Integrated transcriptome and targeted metabolome analyses provide insights into flavonoid biosynthesis in kiwifruit (Actinidia chinensis).

So far, a variety of metabolite components of kiwifruit have been elucidated. However, the identification and analysis of flavonoids in different tissues of kiwifruit are rarely carried out. In this study, we performed transcriptome and metabolome analyses of roots (Gkf_R), stems (Gkf_T), leaves (Gkf_L), and fruits (Gkf_F) to provide insights into the differential accumulation and regulation mechanisms of flavonoids in kiwifruit. Results showed that a total of 301 flavonoids were identified, in four tissues with different accumulation trends, and a large proportion of flavonoids had high accumulation in Gkf_L and Gkf_R. A total of 84 genes have been identified involved in the flavonoid biosynthesis pathway, and the expression levels of five LAR, two DFR, and one HCT were significantly correlated with the accumulation of 16 flavonoids and co-localized in the flavonoid biosynthesis pathway. In addition, a total of 2362 transcription factor genes were identified, mainly MYBs, bHLHs, ERFs, bZIPs and WRKYs, among which the expression level of bHLH74, RAP2.3L/4L/10L, MYB1R1, and WRKY33 were significantly correlated with 25, 56, 43, and 24 kinds of flavonoids. Our research will enrich the metabolomic data and provide useful information for the directed genetic improvement and application in the pharmaceutical industry of kiwifruit.

Identification of Chagas disease biomarkers using untargeted metabolomics.

Untargeted metabolomic analysis is a powerful tool used for the discovery of novel biomarkers. Chagas disease (CD), caused by Trypanosoma cruzi, is a neglected tropical disease that affects 6-7 million people with approximately 30% developing cardiac manifestations. The most significant clinical challenge lies in its long latency period after acute infection, and the lack of surrogate markers to predict disease progression or cure. In this cross-sectional study, we analyzed sera from 120 individuals divided into four groups: 31 indeterminate CD, 41 chronic chagasic cardiomyopathy (CCC), 18 Latin Americans with other cardiomyopathies and 30 healthy volunteers. Using a high-throughput panel of 986 metabolites, we identified three distinct profiles among individuals with cardiomyopathy, indeterminate CD and healthy volunteers. After a more stringent analysis, we identified some potential biomarkers. Among peptides, phenylacetylglutamine and fibrinopeptide B (1-13) exhibited an increasing trend from controls to ICD and CCC. Conversely, reduced levels of bilirubin and biliverdin alongside elevated urobilin correlated with disease progression. Finally, elevated levels of cystathionine, phenol glucuronide and vanillactate among amino acids distinguished CCC individuals from ICD and controls. Our novel exploratory study using metabolomics identified potential biomarker candidates, either alone or in combination that if confirmed, can be translated into clinical practice.

An isotopically-labelled temporal mass spectrometry imaging data analysis workflow to reveal glucose spatial metabolism patterns in bovine lens tissue.

Application of stable isotopically labelled (SIL) molecules in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) over a series of time points allows the temporal and spatial dynamics of biochemical reactions to be tracked in a biological system. However, these large kinetic MSI datasets and the inherent variability of biological replicates presents significant challenges to the rapid analysis of the data. In addition, manual annotation of downstream SIL metabolites involves human input to carefully analyse the data based on prior knowledge and personal expertise. To overcome these challenges to the analysis of spatiotemporal MALDI-MSI data and improve the efficiency of SIL metabolite identification, a bioinformatics pipeline has been developed and demonstrated by analysing normal bovine lens glucose metabolism as a model system. The pipeline consists of spatial alignment to mitigate the impact of sample variability and ensure spatial comparability of the temporal data, dimensionality reduction to rapidly map regional metabolic distinctions within the tissue, and metabolite annotation coupled with pathway enrichment modules to summarise and display the metabolic pathways induced by the treatment. This pipeline will be valuable for the spatial metabolomics community to analyse kinetic MALDI-MSI datasets, enabling rapid characterisation of spatio-temporal metabolic patterns from tissues of interest.

Comprehensive metabolomics and chemometrics unravel potential anti-diabetic metabolites of pumpkin (Cucurbita pepo L.) fruits through UPLC-QqQ-MS and GC-MS analyses.

