Serum metabolic profiling analysis of gout patients based on UPLC-Q-TOF/MS

Untargeted metabolomics analysis of the urinary metabolic signature of acute and chronic gout

BACKGROUND

Gout is a common kind of inflammatory arthritis with metabolic disorders. However, the detailed pathogenesis of gout is complex and not fully clear. We investigated the urine metabolic profiling of gout patients by ultra-performance liquid chromatograph quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS).

METHOD

Urine metabolites were extracted from 26 acute gout patients, 31 chronic gout patients, and 32 healthy controls. Metabolite extracts were analyzed by UPLC-Q-TOF-MS for untargeted metabolomics. The peak area of creatinine was used to correct the content variations of urine samples for the semi-quantitative analysis. The value of variable importance in the projection (VIP) was obtained through the orthogonal partial least squares-discrimination analysis (OPLS-DA), and several differential metabolites were screened out.

RESULTS

The potential metabolic markers of gout in different stages were found based on the t-test. Finally, 18 different metabolites were identified through Human Metabolome Database (HMDB) and Targeted-MS/MS. The receiver operating characteristic (ROC) curve results revealed that all the screened biomarkers exerted high accuracy and diagnostic value. Pathway analysis indicated that the significantly different metabolites were mainly involved in purine metabolism and amino acid metabolism.

CONCLUSION

The identified potential biomarkers are mainly involved in purine metabolism and amino acid metabolism, which leads us to further explore the pathogenesis of gout. This will lead us to further explore the pathogenesis of gout and provide the basis and ideas for the prevention and treatment of gout.

Indolelactic acid as a potential metabolic biomarker for diagnosing gout

Gout is a heterogeneous disease caused by the deposition of monosodium urate crystals in joints, but its pathogenesis is currently poorly understood. The discovery of novel biomarkers is necessary for the early detection and diagnosis of gout. The present study aimed to characterize the metabolic profile of patients with gout using metabolomics, and to uncover the underlying pathological mechanisms leading to gout development. Serum samples were collected from 49 healthy participants and 47 patients with gout. Using ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer non-target metabolomics technology, with a variable importance in the projection >1 and a false discovery rate adjusted P<0.05 was used, while a biomarker panel was screened using receiver operating characteristic (ROC) analysis. The potential differentially expressed markers related to gout were identified by ROC analysis, and the erythrocyte sedimentation rate, uric acid, alanine transaminase, aspartate aminotransferase, creatinine, triglyceride, total cholesterol, high-density lipoprotein and low-density lipoprotein levels were significantly different in the group of patients with gout compared with those in healthy individuals. A total of 186 differentially expressed metabolites were identified, with 156 differential metabolites upregulated and 30 downregulated in the patients with gout compared with healthy individuals. Pathway analysis demonstrated that D-glutamine and D-glutamate metabolism may serve key roles in gout. Compared with healthy people, the indolelactic acid (ILA) level of patients with gout was significantly higher. ILA may serve as a potential biomarker for the diagnosis of gout and could be used to detect or predict gout progression in the future.

A metabolomics perspective reveals the mechanism of the uric acid-lowering effect of Prunus salicina Lindl. cv. "furong" polyphenols in hypoxanthine and potassium oxybate-induced hyperuricemic mice

The incidence of hyperuricemia (HUA) shows a gradually increasing trend towards affecting younger individuals, and it can significantly harm the overall health status of the body. Based on a metabolomics perspective, this study reveals the mechanism of the uric acid-lowering action of Prunus salicina Lindl. cv. "furong" polyphenols (PSLP) on a hyperuricemia mouse model induced by hypoxanthine and potassium oxybutyrate. The results demonstrate that PSLP comprise an effective treatment strategy for reducing the levels of serum uric acid (SUA), serum creatinine (SCr) and blood urea nitrogen (BUN) in HUA mice (p < 0.05), wherein the maximum decrease rates are up to 44.50%, 29.46%, and 32.95%, respectively. PSLP are observed to exert a pronounced inhibitory effect on the activities of xanthine oxidase (XOD) and adenosine deaminase (ADA) in the livers of HUA mice, with reductions of up to 16.36% and 20.13%, respectively. These findings illustrate that PSLP exert a significant uric acid-lowering effect. Subsequent metabolomic analysis of mouse serum identified 28 potential biomarkers for hyperuricemia, whose levels were markedly diminished by PSLP. This process involved alterations in purine, glycine, the pentose phosphate pathway, and galactose metabolism. Twenty-eight potential biomarkers were identified for hyperuricemia by subsequent metabolomic analysis of mouse serum, whose levels were markedly reversed by PSLP intervention. The regulation of HUA by PSLP involved alterations in purine metabolism, glycerolipid metabolism, the pentose phosphate pathway, and galactose metabolism. The mechanism of PSLP ameliorated hyperuricemia might be attributed to reduction of the level of the uric acid precursor ribose-5-phosphate in the pentose phosphate pathway, the inhibition of the activities of uric acid synthase XOD and ADA in purine metabolism, and reduction of the synthesis of the end product uric acid. This study provides a theoretical basis for the development of functional foods based on PSLP, which can potentially reduce uric acid levels.

