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.

Wide-ranging genetic variation in sensitivity to rapamycin in Drosophila melanogaster.

The progress made in aging research using laboratory organisms is undeniable. Yet, with few exceptions, these studies are conducted in a limited number of isogenic strains. The path from laboratory discoveries to treatment in human populations is complicated by the reality of genetic variation in nature. To model the effect of genetic variation on the action of the drug rapamycin, here we use the growth of Drosophila melanogaster larvae. We screened 140 lines from the Drosophila Genetic References Panel for the extent of developmental delay and found wide-ranging variation in their response, from lines whose development time is nearly doubled by rapamycin, to those that appear to be completely resistant. Sensitivity did not associate with any single genetic marker, nor with any gene. However, variation at the level of genetic pathways was associated with rapamycin sensitivity and might provide insight into sensitivity. In contrast to the genetic analysis, metabolomic analysis showed a strong response of the metabolome to rapamycin, but only among the sensitive larvae. In particular, we found that rapamycin altered levels of amino acids in sensitive larvae, and in a direction strikingly similar to the metabolome response to nutrient deprivation. This work demonstrates the need to evaluate interventions across genetic backgrounds and highlights the potential of omic approaches to reveal biomarkers of drug efficacy and to shed light on mechanisms underlying sensitivity to interventions aimed at increasing lifespan.

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.

Selection for early reproduction leads to accelerated aging and extensive metabolic remodeling in Drosophila melanogaster populations.

Experimental evolution studies that feature selection on life-history characters are a proven approach for studying the evolution of aging and variation in rates of senescence. Recently, the incorporation of genomic and transcriptomic approaches into this framework has led to the identification of hundreds of genes associated with different aging patterns. However, our understanding of the specific molecular mechanisms underlying these aging patterns remains limited. Here, we incorporated extensive metabolomic profiling into this framework to generate mechanistic insights into aging patterns in Drosophila melanogaster . Specifically, we characterized metabolomic change over time associated with accelerated aging in populations of D. melanogaster under selection for early reproduction compared to their controls. Using this data we: i) evaluated the evolutionary repeatability across the metabolome; ii) evaluated the value of the metabolome as a predictor of "biological age" in this system; and iii) identified specific metabolic pathways associated with accelerated aging. Generally, our findings suggest that the metabolome is a reliable predictor of age and senescence in populations that share a recent evolutionary history. Metabolomic analysis revealed that generations of selection for early reproduction resulted in highly repeatable alterations to the metabolome. Specifically, changes in carbohydrate, amino acid, and TCA cycle-related metabolite abundances over time point to metabolic remodeling that favors rapid early reproduction with long-term consequences for carbohydrate and protein utilization.

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.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts.

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

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.

Multi-dimensional metabolomic profiling reveals dysregulated ornithine metabolism hallmarks associated with a severe acute pancreatitis phenotype.

To reveal dysregulated metabolism hallmark that was associated with a severe acute pancreatitis (SAP) phenotype. In this study, LC-MS/MS-based targeted metabolomics was used to analyze plasma samples from 106 acute pancreatitis (AP) patients (34 mild, 38 moderate, and 34 severe) admitted within 48 hours from abdominal pain onset and 41 healthy controls. Temporal metabolic profiling was performed on days 1, 3, and 7 after admission. A random forest (RF) was performed to significantly determine metabolite differences between SAP and non-SAP (NSAP) groups. Mass spectrometry imaging (MSI) and immunohistochemistry were conducted for the examination of pancreatic metabolite and metabolic enzyme alterations, respectively, on necrosis and paracancerous tissues. Simultaneously determination of serum and pancreatic tissue metabolic alterations using an L-ornithine-induced AP model to discover metabolic commonalities. Twenty-two significant differential metabolites screened by RF were selected to build an accurate model for the prediction of SAP from NSAP (AUC = 0.955). Six of 22 markers were found by MSI with significant alterations in pancreatic lesions, reduced ornithine-related metabolites were also identified. The abnormally expressed arginase2 and ornithine transcarboxylase were further discovered in combination with time-course metabolic profiling in the SAP animal models, the decreased ornithine catabolites were found at a late stage of inflammation, but ornithine-associated metabolic enzymes were activated during the inflammatory process. The plasma metabolome of AP patients is distinctive, which shows promise for early SAP diagnosis. AP aggravation is linked to the activated ornithine metabolic pathway and its inadequate levels of catabolites in in-situ lesion.

