Untargeted metabolomics for uncovering biological markers of human skeletal muscle ageing

Mechanism of Xiaoyao San in treating non-alcoholic fatty liver disease with liver depression and spleen deficiency: based on bioinformatics, metabolomics and in vivo experiments

Xiaoyao san (XYS) plays an important role in treatment of non-alcoholic fatty liver disease (NAFLD) with liver stagnation and spleen deficiency, but its specific mechanism is still unclear. This study aimed to investigate the material basis and mechanism by means of network pharmacology, metabolomics, systems biology and molecular docking methods. On this basis, NAFLD rat model with liver stagnation and spleen deficiency was constructed and XYS was used to intervene, and liver histopathology, biochemical detection, enzyme-linked immunosorbent assay, quantitative PCR assay and western blotting were used to further verify the mechanism. Through the above research methods, network pharmacology study showed that there were 94 targets in total for XYS in the treatment of NAFLD. Metabolomics study showed that NAFLD with liver depression and spleen deficiency had a total of 73 differential metabolites. Systems biology found that PTGS2 and PPARG were the core targets; Quercetin, kaempferol, naringenin, beta-sitosterol and stigmasterol were the core active components; AA, cAMP were the core metabolites. And molecular docking showed that the core active components can act well on the key targets. Animal experiments showed that XYS could improve liver histopathology, increase 5HT and NA, decrease INS and FBG, improve blood lipids and liver function, decrease AA, increase cAMP, down-regulate PTGS2, up-regulate PPARG, and decrease PGE2 and 15d-PGJ2. In conclusion, XYS might treat NAFLD with liver depression and spleen deficiency by down-regulating PTGS2, up-regulating PPARG, reducing AA content, increasing cAMP, improving insulin resistance, affecting glucose and lipid metabolism, inhibiting oxidative stress and inflammatory response.Communicated by Ramaswamy H. Sarma.

Metabolomic profiles of ovariectomized mice and their associations with body composition and frailty-related parameters in postmenopausal women

BACKGROUND

Menopause, a dramatical estrogen-deficient condition, is considered the most significant milestone in women's health.

PURPOSE

To investigate the metabolite changes attributed to estrogen deficiency using random forest (RF)-based machine learning (ML) modeling strategy in ovariectomized (OVX) mice as well as determine the clinical relevance of selected metabolites in older women.

METHODS AND RESULTS

Untargeted and targeted metabolomic analyses revealed that metabolites related to TCA cycle, sphingolipids, phospholipids, fatty acids, and amino acids, were significantly changed in the plasma and/or muscle of OVX mice. Subsequent ML classifiers based on RF algorithm selected alpha-ketoglutarate (AKG), arginine, carnosine, ceramide C24, phosphatidylcholine (PC) aa C36:6, and PC ae C42:3 in plasma as well as PC aa 34:1, PC aa C34:3, PC aa C36:5, PC aa C32:1, PC aa C36:2, and sphingosine in muscle as top featured metabolites that differentiate the OVX mice from the sham-operated group. When circulating levels of AKG, arginine, and carnosine, which showed the most significant changes in OVX mice blood, were measured in postmenopausal women, higher plasma AKG levels were associated with lower bone mass, weak grip strength, poor physical performance, and increased frailty risk.

CONCLUSIONS

Metabolomics- and ML-based methods identified the key metabolites of blood and muscle that were significantly changed after ovariectomy in mice, and the clinical implication of several metabolites was investigated by looking at their correlation with body composition and frailty-related parameters in postmenopausal women. These findings provide crucial context for understanding the diverse physiological alterations caused by estrogen deficiency in women.

A pilot study of alternative substrates in the critically Ill subject using a ketogenic feed

Bioenergetic failure caused by impaired utilisation of glucose and fatty acids contributes to organ dysfunction across multiple tissues in critical illness. Ketone bodies may form an alternative substrate source, but the feasibility and safety of inducing a ketogenic state in physiologically unstable patients is not known. Twenty-nine mechanically ventilated adults with multi-organ failure managed on intensive care units were randomised (Ketogenic n = 14, Control n = 15) into a two-centre pilot open-label trial of ketogenic versus standard enteral feeding. The primary endpoints were assessment of feasibility and safety, recruitment and retention rates and achievement of ketosis and glucose control. Ketogenic feeding was feasible, safe, well tolerated and resulted in ketosis in all patients in the intervention group, with a refusal rate of 4.1% and 82.8% retention. Patients who received ketogenic feeding had fewer hypoglycaemic events (0.0% vs. 1.6%), required less exogenous international units of insulin (0 (Interquartile range 0-16) vs.78 (Interquartile range 0-412) but had slightly more daily episodes of diarrhoea (53.5% vs. 42.9%) over the trial period. Ketogenic feeding was feasible and may be an intervention for addressing bioenergetic failure in critically ill patients. Clinical Trials.gov registration: NCT04101071.

