Serum metabolomics analysis of patients with chronic obstructive pulmonary disease and 'frequent exacerbator' phenotype.

Chronic obstructive pulmonary disease (COPD) exacerbations accelerate loss of lung function and increased mortality. The complex nature of COPD presents challenges in accurately predicting and understanding frequent exacerbations. The present study aimed to assess the metabolic characteristics of the frequent exacerbation of COPD (COPD­FE) phenotype, identify potential metabolic biomarkers associated with COPD­FE risk and evaluate the underlying pathogenic mechanisms. An internal cohort of 30 stable patients with COPD was recruited. A widely targeted metabolomics approach was used to detect and compare serum metabolite expression profiles between patients with COPD­FE and patients with non­frequent exacerbation of COPD (COPD­NE). Bioinformatics analysis was used for pathway enrichment analysis of the identified metabolites. Spearman's correlation analysis assessed the associations between metabolites and clinical indicators, while receiver operating characteristic (ROC) analysis evaluated the ability of metabolites to distinguish between two groups. An external cohort of 20 patients with COPD validated findings from the internal cohort. Out of the 484 detected metabolites, 25 exhibited significant differences between COPD­FE and COPD­NE. Metabolomic analysis revealed differences in lipid, energy, amino acid and immunity pathways. Spearman's correlation analysis demonstrated associations between metabolites and clinical indicators of acute exacerbation risk. ROC analysis demonstrated that the area under the curve (AUC) values for D­fructose 1,6­bisphosphate (AUC=0.871), arginine (AUC=0.836), L­2­hydroxyglutarate (L­2HG; AUC=0.849), diacylglycerol (DG) (16:0/20:5) (AUC=0.827), DG (16:0/20:4) (AUC=0.818) and carnitine­C18:2 (AUC=0.804) were >0.8, highlighting their discriminative capacity between the two groups. External validation results demonstrated that DG (16:0/20:5), DG (16:0/20:4), carnitine­C18:2 and L­2HG were significantly different between patients with COPD­FE and those with COPD­NE. In conclusion, the present study offers insights into early identification, mechanistic understanding and personalized management of the COPD­FE phenotype.

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000-1400 nm and 1500-1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

Integrated transcriptomics and metabolomics study of embryonic breast muscle of Jiaji ducks.

Because number of matured muscle fibers in poultry does not increase after birth, the meat yield is mainly determined during embryogenesis. We previously indicated breast muscle grew rapidly from 18th day after hatching (E18) to E27, and almost stopped from E27 to E34 of Jiaji ducks, while the mechanism is unclear. This study utilized RNA-seq to explore the related genes of muscle development and their relationship with small molecule metabolites at E18, E27 and E34 of Jiaji ducks. Several thousand differentially expressed genes (DEGs) were detected among E18, E27 and E34. DEGs expression profiles included 8 trend maps, among which trend 1 was opposite to and trend 6 was consistent with breast muscle development trend of Jiaji ducks. Through joint analysis between trend 1 of DEGs and trend 1 of differential metabolites (DEMs), protein digestion and absorption pathway stood out. The decrease of COL8A2 gene expression will lead to the decrease of arginine content, which will inhibit the development of breast muscle in embryonic Jiaji duck. Similarly, joint analysis between trend 6 of DEGs and trend 6 of DEMs indicated the increase of GAMT gene expression will cause the increase of proline content, and then promote the development of breast muscle of Jiaji duck in embryonic period. These results will be helpful for further understanding the mechanism of muscle yields of Jiaji ducks.

Increase of secondary metabolites in sweet basil (Ocimum basilicum L.) leaves by exposure to N2O5 with plasma technology.

Exposure to N2O5 generated by plasma technology activates immunity in Arabidopsis through tryptophan metabolites. However, little is known about the effects of N2O5 exposure on other plant species. Sweet basil synthesizes many valuable secondary metabolites in its leaves. Therefore, metabolomic analyses were performed at three different exposure levels [9.7 (Ex1), 19.4 (Ex2) and 29.1 (Ex3) µmol] to assess the effects of N2O5 on basil leaves. As a result, cinnamaldehyde and phenolic acids increased with increasing doses. Certain flavonoids, columbianetin, and caryophyllene oxide increased with lower Ex1 exposure, cineole and methyl eugenol increased with moderate Ex2 exposure and L-glutathione GSH also increased with higher Ex3 exposure. Furthermore, gene expression analysis by quantitative RT-PCR showed that certain genes involved in the syntheses of secondary metabolites and jasmonic acid were significantly up-regulated early after N2O5 exposure. These results suggest that N2O5 exposure increases several valuable secondary metabolites in sweet basil leaves via plant defense responses in a controllable system.

