PubMed
Gut microbiome encoded purine and amino acid pathways present prospective biomarkers for predicting metformin therapy efficacy in newly diagnosed T2D patients
Gut Microbes. 2024 Jan-Dec;16(1):2361491. doi: 10.1080/19490976.2024.2361491. Epub 2024 Jun 13.ABSTRACTMetformin is widely used for treating type 2 diabetes mellitus (T2D). However, the efficacy of metformin monotherapy is highly variable within the human population. Understanding the potential indirect or synergistic effects of metformin on gut microbiota composition and encoded functions could potentially offer new insights into predicting treatment efficacy and designing more personalized treatments in the future. We combined targeted metabolomics and metagenomic profiling of gut microbiomes in newly diagnosed T2D patients before and after metformin therapy to identify potential pre-treatment biomarkers and functional signatures for metformin efficacy and induced changes in metformin therapy responders. Our sequencing data were largely corroborated by our metabolic profiling and identified that pre-treatment enrichment of gut microbial functions encoding purine degradation and glutamate biosynthesis was associated with good therapy response. Furthermore, we identified changes in glutamine-associated amino acid (arginine, ornithine, putrescine) metabolism that characterize differences in metformin efficacy before and after the therapy. Moreover, metformin Responders' microbiota displayed a shifted balance between bacterial lipidA synthesis and degradation as well as alterations in glutamate-dependent metabolism of N-acetyl-galactosamine and its derivatives (e.g. CMP-pseudaminate) which suggest potential modulation of bacterial cell walls and human gut barrier, thus mediating changes in microbiome composition. Together, our data suggest that glutamine and associated amino acid metabolism as well as purine degradation products may potentially condition metformin activity via its multiple effects on microbiome functional composition and therefore serve as important biomarkers for predicting metformin efficacy.PMID:38868903 | DOI:10.1080/19490976.2024.2361491
Identification of Potential Biomarkers of EGFR Mutation in Pleural Effusion of Non-Small Cell Lung Cancer Patients Based on Metabolomics
Clin Lab. 2024 Jun 1;70(6). doi: 10.7754/Clin.Lab.2023.231105.ABSTRACTBACKGROUND: Malignant pleural effusion (MPE) is a common complication of non-small cell lung cancer (NSCLC). Patients with NSCLC exhibit a high rate of epidermal growth factor receptor (EGFR) mutations. The detection of EGFR mutations is usually time-consuming and costly. This study aimed at identifying potential biomarkers of EGFR mutations in MPE of NSCLC patients by metabolomics.METHODS: In total, 58 MPE samples from 30 EGFR mutant and from 28 wild-type NSCLC patients were collected and analyzed by using hydrogen nuclear magnetic resonance (1H NMR) based metabolomics and UPLC-MS/MS based amino acid analysis.RESULTS: Our 1H NMR study showed a significant increase in the lysine levels but a significant decrease in the alanine levels in MPE of NSCLC patients with EGFR-mutant. Twelve amino acids in MPE were further determined by UPLC-MS/MS. It showed that alanine in MPE (6.34 ± 1.88 vs. 8.73 ± 3.68) were significantly decreased and leucine (3.13 ± 0.57 vs. 2.22 ± 0.13), lysine (2.19 ± 0.50 vs. 1.53 ± 0.40), and tyrosine (2.69 ± 0.71 vs. 1.89 ± 0.46) were increased in the EGFR mutation group; leucine (2.19 ± 0.50 vs. 1.53 ± 0.40), methionine (2.19 ± 0.50 vs. 1.53 ± 0.40), and threonine (2.19 ± 0.50 vs. 1.53 ± 0.40) in MPE were significantly lower in the EGRF 19 mutation compared with 21 mutation patients. The area under the receiver operating characteristic curve of 0.851 and 0.931 would be achieved by the logistic model for classification of EGFR-mutant patients from the wild-type controls or the exon 19 from exon 21 mutant patients.CONCLUSIONS: Amino acids in MPE are significantly altered and helpful in the diagnosis of EGFR-mutant patients from the wild-type controls or the exon 19 from exon 21 mutant patients with high accuracy, which is worthy of further study.PMID:38868885 | DOI:10.7754/Clin.Lab.2023.231105
Non-Targeted Metabolomics Analysis of Mother and Infant in Gestational Diabetes Mellitus and Neonatal Clinical Characterization