This comprehensive study explores the phytoconstituents of different parts of pumpkin (Cucurbita pepo) including flesh, peel, seeds, pumpkin juice, and pumpkin seed oil. Utilizing advanced analytical techniques including UPLC-QqQ-MS and GC-TSQ-MS combined with multivariate statistical analysis, 94 distinct chromatographic peaks from various chemical classes were annotated. Predominant classes included phenolic acids, flavonoids, cucurbitacins, amino acids, triterpenoids, fatty acids, sterols, carotenoids, and other compounds. For more comprehensive chemical profiling of the tested samples, fractionation of the different parts of the fruit was attempted through successive solvent extraction. The unsaponifiable part of the oils, analyzed by GC, showed that the phytosterols, namely ß-sitosterol, and stigmasterol are in the majority. All pumpkin extracts showed significant inhibition of carbohydrase enzymes and glucose uptake promotion by cells. Pumpkin flesh butanol fraction exhibited potent α-glucosidase inhibition, while pumpkin defatted seed methylene chloride fraction showed strong α-amylase inhibition. Additionally, pumpkin seed oil and defatted seed petroleum ether fraction demonstrated high glucose uptake activity. Bioactive metabolites including vaccenic acid, sinapic acid, kuguacin G, luteolin hexoside, delta-7-avenasterol, cucurbitosides and others were unveiled through OPLS multivariate models elucidating the anti-diabetic potential of pumpkin. These findings support the use of pumpkin as a functional food, offering insights into its mechanisms of action in diabetes management.

Integrative metabolomics and chemometrics depict the metabolic alterations of differently processed red kidney beans (Phaseolus vulgaris L.) and in relation to in-vitro anti-diabetic efficacy.

Red kidney beans (RKB) serve as a powerhouse packed with a plethora of largely unexplored extraordinary chemical entities with potential significance. However, their nutraceutical applications as a functional hypoglycemic food still lag behind and warrant further investigation. With a scope to optimize chemical and biological traits of RKB, green modification approaches (processing methods) seem inevitable. Accordingly, the current study offered the first integrative workflow to scrutinize dynamic changes in chemical profiles of differently processed RKB and their potential entanglements on diabetes mitigation using Ultra Performance Liquid Chromatography-mass spectrometry (UPLC-MS/MS) coupled with chemometrics. Different physical and biological processing treatments namely germination, fermentation, cooking and dehulling were preliminarily implemented on RKB. Complementarily, the concomitant metabolite alterations among differently processed RKB were monitored and interpreted. Next, an in-vitro α-amylase and α-glycosidase inhibitory testing of the differently processed samples was conducted and integrated with orthogonal projection to latent structures (OPLS) analysis to pinpoint the possible efficacy compounds. A total of 72 compounds spanning fatty acids and their glycerides, flavonoids, phenolic acids, amino acids, dipeptides, phytosterols and betaxanthins were profiled. Given this analysis and compared with raw unprocessed samples, it was found that flavonoids experienced notable accumulation during germination while both fermentation and dehulling approaches sharply intensified the content of amino acids and dipeptides. Comparably, Fatty acids, phytosterols and betaxanthins were unevenly distributed among the comparable samples. Admittedly, OPLS-DA revealed an evident discrimination among the processed samples assuring their quite compositional discrepancies. In a more targeted approach, kaempferol-O-sophoroside, quercetin, carlinoside and betavulgarin emerged as focal discriminators of sprouted samples while citrulline, linoleic acid, linolenoyl-glycerol and stigmasterol were the determining metabolites in cooked samples. Our efficacy experimental findings emphasized that the different RKB samples exerted profound inhibitory actions against both α-amylase and α-glycosidase enzymes with the most promising observations in the case of sprouted and cooked samples. Coincidently, OPLS analysis revealed selective enhancement of possible efficacy constituents primarily citrulline, formononetin, gamabufotalin, kaempferol-O-sophoroside, carlinoside, oleic acid and ergosterol in sprouted and cooked samples rationalizing their noteworthy α-amylase and α-glucosidase inhibitory activities. Taken together, this integrated work provides insightful perspectives beyond the positive impact of different processing protocols on bioactives accumulation and pharmacological traits of RKB expanding their utilization as functional hypoglycemic food to rectify diabetes.

Non-target and target quantitative metabolomics with quantitative aroma evaluation reveal the influence mechanism of withering light quality on tea aroma and volatile metabolites evolution.