Non-Targeted Metabolomics Analysis of Mother and Infant in Gestational Diabetes Mellitus and Neonatal Clinical Characterization.

BACKGROUND: The goal was to analyze serums of GDM patients and healthy pregnant women using HPLC-MS and preliminarily screen differential metabolites by metabolomics. METHOD: Sixty pregnant women who underwent elective cesarean section at term in Dongguan Dalang Hospital from January 2023 to April 2023 were selected and divided into the GDM group and healthy pregnancy group. Pre-pregnancy and pregnancy examination information, such as age, BMI, OGTT results, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and other clinical data were col-lected for statistical analysis. Non-targeted metabolomics of serum from 30 GDM patients and 30 healthy pregnant women were studied by HPLC-MS, and different ions were searched. The structures of differential metabolites were identified by HMDB database. The metabolic pathways of differential metabolites were analyzed by KEGG database. RESULTS: The OGTT result, pCO2, pO2, HCO3, BE, Apgar score, and bilirubin levels in the GDM group were higher than those in the healthy pregnancy group (p < 0.05). However, there were no significant differences in age, triglyceride, total cholesterol, newborn birth weight, newborn birth blood glucose, and blood gas pH between the two groups (all p > 0.05). Using p < 0.05 as the screening standard, 55 differential metabolites were identified in serum, mainly including fatty acyl, carboxylic acids and their derivatives, steroids and their derivatives, ketoacids and their derivatives, and pyrimidine nucleosides, etc., all of which were up-regulated or down-regulated to varying degrees. The 55 metabolites were mainly involved in the metabolism of pyrimidine, pyruvate, alanine, aspartic acid, glutamic acid, and arachidonic acid, glycolysis, and biosynthesis of unsaturated fatty acids. CONCLUSIONS: The discovery of these metabolites provides a theoretical basis for an indepth understanding of GDM pathogenesis. Non-targeted metabonomics analysis of blood metabonomics research technology has shown great potential value in the early diagnosis of obstetric diseases and the study of disease mechanisms.

Identification of Potential Biomarkers of EGFR Mutation in Pleural Effusion of Non-Small Cell Lung Cancer Patients Based on Metabolomics.

BACKGROUND: Malignant pleural effusion (MPE) is a common complication of non-small cell lung cancer (NSCLC). Patients with NSCLC exhibit a high rate of epidermal growth factor receptor (EGFR) mutations. The detection of EGFR mutations is usually time-consuming and costly. This study aimed at identifying potential biomarkers of EGFR mutations in MPE of NSCLC patients by metabolomics. METHODS: In total, 58 MPE samples from 30 EGFR mutant and from 28 wild-type NSCLC patients were collected and analyzed by using hydrogen nuclear magnetic resonance (1H NMR) based metabolomics and UPLC-MS/MS based amino acid analysis. RESULTS: Our 1H NMR study showed a significant increase in the lysine levels but a significant decrease in the alanine levels in MPE of NSCLC patients with EGFR-mutant. Twelve amino acids in MPE were further determined by UPLC-MS/MS. It showed that alanine in MPE (6.34 ± 1.88 vs. 8.73 ± 3.68) were significantly decreased and leucine (3.13 ± 0.57 vs. 2.22 ± 0.13), lysine (2.19 ± 0.50 vs. 1.53 ± 0.40), and tyrosine (2.69 ± 0.71 vs. 1.89 ± 0.46) were increased in the EGFR mutation group; leucine (2.19 ± 0.50 vs. 1.53 ± 0.40), methionine (2.19 ± 0.50 vs. 1.53 ± 0.40), and threonine (2.19 ± 0.50 vs. 1.53 ± 0.40) in MPE were significantly lower in the EGRF 19 mutation compared with 21 mutation patients. The area under the receiver operating characteristic curve of 0.851 and 0.931 would be achieved by the logistic model for classification of EGFR-mutant patients from the wild-type controls or the exon 19 from exon 21 mutant patients. CONCLUSIONS: Amino acids in MPE are significantly altered and helpful in the diagnosis of EGFR-mutant patients from the wild-type controls or the exon 19 from exon 21 mutant patients with high accuracy, which is worthy of further study.

