Gallic acid(GA),a plant phenol ubiquitously present in fruits and vegetables,has demonstrated efficacy in ameliorating ulcerative colitis(UC).Despite previous reports,the precise mechanism of GA's therapeutic acti...Gallic acid(GA),a plant phenol ubiquitously present in fruits and vegetables,has demonstrated efficacy in ameliorating ulcerative colitis(UC).Despite previous reports,the precise mechanism of GA's therapeutic action remains elusive.Herein,the present study aims to delineate the mechanism underlying the anti-UC effects of GA by focusing on the interplay of gut microbiota,microbial and host cometabolites,and gut immune regulation.The findings revealed that GA treatment improved the colitis symptoms and systematic inflammatory response,reliant on gut microbiota,as evidenced by microbiota depletion and fecal microbiota transplantation.According to the 16S r DNA sequencing results,GA altered the gut microbiota community structure and upregulated the biosynthesis of secondary bile acids(SBAs).Metabolomics and flow cytometry(FCS)analysis revealed a substantial increase in SBAs production,including ursodeoxycholic acid(UDCA),lithocholic acid(LCA),3-oxo-lithocholic acid(3-oxo-LCA)and iso-allolithocholic acid(Isoallo LCA),which further upregulated the proportion of regulatory T(Treg)cells and downregulated the proportion of T helper type 17(Th17)cells in the colon,ultimately resulting in an improved Treg/Th17 balance.Further FCS and real-time quantitative PCR assays provided mechanistic insights,demonstrating that UDCA and Isoallo LCA facilitate Treg cell differentiation through the upregulation of nuclear hormone receptor 4A1(NR4A1).This research elucidated that GA effectively mitigates colitis by modulating the Treg/Th17 balance,facilitated by the enhanced synthesis of microbiota-derived SBAs.These insights unveil innovative pathways through which GA exerts its anti-UC effects,emphasizing the potential therapeutic benefits of incorporating a GAenriched diet into UC management.展开更多
Parkinson's disease(PD) is an aging-associated neurodegenerative movement disorder with increasing morbidity and mortality rates.The current gold standard for diagnosing PD is clinical evaluation,which is often ch...Parkinson's disease(PD) is an aging-associated neurodegenerative movement disorder with increasing morbidity and mortality rates.The current gold standard for diagnosing PD is clinical evaluation,which is often challenging and inaccurate.Metabolomics and lipidomics approaches have been extensively applied because of their potential in discovering valuable biomarkers for medical diagnostics.Here,we used comprehensive untargeted metabolomics and lipidomics methodologies based on liquid chromatographymass spectrometry to evaluate metabolic abnormalities linked with PD.Two well-characterized cohorts of288 plasma samples(143 PD patients and 145 control subjects in total) were used to examine metabolic alterations and identify diagnostic biomarkers.Unbiased multivariate and univariate studies were combined to identify the promising metabolic signatures,based on which the discriminant models for PD were established by integrating multiple machine learning algorithms.A 6-biomarker predictive model was constructed based on the omics profile in the discovery cohort,and the discriminant performance of the biomarker panel was evaluated with an accuracy over 81.6% both in the discovery cohort and validation cohort.The results indicated that PC(40:7),eicosatrienoic acid were negatively correlated with severity of PD,and pentalenic acid,PC(40:6p) and aspartic acid were positively correlated with severity of PD.In summary,we developed a multi-metabolite predictive model which can diagnose PD with over81.6% accuracy based on this unique metabolic signature.Future clinical diagnosis of PD may benefit from the biomarker panel reported in this study.展开更多
Previous studies demonstrated that three-dimensional(3D) multicellular tumor spheroids(MCTS) could more closely mimic solid tumors than two-dimensional(2D) cancer cells in terms of the spatial structure, extracellular...Previous studies demonstrated that three-dimensional(3D) multicellular tumor spheroids(MCTS) could more closely mimic solid tumors than two-dimensional(2D) cancer cells in terms of the spatial structure, extracellular matrix-cell interaction, and gene expression pattern. However, no study has been reported on the differences in lipid metabolism and distribution among 2D cancer cells, MCTS, and solid tumors. Here, we used Hep G2 liver cancer cell lines to establish these three cancer models. The variations of lipid profiles and spatial distribution among them were explored by using mass spectrometry-based lipidomics and matrix-assisted laser desorption/ionization mass spectrometry imaging(MSI). The results revealed that MCTS, relative to 2D cells, had more shared lipid species with solid tumors. Furthermore,MCTS contained more comparable characteristics than 2D cells to solid tumors with respect to the relative abundance of most lipid classes and mass spectra patterns. MSI data showed that 46 of 71 lipids had similar spatial distribution between solid tumors and MCTS, while lipids in 2D cells had no specific spatial distribution. Interestingly, most of detected lipid species in sphingolipids and glycerolipids preferred locating in the necrotic region to the proliferative region of solid tumors and MCTS. Taken together, our study provides the evidence of lipid metabolism and distribution demonstrating that MCTS are a more suitable in vitro model to mimic solid tumors, which may offer insights into tumor metabolism and microenvironment.展开更多