Clin Lab. 2024 Jun 1;70(6). doi: 10.7754/Clin.Lab.2023.230527.ABSTRACTBACKGROUND: The goal was to analyze serums of GDM patients and healthy pregnant women using HPLC-MS and preliminarily screen differential metabolites by metabolomics.METHOD: Sixty pregnant women who underwent elective cesarean section at term in Dongguan Dalang Hospital from January 2023 to April 2023 were selected and divided into the GDM group and healthy pregnancy group. Pre-pregnancy and pregnancy examination information, such as age, BMI, OGTT results, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and other clinical data were col-lected for statistical analysis. Non-targeted metabolomics of serum from 30 GDM patients and 30 healthy pregnant women were studied by HPLC-MS, and different ions were searched. The structures of differential metabolites were identified by HMDB database. The metabolic pathways of differential metabolites were analyzed by KEGG database.RESULTS: The OGTT result, pCO2, pO2, HCO3, BE, Apgar score, and bilirubin levels in the GDM group were higher than those in the healthy pregnancy group (p < 0.05). However, there were no significant differences in age, triglyceride, total cholesterol, newborn birth weight, newborn birth blood glucose, and blood gas pH between the two groups (all p > 0.05). Using p < 0.05 as the screening standard, 55 differential metabolites were identified in serum, mainly including fatty acyl, carboxylic acids and their derivatives, steroids and their derivatives, ketoacids and their derivatives, and pyrimidine nucleosides, etc., all of which were up-regulated or down-regulated to varying degrees. The 55 metabolites were mainly involved in the metabolism of pyrimidine, pyruvate, alanine, aspartic acid, glutamic acid, and arachidonic acid, glycolysis, and biosynthesis of unsaturated fatty acids.CONCLUSIONS: The discovery of these metabolites provides a theoretical basis for an indepth understanding of GDM pathogenesis. Non-targeted metabonomics analysis of blood metabonomics research technology has shown great potential value in the early diagnosis of obstetric diseases and the study of disease mechanisms.PMID:38868866 | DOI:10.7754/Clin.Lab.2023.230527
Dissecting causal relationships between immune cells, plasma metabolites, and COPD: a mediating Mendelian randomization study
Front Immunol. 2024 May 28;15:1406234. doi: 10.3389/fimmu.2024.1406234. eCollection 2024.ABSTRACTOBJECTIVE: This study employed Mendelian Randomization (MR) to investigate the causal relationships among immune cells, COPD, and potential metabolic mediators.METHODS: Utilizing summary data from genome-wide association studies, we analyzed 731 immune cell phenotypes, 1,400 plasma metabolites, and COPD. Bidirectional MR analysis was conducted to explore the causal links between immune cells and COPD, complemented by two-step mediation analysis and multivariable MR to identify potential mediating metabolites.RESULTS: Causal relationships were identified between 41 immune cell phenotypes and COPD, with 6 exhibiting reverse causality. Additionally, 21 metabolites were causally related to COPD. Through two-step MR and multivariable MR analyses, 8 cell phenotypes were found to have causal relationships with COPD mediated by 8 plasma metabolites (including one unidentified), with 1-methylnicotinamide levels showing the highest mediation proportion at 26.4%.CONCLUSION: We have identified causal relationships between 8 immune cell phenotypes and COPD, mediated by 8 metabolites. These findings contribute to the screening of individuals at high risk for COPD and offer insights into early prevention and the precocious diagnosis of Pre-COPD.PMID:38868780 | PMC:PMC11168115 | DOI:10.3389/fimmu.2024.1406234
Microbial community roles and chemical mechanisms in the parasitic development of <em>Orobanche cumana</em>