Withering is the first and key process that influences tea quality, with light quality being a key regulatory factor. However, effects of withering light quality (WLQ) on transformation and formation pathways of tea aroma and volatile metabolites (VMs) remain unclear. In the present study, four WLQs were set up to investigate their effects on tea aroma and VMs. The results showed that blue and red light reduced the grassy aroma and improved the floral and fruity aroma of tea. Based on GC-MS/MS, 83 VMs were detected. Through VIP, significant differences, and OAV analysis, 13 key differential VMs were screened to characterize the differential impacts of WLQ on tea aroma. Further analysis of the evolution and metabolic pathways revealed that glycoside metabolism was the key pathway regulating tea aroma through WLQ. Blue light withering significantly enhanced glycosides hydrolysis and amino acids deamination, which was beneficial for the enrichment of floral and fruity VMs, such as geraniol, citral, methyl salicylate, 2-methyl-butanal, and benzeneacetaldehyde, as well as the transformation of grassy VMs, such as octanal, naphthalene, and cis-3-hexenyl isovalerate, resulting in the formation of tea floral and fruity aroma. The results provide theoretical basis and technical support for the targeted processing of high-quality tea.

Metabolomics and cellular altered pathways in cancer biology: A review.

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis.

Bronchiolitis is the leading cause of infant hospitalization. However, the molecular networks driving bronchiolitis pathobiology remain unknown. Integrative molecular networks, including the transcriptome and metabolome, can identify functional and regulatory pathways contributing to disease severity. Here, we integrated nasopharyngeal transcriptome and metabolome data of 397 infants hospitalized with bronchiolitis in a 17-center prospective cohort study. Using an explainable deep network model, we identified an omics-cluster comprising 401 transcripts and 38 metabolites that distinguishes bronchiolitis severity (test-set AUC, 0.828). This omics-cluster derived a molecular network, where innate immunity-related metabolites (e.g., ceramides) centralized and were characterized by toll-like receptor (TLR) and NF-κB signaling pathways (both FDR < 0.001). The network analyses identified eight modules and 50 existing drug candidates for repurposing, including prostaglandin I2 analogs (e.g., iloprost), which promote anti-inflammatory effects through TLR signaling. Our approach facilitates not only the identification of molecular networks underlying infant bronchiolitis but the development of pioneering treatment strategies.

The diagnostic potential of urine in paediatric patients undergoing initial treatment for tuberculous meningitis.

Tuberculous meningitis (TBM)-the extrapulmonary form of tuberculosis, is the most severe complication associated with tuberculosis, particularly in infants and children. The gold standard for the diagnosis of TBM requires cerebrospinal fluid (CSF) through lumbar puncture-an invasive sample collection method, and currently available CSF assays are often not sufficient for a definitive TBM diagnosis. Urine is metabolite-rich and relatively unexplored in terms of its potential to diagnose neuroinfectious diseases. We used an untargeted proton magnetic resonance (1H-NMR) metabolomics approach to compare the urine from 32 patients with TBM (stratified into stages 1, 2 and 3) against that from 39 controls in a South African paediatric cohort. Significant spectral bins had to satisfy three of our four strict cut-off quantitative statistical criteria. Five significant biological metabolites were identified-1-methylnicotinamide, 3-hydroxyisovaleric acid, 5-aminolevulinic acid, N-acetylglutamine and methanol-which had no correlation with medication metabolites. ROC analysis revealed that methanol lacked diagnostic sensitivity, but the other four metabolites showed good diagnostic potential. Furthermore, we compared mild (stage 1) TBM and severe (stages 2 and 3) TBM, and our multivariate metabolic model could successfully classify severe but not mild TBM. Our results show that urine can potentially be used to diagnose severe TBM.

Study the local metabolic changes of aneurysms through microcatheter sampling.

Intracranial aneurysm is the primary cause of nontraumatic subarachnoid hemorrhage. To assess aneurysm metabolism, we present a method of intra-operatively collecting blood samples from the aneurysm neck, as well as the proximal and distal responsible vessels, using microcatheters. Through these paired comparisons, we can eliminate the interpatient variation usually observed in plasma samples taken from the peripheral vein. We utilized 39 plasma samples from 13 intracranial patients to characterize the metabolite profiles using untargeted liquid chromatography-mass spectrometry. Our findings revealed that L-tyrosine is upregulated at relatively high levels at the aneurysm neck than the proximal and distal aneurysm, whereas phenylpyruvic acid, L-cystine, and L-ornithine are downregulated. Based on this, there was also a significant decrease in arginine within small aneurysm of the internal carotid artery. The 6-month follow-up indicated that patients who experienced good recovery had lower levels of biliverdin, bilirubin, and metabolites of coenzyme Q within the aneurysm. In conclusion, our investigation provides a comprehensive overview of plasma metabolites in patients with intracranial aneurysms, shedding light on potential pathogenetic mechanisms in unruptured intracranial aneurysms. Moreover, the study proposes innovative ideas for establishing postoperative follow-up timelines for flow diverter devices.