Database-assisted global metabolomics has received growing attention due to its capability for unbiased identification of metabolites in various biological samples.Herein,we established a mass spectrometry(MS)-based d...Database-assisted global metabolomics has received growing attention due to its capability for unbiased identification of metabolites in various biological samples.Herein,we established a mass spectrometry(MS)-based database-assisted global metabolomics method and investigated metabolic distance between pleural effusion induced by tuberculosis and malignancy,which are difficult to be distinguished due to their similar clinical symptoms.The present method utilized a liquid chromatography(LC) system coupled with high resolution mass spectrometry(MS) working on full scan and data dependent mode for data acquisition.Unbiased identification of metabolites was performed through mass spectral searching and then confirmed by using authentic standards.As a result,a total of 194 endogenous metabolites were identified and 33 metabolites were found to be differentiated between tuberculous and malignant pleural effusions.These metabolites involved in tryptophan catabolism,bile acid biosynthesis,and β-oxidation of fatty acids,provided non-invasive biomarkers for differentiation of the pleural effusion samples with high sensitivity and specificity.展开更多
Parkinson’s disease(PD)is a complex neurological disorder that typically worsens with age.A wide range of pathologies makes PD a very heterogeneous condition,and there are currently no reliable diagnostic tests for t...Parkinson’s disease(PD)is a complex neurological disorder that typically worsens with age.A wide range of pathologies makes PD a very heterogeneous condition,and there are currently no reliable diagnostic tests for this disease.The application of metabolomics to the study of PD has the potential to identify disease biomarkers through the systematic evaluation of metabolites.In this study,urine metabolic profiles of 215 urine samples from 104 PD patients and 111 healthy individuals were assessed based on liquid chromatography-mass spectrometry.The urine metabolic profile was first evaluated with partial leastsquares discriminant analysis,and then we integrated the metabolomic data with ensemble machine learning techniques using the voting strategy to achieve better predictive performance.A combination of 8-metabolite predictive panel performed well with an accuracy of over 90.7%.Compared to control subjects,PD patients had higher levels of 3-methoxytyramine,N-acetyl-l-tyrosine,orotic acid,uric acid,vanillic acid,and xanthine,and lower levels of 3,3-dimethylglutaric acid and imidazolelactic acid in their urine.The multi-metabolite prediction model developed in this study can serve as an initial point for future clinical studies.展开更多
基金funded by the National Natural Science Foundation of China(82174104 and 22104158)Guangzhou Science and Technology Program(2023B03J1382)+2 种基金Nansha Science and Technology Program(2022ZD004)Science and Technology Innovation Strategy of Guangdong Province(2020A1111350001)Natural Science Foundation of Hunan Province(2021JJ40041)。
文摘Gallic acid(GA),a plant phenol ubiquitously present in fruits and vegetables,has demonstrated efficacy in ameliorating ulcerative colitis(UC).Despite previous reports,the precise mechanism of GA's therapeutic action remains elusive.Herein,the present study aims to delineate the mechanism underlying the anti-UC effects of GA by focusing on the interplay of gut microbiota,microbial and host cometabolites,and gut immune regulation.The findings revealed that GA treatment improved the colitis symptoms and systematic inflammatory response,reliant on gut microbiota,as evidenced by microbiota depletion and fecal microbiota transplantation.According to the 16S r DNA sequencing results,GA altered the gut microbiota community structure and upregulated the biosynthesis of secondary bile acids(SBAs).Metabolomics and flow cytometry(FCS)analysis revealed a substantial increase in SBAs production,including ursodeoxycholic acid(UDCA),lithocholic acid(LCA),3-oxo-lithocholic acid(3-oxo-LCA)and iso-allolithocholic acid(Isoallo LCA),which further upregulated the proportion of regulatory T(Treg)cells and downregulated the proportion of T helper type 17(Th17)cells in the colon,ultimately resulting in an improved Treg/Th17 balance.Further FCS and real-time quantitative PCR assays provided mechanistic insights,demonstrating that UDCA and Isoallo LCA facilitate Treg cell differentiation through the upregulation of nuclear hormone receptor 4A1(NR4A1).This research elucidated that GA effectively mitigates colitis by modulating the Treg/Th17 balance,facilitated by the enhanced synthesis of microbiota-derived SBAs.These insights unveil innovative pathways through which GA exerts its anti-UC effects,emphasizing the potential therapeutic benefits of incorporating a GAenriched diet into UC management.
