Potential Metabolite Biomarkers Research Articles

Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction.

Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.

The Current State of Proteomics and Metabolomics for Inner Ear Health and Disease.

Characterising inner ear disorders represents a significant challenge due to a lack of reliable experimental procedures and identified biomarkers. It is also difficult to access the complex microenvironments of the inner ear and investigate specific pathological indicators through conventional techniques. Omics technologies have the potential to play a vital role in revolutionising the diagnosis of ear disorders by providing a comprehensive understanding of biological systems at various molecular levels. These approaches reveal valuable information about biomolecular signatures within the cochlear tissue or fluids such as the perilymphatic and endolymphatic fluid. Proteomics identifies changes in protein abundance, while metabolomics explores metabolic products and pathways, aiding the characterisation and early diagnosis of diseases. Although there are different methods for identifying and quantifying biomolecules, mass spectrometry, as part of proteomics and metabolomics analysis, could be utilised as an effective instrument for understanding different inner ear disorders. This study aims to review the literature on the application of proteomic and metabolomic approaches by specifically focusing on Meniere's disease, ototoxicity, noise-induced hearing loss, and vestibular schwannoma. Determining potential protein and metabolite biomarkers may be helpful for the diagnosis and treatment of inner ear problems.

Serum bile acid and unsaturated fatty acid profiles of non-alcoholic fatty liver disease in type 2 diabetic patients.

The understanding of bile acid (BA) and unsaturated fatty acid (UFA) profiles, as well as their dysregulation, remains elusive in individuals with type 2 diabetes mellitus (T2DM) coexisting with non-alcoholic fatty liver disease (NAFLD). Investigating these metabolites could offer valuable insights into the pathophy-siology of NAFLD in T2DM. To identify potential metabolite biomarkers capable of distinguishing between NAFLD and T2DM. A training model was developed involving 399 participants, comprising 113 healthy controls (HCs), 134 individuals with T2DM without NAFLD, and 152 individuals with T2DM and NAFLD. External validation encompassed 172 participants. NAFLD patients were divided based on liver fibrosis scores. The analytical approach employed univariate testing, orthogonal partial least squares-discriminant analysis, logistic regression, receiver operating characteristic curve analysis, and decision curve analysis to pinpoint and assess the diagnostic value of serum biomarkers. Compared to HCs, both T2DM and NAFLD groups exhibited diminished levels of specific BAs. In UFAs, particular acids exhibited a positive correlation with NAFLD risk in T2DM, while the ω-6:ω-3 UFA ratio demonstrated a negative correlation. Levels of α-linolenic acid and γ-linolenic acid were linked to significant liver fibrosis in NAFLD. The validation cohort substantiated the predictive efficacy of these biomarkers for assessing NAFLD risk in T2DM patients. This study underscores the connection between altered BA and UFA profiles and the presence of NAFLD in individuals with T2DM, proposing their potential as biomarkers in the pathogenesis of NAFLD.

Serum metabolomic profiling of patients with liver cirrhosis at different stages.

An accurate and non-invasive diagnosis of the clinical stage is critical for effectively managing liver cirrhosis. This study aimed to identify serum metabolite biomarkers and clinical features that may reliably predict high-risk cirrhosis. This cross-sectional study recruited 94 cirrhotic patients (70 for identification cohort, 24 for validation cohort) from Minhang Hospital Affiliated with Fudan University between 2018 and 2021, who were analyzed by targeted quantitative metabolomics technique. Baseline clinical characteristics were collected, and different stage cirrhosis classification was performed according to the presence or absence of decompensated events. Potential metabolite biomarkers were screened, and a model for predicting the decompensation stage was created. Finally, the incidence of decompensated outcomes was analyzed. A total of 560 metabolites were detected in the identification cohort. Indole-3-propionic acid (IPA) was the most significantly decreased metabolic biomarker in the decompensated group (P<0.01, |log2FC| >2), having the strongest correlation with hyaluronic acid (r=-0.50, P<0.01). It also performed well for differentiating decompensated cirrhosis with an area under the curve (AUC) of 0.79(0.75 at internal validation). Another diagnostic model consisting of indole-3-propionic acid, hemoglobin, and albumin showed better predictive performance with an AUC of 0.97 (0.91 at internal validation). Also, 31 (44.29%) patients developed decompensated events at a median follow-up of 22.76±15.24 months. The cumulative incidence of decompensated events based on IPA subgroups (IPA <39.67ng/ml and ≥39.67ng/ml) showed a significant difference (P<0.01). "Indole-3-propionic acid" and a diagnostic model of hemoglobin and albumin can non-invasively identify cirrhotic populations at risk for decompensation, aiding in future management of liver cirrhosis.

