4-Nitrophenol at environmentally relevant concentrations mediates reproductive toxicity in Caenorhabditis elegans via metabolic disorders-induced estrogen signaling pathway.

4-Nitrophenol (4-NP), as a toxic and refractory pollutant, has generated significant concern due to its adverse effects. However, the potential toxic effects and mechanism remained unclear. In this study, the reproduction, development, locomotion and reactive oxygen species (ROS) production of Caenorhabditis elegans were investigated to evaluate the 4-NP toxicity. We used metabolomics to assess the potential damage mechanisms. The role of metabolites in mediating the relationship between 4-NP and phenotypes was examined by correlation and mediation analysis. 4-NP (8 ng/L and 8 µg/L) caused significant reduction of brood size, ovulation rate, total germ cells numbers, head thrashes and body bends, and an increase in ROS. However, the oosperm numbers in uterus, body length and body width were decreased in 8 µg/L. Moreover, 36 differential metabolites were enriched in the significant metabolic pathways, including lysine biosynthesis, ß-alanine metabolism, tryptophan metabolism, pentose phosphate pathway, pentose and glucuronate interconversions, amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, galactose metabolism, propanoate metabolism, glycerolipid metabolism, and estrogen signaling pathway. The mechanism of 4-NP toxicity was that oxidative stress caused by the perturbation of amino acid, which had effects on energy metabolism through disturbing carbohydrate and lipid metabolism, and finally affected the estrogen signaling pathway to exert toxic effects. Moreover, correlation and mediation analysis showed glycerol-3P, glucosamine-6P, glucosamine-1P, UDP-galactose, L-aspartic acid, and uracil were potential markers for the reproduction and glucose-1,6P2 for developmental toxicity. The results provided insight into the pathways involved in the toxic effects caused by 4-NP and developed potential biomarkers to evaluate 4-NP toxicity.

Metabolomic profiling of upper GI malignancies in blood and tissue: a systematic review and meta-analysis.

OBJECTIVE: To conduct a systematic review and meta-analysis of case-control and cohort human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on esophageal cancer (EC), cancer of the gastroesophageal junction (GEJ), and gastric cancer (GC) in blood and tissue. BACKGROUND: Upper gastrointestinal cancers (UGC), predominantly EC, GEJ, and GC, are malignant tumour types with high morbidity and mortality rates. Numerous studies have focused on metabolomic profiling of UGC in recent years. In this systematic review and meta-analysis, we have provided a collective summary of previous findings on metabolites and metabolomic profiling associated with EC, GEJ and GC. METHODS: Following the PRISMA procedure, a systematic search of four databases (Embase, PubMed, MEDLINE, and Web of Science) for molecular epidemiologic studies on the metabolomic profiles of EC, GEJ and GC was conducted and registered at PROSPERO (CRD42023486631). The Newcastle-Ottawa Scale (NOS) was used to benchmark the risk of bias for case-controlled and cohort studies. QUADOMICS, an adaptation of the QUADAS-2 (Quality Assessment of Diagnostic Accuracy) tool, was used to rate diagnostic accuracy studies. Original articles comparing metabolite patterns between patients with and without UGC were included. Two investigators independently completed title and abstract screening, data extraction, and quality evaluation. Meta-analysis was conducted whenever possible. We used a random effects model to investigate the association between metabolite levels and UGC. RESULTS: A total of 66 original studies involving 7267 patients that met the required criteria were included for review. 169 metabolites were differentially distributed in patients with UGC compared to healthy patients among 44 GC, 9 GEJ, and 25 EC studies including metabolites involved in glycolysis, anaerobic respiration, tricarboxylic acid cycle, and lipid metabolism. Phosphatidylcholines, eicosanoids, and adenosine triphosphate were among the most frequently reported lipids and metabolites of cellular respiration, while BCAA, lysine, and asparagine were among the most commonly reported amino acids. Previously identified lipid metabolites included saturated and unsaturated free fatty acids and ketones. However, the key findings across studies have been inconsistent, possibly due to limited sample sizes and the majority being hospital-based case-control analyses lacking an independent replication group. CONCLUSION: Thus far, metabolomic studies have provided new opportunities for screening, etiological factors, and biomarkers for UGC, supporting the potential of applying metabolomic profiling in early cancer diagnosis. According to the results of our meta-analysis especially BCAA and TMAO as well as certain phosphatidylcholines should be implicated into the diagnostic procedure of patients with UGC. We envision that metabolomics will significantly enhance our understanding of the carcinogenesis and progression process of UGC and may eventually facilitate precise oncological and patient-tailored management of UGC.