Bifidobacterium infantis associates with T cell immunity in human infants and is sufficient to enhance antigen-specific T cells in mice.

Bacille Calmette-Guerin (BCG) vaccine can elicit good TH1 responses in neonates. We hypothesized that the pioneer gut microbiota affects vaccine T cell responses. Infants who are HIV exposed but uninfected (iHEU) display an altered immunity to vaccination. BCG-specific immune responses were analyzed at 7 weeks of age in iHEU, and responses were categorized as high or low. Bifidobacterium longum subsp. infantis was enriched in the stools of high responders, while Bacteroides thetaiotaomicron was enriched in low responders at time of BCG vaccination. Neonatal germ-free or SPF mice orally gavaged with live B. infantis exhibited significantly higher BCG-specific T cells compared with pups gavaged with B. thetaiotaomicron. B. infantis and B. thetaiotaomicron differentially affected stool metabolome and colonic transcriptome. Human colonic epithelial cells stimulated with B. infantis induced a unique gene expression profile versus B. thetaiotaomicron. We thus identified a causal role of B. infantis in early-life antigen-specific immunity.

Raising NAD+ Level Stimulates Short-Chain Dehydrogenase/Reductase Proteins to Alleviate Heart Failure Independent of Mitochondrial Protein Deacetylation.

BACKGROUND: Strategies to increase cellular NAD+ (oxidized nicotinamide adenine dinucleotide) level have prevented cardiac dysfunction in multiple models of heart failure, but molecular mechanisms remain unclear. Little is known about the benefits of NAD+-based therapies in failing hearts after the symptoms of heart failure have appeared. Most pretreatment regimens suggested mechanisms involving activation of sirtuin, especially Sirt3 (sirtuin 3), and mitochondrial protein acetylation. METHODS: We induced cardiac dysfunction by pressure overload in SIRT3-deficient (knockout) mice and compared their response with nicotinamide riboside chloride treatment with wild-type mice. To model a therapeutic approach, we initiated the treatment in mice with established cardiac dysfunction. RESULTS: We found nicotinamide riboside chloride improved mitochondrial function and blunted heart failure progression. Similar benefits were observed in wild-type and knockout mice. Boosting NAD+ level improved the function of NAD(H) redox-sensitive SDR (short-chain dehydrogenase/reductase) family proteins. Upregulation of Mrpp2 (mitochondrial ribonuclease P protein 2), a multifunctional SDR protein and a subunit of mitochondrial ribonuclease P, improves mitochondrial DNA transcripts processing and electron transport chain function. Activation of SDRs in the retinol metabolism pathway stimulates RXRα (retinoid X receptor α)/PPARα (proliferator-activated receptor α) signaling and restores mitochondrial oxidative metabolism. Downregulation of Mrpp2 and impaired mitochondrial ribonuclease P were found in human failing hearts, suggesting a shared mechanism of defective mitochondrial biogenesis in mouse and human heart failure. CONCLUSIONS: These findings identify SDR proteins as important regulators of mitochondrial function and molecular targets of NAD+-based therapy. Furthermore, the benefit is observed regardless of Sirt3-mediated mitochondrial protein deacetylation, a widely held mechanism for NAD+-based therapy for heart failure. The data also show that NAD+-based therapy can be useful in pre-existing heart failure.

Amygdalobacter indicium gen. nov., sp. nov., and Amygdalobacter nucleatus sp. nov., gen. nov.: novel bacteria from the family Oscillospiraceae isolated from the female genital tract.

Four obligately anaerobic Gram-positive bacteria representing one novel genus and two novel species were isolated from the female genital tract. Both novel species, designated UPII 610-JT and KA00274T, and an additional isolate of each species were characterized utilizing biochemical, genotypic and phylogenetic analyses. All strains were non-motile and non-spore forming, asaccharolytic, non-cellulolytic and indole-negative coccobacilli. Fatty acid methyl ester analysis for UPII 610-JT and KA00274T and additional isolates revealed C16 : 0, C18 : 0, C18:1ω9c and C18:2ω6,9c to be the major fatty acids for both species. UPII 610-JT had a 16S rRNA gene sequence similarity of 99.4 % to an uncultured clone sequence (AY724740) designated as Bacterial Vaginosis Associated Bacterium 2 (BVAB2). KA00274T had a 16S rRNA gene sequence similarity of 96.5 % to UPII 610-JT. Whole genomic DNA mol% G+C content was 42.2 and 39.3 % for UPII 610-JT and KA00274T, respectively. Phylogenetic analyses indicate these isolates represent a novel genus and two novel species within the Oscillospiraceae family. We propose the names Amygdalobacter indicium gen. nov., sp. nov., for UPII 610-JT representing the type strain of this species (=DSM 112989T, =ATCC TSD-274T) and Amygdalobacter nucleatus gen. nov., sp. nov., for KA00274T representing the type strain of this species (=DSM 112988T, =ATCC TSD-275T).