Metabolome-microbiome insights into therapeutic impact of 8-O-acetylharpagide against breast cancer in a murine model.

Iridoid glycosides extract, which is the main active extract of Ajuga decumbens Thunb, has been proved to have anti-breast cancer activity in previous studies. However, it is still unknown whether 8-O-acetylharpagide, a main active compound in the extract, has anti-breast cancer activity. In this study, 4T1 breast cancer mice model was first successfully established. Then the anti-breast cancer effect of 8-O-acetylharpagide was systematically investigated. Feces were collected for metabolomics and 16S rRNA analysis to assess the potential mechanism. The results showed that 8-O-acetylharpagide was effective in reducing 4T1 mouse tumor volume and weight compared with the model group. Metabolome analysis revealed 12 potential metabolite biomarkers in feces, mainly involved in primary bile acid biosynthesis and arachidonic acid metabolism. The 16S rRNA sequencing results demonstrated that 8-O-acetylharpagide modulated the abundance of the intestinal flora in 4T1 mice. Spearman correlation analysis showed that calcitriol and prostaglandin G2 strongly correlated with Akkermansia, Firmicutes and Muribaculum. Overall, the active compound 8-O-acetylharpagide could inhibit significantly breast cancer growth in 4T1 breast cancer model mice. The mechanism of the anti-breast cancer effect of 8-O-acetylharpagide may be related to the regulation of primary bile acid biosynthesis and arachidonic acid metabolism and modulation of the abundance of Akkermansia and Firmicutes.

Abstract 1068: Plasma metabolite biomarkers for EGFR-mutated non-small cell lung cancer (NSCLC)

Abstract Background: Lung cancer is the leading cause of cancer death. More than half of the patients are diagnosed in the advanced stage for which the 5-year survival is below 10%. Lung cancer screening using low-dose CT scan reduced lung cancer mortality among heavy smokers. However, no standard screening method exists for non-smokers. Up to 35% and 90% of male and female lung cancer patients in Southeast Asia, respectively, do not smoke. Half of NSCLC patients in this region harbor EGFR mutations, which is common in women and non-smokers. Thus, we sought to explore novel biomarkers that help to identify lung cancer patients and those who have EGFR mutation. Metabolic reprogramming is one of the hallmarks of cancer. It leads to changes in metabolite levels in the blood. Hence, we conducted plasma metabolomic analysis to identify potential metabolite biomarkers in lung cancer patients. Methods: Plasma samples were collected from 69 lung cancer patients at Siriraj hospital and 10 healthy individuals during 2018-2022. Plasma metabolites from 29 treatment-naïve EGFR-mutated NSCLC patients, 30 treatment-naïve EGFR-wild-type NSCLC patients, 10 treatment-resistant EGFR T790M-mutated NSCLC patients, and 10 healthy individuals were profiled using GC-MS and untargeted LC-MS with Metlin database matching. EGFR mutation was detected from plasma ctDNA using real-time PCR. Negative plasma EGFR mutation results were confirmed with corresponding tumor tissues. Metabolomics data were processed by the metabox R package. Results: Among 114 candidate metabolites that passed the variable importance in projection (VIP) cutoff of 1.5, the top ten metabolites that differentiate NSCLC patients with EGFR mutation from those without were shown in Table 1. Many of them can also separate between NSCLC patients and healthy individuals. Table 1. ROC curve analysis Discriminant metabolites (Mutated EGFR vs Wild-type EGFR NSCLC) AUC Accuracy Sensitivity Specificity PG(a-13:0/i-12:0) 0.8000 0.7833 0.8333 0.7333 Nervonic ceramide 0.7736 0.6772 0.7259 0.6333 Valine 0.7678 0.7298 0.7259 0.7333 L-Histidinol 0.7644 0.7281 0.7222 0.7333 Leucine 0.7563 0.7632 0.7259 0.8000 Tryptophan 0.7425 0.6289 0.5593 0.7000 8-methylcaffeine 0.6793 0.6096 0.6222 0.6000 Formic acid 0.6759 0.6447 0.5889 0.7000 PC(20:0/24:0) 0.6506 0.6614 0.8259 0.5000 Sucrose 0.6471 0.6596 0.7593 0.5667 Discriminant metabolites (NSCLC patients vs Healthy individuals) AUC Accuracy Sensitivity Specificity Tryptophan 0.9986 0.9867 1.0000 0.9848 PC(20:0/24:0) 0.8609 0.9505 0.6111 1.0000 L-Histidinol 0.8572 0.9625 0.7778 0.9861 Formic acid 0.6696 0.9262 0.6667 0.9704 Conclusions: Plasma L-Histidinol, tryptophan, PC(20:0/24:0), and formic acid are potential metabolite biomarkers for NSCLC patients harboring EGFR mutation. Citation Format: Lucksamon Thamlikitkul, Siriphan Manocheewa, Suphitcha Limjiasahapong, Kwanjeera Wanichthanarak, Atikorn Panya, Naravat Poungvarin, Yotsawat Pomyen, Sakda Khoomrung. Plasma metabolite biomarkers for EGFR-mutated non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1068.

Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids.

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.

Metabolomics identifies and validates serum androstenedione as novel biomarker for diagnosing primary angle closure glaucoma and predicting the visual field progression

Background:Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness in Asia, and no reliable, effective diagnostic, and predictive biomarkers are used in clinical routines. A growing body of evidence shows metabolic alterations in patients with glaucoma. We aimed to develop and validate potential metabolite biomarkers to diagnose and predict the visual field progression of PACG.Methods:Here, we used a five-phase (discovery phase, validation phase 1, validation phase 2, supplementary phase, and cohort phase) multicenter (EENT hospital, Shanghai Xuhui Central Hospital), cross-sectional, prospective cohort study designed to perform widely targeted metabolomics and chemiluminescence immunoassay to determine candidate biomarkers. Five machine learning (random forest, support vector machine, lasso, K-nearest neighbor, and GaussianNaive Bayes [NB]) approaches were used to identify an optimal algorithm. The discrimination ability was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed by Hosmer-Lemeshow tests and calibration plots.Results:Studied serum samples were collected from 616 participants, and 1464 metabolites were identified. Machine learning algorithm determines that androstenedione exhibited excellent discrimination and acceptable calibration in discriminating PACG across the discovery phase (discovery set 1, AUCs=1.0 [95% CI, 1.00–1.00]; discovery set 2, AUCs = 0.85 [95% CI, 0.80–0.90]) and validation phases (internal validation, AUCs = 0.86 [95% CI, 0.81–0.91]; external validation, AUCs = 0.87 [95% CI, 0.80–0.95]). Androstenedione also exhibited a higher AUC (0.92–0.98) to discriminate the severity of PACG. In the supplemental phase, serum androstenedione levels were consistent with those in aqueous humor (r=0.82, p=0.038) and significantly (p=0.021) decreased after treatment. Further, cohort phase demonstrates that higher baseline androstenedione levels (hazard ratio = 2.71 [95% CI: 1.199–6.104], p=0.017) were associated with faster visual field progression.Conclusions:Our study identifies serum androstenedione as a potential biomarker for diagnosing PACG and indicating visual field progression.Funding:This work was supported by Youth Medical Talents – Clinical Laboratory Practitioner Program (2022-65), the National Natural Science Foundation of China (82302582), Shanghai Municipal Health Commission Project (20224Y0317), and Higher Education Industry-Academic-Research Innovation Fund of China (2023JQ006).