基金support from the Collaborative Research Fund (No.C2011-21GF)Guangdong Province Basic and Applied Basic Research Foundation (No.2021B1515120051)。
文摘Parkinson's disease(PD) is an aging-associated neurodegenerative movement disorder with increasing morbidity and mortality rates.The current gold standard for diagnosing PD is clinical evaluation,which is often challenging and inaccurate.Metabolomics and lipidomics approaches have been extensively applied because of their potential in discovering valuable biomarkers for medical diagnostics.Here,we used comprehensive untargeted metabolomics and lipidomics methodologies based on liquid chromatographymass spectrometry to evaluate metabolic abnormalities linked with PD.Two well-characterized cohorts of288 plasma samples(143 PD patients and 145 control subjects in total) were used to examine metabolic alterations and identify diagnostic biomarkers.Unbiased multivariate and univariate studies were combined to identify the promising metabolic signatures,based on which the discriminant models for PD were established by integrating multiple machine learning algorithms.A 6-biomarker predictive model was constructed based on the omics profile in the discovery cohort,and the discriminant performance of the biomarker panel was evaluated with an accuracy over 81.6% both in the discovery cohort and validation cohort.The results indicated that PC(40:7),eicosatrienoic acid were negatively correlated with severity of PD,and pentalenic acid,PC(40:6p) and aspartic acid were positively correlated with severity of PD.In summary,we developed a multi-metabolite predictive model which can diagnose PD with over81.6% accuracy based on this unique metabolic signature.Future clinical diagnosis of PD may benefit from the biomarker panel reported in this study.
基金supported by National Natural Science Foundation of China (Nos. 22036001, 22106130 and 91843301)Research Grant Council (Nos. 463612 and 14104314) of Hong Kong。
文摘Previous studies demonstrated that three-dimensional(3D) multicellular tumor spheroids(MCTS) could more closely mimic solid tumors than two-dimensional(2D) cancer cells in terms of the spatial structure, extracellular matrix-cell interaction, and gene expression pattern. However, no study has been reported on the differences in lipid metabolism and distribution among 2D cancer cells, MCTS, and solid tumors. Here, we used Hep G2 liver cancer cell lines to establish these three cancer models. The variations of lipid profiles and spatial distribution among them were explored by using mass spectrometry-based lipidomics and matrix-assisted laser desorption/ionization mass spectrometry imaging(MSI). The results revealed that MCTS, relative to 2D cells, had more shared lipid species with solid tumors. Furthermore,MCTS contained more comparable characteristics than 2D cells to solid tumors with respect to the relative abundance of most lipid classes and mass spectra patterns. MSI data showed that 46 of 71 lipids had similar spatial distribution between solid tumors and MCTS, while lipids in 2D cells had no specific spatial distribution. Interestingly, most of detected lipid species in sphingolipids and glycerolipids preferred locating in the necrotic region to the proliferative region of solid tumors and MCTS. Taken together, our study provides the evidence of lipid metabolism and distribution demonstrating that MCTS are a more suitable in vitro model to mimic solid tumors, which may offer insights into tumor metabolism and microenvironment.
基金supported by grants from National Key Research and Development Program of China (No.2017YFC1600500)National Nature Science Foundation of China (Nos.21575120and 21707112)Hong Kong General Research Fund (No.12302317)。
文摘Database-assisted global metabolomics has received growing attention due to its capability for unbiased identification of metabolites in various biological samples.Herein,we established a mass spectrometry(MS)-based database-assisted global metabolomics method and investigated metabolic distance between pleural effusion induced by tuberculosis and malignancy,which are difficult to be distinguished due to their similar clinical symptoms.The present method utilized a liquid chromatography(LC) system coupled with high resolution mass spectrometry(MS) working on full scan and data dependent mode for data acquisition.Unbiased identification of metabolites was performed through mass spectral searching and then confirmed by using authentic standards.As a result,a total of 194 endogenous metabolites were identified and 33 metabolites were found to be differentiated between tuberculous and malignant pleural effusions.These metabolites involved in tryptophan catabolism,bile acid biosynthesis,and β-oxidation of fatty acids,provided non-invasive biomarkers for differentiation of the pleural effusion samples with high sensitivity and specificity.
基金support from the Collaborative Research Fund(No.C2011–21GF)from Guangdong Province Basic and Applied Basic Research Foundation(No.2021B1515120051).
文摘Parkinson’s disease(PD)is a complex neurological disorder that typically worsens with age.A wide range of pathologies makes PD a very heterogeneous condition,and there are currently no reliable diagnostic tests for this disease.The application of metabolomics to the study of PD has the potential to identify disease biomarkers through the systematic evaluation of metabolites.In this study,urine metabolic profiles of 215 urine samples from 104 PD patients and 111 healthy individuals were assessed based on liquid chromatography-mass spectrometry.The urine metabolic profile was first evaluated with partial leastsquares discriminant analysis,and then we integrated the metabolomic data with ensemble machine learning techniques using the voting strategy to achieve better predictive performance.A combination of 8-metabolite predictive panel performed well with an accuracy of over 90.7%.Compared to control subjects,PD patients had higher levels of 3-methoxytyramine,N-acetyl-l-tyrosine,orotic acid,uric acid,vanillic acid,and xanthine,and lower levels of 3,3-dimethylglutaric acid and imidazolelactic acid in their urine.The multi-metabolite prediction model developed in this study can serve as an initial point for future clinical studies.