Dysregulated arginine metabolism in precursor B-cell acute lymphoblastic leukemia in children: a metabolomic study.

BACKGROUND: Precursor B-cell acute lymphoblastic leukemia (B-ALL) is the most common cancers in children. Failure of induction chemotherapy is a major factor leading to relapse and death in children with B-ALL. Given the importance of altered metabolites in the carcinogenesis of pediatric B-ALL, studying the metabolic profile of children with B-ALL during induction chemotherapy and in different minimal residual disease (MRD) status may contribute to the management of pediatric B-ALL. METHODS: We collected paired peripheral blood plasma samples from children with B-ALL at pre- and post-induction chemotherapy and analyzed the metabolomic profiling of these samples by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). Healthy children were included as controls. We selected metabolites that were depleted in pediatric B-ALL and analyzed the concentrations in pediatric B-ALL samples. In vitro, we study the effects of the selected metabolites on the viability of ALL cell lines and the sensitivity to conventional chemotherapeutic agents in ALL cell lines. RESULTS: Forty-four metabolites were identified with different levels between groups. KEGG pathway enrichment analyses revealed that dysregulated linoleic acid (LA) metabolism and arginine (Arg) biosynthesis were closely associated with pediatric B-ALL. We confirmed that LA and Arg were decreased in pediatric B-ALL samples. The treatment of LA and Arg inhibited the viability of NALM-6 and RS4;11 cells in a dose-dependent manner, respectively. Moreover, Arg increased the sensitivity of B-ALL cells to L-asparaginase and daunorubicin. CONCLUSION: Arginine increases the sensitivity of B-ALL cells to the conventional chemotherapeutic drugs L-asparaginase and daunorubicin. This may represent a promising therapeutic approach.

Leech Poecilobdella manillensis protein extract ameliorated hyperuricemia by restoring gut microbiota dysregulation and affecting serum metabolites.

BACKGROUND: Hyperuricemia (HUA) is a public health concern that needs to be solved urgently. The lyophilized powder of Poecilobdella manillensis has been shown to significantly alleviate HUA; however, its underlying metabolic regulation remains unclear. AIM: To explore the underlying mechanisms of Poecilobdella manillensis in HUA based on modulation of the gut microbiota and host metabolism. METHODS: A mouse model of rapid HUA was established using a high-purine diet and potassium oxonate injections. The mice received oral drugs or saline. Additionally, 16S rRNA sequencing and ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics were performed to identify changes in the microbiome and host metabolome, respectively. The levels of uric acid transporters and epithelial tight junction proteins in the renal and intestinal tissues were analyzed using an enzyme-linked immunosorbent assay. RESULTS: The protein extract of Poecilobdella manillensis lyophilized powder (49 mg/kg) showed an enhanced anti-trioxypurine ability than that of allopurinol (5 mg/kg) (P < 0.05). A total of nine bacterial genera were identified to be closely related to the anti-trioxypurine activity of Poecilobdella manillensis powder, which included the genera of Prevotella, Delftia, Dialister, Akkermansia, Lactococcus, Escherichia_Shigella, Enterococcus, and Bacteroides. Furthermore, 22 metabolites in the serum were found to be closely related to the anti-trioxypurine activity of Poecilobdella manillensis powder, which correlated to the Kyoto Encyclopedia of Genes and Genomes pathways of cysteine and methionine metabolism, sphingolipid metabolism, galactose metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis. Correlation analysis found that changes in the gut microbiota were significantly related to these metabolites. CONCLUSION: The proteins in Poecilobdella manillensis powder were effective for HUA. Mechanistically, they are associated with improvements in gut microbiota dysbiosis and the regulation of sphingolipid and galactose metabolism.

Dissection of potential anti-osteoporosis mechanism of isopsoralen - a quality control marker in Psoraleae Fructus - by metabolite profiling and network pharmacology.