Pollen foraging preferences in honey bees and the nutrient profiles of the pollen.

Honey bees are important insect pollinators that provide critical pollination services to fruit and nut crops in the US. They face challenges likely due to pressures associated with agricultural intensification related habitat loss. To better understand this, pollen preferences of foraging bees and the nutritional profile of pollen brought into hives by foraging bees in crop fields and nut orchards can provide valuable information. We trained bees to forage on bee-collected pollen from hives placed for pollination services in almond orchards, sunflower fields, or mixed species from inter-row plantings. Using bees trained to a certain kind of hive pollen, we applied a binary scoring system, to test preferences of these preconditioned foragers. We also performed metabolomic analyses of the hive pollen used for training and testing to elucidate their nutritional content. Irrespective of preconditioning, bees collected all the available choice pollen types, predominantly choosing hive-collected mixed species pollen (MSP), followed by almond orchard pollen. The hive-collected MSP was chemically diverse, richest in cholesterol, vitamins, and phytochemicals quercetin, kaempferol, coumarin, and quinine, but was not consistently high for essential amino acids and polyunsaturated fatty acids. Although diversity in chemical profiles may not directly relate to plant species diversity, our results suggest that foragers collect a variety of pollen types when available reiterating the importance of diverse floral resources.

Vibrational imaging of metabolites for improved microbial cell strains.

Significance: Biomanufacturing utilizes modified microbial systems to sustainably produce commercially important biomolecules for use in agricultural, energy, food, material, and pharmaceutical industries. However, technological challenges related to non-destructive and high-throughput metabolite screening need to be addressed to fully unlock the potential of synthetic biology and sustainable biomanufacturing. Aim: This perspective outlines current analytical screening tools used in industrial cell strain development programs and introduces label-free vibrational spectro-microscopy as an alternative contrast mechanism. Approach: We provide an overview of the analytical instrumentation currently used in the "test" portion of the design, build, test, and learn cycle of synthetic biology. We then highlight recent progress in Raman scattering and infrared absorption imaging techniques, which have enabled improved molecular specificity and sensitivity. Results: Recent developments in high-resolution chemical imaging methods allow for greater throughput without compromising the image contrast. We provide a roadmap of future work needed to support integration with microfluidics for rapid screening at the single-cell level. Conclusions: Quantifying the net expression of metabolites allows for the identification of cells with metabolic pathways that result in increased biomolecule production, which is essential for improving the yield and reducing the cost of industrial biomanufacturing. Technological advancements in vibrational microscopy instrumentation will greatly benefit biofoundries as a complementary approach for non-destructive cell screening.

Integration of Gut Microbiota, Serum Metabolomic, and Network Pharmacology to Reveal the Anti Insomnia Mechanism of Mongolian Medicine Sugemule-4 Decoction on Insomnia Model Rats.