Imeta. 2022 Jun 13;1(3):e31. doi: 10.1002/imt2.31. eCollection 2022 Sep.ABSTRACTOrobanche cumana Wallr. is a holoparasite weed that extracts water and nutrients from its host the sunflower, thereby causing yield reductions and quality losses. However, the number of O. cumana parasites in the same farmland is distinctly different. The roots of some hosts have been heavily parasitized, while others have not been parasitized. What are the factors contributing to this phenomenon? Is it possible that sunflower interroot microorganisms are playing a regulatory role in this phenomenon? The role of the microbial community in this remains unclear. In this study, we investigated the rhizosphere soil microbiome for sunflowers with different degrees of O. cumana parasitism, that is, healthy, light infection, moderate infection, and severe infection on the sunflower roots. The microbial structures differed significantly according to the degree of parasitism, where Xanthomonadaceae was enriched in severe infections. Metagenomic analyses revealed that amino acid, carbohydrate, energy, and lipid metabolism were increased in the rhizosphere soils of severely infected sunflowers, which were attributed to the proliferation of Lysobacter. Lysobacter antibioticus (HX79) was isolated and its capacity to promote O. cumana seed germination and increase the germ tube length was confirmed by germination and pot experiments. Cyclo(Pro-Val), an active metabolite of strain HX79, was identified and metabolomic and molecular docking approaches confirmed it was responsible for promoting O. cumana seed germination and growth. And we found that Pseudomonas mandelii HX1 inhibited the growth of O. cumana in the host rhizosphere soil. Our findings clarify the role of rhizosphere microbiota in regulating the parasite O. cumana to possibly facilitate the development of a new weed suppression strategy.PMID:38868712 | PMC:PMC10989955 | DOI:10.1002/imt2.31
Majorbio Cloud: A one-stop, comprehensive bioinformatic platform for multiomics analyses
Imeta. 2022 Mar 16;1(2):e12. doi: 10.1002/imt2.12. eCollection 2022 Jun.ABSTRACTThe platform consists of three modules, which are pre-configured bioinformatic pipelines, cloud toolsets, and online omics' courses. The pre-configured bioinformatic pipelines not only combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics, but also provide users with powerful and convenient interactive analysis reports, which allow them to analyze and mine data independently. As a useful supplement to the bioinformatics pipelines, a wide range of cloud toolsets can further meet the needs of users for daily biological data processing, statistics, and visualization. The rich online courses of multi-omics also provide a state-of-art platform to researchers in interactive communication and knowledge sharing.PMID:38868573 | PMC:PMC10989754 | DOI:10.1002/imt2.12
The impact of aquaculture system on the microbiome and gut metabolome of juvenile Chinese softshell turtle (Pelodiscus sinensis)
Imeta. 2022 Apr 5;1(2):e17. doi: 10.1002/imt2.17. eCollection 2022 Jun.ABSTRACTThe commercial aquatic animal microbiome may markedly affect the successful host's farming in various aquaculture systems. However, very little was known about it. Here, two different aquaculture systems, the rice-fish culture (RFC) and intensive pond culture (IPC) systems, were compared to deconstruct the skin, oral, and gut microbiome, as well as the gut metabolome of juvenile Chinese softshell turtle (Pelodiscus sinensis). Higher alpha-diversity and functional redundancy of P. sinensis microbial community were found in the RFC than those of the IPC. The aquaculture systems have the strongest influence on the gut microbiome, followed by the skin microbiome, and finally the oral microbiome. Source-tracking analysis showed that the RFC's microbial community originated from more unknown sources than that of the IPC across all body regions. Strikingly, the RFC's oral and skin microbiome exhibited a significantly higher proportion of generalists and broader habitat niche breadth than those of the IPC, but not the gut. Null model analysis revealed that the RFC's oral and skin microbial community assembly was governed by a significantly greater proportion of deterministic processes than that of the IPC, but not the gut. We further identified the key gene and microbial contribution to five significantly changed gut metabolites, 2-oxoglutarate, N-acetyl-d-mannosamine, cis-4-hydroxy-d-proline, nicotinamide, and l-alanine, which were significantly correlated with important categories of microbe-mediated processes, including the amino acid metabolism, GABAergic synapse, ABC transporters, biosynthesis of unsaturated fatty acids, as well as citrate cycle. Moreover, different aquaculture systems have a significant impact on the hepatic lipid metabolism and body shape of P. sinensis. Our results provide new insight into the influence of aquaculture systems on the microbial community structure feature and assembly mechanism in an aquatic animal, also highlighting the key microbiome and gene contributions to the metabolite variation in the gut microbiome-metabolome association.PMID:38868566 | PMC:PMC10989827 | DOI:10.1002/imt2.17