RATIONALE: Isopsoralen (ISO), a quality control marker (Q-marker) in Psoraleae Fructus, is proven to present an obvious anti-osteoporosis effect. Until now, the metabolism and anti-osteoporosis mechanisms of ISO have not been fully elucidated, greatly restricting its drug development. METHODS: The metabolites of ISO in rats were profiled by using ultrahigh-performance liquid chromatography coupled with time-of-flight mass spectrometry. The potential anti-osteoporosis mechanism of ISO in vivo was predicted by using network pharmacology. RESULTS: A total of 15 metabolites were characterized in rats after ingestion of ISO (20 mg/kg/day, by gavage), including 2 in plasma, 12 in urine, 6 in feces, 1 in heart, 3 in liver, 1 in spleen, 1 in lung, 3 in kidney, and 2 in brain. The pharmacology network results showed that ISO and its metabolites could regulate AKT1, SRC, NFKB1, EGFR, MAPK3, etc., involved in the prolactin signaling pathway, ErbB signaling pathway, thyroid hormone pathway, and PI3K-Akt signaling pathway. CONCLUSIONS: This is the first time for revealing the in vivo metabolism features and potential anti-osteoporosis mechanism of ISO by metabolite profiling and network pharmacology, providing data for further verification of pharmacological mechanism.

MRMQuant: Automated MRM Data Quantitation for Large-Scale Targeted Metabolomics Analysis.

Multiple reaction monitoring (MRM) is a powerful and popular technique used for metabolite quantification in targeted metabolomics. Accurate and consistent quantitation of metabolites from the MRM data is essential for subsequent analyses. Here, we developed an automated tool, MRMQuant, for targeted metabolomic quantitation using high-throughput liquid chromatography-tandem mass spectrometry MRM data to provide users with an easy-to-use tool for accurate MRM data quantitation with minimal human intervention. This tool has many user-friendly functions and features to inspect and correct the quantitation results as required. MRMQuant possesses the following features to ensure accurate quantitation: (1) dynamic signal smoothing, (2) automatic deconvolution of coeluted peaks, (3) absolute quantitation via standard curves and/or internal standards, (4) visualized inspection and correction, (5) corrections applicable to multiple samples, and (6) batch-effect correction.

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.

Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability.

Biomarkers for congenital ventricular outflow tract malformations based on maternal serum lipid metabolomics analysis.

BACKGROUND: The congenital ventricular outflow tract malformations (CVOTMs) is a major congenital heart diseases (CHDs) subtype, and its pathogenesis is complex and unclear. Lipid metabolic plays a crucial role in embryonic cardiovascular development. However, due to the limited types of detectable metabolites in previous studies, findings on lipid metabolic and CHDs are still inconsistent, and the possible mechanism of CHDs remains unclear. METHODS: The nest case-control study obtained subjects from the multicenter China Teratology Birth Cohort (CTBC), and maternal serum from the pregnant women enrolled during the first trimester was utilized. The subjects were divided into a discovery set and a validation set. The metabolomics of CVOTMs and normal fetuses were analyzed by targeted lipid metabolomics. Differential comparison, random forest and lasso regression were used to screen metabolic biomarkers. RESULTS: The lipid metabolites were distributed differentially between the cases and controls. Setting the selection criteria of P value < 0.05, and fold change (FC) > 1.2 or < 0.833, we screened 70 differential metabolites. Within the prediction model by random forest and lasso regression, DG (14:0_18:0), DG (20:0_18:0), Cer (d18:2/20:0), Cer (d18:1/20:0) and LPC (0:0/18:1) showed good prediction effects in discovery and validation sets. Differential metabolites were mainly concentrated in glycerolipid and glycerophospholipids metabolism, insulin resistance and lipid & atherosclerosis pathways, which may be related to the occurrence and development of CVOTMs. CONCLUSION: Findings in this study provide a new metabolite data source for the research on CHDs. The differential metabolites and involved metabolic pathways may suggest new ideas for further mechanistic exploration of CHDs, and the selected biomarkers may provide some new clues for detection of COVTMs.

Metabolomics analysis of patients with Schistosoma japonicum infection based on UPLC-MS method.