Objective: To explored the potential molecular mechanism of Sugemule-4 decoction (MMS-4D) in treating insomnia. Methods: DL-4-chlorophenylalanine (PCPA) + chronic unpredictable mild stress stimulation (CUMS) was used to induce an insomnia model in rats. After the model was successfully established, MMS-4D was intervened at low, medium, and high doses for 7 days. The open-field test (OFT) was used to preliminarily evaluate the efficacy. The potential mechanism of MMS-4D in treating insomnia was investigated using gut microbiota, serum metabolomics, and network pharmacology (NP). Experimental validation of the main components of the key pathways was carried out using ELISA and Western blot. Results: The weights of the insomnia-model rats were significantly raised (p ≤ 0.05), the total exercise distance in the OFT increased (p ≤ 0.05), the rest time shortened, and the number of standing times increased (p ≤ 0.05), after treatment with MMS-4D. Moreover, there was a substantial recovery in the 5-HT, DA, GABA, and Glu levels in the hypothalamus tissue and the 5-HT and GABA levels in the colon tissue of rats. The expression of DAT and DRD1 proteins in the hippocampus of insomnia rats reduced after drug treatment. MMS-4D may treat insomnia by regulating different crucial pathways including 5-HT -, DA -, GABA -, and Glu-mediated neuroactive light receiver interaction, cAMP signaling pathway, serotonergic, glutamatergic, dopaminergic, and GABAergic synapses. Conclusion: This study revealed that MMS-4D can improve the general state and behavioral changes of insomnia model rats. Its mechanism may be related to the reversal of abnormal pathways mediated by 5-HT, DA, GABA, and Glu, such as Serotonergic synapse, Dopaminergic synapse, Glutamatergic synapse, and GABAergic synapse.

Widely targeted metabolomics reveals the phytoconstituent changes in Platostoma palustre leaves and stems at different growth stages.

Platostoma palustre (Blume) A. J. Paton is an important edible and medicinal plant. To gain a comprehensive and clear understanding of the variation patterns of metabolites in P. palustre, we employed the UPLC-MS platform along with widely targeted metabolomics techniques to analyze the metabolites in the stems and leaves of P. palustre at different stages. Our results revealed a total of 1228 detected metabolites, including 241 phenolic acids, 203 flavonoids, 152 lipids, 128 terpenes, 106 amino acids, 79 organic acids, 74 saccharides, 66 alkaloids, 44 lignans, etc. As the growth time increased, the differential metabolites (DAMs) mainly enriched in P. palustre leaves were terpenoids, phenolic acids, and lipids, while the DAMs primarily enriched in stems were terpenoids. Compared to stems, there were more differential flavonoids in leaves, and saccharides and flavonoids were significantly enriched in leaves during the S1 and S2 stages. Additionally, we identified 13, 10, and 23 potential markers in leaf, stem, and leaf vs. stem comparison groups. KEGG enrichment analysis revealed that arginine biosynthesis was the common differential metabolic pathway in different growth stages and tissues. Overall, this study comprehensively analyzed the metabolic profile information of P. palustre, serving as a solid foundation for its further development and utilization.

Unveiling the "hidden quality" of the walnut pellicle: a precious source of bioactive lipids.

Tree nut consumption has been widely associated with various health benefits, with walnuts, in particular, being linked with improved cardiovascular and neurological health. These benefits have been attributed to walnuts' vast array of phenolic antioxidants and abundant polyunsaturated fatty acids. However, recent studies have revealed unexpected clinical outcomes related to walnut consumption, which cannot be explained simply with the aforementioned molecular hallmarks. With the goal of discovering potential molecular sources of these unexplained clinical outcomes, an exploratory untargeted metabolomics analysis of the isolated walnut pellicle was conducted. This analysis revealed a myriad of unusual lipids, including oxylipins and endocannabinoids. These lipid classes, which are likely present in the pellicle to enhance the seeds' defenses due to their antimicrobial properties, also have known potent bioactivities as mammalian signaling molecules and homeostatic regulators. Given the potential value of this tissue for human health, with respect to its "bioactive" lipid fraction, we sought to quantify the amounts of these compounds in pellicle-enriched waste by-products of mechanized walnut processing in California. An impressive repertoire of these compounds was revealed in these matrices, and in notably significant concentrations. This discovery establishes these low-value agriculture wastes promising candidates for valorization and translation into high-value, health-promoting products; as these molecules represent a potential explanation for the unexpected clinical outcomes of walnut consumption. This "hidden quality" of the walnut pellicle may encourage further consumption of walnuts, and walnut industries may benefit from a revaluation of abundant pellicle-enriched waste streams, leading to increased sustainability and profitability through waste upcycling.