Age-related changes of skeletal muscle metabolic response to contraction are also sex-dependent.

Mitochondria adapt to increased energy demands during muscle contraction by acutely altering metabolite fluxes and substrate oxidation. With age, an impaired mitochondrial metabolic response may contribute to reduced exercise tolerance and decreased skeletal muscle mass, specific force, increased overall fatty depositions in the skeletal muscle, frailty and depressed energy maintenance. We hypothesized that elevated energy stress in mitochondria with age alters the capacity of mitochondria to utilize different substrates following muscle contraction. To test this hypothesis, we used in vivo electrical stimulation to simulate high-intensity intervals (HII) or low intensity steady-state (LISS) exercise in young (5-7 months) and aged (27-29 months) male and female mice to characterize effects of age and sex on mitochondrial substrate utilization in skeletal muscle following contraction. Mitochondrial respiration using glutamate decreased in aged males following HII and glutamate oxidation was inhibited following HII in both the contracted and non-stimulated muscle of aged female muscle. Analyses of the muscle metabolome of female mice indicated that changes in metabolic pathways induced by HII and LISS contractions in young muscle are absent in aged muscle. To test improved mitochondrial function on substrate utilization following HII, we treated aged females with elamipretide (ELAM), a mitochondrially-targeted peptide shown to improve mitochondrial bioenergetics and restore redox status in aged muscle. ELAM removed inhibition of glutamate oxidation and showed increased metabolic pathway changes following HII, suggesting rescuing redox status and improving bioenergetic function in mitochondria from aged muscle increases glutamate utilization and enhances the metabolic response to muscle contraction in aged muscle. KEY POINTS: Acute local contraction of gastrocnemius can systemically alter mitochondrial respiration in non-stimulated muscle. Age-related changes in mitochondrial respiration using glutamate or palmitoyl carnitine following contraction are sex-dependent. Respiration using glutamate after high-intensity contraction is inhibited in aged female muscle. Metabolite level and pathway changes following muscle contraction decrease with age in female mice. Treatment with the mitochondrially-targeted peptide elamipretide can partially rescue metabolite response to muscle contraction.

Labile Metabolite Profiling in Human Blood Using Phosphorus NMR Spectroscopy.

Phosphorus metabolites occupy a unique place in cellular function as critical intermediates and products of cellular metabolism. Human blood is the most widely used biospecimen in the clinic and in the metabolomics field, and hence an ability to profile phosphorus metabolites in blood, quantitatively, would benefit a wide variety of investigations of cellular functions in health and diseases. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the two premier analytical platforms used in the metabolomics field. However, detection and quantitation of phosphorus metabolites by MS can be challenging due to their lability, high polarity, structural isomerism, and interaction with chromatographic columns. The conventionally used 1H NMR, on the other hand, suffers from poor resolution of these compounds. As a remedy, 31P NMR promises an important alternative to both MS and 1H NMR. However, numerous challenges including the instability of phosphorus metabolites, their chemical shift sensitivity to solvent composition, pH, salt, and temperature, and the lack of identified metabolites have so far restricted the scope of 31P NMR. In the current study, we describe a method to analyze nearly 25 phosphorus metabolites in blood using a simple one-dimensional (1D) NMR spectrum. Establishment of the identity of unknown metabolites involved a combination of (a) comprehensively analyzing an array of 1D and two-dimensional (2D) 1H/31P homonuclear and heteronuclear NMR spectra of blood; (b) mapping the central carbon metabolic pathway; (c) developing and using 1H and 31P spectral and chemical shift databases; and finally (d) confirming the putative metabolite peaks with spiking using authentic compounds. The resulting simple 1D 31P NMR-based method offers an ability to visualize and quantify the levels of intermediates and products of multiple metabolic pathways, including central carbon metabolism, in one step. Overall, the findings represent a new dimension for blood metabolite analysis and are anticipated to greatly impact the blood metabolomics field.

Anomalous Dynamics of Labile Metabolites in Cold Human Blood Detected Using 1H NMR Spectroscopy.