DOP33 Distinct perturbances in metabolic pathways associate with disease progression in patients with Inflammatory Bowel Disease

Abstract Background Patients with inflammatory bowel diseases (IBD) exhibit distinct shifts in circulating metabolite levels that have been associated with clinical disease activity and disease phenotype. However, there are limited data on the impact of metabolomic changes on disease progression in IBD. We aimed to understand the association of circulating metabolite levels and related pathways with the risk of disease progression in patients with IBD. Methods We conducted an observational cohort study using the Mount Sinai Crohn’s and Colitis Registry (MSCCR). Patients were prospectively enrolled with baseline questionnaires and biospecimens that were then linked to longitudinal electronic health record data. A total of 1,456 serum metabolites were measured in 277 patients with UC and 375 patients with CD using untargeted metabolomics. Disease progression was defined as any new steroid or biological prescriptions, IBD-related hospitalizations, or surgery. Multivariable Cox proportional hazards regression models, adjusted for age, sex, smoking, biologic use, and surgery were used to explore metabolite associations with risk of disease progression. Metabolite set enrichment analysis (MSEA) was performed to identify underlying metabolic pathways associated with disease progression. Results During a median 2-year follow-up time (IQR 0.3-5.1 years), 375 patients (57.5%) had a disease progression event. Among CD, 151 metabolites significantly correlated with the composite outcome (FDR&amp;lt;0.1). Of these, 81 (53.6%) were linked to higher risk, mainly enriched in amino acids, purine, and pyrimidine metabolism, fibrinogen cleavage peptides, and bile acid pathways. Additionally, 70 (46.4%) metabolites associated with lower risk with enrichment in fatty acid metabolism, steroid biosynthesis, histidine, tryptophan, and antioxidant pathways. In CD, aspartate metabolism was most significantly associated with increased risk and linoleic metabolism with decreased risk. In UC, 84 metabolites were significantly associated with disease progression (FDR&amp;lt;0.1), with 29 (34.5%) associated with increased and 55 (65.5%) with decreased risk. Metabolic pathways linked to higher risk were sphingolipid, benzoate, hydrogen sulfide, polyamine, and tyrosine metabolism, while protective metabolites were enriched in branched-chain amino acid (BCAA), steroid biosynthesis, histidine, and phenylalanine pathways. In UC, sphingolipid metabolism was prominently associated with increased risk and BCAA metabolism with decreased risk. Conclusion Specific metabolites and metabolic pathways are associated with risk of disease progression in patients with IBD, highlighting potential prognostic metabolite biomarkers and pathways that may be important in modulating risk of disease progression.

Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On–Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.

Predicting effect of anti-PD-1/PD-L1 inhibitors therapy for hepatocellular carcinoma by detecting plasma metabolite based on UHPLC-MS.

Anti-PD-1/PD-L1 inhibitors therapy has become a promising treatment for hepatocellular carcinoma (HCC), while the therapeutic efficacy varies significantly among effects for individual patients are significant difference. Unfortunately, specific predictive biomarkers indicating the degree of benefit for patients and thus guiding the selection of suitable candidates for immune therapy remain elusive.no specific predictive biomarkers are available indicating the degree of benefit for patients and thus screening the preferred population suitable for the immune therapy. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) considered is an important method for analyzing biological samples, since it has the advantages of high rapid, high sensitivity, and high specificity. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) has emerged as a pivotal method for analyzing biological samples due to its inherent advantages of rapidity, sensitivity, and specificity. In this study, potential metabolite biomarkers that can predict the therapeutic effect of HCC patients receiving immune therapy were identified by UHPLC-MS. A partial least-squares discriminant analysis (PLS-DA) model was established using 14 glycerophospholipid metabolites mentioned above, and good prediction parameters (R2 = 0.823, Q2 = 0.615, prediction accuracy = 0.880 and p < 0.001) were obtained. The relative abundance of glycerophospholipid metabolite ions is closely related to the survival benefit of HCC patients who received immune therapy. This study reveals that glycerophospholipid metabolites play a crucial role in predicting the efficacy of immune therapy for HCC.