Diet influences knee osteoarthritis osteophyte formation via gut microbiota and serum metabolites.

Osteophyte formation, a key indicator of osteoarthritis (OA) severity, remains poorly understood in its relation to gut microbiota and metabolites in knee osteoarthritis (KOA). We conducted 16S rDNA sequencing and untargeted metabolomics on fecal and serum samples from 20 healthy volunteers, 80 KOA patients in Guangdong, and 100 in Inner Mongolia, respectively. Through bioinformatics analysis, we identified 3 genera and 5 serum metabolites associated with KOA osteophyte formation. Blautia abundance negatively correlated with meat, cheese, and bean consumption. The 5 serum metabolites negatively correlated with dairy, beef, cheese, sugar, and salt intake, yet positively with age and oil consumption. Higher Blautia levels in the gut may contribute to KOA osteophyte formation, with serum metabolites LTB4 and PGD2 potentially serving as biomarkers. KOA patients in Inner Mongolia exhibited lower Blautia levels and reduced expression of 5 serum metabolites, possibly due to cheese consumption habits, resulting in less osteophyte formation.

Investigation on the mechanisms of scorpion venom in hepatocellular carcinoma model mice via untargeted metabolomics profiling.

Metabolic reprogramming is frequently accompanied by hepatocellular carcinoma (HCC) progression. Disrupted metabolites act as potential biomarkers and drug therapeutic targets for HCC. Peptide extract of scorpion venom (PESV) induces cytotoxic anti-proliferative effects and apoptosis in tumors. However, the action mechanisms of PESV remain unknown. This study aimed to explore the serum metabolic profiles of tumor-bearing mouse model. We generated an orthotopic HCC xenograft mouse model by implanting H22 cells into the left hepatic lobe of male C57BL/6 mice. After surgery, the mice were assigned to two groups randomly: PESV (PESV-treated 40 mg/kg daily, i.g.; n = 6) and control (treated with the solvent equally for 14 d, n = 6) groups. Based on an untargeted metabolomics approach using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry, differential metabolites were screened via univariate and multivariate data analyses. A total of 48 differential metabolites in negative ion mode and 63 in positive ion mode were identified in the serum samples. Furthermore, metabolic pathway analysis revealed that aminoacyl-tRNA biosynthesis, amino acid pathway, glutathione metabolism, protein transports, protein digestion and absorption, and cAMP signaling pathways play vital roles in PESV-induced inhibition of tumors. These findings highlight the distinct changes in the metabolic profiles of HCC-bearing mice after PESV treatment, suggesting the potential of the identified metabolic molecules as therapeutic targets for HCC.

Unique metabolomics characteristics for distinguishing cirrhosis related to different liver diseases: A systematic review and meta-analysis.