The aging tumor metabolic microenvironment

Despite the higher incidence of cancer with increasing age, few preclinical or clinical studies incorporate age. This, coupled with an aging world population, requires that we improve our understanding of how aging affects cancer development, progression, and treatment. One key area will be how the tumor microenvironment (TME) changes with age. Metabolite levels are an essential component of the TME, and they are affected by the metabolic requirements of the cells present and systemic metabolite availability. These factors are affected by aging, causing different TME metabolic states between young and older adults. In this review, we will summarize what is known about how aging impacts the TME metabolic state, and suggest how we can improve our understanding of it.

Identification of the hub genes in polycystic ovary syndrome based on disease-associated molecule network

Revealing the key genes involved in polycystic ovary syndrome (PCOS) and elucidating its pathogenic mechanism is of extreme importance for the development of targeted clinical therapy for PCOS. Investigating disease by integrating several associated and interacting molecules in biological systems will make it possible to discover new pathogenic genes. In this study, an integrative disease-associated molecule network, combining protein-protein interactions and protein-metabolites interactions (PPMI) network was constructed based on the PCOS-associated genes and metabolites systematically collected. This new PPMI strategy identified several potential PCOS-associated genes, which have unreported in previous publications. Moreover, the systematic analysis of five benchmarks data sets indicated the DERL1 was identified as downregulated in PCOS granulosa cell and has good classification performance between PCOS patients and healthy controls. CCR2 and DVL3 were upregulated in PCOS adipose tissues and have good classification performance. The expression of novel gene FXR2 identified in this study is significantly increased in ovarian granulosa cells of PCOS patients compared with controls via quantitative analysis. Our study uncovers substantial differences in the PCOS-specific tissue and provides a plethora of information on dysregulated genes and metabolites that are linked to PCOS. This knowledgebase could have the potential to benefit the scientific and clinical community. In sum, the identification of novel gene associated with PCOS provides valuable insights into the underlying molecular mechanisms of PCOS and could potentially lead to the development of new diagnostic and therapeutic strategies.

Metabolomics in aging research: aging markers from organs

Metabolism plays an important role in regulating aging at several levels, and metabolic reprogramming is the main driving force of aging. Due to the different metabolic needs of different tissues, the change trend of metabolites during aging in different organs and the influence of different levels of metabolites on organ function are also different, which makes the relationship between the change of metabolite level and aging more complex. However, not all of these changes lead to aging. The development of metabonomics research has opened a door for people to understand the overall changes in the metabolic level in the aging process of organisms. The omics-based "aging clock" of organisms has been established at the level of gene, protein and epigenetic modifications, but there is still no systematic summary at the level of metabolism. Here, we reviewed the relevant research published in the last decade on aging and organ metabolomic changes, discussed several metabolites with high repetition rate, and explained their role in vivo, hoping to find a group of metabolites that can be used as metabolic markers of aging. This information should provide valuable information for future diagnosis or clinical intervention of aging and age-related diseases.

Exploring the aging process of cognitively healthy adults by analyzing cerebrospinal fluid metabolomics using liquid chromatography-tandem mass spectrometry

BACKGROUND

During biological aging, significant metabolic dysregulation in the central nervous system may lead to cognitive decline and neurodegeneration. However, the metabolomics of the aging process in cerebrospinal fluid (CSF) has not been thoroughly explored.

METHODS

In this cohort study of CSF metabolomics using liquid chromatography-mass spectrometry (LC-MS), fasting CSF samples collected from 92 cognitively unimpaired adults aged 20-87 years without obesity or diabetes were analyzed.

RESULTS

We identified 37 metabolites in these CSF samples with significant positive correlations with aging, including cysteine, pantothenic acid, 5-hydroxyindoleacetic acid (5-HIAA), aspartic acid, and glutamate; and two metabolites with negative correlations, asparagine and glycerophosphocholine. The combined alterations of asparagine, cysteine, glycerophosphocholine, pantothenic acid, sucrose, and 5-HIAA showed a superior correlation with aging (AUC = 0.982). These age-correlated changes in CSF metabolites might reflect blood-brain barrier breakdown, neuroinflammation, and mitochondrial dysfunction in the aging brain. We also found sex differences in CSF metabolites with higher levels of taurine and 5-HIAA in women using propensity-matched comparison.