Predictive value of bile acids as metabolite biomarkers for gallstone disease: A systematic review and meta-analysis.

The profiles of bile acids (BAs) in patients with gallstone disease (GSD) have been found to be altered markedly though in an inconsistent pattern. This study aims to characterize the variation of the BA profiles in GSD patients, thereby to discover the potential metabolite biomarkers for earlier detection of GSD. Literature search of eight electronic database in both English and Chinese was completed on May 11, 2023. The qualitative and quantitative reviews were performed to summarize the changes of BA profiles in GSD patients compared with healthy subjects. The concentrations of BAs were adopted as the primary outcomes and the weighted mean differences (WMDs) and 95% confidence interval (CI) were generated by random-effects meta-analysis models. A total of 30 studies were enrolled which included 2313 participants and reported the 39 BAs or their ratios. Qualitative review demonstrated serum Taurocholic Acid (TCA), Glycochenodeoxycholic acid (GCDCA), Glycocholic acid (GCA), Taurochenodeoxycholic acid (TCDCA), Glycodeoxycholic acid (GDCA) and Deoxycholic acid (DCA) were significantly increased in GSD patients compared with healthy subjects. Meta analysis was performed in 16 studies and showed that serum Total BAs (TBA) (WMD = 1.36μmol/L, 95%CI = 0.33; 2.4) was elevated however bile TBA (WMD = -36.96mmol/L, 95%CI = -52.32; -21.6) was declined in GSD patients. GCA (WMD = 0.83μmol/L, 95%CI = 0.06; 1.6) and TCA (WMD = 0.51μmol/L; 95%CI = 0.18; 0.85) were both increased in serum sample; TCDCA (WMD = 2.64mmol/L, 95%CI = 0.16; 5.12) was rising, however GCDCA (WMD = -13.82mmol/L, 95%CI = -21.86; -5.78) was falling in bile sample of GSD patients. The level of serum DCA in the GSD patients was found to be increased by using chromatography, yet decreased by chromatography mass spectrometry. The profiles of BAs demonstrated distinctive changes in GSD patients compared with healthy control subjects. Serum GCA, TCA and GCDCA, as the typically variant BAs, presented as a potential marker for earlier diagnosis of GSD, which could facilitate early prophylactic intervention. Yet, further validation of these biomarkers by longitudinal studies is still warranted in the future. PROSPERO registration number CRD42022339649.

Metabolomic analysis identifies dysregulation of lipid metabolism in the immune clearance phase of chronic hepatitis B patients

There is an accelerated progression of liver necroinflammation and fibrosis in the liver during the immune clearance (IC) phase of Chronic hepatitis B virus (HBV) infection, which are critical indicators of antiviral treatment for chronic hepatitis B (CHB) infection. This study applied serum metabolomics to identify the potential metabolite biomarkers for differential diagnosis between the CHB immune tolerance (IT) and Immune clearance (IC) phases. A liquid chromatography-mass spectrometry (LC-MS)-based approach was applied to evaluate and compared the serum metabolic profiles of 28 patients in IT phase and 33 patients in IC phase and appropriate statistical methods with MetaboAnalystR 2.0 R package to analyze those metabolites. The differential metabolites between IT and TC groups were classified and the top altered classification were lipids and lipid-like molecules and fatty acyls, clearly indicating that there were differences in the lipid metabolomic profile of HBV-infected patients with IT vs. IR phase. We identified the top 10 potential metabolite biomarkers for differential diagnosis between IT and IR. There were four lipid metabolites among them and the AUC of two of them, octadecadienoyl-sn-glycero-3-phosphocholine and 3-Cycloheptene-l-acetic acid, were 0.983 and 0.933. octadecadienoyl-sn-glycero-3-phosphocholine is Diacylglycerol (18:2n6/18:0) and 3-Cycloheptene-l-acetic acid is hydroxy fatty acids, both of which were associated with lipid metabolism. This study not only provides the potential metabolic biomarkers but also insight into the mechanism of CHB progression during IT clearance phase.