BACKGROUND AND AIM: Clinical evidence for early identification and diagnosis of liver cirrhosis (LC) caused by different types of liver disease is limited. We investigated this topic through a meta-analysis of quantitative metabolomics. METHODS: Four databases were searched until October 31, 2022 for studies comparing metabolite levels between patients with different types of liver disease and control individuals. A random-effects model was applied for the meta-analysis. RESULTS: This study included 55 studies with 8266 clinical participants, covering 348 metabolites. In LC related to drug-induced liver injury (DILI), hepatitis B virus (HBV) infection, and non-alcoholic fatty liver disease (NAFLD), the primary bile acid biosynthesis (taurocholic acid: SMD, 1.08[0.81, 1.35]; P < 0.00001; glycocholic acid: SMD, 1.35[1.07, 1.62]; P < 0.00001; taurochenodeoxycholic acid: SMD, 1.36[0.94, 1.78]; P < 0.00001; glycochenodeoxycholic acid: SMD, 1.49[0.93, 2.06]; P < 0.00001), proline and arginine (l-proline: SMD, 1.06[0.53, 1.58]; P < 0.0001; hydroxyproline: SMD, 0.81[0.30, 1.33]; P = 0.002), and fatty acid biosynthesis (palmitic acid: SMD, 0.44[0.21, 0.67]; P = 0.0002; oleic acid: SMD, 0.46[0.19, 0.73]; P = 0.0008; stearic acid: SMD, 0.37[0.07, 0.68]; P = 0.02) metabolic pathways were significantly altered. CONCLUSION: We identified key biomarkers and metabolic characteristics for distinguishing and identifying LC related to different types of liver disease, providing a new perspective for early diagnosis, disease monitoring, and precise treatment.

Multi-omics combined to explore the purging mechanism of Rhei Radix et Rhizoma and Magnoliae Officinalis Cortex.

Rhei Radix et Rhizoma and Magnoliae Officinalis Cortex have been used together to treat constipation in the clinical practices for more than 2000 years. Nonetheless, their compatibility mechanism is still unclear. In this study, the amelioration of Rhei Radix et Rhizoma combined with Magnoliae Officinalis Cortex on constipation was systematically and comprehensively evaluated. The results showed that their compatibility could markedly shorten gastrointestinal transport time, increase fecal water content and frequency of defecation, improve gastrointestinal hormone disorders and protect colon tissue of constipation rats compared with the single drug. Furthermore, according to 16S rRNA sequencing in conjunction with UPLC-Q-TOF/MS, the combination of two herbal medications could greatly raise the number of salutary bacteria (Lachnospiraceae, Romboutsia and Subdoligranulum) while decreasing the abundance of pathogenic bacteria (Erysipelatoclostridiaceae). And two herb drugs could markedly improve the disorder of fecal metabolic profiles. A total of 7 different metabolites associated with constipation were remarkably shifted by the compatibility of two herbs, which were mainly related to arachidonic acid metabolism, alpha-linolenic acid metabolism, unsaturated fatty acid biosynthesis and other metabolic ways. Thus, the regulation of intestinal microbiome and its metabolism could be a potential target for Rhei Radix et Rhizoma and Magnoliae Officinalis Cortex herb pair to treat constipation. Furthermore, the multi-omics approach utilized in this study, which integrated the microbiome and metabolome, had potential for investigating the mechanism of traditional Chinese medicines.

Selenium-enriched yeast, a selenium supplement, improves the rheological properties and processability of dough: From the view of yeast metabolism and gluten alteration.

This study investigated the effect mechanism of selenium (Se)-enriched yeast on the rheological properties of dough from the perspective of yeast metabolism and gluten alteration. As the yeast Se content increased, the gas production rate of Se-enriched yeast slowed down, and dough viscoelasticity decreased. The maximum creep of Se-enriched dough increased by 29%, while the final creep increased by 54%, resulting in a softer dough. Non-targeted metabolomics analyses showed that Se inhibited yeast energy metabolism and promoted the synthesis of stress-resistance related components. Glutathione, glycerol, and linoleic acid contributed to the rheological property changes of the dough. The fractions and molecular weight distribution of protein demonstrated that the increase in yeast Se content resulted in the depolymerization of gluten. The intermolecular interactions, fluorescence spectrum and disulfide bond analysis showed that the disruption of intermolecular disulfide bond induced by Se-enriched yeast metabolites played an important role in the depolymerization of gluten.

Metabolic and mitochondria alterations induced by SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10.