Macronutrient-differential dietary pattern impacts on body weight, hepatic inflammation, and metabolism
Front Nutr. 2024 May 29;11:1356038. doi: 10.3389/fnut.2024.1356038. eCollection 2024.ABSTRACTINTRODUCTION: Obesity is a multi-factorial disease frequently associated with poor nutritional habits and linked to many detrimental health outcomes. Individuals with obesity are more likely to have increased levels of persistent inflammatory and metabolic dysregulation. The goal of this study was to compare four dietary patterns differentiated by macronutrient content in a postmenopausal model. Dietary patterns were high carbohydrate (HC), high fat (HF), high carbohydrate plus high fat (HCHF), and high protein (HP) with higher fiber.METHODS: Changes in body weight and glucose levels were measured in female, ovariectomized C57BL/6 mice after 15 weeks of feeding. One group of five mice fed the HCHF diet was crossed over to the HP diet on day 84, modeling a 21-day intervention. In a follow-up study comparing the HCHF versus HP dietary patterns, systemic changes in inflammation, using an 80-cytokine array and metabolism, by untargeted liquid chromatography-mass spectrometry (LCMS)-based metabolomics were evaluated.RESULTS: Only the HF and HCHF diets resulted in obesity, shown by significant differences in body weights compared to the HP diet. Body weight gains during the two-diet follow-up study were consistent with the four-diet study. On Day 105 of the 4-diet study, glucose levels were significantly lower for mice fed the HP diet than for those fed the HC and HF diets. Mice switched from the HCHF to the HP diet lost an average of 3.7 grams by the end of the 21-day intervention, but this corresponded with decreased food consumption. The HCHF pattern resulted in dramatic inflammatory dysregulation, as all 80 cytokines were elevated significantly in the livers of these mice after 15 weeks of HCHF diet exposure. Comparatively, only 32 markers changed significantly on the HP diet (24 up, 8 down). Metabolic perturbations in several endogenous biological pathways were also observed based on macronutrient differences and revealed dysfunction in several nutritionally relevant biosynthetic pathways.CONCLUSION: Overall, the HCHF diet promoted detrimental impacts and changes linked to several diseases, including arthritis or breast neoplasms. Identification of dietary pattern-specific impacts in this model provides a means to monitor the effects of disease risk and test interventions to prevent poor health outcomes through nutritional modification.PMID:38868554 | PMC:PMC11168494 | DOI:10.3389/fnut.2024.1356038
Multi-omics unveils tryptophan metabolic pathway as a key pathway influencing residual feed intake in Duroc swine
Front Vet Sci. 2024 May 29;11:1403493. doi: 10.3389/fvets.2024.1403493. eCollection 2024.ABSTRACTThe genetic trait of residual feed intake (RFI) holds considerable importance in the swine industry. Recent research indicates that the gut microbiota of pigs plays a pivotal role in the manifestation of the RFI trait. Nevertheless, the metabolic pathways involved in the functioning of these microorganisms remain elusive. Thus, based on the ranking of the RFI trait in Duroc pigs, the present study selected the top 10 and bottom 10 pigs as the experimental subjects. The distribution and metabolite differences of cecal microbiota were analyzed using 16S rRNA gene sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques. The low RFI cecal group was named LRC, and the high RFI cecal group was named HRC. The results indicate that the LRC group had lower RFI, feed conversion ratio (FCR), average daily feed intake (ADFI) (p < 0.001), and thinner backfat (p < 0.05) compared with the HRC group. We simultaneously recorded the foraging behavior as well, the LRC group had a significant increase in total time spent at the feeder per day (TPD) (p < 0.05) and a significant increase in average feed intake per mins (AFI) and the number of visits to the feeder per day (NVD) compared to the HRC group (p < 0.001). Clostridium_XVIII, Bulleidia, and Intestinimonas were significantly enriched in the LRC group (p < 0.01), while Sutterella, Fusobacterium, and Bacteroides were significantly increased in the HRC group (p < 0.01). In the metabolome, we detected 390 (248 metabolites up and 142 down in the LRC compared with HRC), and 200 (97 metabolites up and 103 down in the LRC compared with HRC) differential metabolites in positive and negative ionization modes. The comprehensive analysis found that in the LRC group, Escherichia and Eubacterium in the gut may increase serotonin content, respectively. Bacteroides may deplete serotonin. We suggest that the RFI may be partly achieved through tryptophan metabolism in gut microbes. In individuals with low RFI, gut microbes may enhance feed efficiency by enhancing host synthesis and metabolism of tryptophan-related metabolites.PMID:38868499 | PMC:PMC11168206 | DOI:10.3389/fvets.2024.1403493
Quantitative microbiome profiling reveals the developmental trajectory of the chicken gut microbiota and its connection to host metabolism
Imeta. 2023 Apr 25;2(2):e105. doi: 10.1002/imt2.105. eCollection 2023 May.ABSTRACTRevealing the assembly and succession of the chicken gut microbiota is critical for a better understanding of its role in chicken physiology and metabolism. However, few studies have examined dynamic changes of absolute chicken gut microbes using the quantitative microbiome profiling (QMP) method. Here, we revealed the developmental trajectory of the broiler chicken gut bacteriome and mycobiome by combining high-throughput sequencing with a microbial load quantification assay. We showed that chicken gut microbiota abundance and diversity reached a plateau at 7 days posthatch (DPH), forming segment-specific community types after 1 DPH. The bacteriome was more impacted by deterministic processes, and the mycobiome was more affected by stochastic processes. We also observed stage-specific microbes in different gut segments, and three microbial occurrence patterns including "colonization," "disappearance," and "core" were defined. The microbial co-occurrence networks were very different among gut segments, with more positive associations than negative associations. Furthermore, we provided links between the absolute changes in chicken gut microbiota and their serum metabolite variations. Time-course untargeted metabolomics revealed six metabolite clusters with different changing patterns of abundance. The foregut microbiota had more connections with chicken serum metabolites, and the gut microbes were closely related to chicken lipid and amino acid metabolism. The present study provided a full landscape of chicken gut microbiota development in a quantitative manner, and the associations between gut microbes and chicken serum metabolites further highlight the impact of gut microbiota in chicken growth and development.PMID:38868437 | PMC:PMC10989779 | DOI:10.1002/imt2.105
Applying multi-omics toward tumor microbiome research
Imeta. 2023 Jan 9;2(1):e73. doi: 10.1002/imt2.73. eCollection 2023 Feb.ABSTRACTRather than a "short-term tenant," the tumor microbiome has been shown to play a vital role as a "permanent resident," affecting carcinogenesis, cancer development, metastasis, and cancer therapies. As the tumor microbiome has great potential to become a target for the early diagnosis and treatment of cancer, recent research on the relevance of the tumor microbiota has attracted a wide range of attention from various scientific fields, resulting in remarkable progress that benefits from the development of interdisciplinary technologies. However, there are still a great variety of challenges in this emerging area, such as the low biomass of intratumoral bacteria and unculturable character of some microbial species. Due to the complexity of tumor microbiome research (e.g., the heterogeneity of tumor microenvironment), new methods with high spatial and temporal resolution are urgently needed. Among these developing methods, multi-omics technologies (combinations of genomics, transcriptomics, proteomics, and metabolomics) are powerful approaches that can facilitate the understanding of the tumor microbiome on different levels of the central dogma. Therefore, multi-omics (especially single-cell omics) will make enormous impacts on the future studies of the interplay between microbes and tumor microenvironment. In this review, we have systematically summarized the advances in multi-omics and their existing and potential applications in tumor microbiome research, thus providing an omics toolbox for investigators to reference in the future.PMID:38868335 | PMC:PMC10989946 | DOI:10.1002/imt2.73