Identification of Novel and Early Biomarkers for Cisplatin-induced Nephrotoxicity and the Nephroprotective Role of Cimetidine using a Pharmacometabolomic-based Approach Coupled with In Vitro Toxicodynamic Modeling and Simulation

Cisplatin is widely used for the treatment of various types of cancer. However, cisplatin-induced nephrotoxicity (CIN) is frequently observed in patients receiving cisplatin therapy which poses a challenge in its clinical utility. Currently used clinical biomarkers for CIN are not adequate for early detection of nephrotoxicity, hence there is a need to identify potential early biomarkers in predicting CIN. In the current study, a combination of in vitro toxicodynamic (TD) modeling and untargeted global metabolomics approach was used to identify novel potential metabolite biomarkers for early detection of CIN. In addition, we investigated the protective role of cimetidine (CIM), an inhibitor of the organic cation transporter 2 (OCT2), in suppressing CIN. We first characterized the time-course of nephrotoxic effects of cisplatin (CIS) and the protective effects of CIM in a human pseudo-immortalized renal proximal tubule epithelial cell line (RPTEC), SA7K cell line. Secondly, we used a mathematical cell-level, in vitro TD modeling approach to quantitatively characterize the time-course effects of CIS and CIM as single agents and combination in SA7K cells. Based on the experimental and modeling results, we selected relevant concentrations of CIS and CIM for our metabolomics study. With the help of PCA (Principal Component Analysis) and PLS-DA (Projection to Latent Structure – Discriminate Analysis) analyses, we confirmed global metabolome changes for different groups (CIS, CIM, CIS+CIM vs control) in SA7K cells. Based on the criterion of a p-value ≤ 0.05 and a fold change ≥ 2 or ≤ 0.5, we identified 20 top metabolites that were significantly changed during the early phase i.e. within first 12 h of CIS treatment. Finally, pathway analysis was conducted that revealed the key metabolic pathways that were most impacted in CIN.

Identification of metabolite biomarkers in Salmonella enteritidis-contaminated chickens using UHPLC-QTRAP-MS-based targeted metabolomics

This study aimed to characterize the metabolic profile of Salmonella enteritidis (S. enteritidis) in chicken matrix and to identify metabolic biomarkers of S. enteritidis in chicken. The UHPLC-QTRAP-MS high-throughput targeted metabolomics approach was employed to analyze the metabolic profiles of contaminated and control group chickens. A total of 348 metabolites were quantified, and the application of deep learning least absolute shrinkage and selection operator (LASSO) modelling analysis obtained eight potential metabolite biomarkers for S. enteritidis. Metabolic abundance change analysis revealed significantly enriched abundances of anthranilic acid, l-pyroglutamic acid, 5-hydroxylysine, n,n-dimethylarginine, 4-hydroxybenzoic acid, and menatetrenone in contaminated chicken samples. The receiver operating characteristic (ROC) curve analysis demonstrated the strong ability of these six metabolites as biomarkers to distinguish S. enteritidis contaminated and fresh chicken samples. The findings presented in this study offer a theoretical foundation for developing an innovative approach to identify and detect foodborne contamination caused by S. enteritidis.

Combined metabolomic and lipidomic analysis uncovers metabolic profile and biomarkers for papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy with a rapidly increasing incidence. The pathogenesis of PTC is unclear, but metabolic and lipidomic reprogramming may play a role in tumor growth. We applied ultra-performance liquid chromatography-tandem mass spectrometry to perform widely targeted metabolomics and lipidomics on plasma samples from 94 patients with PTC and 100 healthy controls. We identified 113 differential metabolites and 236 differential lipids, mainly involved in branched-chain amino acid metabolism, glutamate and glutamine metabolism, tricarboxylic acid cycle, and lipid metabolism. We also screened three potential metabolite biomarkers: sebacic acid, L-glutamine, and indole-3-carboxaldehyde. These biomarkers showed excellent diagnostic performance for PTC in both discovery and validation cohorts, with areas under the receiver operating characteristic curves of 0.994 and 0.925, respectively. Our findings reveal distinct metabolic and lipidomic features of PTC and provide novel targets for diagnosis and treatment.