Antiviral signaling, immune response and cell metabolism are dysregulated by SARS-CoV-2, the causative agent of COVID-19. Here, we show that SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10 induce a significant mitochondrial and metabolic reprogramming in A549 lung epithelial cells. While ORF9b, ORF9c and ORF10 induced largely overlapping transcriptomes, ORF3a induced a distinct transcriptome, including the downregulation of numerous genes with critical roles in mitochondrial function and morphology. On the other hand, all four ORFs altered mitochondrial dynamics and function, but only ORF3a and ORF9c induced a marked alteration in mitochondrial cristae structure. Genome-Scale Metabolic Models identified both metabolic flux reprogramming features both shared across all accessory proteins and specific for each accessory protein. Notably, a downregulated amino acid metabolism was observed in ORF9b, ORF9c and ORF10, while an upregulated lipid metabolism was distinctly induced by ORF3a. These findings reveal metabolic dependencies and vulnerabilities prompted by SARS-CoV-2 accessory proteins that may be exploited to identify new targets for intervention.

Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study.

INTRODUCTION: Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these. OBJECTIVE: To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD. METHODS: We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling. RESULTS: 499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides. CONCLUSIONS: Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Biochemical indicators, cell apoptosis, and metabolomic analyses of the low-temperature stress response and cold tolerance mechanisms in Litopenaeus vannamei.

The cold tolerance of Litopenaeus vannamei is important for breeding in specific areas. To explore the cold tolerance mechanism of L. vannamei, this study analyzed biochemical indicators, cell apoptosis, and metabolomic responses in cold-tolerant (Lv-T) and common (Lv-C) L. vannamei under low-temperature stress (18 °C and 10 °C). TUNEL analysis showed a significant increase in apoptosis of hepatopancreatic duct cells in L. vannamei under low-temperature stress. Biochemical analysis showed that Lv-T had significantly increased levels of superoxide dismutase (SOD) and triglycerides (TG), while alanine aminotransferase (ALT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH-L), and uric acid (UA) levels were significantly decreased compared to Lv-C (p < 0.05). Metabolomic analysis displayed significant increases in metabolites such as LysoPC (P-16:0), 11beta-Hydroxy-3,20-dioxopregn-4-en-21-oic acid, and Pirbuterol, while metabolites such as 4-Hydroxystachydrine, Oxolan-3-one, and 3-Methyldioxyindole were significantly decreased in Lv-T compared to Lv-C. The differentially regulated metabolites were mainly enriched in pathways such as Protein digestion and absorption, Central carbon metabolism in cancer and ABC transporters. Our study indicate that low temperature induces damage to the hepatopancreatic duct of shrimp, thereby affecting its metabolic function. The cold resistance mechanism of Lv-T L. vannamei may be due to the enhancement of antioxidant enzymes and lipid metabolism.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS.

BACKGROUND: The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified. METHODS: We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis. RESULTS: In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008). INTERPRETATION: Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review).

Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end­stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in­depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.

Potential nutritional and functional matters in yeast culture prepared by soybean meal fermentation.

BACKGROUND: Yeast culture (YC) is a product fermented on a specific medium, which is a type of postbiotic of anaerobic solid-state fermentation. Although YC has positive effects on the animal growth and health, it contains a variety of beneficial metabolites as dark matter, which have not been quantified. In the present study, liquid chromatography-tandem mass spectrometry is employed to identify the unknown metabolites. Following their identification, the important chemicals are quantified using HPLC-diode array detection methods. RESULTS: Non-targeted metabolomics studies showed that 670 metabolites in total were identified in YC, of which 23 metabolites significantly increased, including organic acids, amino acids, nucleosides and purines, isoflavones, and other substances. The chemical quantitative analysis showed that the contents of succinic acid, aminobutyric acid, glutamine, purine and daidzein increased by 84.42%, 51.07%, 100%, 68.85% and 4.60%, respectively. CONCLUSION: Therefore, the use of non-targeted metabolomics combined with chemical quantitative analysis to reveal the nutritional and functional substances of YC could help to elucidate the postbiotic mechanism and provide theoretical support for the regulation of the directional accumulation of beneficial metabolites. © 2024 Society of Chemical Industry.

The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors.