Multi-omic analysis tools for microbial metabolites prediction.

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

[Differential analysis of aqueous humor metabolomics between presenile cataract and senile cataract].

Objective: To explore the differences in metabolites and metabolic pathways in the aqueous humor between patients with presenile cataracts and senile cataracts. Methods: This metabolomic study was conducted at Tianjin Medical University Eye Hospital from August 2020 to September 2022. Eight patients with presenile cataracts (8 eyes) and 8 patients with senile cataracts (9 eyes) were included. Data were collected, including age, gender, preoperative uncorrected visual acuity, intraocular pressure, lens dysfunction index, and axial length. Aqueous humor and anterior capsule tissue samples were obtained during cataract surgery. Metabolites in the aqueous humor were detected using Liquid Chromatography-Mass Spectrometry in a non-targeted approach. The principal component analysis, differential analysis, clustering analysis, and correlation analysis were performed to identify differentially expressed metabolites. These metabolites were ranked based on the fold change (FC). The receiver operating characteristic (ROC) curve analysis and metabolic enrichment analysis were used to identify differential pathways and potential biomarkers for presenile cataracts. Immunohistochemistry was conducted on anterior capsule tissues, and pyruvate levels were measured by colorimetry to validate metabolomic results. Results: Patients with presenile cataracts included 7 males and 1 female, with a mean age of (37.50±4.90) years. Patients with senile cataracts were 7 males and 1 female, with a mean age of (73.44±5.22) years. Except for age, there were no significant differences in baseline data (P>0.05). A total of 347 differential metabolites were identified, 10 of which were potential biomarkers for presenile cataract according to the ROC curve analysis (all P<0.05), including propoxycaine (log2FC=7.26), 2-methyl-2, 3, 4, 5-tetrahydro-1, 5-benzodiazepine-4-ketone (log2FC=6.35), l-pyroglutamic acid (log2FC=-1.72), leanly-proline (log2FC=-0.77), and choline (log2FC=-0.56) in the positive ion mode, and N-phenylacetyl glutamine (log2FC=-1.84), pyruvate (log2FC=1.07), ascorbic acid (log2FC=0.92), pseudouracil nucleoside (log2FC=-0.68), and palmitic acid (log2FC=-0.51) in the negative ion mode. The metabolic enrichment analysis identified 72 differential pathways (32 cationic and 40 anionic), with significant differences in glutathione metabolism, cysteine and methionine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, and the citric acid cycle (P<0.05). The experimental validation showed reduced lactate dehydrogenase and increased pyruvate levels in patients with presenile cataracts (P<0.05). Conclusions: Pyruvate and nine other metabolites may serve as potential biomarkers for presenile cataracts. Pathways involving glutathione metabolism, cysteine and methionine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, and the citric acid cycle are notably dysregulated in patients with presenile cataracts.

Integrative metagenomics, volatilomics and chemometrics for deciphering the microbial structure and core metabolic network during Chinese rice wine (Huangjiu) fermentation in different regions.

Huangjiu is a spontaneously fermented alcoholic beverage, that undergoes intricate microbial compositional changes. This study aimed to unravel the flavor and quality formation mechanisms based on the microbial metabolism of Huangjiu. Here, metagenome techniques, chemometrics analysis, and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) metabolomics combined with microbial metabolic network were employed to investigate the distinctions and relationship between the microbial profiles and the quality characteristics, flavor metabolites, functional metabolic patterns of Huangjiu across three regions. Significant variations (P < 0.05) were observed in metabolic rate of physicochemical parameters and biogenic amine concentration among three regions. 8 aroma compounds (phenethyl acetate, phenylethyl alcohol, isobutyl alcohol, ethyl octanoate, ethyl acetate, ethyl hexanoate, isoamyl alcohol, and diethyl succinate) out of 448 volatile compounds were identified as the regional chemical markers. 25 dominant microbial genera were observed through metagenomic analysis, and 13 species were confirmed as microbial markers in three regions. A metabolic network analysis revealed that Saccharomycetales (Saccharomyces), Lactobacillales (Lactobacillus, Weissella, and Leuconostoc), and Eurotiales (Aspergillus) were the predominant populations responsible for substrate, flavor (mainly esters and phenylethyl alcohol) metabolism, Lactobacillales and Enterobacterales were closely linked with biogenic amine. These findings provide scientific evidence for regional microbial contributions to geographical characteristics of Huangjiu, and perspectives for optimizing microbial function to promote Huangjiu quality.