Are lipids, phenylpropanoids, and benzenoids potential metabolite biomarkers for succession in desert biocrusts?

Are lipids, phenylpropanoids, and benzenoids potential metabolite biomarkers for succession in desert biocrusts?

Distinctive metabolic remodeling in TYMP deficiency beyond mitochondrial dysfunction.

Mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) is caused by mutations in the TYMP gene, which encodes thymidine phosphorylase (TP). As a cytosolic metabolic enzyme, TP defects affect biological processes that are thought to not be limited to the abnormal replication of mitochondrial DNA. This study aimed to elucidate the characteristic metabolic alterations and associated homeostatic regulation caused by TYMP deficiency. The pathogenicity of novel TYMP variants was evaluated in terms of clinical features, genetic analysis, and structural instability. We analyzed plasma samples from three patients with MNGIE; three patients with m.3243A > G mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS); and four healthy controls (HC) using both targeted and untargeted metabolomics techniques. Transcriptomics analysis and bioenergetic studies were performed on skin fibroblasts from participants in these three groups. A TYMP overexpression experiment was conducted to rescue the observed changes. Compared with controls, specific alterations in nucleosides, bile acids, and steroid metabolites were identified in the plasma of MNGIE patients. Comparable mitochondrial dysfunction was present in fibroblasts from patients with TYMP deficiency and in those from patients with the m.3243A > G mutation. Distinctively decreased sterol regulatory element binding protein (SREBP) regulated cholesterol metabolism and fatty acid (FA) biosynthesis as well as reduced FA degradation were revealed in fibroblasts with TYMP deficiency. The restoration of thymidine phosphorylase activity rescued the observed changes in MNGIE fibroblasts. Our findings indicated that more widespread metabolic disturbance may be caused by TYMP deficiency in addition to mitochondrial dysfunction, which expands our knowledge of the biochemical outcome of TYMP deficiency. KEY MESSAGES: Distinct metabolic profiles in patients with TYMP deficiency compared to those with m.3243A > G mutation. TYMP deficiency leads to a global disruption of nucleoside metabolism. Cholesterol and fatty acid metabolism are inhibited in individuals with MNGIE. TYMP is functionally related to SREBP-regulated pathways. Potential metabolite biomarkers that could be valuable clinical tools to improve the diagnosis of MNGIE.

Urinary metabolites associate with the presence of diabetic kidney disease in type 2 diabetes and mediate the effect of inflammation on kidney complication

AimsDiabetic kidney disease (DKD) is the one of the leading causes of end-stage kidney disease. Unraveling novel biomarker signatures capable to identify patients with DKD is favorable for tackle the burden. Here, we investigated the possible association between urinary metabolites and the presence of DKD in type 2 diabetes (T2D), and further, whether the associated metabolites improve discrimination of DKD and mediate the effect of inflammation on kidney involvement was evaluated.MethodsTwo independent cohorts comprising 192 individuals (92 DKD) were analyzed. Urinary metabolites were analyzed by targeted metabolome profiling and inflammatory cytokine IL-18 were measured by ELISA. Differentially expressed metabolites were selected and mediation analysis was carried out.ResultsSeven potential metabolite biomarkers (i.e., S-Adenosyl-L-homocysteine, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid) were identified using the discovery and validation design. In the pooled analysis, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid were markedly and independently associated with DKD. The composite index of 7 potential metabolite biomarkers (CMI) mediated 32.99% of the significant association between the inflammatory IL-18 and DKD. Adding the metabolite biomarkers improved the discrimination of DKD.ConclusionsIn T2D, several associated urinary metabolites were identified to improve the prediction of DKD. Whether interventions aimed at reducing CMI also reduce the risk of DKD especially in patients with high IL-18 needs further investigations.

A Metabolomics study of metabolites associated with the glomerular filtration rate

BackgroundChronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates.MethodsAn untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS). The 145 samples were divided into four groups based on the patient’s measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software.ResultsA large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function.ConclusionsThis global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function.