Prognostic information is needed to balance benefits and risks of cancer treatment in older patients. Metabolomics-based scores were previously developed to predict 5- and 10-year mortality (MetaboHealth) and biological age (MetaboAge). This study aims to investigate the association of MetaboHealth and MetaboAge with 1-year mortality in older patients with solid tumors, and to study their predictive value for mortality in addition to established clinical predictors. This prospective cohort study included patients aged ≥ 70 years with a solid malignant tumor, who underwent blood sampling and a geriatric assessment before treatment initiation. The outcome was all-cause 1-year mortality. Of the 192 patients, the median age was 77 years. With each SD increase of MetaboHealth, patients had a 2.32 times increased risk of mortality (HR 2.32, 95% CI 1.59-3.39). With each year increase in MetaboAge, there was a 4% increased risk of mortality (HR 1.04, 1.01-1.07). MetaboHealth and MetaboAge showed an AUC of 0.66 (0.56-0.75) and 0.60 (0.51-0.68) for mortality prediction accuracy, respectively. The AUC of a predictive model containing age, primary tumor site, distant metastasis, comorbidity, and malnutrition was 0.76 (0.68-0.83). Addition of MetaboHealth increased AUC to 0.80 (0.74-0.87) (p = 0.09) and AUC did not change with MetaboAge (0.76 (0.69-0.83) (p = 0.89)). Higher MetaboHealth and MetaboAge scores were associated with 1-year mortality. The addition of MetaboHealth to established clinical predictors only marginally improved mortality prediction in this cohort with various types of tumors. MetaboHealth may potentially improve identification of older patients vulnerable for adverse events, but numbers were too small for definitive conclusions. The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.

Urine metabolomics unravel the effects of short-term dietary interventions on oxidative stress and inflammation: a randomized controlled crossover trial.

Dietary biomarkers in urine remain elusive when evaluating diet-induced oxidative stress and inflammation. In our previous study, we conducted a randomized controlled crossover trial to compare the short-term (4-weeks) effects of the balanced Korean diet (BKD) with Western diets, including the 2010 dietary guidelines for Americans (2010 DGA) and typical American diet (TAD), on various metabolic indices in obese Korean adults. Building on this work, the current research focuses on the impact of these dietary interventions on oxidative stress (d-ROMs and BAP) and inflammation (CRP, TNF-α, IL-6, IL-1ß, MCP-1) biomarkers in serum, and the concurrent urine metabolomes. Each dietary regimen was in silico and experimentally examined for their antioxidant levels using ABTS, DPPH, and FRAP assays, as well as total flavonoid (TFC) and total phenolic (TPC) contents. We assessed post-intervention variations in oxidative stress and inflammation biomarkers in serum, as well as the urine metabolite profiles for the participants (n = 48, average age: 41 years). Antioxidant contents and associated total antioxidant capacity (TAC) were significantly higher for the recommended diets (BKD and 2010 DGA) compared to TAD (p < 0.05). Butanol extracts from recommended diets (BKD and 2010 DGA) showed significantly higher antioxidant activity compared to TAD in ABTS (p < 0.01), DPPH, and FRAP (p < 0.05) assays. Consistent results were observed in total phenolic and flavonoid contents, mirroring their respective antioxidant activities. Following the intervention period, oxidative stress & inflammation markers in serum varied marginally, however, the urine metabolite profiles were clearly demarcated for the BKD and Western dietary groups (PC1 = 5.41%). For BKD group, the pre- and post-intervention urine metabolite profiles were clearly segregated (PLS2 = 2.93%). Compared to TAD, urine extracts from the recommended dietary group showed higher abundance of benzoic acid & phenolic derivatives (VIP > 0.7, p < 0.05). Metabolites associated with oxidative stress were observed higher in the urine samples from Western dietary groups compared to BKD. Urine metabolomics data delineated the post-intervention effects of three dietary interventions which corroborates the respective findings for their effects on metabolic indices.