Metabolomics-based study on the significance of differential metabolite binding IgG isoforms in Hemolytic disease of newborn.

BACKGROUND: Hemolytic disease of the newborn (HDN) is a common condition that can have a severe impact on the health of newborns due to the hemolytic reactions it triggers. Although numerous studies have focused on understanding the pathogenesis of HDN, there are still many unanswered questions. METHODS: In this retrospective study, serum samples were collected from 15 healthy newborns and 8 infants diagnosed with hemolytic disease. The relationship between different metabolites and various IgG subtypes in Healthy, HDN and BLI groups was studied by biochemical technique and enzyme-linked immunosorbent assay (ELISA). Metabolomics analysis was conducted to identify the differential metabolites associated with HDN. Subsequently, Pearson's correlation analysis was used to determine the relation of these differential metabolites with IgG isoforms. The relationship between the metabolites and IgG subtypes was observed after treatment. RESULTS: The study results revealed that infants with hemolytic disease exhibited abnormal elevations in TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4 levels when compared to healthy newborns. Additionally, differences in metabolite contents were also observed. N, N-DIMETHYLARGININE showed negative correlations with TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4, while 2-HYDROXYBUTYRATE, AMINOISOBUTANOATE, Inosine, and ALLYL ISOTHIOCYANATE exhibited positive correlations with TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4. Through metabolomics-based research, we have discovered associations between differential metabolites and different IgG isoforms during the onset of HDN. CONCLUSION: These findings suggest that changes in metabolite and IgG isoform levels are linked to HDN. Understanding the involvement of IgG isoforms and metabolites can provide valuable guidance for the diagnosis and treatment of HDN.

Exploring sample treatment strategies for untargeted metabolomics: A comparative study of solid phase microextraction (SPME) and homogenization with solid-liquid extraction (SLE) in renal tissue.

BACKGROUND: The selection of the sample treatment strategy is a crucial step in the metabolomics workflow. Solid phase microextraction (SPME) is a sample processing methodology with great potential for use in untargeted metabolomics of tissue samples. However, its utilization is not as widespread as other standard protocols involving steps of tissue collection, metabolism quenching, homogenization, and extraction of metabolites by solvents. Since SPME allows us to perform all these steps in one action in tissue samples, in addition to other advantages, it is necessary to know whether this methodology produces similar or comparable metabolome and lipidome coverage and performance to classical methods. RESULTS: SPME and homogenization with solid-liquid extraction (Homo-SLE) sample treatment methods were applied to healthy murine kidney tissue, followed by comprehensive metabolomics and lipidomics analyses. In addition, it has been tested whether freezing and storage of the tissue causes alterations in the renal metabolome and lipidome, so the analyses were performed on fresh and frozen tissue samples Lipidomics analysis revealed the exclusive presence of different structural membrane and intracellular lipids in the Homo-SLE group. Conversely, all annotated metabolites were detected in both groups. Notably, the freezing of the sample mainly causes a decrease in the levels of most lipid species and an increase in metabolites such as amino acids, purines, and pyrimidines. These alterations are principally detected in a statistically significant way by SPME methodology. Finally, the samples of both methodologies show a positive correlation in all the analyses. SIGNIFICANCE: These results demonstrate that in SPME processing, as long as the fundamentals of non-exhaustive extraction in a pre-equilibrium kinetic regime, extraction in a tissue localized area, the chemistry of the fiber coating and non-homogenization of the tissue are taken into account, is an excellent method to use in kidney tissue metabolomics; since this methodology presents an easy-to-use, efficient, and less invasive approach that simplifies the different sample processing steps.

Metabolomic analysis reveals Ligilactobacillus salivarius CCFM 1266 fermentation improves dairy product quality.