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares

OBJECTIVE

The objective of this study was to discover differential metabolites and pathways underlying infrequent gout flares (InGF) and frequent gout flares (FrGF) using metabolomics and to establish a predictive model by machine learning (ML) algorithms.

METHODS

Serum samples from a discovery cohort of 163 patients with InGF and 239 patients with FrGF were analyzed by mass spectrometry-based untargeted metabolomics to profile differential metabolites and explore dysregulated metabolic pathways using pathway enrichment analysis and network propagation-based algorithms. ML algorithms were performed to establish a predictive model based on selected metabolites, which was further optimized by a quantitative targeted metabolomics method and validated in an independent validation cohort with 97 participants with InGF and 139 participants with FrGF.

RESULTS

A total of 439 differential metabolites between InGF and FrGF groups were identified. Top dysregulated pathways included carbohydrates, amino acids, bile acids, and nucleotide metabolism. Subnetworks with maximum disturbances in the global metabolic networks featured cross-talk between purine metabolism and caffeine metabolism, as well as interactions among pathways involving primary bile acid biosynthesis, taurine and hypotaurine metabolism, alanine, aspartate, and glutamate metabolism, suggesting epigenetic modifications and gut microbiome in metabolic alterations underlying InGF and FrGF. Potential metabolite biomarkers were identified using ML-based multivariable selection and further validated by targeted metabolomics. Area under receiver operating characteristics curve for differentiating InGF and FrGF achieved 0.88 and 0.67 for the discovery and validation cohorts, respectively.

CONCLUSION

Systematic metabolic alterations underlie InGF and FrGF, and distinct profiles are associated with differences in gout flare frequencies. Predictive modeling based on selected metabolites from metabolomics can differentiate InGF and FrGF.

Analysis of Metabolites in Gout: A Systematic Review and Meta-Analysis

(1) Background: Many studies have attempted to explore potential biomarkers for the early detection of gout, but consistent and high levels of evidence are lacking. In this study, metabolomics was used to summarize the changes of metabolites in the literature and explore the potential value of metabolites in predicting the occurrence and development of gout. (2) Methods: We searched the databases including the EMBASE, the Cochrane Library, PubMed, Web of Science, VIP Date, Wanfang Data, and CNKI, and the screening was fulfilled on 30 July 2022. The records were screened according to the inclusion criteria and the risk of bias was assessed. Qualitative analysis was performed for all metabolites, and meta-analysis was performed for metabolite concentrations using random effects to calculate the Std mean difference and 95% confidence interval. (3) Results: A total of 2738 records were identified, 33 studies with 3422 participants were included, and 701 metabolites were identified. The qualitative analysis results showed that compared with the healthy control group, the concentration of 56 metabolites increased, and 22 metabolites decreased. The results of the meta-analysis indicated that 17 metabolites were statistically significant. (4) Conclusions: Metabolites are associated with gout. Some specific metabolites such as uric acid, hypoxanthine, xanthine, KYNA, guanosine, adenosine, creatinine, LB4, and DL-2-Aminoadipic acid have been highlighted in the development of gout.

An Exploratory Study of the Metabolite Profiling from Pesticides Exposed Workers

Pesticides constitute a category of chemical products intended specifically for the control and mitigation of pests. With their constant increase in use, the risk to human health and the environment has increased proportionally due to occupational and environmental exposure to these compounds. The use of these chemicals is associated with several toxic effects related to acute and chronic toxicity, such as infertility, hormonal disorders and cancer. The present work aimed to study the metabolic profile of individuals occupationally exposed to pesticides, using a metabolomics tool to identify potential new biomarkers. Metabolomics analysis was carried out on plasma and urine samples from individuals exposed and non-exposed occupationally, using liquid chromatography coupled with mass spectrometry (UPLC-MS). Non-targeted metabolomics analysis, using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or partial least squares discriminant orthogonal analysis (OPLS-DA), demonstrated good separation of the samples and identified 21 discriminating metabolites in plasma and 17 in urine. The analysis of the ROC curve indicated the compounds with the greatest potential for biomarkers. Comprehensive analysis of the metabolic pathways influenced by exposure to pesticides revealed alterations, mainly in lipid and amino acid metabolism. This study indicates that the use of metabolomics provides important information about complex biological responses.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics

Background

Hyperuricemia and gout are a group of disorders of purine metabolism. In recent years, the incidence of hyperuricemia and gout has been increasing, which is a severe threat to people's health. Several studies on hyperuricemia and gout in proteomics and metabolomics have been conducted recently. Some literature has identified biomarkers that distinguish asymptomatic hyperuricemia from acute gout or remission of gout. We summarize the physiological processes in which these biomarkers may be involved and their role in disease progression.