CONCLUSIONS

Our LC-MS metabolomics of the aging process in a Taiwanese population revealed several significantly altered CSF metabolites during aging and between the sexes. These metabolic alterations in CSF might provide clues for healthy brain aging and deserve further exploration.

The 'ForensOMICS' approach for postmortem interval estimation from human bone by integrating metabolomics, lipidomics, and proteomics

The combined use of multiple omics allows to study complex interrelated biological processes in their entirety. We applied a combination of metabolomics, lipidomics and proteomics to human bones to investigate their combined potential to estimate time elapsed since death (i.e., the postmortem interval [PMI]). This 'ForensOMICS' approach has the potential to improve accuracy and precision of PMI estimation of skeletonized human remains, thereby helping forensic investigators to establish the timeline of events surrounding death. Anterior midshaft tibial bone was collected from four female body donors before their placement at the Forensic Anthropology Research Facility owned by the Forensic Anthropological Center at Texas State (FACTS). Bone samples were again collected at selected PMIs (219-790-834-872days). Liquid chromatography mass spectrometry (LC-MS) was used to obtain untargeted metabolomic, lipidomic, and proteomic profiles from the pre- and post-placement bone samples. The three omics blocks were investigated independently by univariate and multivariate analyses, followed by Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), to identify the reduced number of markers describing postmortem changes and discriminating the individuals based on their PMI. The resulting model showed that pre-placement metabolome, lipidome and proteome profiles were clearly distinguishable from post-placement ones. Metabolites in the pre-placement samples suggested an extinction of the energetic metabolism and a switch towards another source of fuelling (e.g., structural proteins). We were able to identify certain biomolecules with an excellent potential for PMI estimation, predominantly the biomolecules from the metabolomics block. Our findings suggest that, by targeting a combination of compounds with different postmortem stability, in the future we could be able to estimate both short PMIs, by using metabolites and lipids, and longer PMIs, by using proteins.

Study on potential markers for diagnosis of renal cell carcinoma by serum untargeted metabolomics based on UPLC-MS/MS

Objective: Renal cell carcinoma (RCC) is the most common malignancy of the kidney. However, there is no reliable biomarker with high sensitivity and specificity for diagnosis and differential diagnosis. This study aims to analyze serum metabolite profile of patients with RCC and screen for potential diagnostic biomarkers. Methods: Forty-five healthy controls (HC), 40 patients with benign kidney tumor (BKT) and 46 patients with RCC were enrolled in this study. Serum metabolites were detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and then subjected to multivariate statistical analysis, metabolic pathway analysis and diagnostic performance evaluation. Results: The changes of glycerophospholipid metabolism, phosphatidylinositol signaling system, glycerolipid metabolism, d-glutamine and d-glutamate metabolism, galactose metabolism, and folate biosynthesis were observed in RCC group. Two hundred and forty differential metabolites were screened between RCC and HC groups, and 64 differential metabolites were screened between RCC and BKT groups. Among them, 4 differential metabolites, including 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, lysophosphatidylcholine (LPC) 19:2, and γ-Aminobutyryl-lysine (an amino acid metabolite), were of high clinical value not only in the diagnosis of RCC (RCC group vs. HC group; AUC = 0.990, 0.916, 0.909, and 0.962; Sensitivity = 97.73%, 97.73%, 93.18%, and 86.36%; Specificity = 100.00%, 73.33%, 80.00%, and 95.56%), but also in the differential diagnosis of benign and malignant kidney tumors (RCC group vs. BKT group; AUC = 0.989, 0.941, 0.845 and 0.981; Sensitivity = 93.33%, 93.33%, 77.27% and 93.33%; Specificity = 100.00%, 84.21%, 78.38% and 92.11%). Conclusion: The occurrence of RCC may involve changes in multiple metabolic pathways. The 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, LPC 19:2 and γ-Aminobutyryl-lysine may be potential biomarkers for the diagnosis or differential diagnosis of RCC.

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

OBJECTIVE

To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

MATERIAL AND METHODS

Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.

RESULTS

The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19.