Previous studies have demonstrated that Ligilactobacillus salivarius CCFM 1266 exhibits anti-inflammatory properties and the capability to synthesize niacin. This study aimed to investigate the fermentative abilities of L. salivarius CCFM 1266 in fermented milk. Metabonomic analysis revealed that fermentation by L. salivarius CCFM 1266 altered volatile flavor compounds and metabolite profiles, including heptanal, nonanal, and increased niacin production. Genomic investigations confirmed that L. salivarius CCFM 1266 possess essential genes for the metabolism of fructose and mannose, affirming its proficiency in utilizing fructooligosaccharides and mannan oligosaccharides. The addition of fructooligosaccharides and mannan oligosaccharides during the fermentation process significantly facilitated the proliferation of L. salivarius CCFM 1266 in fermented milk, with growth exceeding 107 colony-forming units (CFU)/mL. This intervention not only augmented the microbial density but also modified the metabolite composition of fermented milk, resulting in an elevated presence of advantageous flavor compounds such as nonanal, 2,3-pentanedione, and 3-methyl-2-butanone. However, its influence on improving the texture of fermented milk was observed to be minimal. Co-fermentation of L. salivarius CCFM 1266 with commercial fermentation starters indicated that L. salivarius CCFM 1266 was compatible, similarly altering metabolite composition and increasing niacin content in fermented milk. In summary, the findings suggest that L. salivarius CCFM 1266 holds substantial promise as an adjunctive fermentation starter, capable of enhancing the nutritional diversity of fermented milk products.

Biomarkers for ideal protein: rabbit diet metabolomics varying key amino acids.

With the main aim of identifying biomarkers that contribute to defining the concept of ideal protein in growing rabbits under the most diverse conditions possible this work describes two different experiments. Experiment 1: 24 growing rabbits are included at 56 days of age. The rabbits are fed ad libitum one of the two experimental diets only differing in lysine levels. Experiment 2: 53 growing rabbits are included at 46 days of age, under a fasting and eating one of the five experimental diets, with identical chemical composition except for the three typically limiting amino acids (being fed commercial diets ad libitum in both experiments). Blood samples are taken for targeted and untargeted metabolomics analysis. Here we show that the metabolic phenotype undergoes alterations when animals experience a rapid dietary shift in the amino acid levels. While some of the differential metabolites can be attributed directly to changes in specific amino acids, creatinine, urea, hydroxypropionic acid and hydroxyoctadecadienoic acid are suggested as a biomarker of amino acid imbalances in growing rabbits' diets, since its changes are not attributable to a single amino acid. The fluctuations in their levels suggest intricate amino acid interactions. Consequently, we propose these metabolites as promising biomarkers for further research into the concept of the ideal protein using rabbit as a model.

Identification of Stress-Responsive Metabolites in Plants Using an Untargeted Metabolomics Approach.

Stress can affect different groups of plant metabolites and multiple signaling pathways. Untargeted metabolomics enables the collection of whole-spectrum data for the entire metabolite content present in plant tissues at that point in time. We present a thorough approach for large-scale, untargeted metabolomics of plant tissues using reverse-phase liquid chromatography connected to high-resolution mass spectrometry (LC-MS) of dilute methanolic extract. MZmine is a specialized computer software that automates the alignment and baseline modification of all derived mass peaks across all samples, resulting in precise information on the relative abundance of hundreds of metabolites reflected by thousands of mass signals. Further processing with statistic and bioinformatic techniques will provide a comprehensive perspective of the variations and connections among groups of samples.

Investigating gut microbiota-blood and urine metabolite correlations in early sepsis-induced acute kidney injury: insights from targeted KEGG analyses.