Methodology

We used professional databases including PubMed, Web of Science to conduct the literature review. This review addresses the current landscape of hyperuricemia and gout biomarkers with a focus on proteomics and metabolomics.

Results

Proteomic methods are used to identify differentially expressed proteins to find specific biomarkers. These findings may be suggestive for the diagnosis and treatment of hyperuricemia and gout to explore the disease pathogenesis. The identified biomarkers may be mediators of the link between hyperuricemia, gout and kidney disease, metabolic syndrome, diabetes and hypertriglyceridemia. Metabolomics reveals the main influential pathways through small molecule metabolites, such as amino acid metabolism, lipid metabolism, or other characteristic metabolic pathways. These studies have contributed to the discovery of Chinese medicine. Some traditional Chinese medicine compounds can improve the metabolic disorders of the disease.

Conclusions

We suggest some possible relationships of potential biomarkers with inflammatory episodes, complement activation, and metabolic pathways. These biomarkers are able to distinguish between different stages of disease development. However, there are relatively few proteomic as well as metabolomic studies on hyperuricemia and gout, and some experiments are only primary screening tests, which need further in-depth study.

Metabolomics of Synovial Fluid and Infrapatellar Fat Pad in Patients with Osteoarthritis or Rheumatoid Arthritis

Osteoarthritis (OA) and autoimmune-driven rheumatoid arthritis (RA) are inflammatory joint diseases with complex and insufficiently understood pathogeneses. Our objective was to characterize the metabolic fingerprints of synovial fluid (SF) and its adjacent infrapatellar fat pad (IFP) obtained during the same surgical operation from OA and RA knees. Non-targeted metabolite profiling was performed for 5 non-inflammatory trauma controls, 10 primary OA (pOA) patients, and 10 seropositive RA patients with high-resolution mass spectrometry-based techniques, and metabolites were matched with known metabolite identities. Groupwise differences in metabolic features were analyzed with the univariate Welch’s t-test and the multivariate linear discriminant analysis (LDA) and principal component analysis (PCA). Significant discrimination of metabolite profiles was discovered by LDA for both SF and IFP and by PCA for SF based on diagnosis. In addition to a few drug-derived substances, there were 16 and 13 identified metabolites with significant differences between the diagnoses in SF and IFP, respectively. The pathways downregulated in RA included androgen, bile acid, amino acid, and histamine metabolism, and those upregulated included biotin metabolism in pOA and purine metabolism in RA and pOA. The RA-induced downregulation of androgen and bile acid metabolism was observed for both SF and IFP. The levels of 11 lipid metabolites, mostly glycerophospholipids and fatty acid amides, were also altered by these inflammatory conditions. The identified metabolic pathways could be utilized in the future to deepen our understanding of the pathogeneses of OA and RA and to develop not only biomarkers for their early diagnosis but also therapeutic targets.

Screening and identification of differential metabolites in serum and urine of bamaxiang pigs bitten by trimeresurus stejnegeri based on UPLC-Q-TOF/MS metabolomics technology

Trimeresurus stejnegeri is one of the top ten venomous snakes in China, and its bite causes acute and severe diseases. Elucidating the metabolic changes of the body caused by Trimeresurus stejnegeri bite will be beneficial to the diagnosis and treatment of snakebite. Thus, an animal pig model of Trimeresurus stejnegeri bite was established, and then the metabolites of serum and urine were subsequently screened and identified in both ESI+ and ESI- modes identified by ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS) methods. There are 9 differential metabolites in serum, including Oleic acid, Lithocholic acid, Deoxycholic acid, Hypoxanthine, etc. There are 11 differential metabolites in urine, including Dopamine, Thiocysteine, Arginine, Indoleacetaldehyde, etc. Serum enrichment pathway analysis showed that 5 metabolic pathways, including Tryptophanuria, Liver disease due to cystic fibrosis, Hartnup disease, Hyperbaric oxygen exposure and Biliary cirrhosis, the core metabolites in these pathways, including deoxycholic acid, lithocholic acid, tryptophan and hypoxanthine, changed significantly. Urine enrichment pathway analysis showed that 4 metabolic pathways, including Aromatic L-Amino Acid Decarboxylase, Vitiligo, Blue Diaper Syndrome and Hyperargininemia, the core metabolites in these pathways including dopamine, 5-hydroxyindole acetic acid and arginine. Taken together, the current study has successfully established an animal model of Trimeresurus stejnegeri bite, and identified the metabolic markers and metabolic pathways of Trimeresurus stejnegeri bite. These metabolites and pathways may have potential application value and provide a therapeutic basis for the treatment of Trimeresurus stejnegeri bite.