Recent efforts in our laboratory have enabled access to an unprecedented number (∼90) of quantifiable metabolites in human blood by a simple nuclear magnetic resonance (NMR) spectroscopy method, which includes energy coenzymes, redox coenzymes, and antioxidants that are fundamental to cellular functions [ J. Magn. Reson. Open 2022, 12-13, 100082]. The coenzymes and antioxidants, however, are notoriously labile and are extremely sensitive to specimen harvesting, extraction, and measurement conditions. This problem is largely underappreciated and carries the risk of grossly inaccurate measurements and incorrect study outcomes. As a part of addressing this challenge, in this study, human blood specimens were comprehensively and quantitatively investigated using 1H NMR spectroscopy. Freshly drawn human blood specimens were treated or not treated with methanol, ethanol, or a mixture of methanol and chloroform, and stored on ice or on bench, at room temperature for different time periods from 0 to 24 h, prior to storing at -80 °C. Interestingly, the labile metabolite levels were stable in blood treated with an organic solvent. However, their levels in blood in untreated samples increased or decreased by factors of up to 5 or more within 3 h. Further, surprisingly, and contrary to the current knowledge about metabolite stability, the variation of coenzyme levels was more dramatic in blood stored on ice than on bench, at room temperature. In addition, unlike the generally observed phenomenon of oxidation of redox coenzymes, reduction was observed in untreated blood. Such preanalytical dynamics of the labile metabolites potentially arises from the active cellular metabolism. From the metabolomics perspective, the massive variation of the labile metabolite levels even in blood stored on ice is alarming and stresses the critical need to immediately quench the cellular metabolism for reliable analyses. Overall, the results provide compelling evidence that warrants a paradigm shift in the sample collection protocol for blood metabolomics involving labile metabolites.

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.

Systems biology and machine learning approaches identify metabolites that influence dietary lifespan and healthspan responses across flies and humans.

Dietary restriction (DR) is a potent method to enhance lifespan and healthspan, but individual responses are influenced by genetic variations. Understanding how metabolism-related genetic differences impact longevity and healthspan are unclear. To investigate this, we used metabolites as markers to reveal how different genotypes respond to diet to influence longevity and healthspan traits. We analyzed data from Drosophila Genetic Reference Panel strains raised under AL and DR conditions, combining metabolomic, phenotypic, and genome-wide information. Employing two computational methods across species-random forest modeling within the DGRP and Mendelian randomization in the UK Biobank-we pinpointed key traits with cross-species relevance that influence lifespan and healthspan. Notably, orotate was linked to parental age at death in humans and counteracted DR effects in flies, while threonine extended lifespan, in a strain- and sex-specific manner. Thus, utilizing natural genetic variation data from flies and humans, we employed a systems biology approach to elucidate potential therapeutic pathways and metabolomic targets for diet-dependent changes in lifespan and healthspan.

Age and sex dependent effects of metabolic response to muscle contraction.

Sarcopenia, the age-related loss of muscle mass and function, contributes to decreased quality of life in the elderly and increased healthcare costs. Decreased skeletal muscle mass, specific force, increased overall fatty depositions in the skeletal muscle, frailty and depressed energy maintenance are all associated with increased oxidative stress and the decline in mitochondrial function with age. We hypothesized that elevated mitochondrial stress with age alters the capacity of mitochondria to utilize different substrates following muscle contraction. To test this hypothesis, we designed two in vivo muscle-stimulation protocols to simulate high-intensity intervals (HII) or low intensity steady-state (LISS) exercise to characterize the effect of age and sex on mitochondrial substrate utilization in skeletal muscle following muscle contraction. Following HII stimulation, mitochondria from young skeletal muscle increased fatty acid oxidation compared to non-stimulated control muscle; however, mitochondria from aged muscle decreased fatty acid oxidation. In contrast, following LISS, mitochondrial from young skeletal muscle decreased fatty acid oxidation, whereas aged mitochondria increased fatty acid oxidation. We also found that HII can inhibit mitochondrial oxidation of glutamate in both stimulated and non-stimulated aged muscle, suggesting HII initiates circulation of an exerkine capable of altering whole-body metabolism. Analyses of the muscle metabolome indicates that changes in metabolic pathways induced by HII and LISS contractions in young muscle are absent in aged muscle. Treatment with elamipretide, a mitochondrially targeted peptide, restored glutamate oxidation and metabolic pathway changes following HII suggesting rescuing redox status and improving mitochondrial function in aged muscle enhances the metabolic response to muscle contraction.