Background: The interplay between gut microbiota and metabolites in the early stages of sepsis-induced acute kidney injury (SA-AKI) is not yet clearly understood. This study explores the characteristics and interactions of gut microbiota, and blood and urinary metabolites in patients with SA-AKI. Methods: Utilizing a prospective observational approach, we conducted comparative analyses of gut microbiota and metabolites via metabolomics and metagenomics in individuals diagnosed with SA-AKI compared to those without AKI (NCT06197828). Pearson correlations were used to identify associations between microbiota, metabolites, and clinical indicators. The Comprehensive Antibiotic Resistance Database was employed to detect antibiotic resistance genes (ARGs), while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways informed on metabolic processes and microbial resistance patterns. Results: Our study included analysis of four patients with SA-AKI and five without AKI. Significant disparities in bacterial composition were observed, illustrated by diversity indices (Shannon index: 2.0 ± 0.4 vs. 1.4 ± 0.6, P = 0.230; Simpson index: 0.8 ± 0.1 vs. 0.6 ± 0.2, P = 0.494) between the SA-AKI group and the non-AKI group. N6, N6, N6-Trimethyl-L-lysine was detected in both blood and urine metabolites, and also showed significant correlations with specific gut microbiota (Campylobacter hominis and Bacteroides caccae, R > 0, P < 0.05). Both blood and urine metabolites were enriched in the lysine degradation pathway. We also identified the citrate cycle (TCA cycle) as a KEGG pathway enriched in sets of differentially expressed ARGs in the gut microbiota, which exhibits an association with lysine degradation. Conclusions: Significant differences in gut microbiota and metabolites were observed between the SA-AKI and non-AKI groups, uncovering potential biomarkers and metabolic changes linked to SA-AKI. The lysine degradation pathway may serve as a crucial link connecting gut microbiota and metabolites.

Exploratory metabolomic analysis for characterizing the metabolic profile of the urinary bladder under estrogen deprivation.

Background: Estrogen homeostasis is crucial for bladder function, and estrogen deprivation resulting from menopause, ovariectomy or ovarian dysfunction may lead to various bladder dysfunctions. However, the specific mechanisms are not fully understood. Methods: We simulated estrogen deprivation using a rat ovariectomy model and supplemented estrogen through subcutaneous injections. The metabolic characteristics of bladder tissue were analyzed using non-targeted metabolomics, followed by bioinformatics analysis to preliminarily reveal the association between estrogen deprivation and bladder function. Results: We successfully established a rat model with estrogen deprivation and, through multivariate analysis and validation, identified several promising biomarkers represented by 3, 5-tetradecadiencarnitine, lysoPC (15:0), and cortisol. Furthermore, we explored estrogen deprivation-related metabolic changes in the bladder primarily characterized by amino acid metabolism imbalance. Conclusion: This study, for the first time, depicts the metabolic landscape of bladder resulting from estrogen deprivation, providing an important experimental basis for future research on bladder dysfunctions caused by menopause.

Multi-omics reveals new links between Fructosamine-3-Kinase (FN3K) and core metabolic pathways.

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the Tree of Life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) HepG2 cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), and carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3K and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. Our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance and NAD-dependent metabolic processes are altered.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Integrative analysis of metabolomics and proteomics reveals mechanism of berberrubine-induced nephrotoxicity.

Berberrubine (BRB), a main metabolite of berberine, has stronger hypoglycemic and lipid-lowering activity than its parent form. We previously found that BRB could cause obvious nephrotoxicity, but the molecular mechanism involved remains unknown. In this study, we systematically integrated metabolomics and quantitative proteomics to reveal the potential mechanism of nephrotoxicity caused by BRB. Metabolomic analysis revealed that 103 significant- differentially metabolites were changed. Among the mentioned compounds, significantly upregulated metabolites were observed for phosphorylcholine, sn-glycerol-3-phosphoethanolamine, and phosphatidylcholine. The top three enriched KEGG pathways were the mTOR signaling pathway, central carbon metabolism in cancer, and choline metabolism in cancer. ERK1/2 plays key roles in all three metabolic pathways. To further confirm the main signaling pathways involved, a proteomic analysis was conducted to screen for key proteins (such as Mapk1, Mapk14, and Caspase), indicating the potential involvement of cellular growth and apoptosis. Moreover, combined metabolomics and proteomics analyses revealed the participation of ERK1/2 in multiple metabolic pathways. These findings indicated that ERK1/2 regulated the significant- differentially abundant metabolites determined via metabolomics analysis. Notably, through a cellular thermal shift assay (CETSA) and molecular docking, ERK1/2 were revealed to be the direct binding target involved in BRB-induced nephrotoxicity. To summarize, this study sheds light on the understanding of severe nephrotoxicity caused by BRB and provides scientific basis for its safe use and rational development.