive Oxygen Species (ROS) and the Activity of the Pentose Phosphate Pathway (PPP) in Mammalian Cells.

Inorganic polyphosphate (polyP) is an ancient biopolymer that is well preserved throughout evolution and present in all studied organisms. In mammals, it shows a high co-localization with mitochondria, and it has been demonstrated to be involved in the homeostasis of key processes within the organelle, including mitochondrial bioenergetics. However, the exact extent of the effects of polyP on the regulation of cellular bioenergetics, as well as the mechanisms explaining these effects, still remain poorly understood. Here, using HEK293 mammalian cells under Wild-type (Wt) and MitoPPX (cells enzymatically depleted of mitochondrial polyP) conditions, we show that depletion of polyP within mitochondria increased oxidative stress conditions. This is characterized by enhanced mitochondrial O2- and intracellular H2O2 levels, which may be a consequence of the dysregulation of oxidative phosphorylation (OXPHOS) that we have demonstrated in MitoPPX cells in our previous work. These findings were associated with an increase in basal peroxiredoxin-1 (Prx1), superoxide dismutase-2 (SOD2), and thioredoxin (Trx) antioxidant protein levels. Using 13C-NMR and immunoblotting, we assayed the status of glycolysis and the pentose phosphate pathway (PPP) in Wt and MitoPPX cells. Our results show that MitoPPX cells display a significant increase in the activity of the PPP and an increase in the protein levels of transaldolase (TAL), which is a crucial component of the non-oxidative phase of the PPP and is involved in the regulation of oxidative stress. In addition, we observed a trend towards increased glycolysis in MitoPPX cells, which corroborates our prior work. Here, for the first time, we show the crucial role played by mitochondrial polyP in the regulation of mammalian redox homeostasis. Moreover, we demonstrate a significant effect of mitochondrial polyP on the regulation of global cellular bioenergetics in these cells.

Biomarker-Calibrated Red and Combined Red and Processed Meat Intakes with Chronic Disease Risk in a Cohort of Postmenopausal Women.

BACKGROUND: The associations of red and processed meat with chronic disease risk remain to be clarified, in part because of measurement error in self-reported diet. OBJECTIVES: We sought to develop metabolomics-based biomarkers for red and processed meat, and to evaluate associations of biomarker-calibrated meat intake with chronic disease risk among postmenopausal women. METHODS: Study participants were women who were members of the Women's Health Initiative (WHI) study cohorts. These participants were postmenopausal women aged 50-79 y when enrolled during 1993-1998 at 40 US clinical centers with embedded human feeding and nutrition biomarker studies. Literature reports of metabolomics correlates of meat consumption were used to develop meat intake biomarkers from serum and 24-h urine metabolites in a 153-participant feeding study (2010-2014). Resulting biomarkers were used in a 450-participant biomarker study (2007-2009) to develop linear regression calibration equations that adjust FFQ intakes for random and systematic measurement error. Biomarker-calibrated meat intakes were associated with cardiovascular disease, cancer, and diabetes incidence among 81,954 WHI participants (1993-2020). RESULTS: Biomarkers and calibration equations meeting prespecified criteria were developed for consumption of red meat and red plus processed meat combined, but not for processed meat consumption. Following control for nondietary confounding factors, hazard ratios were calculated for a 40% increment above the red meat median intake for coronary artery disease (HR: 1.10; 95% CI: 1.07, 1.14), heart failure (HR: 1.26; 95% CI: 1.20, 1.33), breast cancer (HR: 1.10; 95% CI: 1.07, 1.13) for, total invasive cancer (HR: 1.07; 95% CI: 1.05, 1.09), and diabetes (HR: 1.37; 95% CI: 1.34, 1.39). HRs for red plus processed meat intake were similar. HRs were close to the null, and mostly nonsignificant following additional control for dietary potential confounding factors, including calibrated total energy consumption. CONCLUSIONS: A relatively high-meat dietary pattern is associated with somewhat higher chronic disease risks. These elevations appear to be largely attributable to the dietary pattern, rather than to consumption of red or processed meat per se.

Mitochondrial Inorganic Polyphosphate (polyP) Is a Potent Regulator of Mammalian Bioenergetics in SH-SY5Y Cells: A Proteomics and Metabolomics Study.

Inorganic polyphosphate (polyP) is an ancient, ubiquitous, and well-conserved polymer which is present in all the studied organisms. It is formed by individual subunits of orthophosphate which are linked by structurally similar bonds and isoenergetic to those found in ATP. While the metabolism and the physiological roles of polyP have already been described in some organisms, including bacteria and yeast, the exact role of this polymer in mammalian physiology still remains poorly understood. In these organisms, polyP shows a co-localization with mitochondria, and its role as a key regulator of the stress responses, including the maintenance of appropriate bioenergetics, has already been demonstrated by our group and others. Here, using Wild-type (Wt) and MitoPPX (cells enzymatically depleted of mitochondrial polyP) SH-SY5Y cells, we have conducted a comprehensive study of the status of cellular physiology, using proteomics and metabolomics approaches. Our results suggest a clear dysregulation of mitochondrial physiology, especially of bioenergetics, in MitoPPX cells when compared with Wt cells. Moreover, the effects induced by the enzymatic depletion of polyP are similar to those present in the mitochondrial dysfunction that is observed in neurodegenerative disorders and in neuronal aging. Based on our findings, the metabolism of mitochondrial polyP could be a valid and innovative pharmacological target in these conditions.

Four-Day Food Record Macronutrient Intake, With and Without Biomarker Calibration, and Chronic Disease Risk in Postmenopausal Women.

We recently evaluated associations of biomarker-calibrated protein intake, protein density, carbohydrate intake, and carbohydrate density with the incidence of cardiovascular disease, cancer, and diabetes among postmenopausal women in the Women's Health Initiative (1993-present, 40 US clinical centers). The biomarkers relied on serum and urine metabolomics profiles, and biomarker calibration used regression of biomarkers on food frequency questionnaires. Here we develop corresponding calibration equations using food records and dietary recalls. In addition, we use calibrated intakes based on food records in disease association estimation in a cohort subset (n = 29,294) having food records. In this analysis, more biomarker variation was explained by food records than by FFQs for absolute macronutrient intake, with 24-hour recalls being intermediate. However, the percentage of biomarker variation explained was similar for each assessment approach for macronutrient densities. Invasive breast cancer risk was related inversely to carbohydrate and protein densities using food records, in analyses that included (calibrated) total energy intake and body mass index. Corresponding analyses for absolute intakes did not differ from the null, nor did absolute or relative intakes associate significantly with colorectal cancer or coronary heart disease. These analyses do not suggest major advantages for food records or dietary recalls in comparison with less costly and logistically simpler food frequency questionnaires for these nutritional variables.