BACKGROUND: Schistosomiasis is still one of the most serious parasitic diseases. Evidence showed that the metabolite profile in serum can potentially act as a marker for parasitic disease diagnosis and evaluate disease progression and prognosis. However, the serum metabolome in patients with Schistosoma japonicum infection is not well defined. In this study, we investigated the metabolite profiles of patients with chronic and with advanced S. japonicum infection. METHODS: The sera of 33 chronic S. japonicum patients, 15 patients with advanced schistosomiasis and 17 healthy volunteers were collected. Samples were extracted for metabolites and analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). RESULTS: We observed significant differences in metabolite profiles in positive and negative ion modes between patients with advanced and chronic S. japonicum infection. In patients with chronic S. japonicum infection, 199 metabolites were significantly upregulated while 207 metabolites were downregulated in advanced infection. These differential metabolites were mainly concentrated in steroid hormone biosynthesis, cholesterol metabolism and bile secretion pathways. We also found that certain bile acid levels were significantly upregulated in the progression from chronic to advanced S. japonicum infection. In receiver operator characteristic (ROC) analysis, we identified three metabolites with area under the curve (AUC) > 0.8, including glycocholic (GCA), glycochenodeoxycholate (GCDCA) and taurochenodeoxycholic acid (TCDCA) concentrated in cholesterol metabolism, biliary secretion and primary bile acid biosynthesis. CONCLUSIONS: This study provides evidence that GCA, GCDCA and TCDCA can potentially act as novel metabolite biomarkers to distinguish patients in different stages of S. japonicum infection. This study will contribute to the understanding of the metabolite mechanisms of the transition from chronic to advanced S. japonicum infection, although more studies are needed to validate this potential role and explore the underlying mechanisms.

Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice.

Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction between gut microbiota and metabolites plays an important role in early life. It is unclear whether the composition of the gut microbiota and metabolites can be used as an early indicator of NJ or to aid clinical decision-making. This study involved a total of 196 neonates and conducted two rounds of "discovery-validation" research on the gut microbiome-metabolome. It utilized methods of machine learning, causal inference, and clinical prediction model evaluation to assess the significance of gut microbiota and metabolites in classifying neonatal jaundice (NJ), as well as the potential causal relationships between corresponding clinical variables and NJ. In the discovery stage, NJ-associated gut microbiota, network modules, and metabolite composition were identified by gut microbiome-metabolome association analysis. The NJ-associated gut microbiota was closely related to bile acid metabolites. By Lasso machine learning assessment, we found that the gut bacteria were associated with abnormal bile acid metabolism. The machine learning-causal inference approach revealed that gut bacteria affected serum total bilirubin and NJ by influencing bile acid metabolism. NJ-associated gut bile acids are potential biomarkers of NJ, and clinical prediction models constructed based on these biomarkers have some clinical effects and the model may be used for disease risk prediction. In the validation stage, it was found that intestinal metabolites can predict NJ, and the machine learning-causal inference approach revealed that bile acid metabolites affected NJ itself by affecting the total bilirubin content. Intestinal bile acid metabolites are potential biomarkers of NJ. By applying machine learning-causal inference methods to gut microbiome-metabolome association studies, we found NJ-associated intestinal bacteria and their network modules and bile acid metabolite composition. The important role of intestinal bacteria and bile acid metabolites in NJ was determined, which can predict the risk of NJ.

Association analysis of the intestinal microbiome-metabolome found that neonatal jaundice (NJ)-related intestinal microbiota, network modules and metabolite composition, and the intestinal microbiota are closely related to bile acid metabolites.Gut bacteria were found to affect serum total bilirubin (TBIL) and NJ by influencing bile acid metabolism through a machine learning-causal inference approach, and bile acid metabolites affected NJ itself by affecting the TBIL content.NJ-associated gut bacteria and bile acids are potential biomarkers of NJ, and clinical decision-making models based on these biomarkers have some clinical effects for disease risk prediction.

Nontargeted metabolomic profiling analysis of patients with type 2 diabetes mellitus undergoing corn silk treatment.

To explore the corn silk's effect and possible mechanism on patients with type 2 diabetes mellitus (T2DM) by untargeted metabolomics. Newly diagnosed patients with T2DM admitted to the endocrinology department of the author's hospital from March 2020 to September 2021 were chosen and then allocated to either the intervention or the control group (NC) randomly. Patients in the intervention group were administered corn silk in the same way as the patients in the NC were given a placebo. A hypoglycemic effect was observed, and an untargeted metabolomics study was done on patients of both groups. Compared with the NC, the glycosylated hemoglobin and fasting blood glucose of patients in the intervention group significantly decreased after 3 months of treatment (P < .05), identified using tandem mass spectrometry, and analyzed by orthogonal partial least squares-discriminant analysis. A total of 73 differential metabolites were screened under the conditions of variable important in projection value >1.0 and P < .05. Differential metabolites are mainly enriched in signaling pathways such as oxidative phosphorylation, purine metabolism, and endocrine resistance. Through untargeted metabolomic analysis, it is found that corn silk water extract may reduce blood glucose in patients with T2DM through multiple pathways, including oxidative phosphorylation and purine metabolism.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes.

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.