CONCLUSION

The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

Metabolomic Profiles of Mouse Tissues Reveal an Interplay between Aging and Energy Metabolism

Energy metabolism, including alterations in energy intake and expenditure, is closely related to aging and longevity. Metabolomics studies have recently unraveled changes in metabolite composition in plasma and tissues during aging and have provided critical information to elucidate the molecular basis of the aging process. However, the metabolic changes in tissues responsible for food intake and lipid storage have remained unexplored. In this study, we aimed to investigate aging-related metabolic alterations in these tissues. To fill this gap, we employed NMR-based metabolomics in several tissues, including different parts of the intestine (duodenum, jejunum, ileum) and brown/white adipose tissues (BAT, WAT), of young (9-10 weeks) and old (96-104 weeks) wild-type (mixed genetic background of 129/J and C57BL/6) mice. We, further, included plasma and skeletal muscle of the same mice to verify previous results. Strikingly, we found that duodenum, jejunum, ileum, and WAT do not metabolically age. In contrast, plasma, skeletal muscle, and BAT show a strong metabolic aging phenotype. Overall, we provide first insights into the metabolic changes of tissues essential for nutrient uptake and lipid storage and have identified biomarkers for metabolites that could be further explored, to study the molecular mechanisms of aging.

Bridging the Polar and Hydrophobic Metabolome in Single-Run Untargeted Liquid Chromatography-Mass Spectrometry Dried Blood Spot Metabolomics for Clinical Purposes

Dried blood spot (DBS) metabolite analysis is a central tool for the clinic, e.g., newborn screening. Instead of applying multiple analytical methods, a single liquid chromatography-mass spectrometry (LC-MS) method was developed for metabolites spanning from highly polar glucose to hydrophobic long-chain acylcarnitines. For liquid chromatography, a diphenyl column and a multi-linear solvent gradient operated at elevated flow rates allowed for an even-spread resolution of diverse metabolites. Injecting moderate volumes of DBS organic extracts directly, in contrast to evaporation and reconstitution, provided substantial increases in analyte recovery. Q Exactive MS settings were also tailored for sensitivity increases, and the method allowed for analyte retention time and peak area repeatabilities of 0.1-0.4 and 2-10%, respectively, for a wide polarity range of metabolites (log P -4.4 to 8.8). The method's performance was suited for both untargeted analysis and targeted approaches evaluated in clinically relevant experiments.

Common Muscle Metabolic Signatures Highlight Arginine and Lysine Metabolism as Potential Therapeutic Targets to Combat Unhealthy Aging

Biological aging research is expected to reveal modifiable molecular mechanisms that can be harnessed to slow or possibly reverse unhealthy trajectories. However, there is first an urgent need to define consensus molecular markers of healthy and unhealthy aging. Established aging hallmarks are all linked to metabolism, and a ‘rewired’ metabolic circuitry has been shown to accelerate or delay biological aging. To identify metabolic signatures distinguishing healthy from unhealthy aging trajectories, we performed nontargeted metabolomics on skeletal muscles from 2-month-old and 21-month-old mice, and after dietary and lifestyle interventions known to impact biological aging. We hypothesized that common metabolic signatures would highlight specific pathways and processes promoting healthy aging, while revealing the molecular underpinnings of unhealthy aging. Here, we report 50 metabolites that commonly distinguished aging trajectories in all cohorts, including 18 commonly reduced under unhealthy aging and 32 increased. We stratified these metabolites according to known relationships with various aging hallmarks and found the greatest associations with oxidative stress and nutrient sensing. Collectively, our data suggest interventions aimed at maintaining skeletal muscle arginine and lysine may be useful therapeutic strategies to minimize biological aging and maintain skeletal muscle health, function, and regenerative capacity in old age.

Tissue-Specific Landscape of Metabolic Dysregulation during Ageing

The dysregulation of cellular metabolism is a hallmark of ageing. To understand the metabolic changes that occur as a consequence of the ageing process and to find biomarkers for age-related diseases, we conducted metabolomic analyses of the brain, heart, kidney, liver, lung and spleen in young (9-10 weeks) and old (96-104 weeks) wild-type mice [mixed genetic background of 129/J and C57BL/6] using NMR spectroscopy. We found differences in the metabolic fingerprints of all tissues and distinguished several metabolites to be altered in most tissues, suggesting that they may be universal biomarkers of ageing. In addition, we found distinct tissue-clustered sets of metabolites throughout the organism. The associated metabolic changes may reveal novel therapeutic targets for the treatment of ageing and age-related diseases. Moreover, the identified metabolite biomarkers could provide a sensitive molecular read-out to determine the age of biologic tissues and organs and to validate the effectiveness and potential off-target effects of senolytic drug candidates on both a systemic and tissue-specific level.