Multi-omics analysis reveals a crosstalk between ferroptosis and peroxisomes on steatotic graft failure after liver transplantation
MedComm (2020). 2024 Jun 12;5(6):e588. doi: 10.1002/mco2.588. eCollection 2024 Jun.ABSTRACTTo identify the mechanism underlying macrosteatosis (MaS)-related graft failure (GF) in liver transplantation (LT) by multi-omics network analysis. The transcriptome and metabolome were assayed in graft and recipient plasma in discovery (n = 68) and validation (n = 89) cohorts. Differentially expressed molecules were identified by MaS and GF status. Transcriptional regulatory networks were generated to explore the mechanism for MaS-related inferior post-transplant prognosis. The differentially expressed molecules associated with MaS and GF were enriched in ferroptosis and peroxisome-related pathways. Core features of MaS-related GF were presented on decreased transferrin and impaired anti-oxidative capacity dependent upon dysregulation of transcription factors hepatocyte nuclear factor 4A (HNF4A) and hypoxia-inducible factor 1A (HIF1A). Furthermore, miR-362-3p and miR-299-5p inhibited transferrin and HIF1A expression, respectively. Lower M2 macrophages but higher memory CD4 T cells were observed in MaS-related GF cases. These results were validated in clinical specimens and cellular models. Systemic analysis of multi-omics data depicted a panorama of biological pathways deregulated in MaS-related GF. Transcriptional regulatory networks centered on transferrin and anti-oxidant responses were associated with poor MaS graft quality, qualifying as potential targets to improve prognosis of patients after LT.PMID:38868330 | PMC:PMC11167151 | DOI:10.1002/mco2.588
Correction to "MetOrigin: Discriminating the origins of microbial metabolites for integrative analysis of the gut microbiome and metabolome"
Imeta. 2023 Nov 6;2(4):e149. doi: 10.1002/imt2.149. eCollection 2023 Nov.ABSTRACT[This corrects the article DOI: 10.1002/imt2.10.].PMID:38868231 | PMC:PMC10989943 | DOI:10.1002/imt2.149
Correction to "The impact of aquaculture system on the microbiome and gut metabolome of juvenile Chinese softshell turtle (Pelodiscus sinensis)"
Imeta. 2023 Nov 6;2(4):e151. doi: 10.1002/imt2.151. eCollection 2023 Nov.ABSTRACT[This corrects the article DOI: 10.1002/imt2.17.].PMID:38868225 | PMC:PMC10989940 | DOI:10.1002/imt2.151
Intraspecific difference of Latilactobacillus sakei in inflammatory bowel diseases: Insights into potential mechanisms through comparative genomics and metabolomics analyses
Imeta. 2023 Sep 25;2(4):e136. doi: 10.1002/imt2.136. eCollection 2023 Nov.ABSTRACTInflammatory bowel diseases (IBDs) are chronic inflammatory diseases of the gastrointestinal tract that have become a global health burden. Studies have revealed that Latilactobacillus sakei can effectively alleviate various immune diseases, including colitis, rheumatoid arthritis, and metabolic disorders. Here, we obtained 72 strains of L. sakei from 120 fermentation and fecal samples across China. In total, 16 strains from different sources were initially screened in an in vitro Caco-2 model induced by dextran sulfate sodium. Subsequently, six strains (four exhibiting effectiveness and two exhibiting ineffectiveness) were selected for further validation in an in vivo colitis mouse model. The results demonstrated that L. sakei strains exhibited varying degrees of amelioration of the colitis disease process. Notably, L. sakei CCFM1267, the most effective strain, significantly restored colon length and tight-junction protein expression, and reduced the levels of cytokines and associated inflammatory enzymes. Moreover, L. sakei CCFM1267 upregulated the abundance of Enterorhabdus, Alloprevotella, and Roseburia, leading to increased levels of acetic acid and propionic acid. Conversely, the other four strains (L. sakei QJSSZ1L4, QJSSZ4L10, QGZZYRHMT1L6, and QGZZYRHMT2L6) only exhibited a partial remission effect, while L. sakei QJSNT1L10 displayed minimal impact. Therefore, L. sakei CCFM1267 and QJSNT1L10 were selected for further exploration of the mechanisms underlying their differential mitigating effects. Comparative genomics analysis revealed significant variations between the two strains, particularly in genes associated with carbohydrate-active enzymes, such as the glycoside hydrolase family, which potentially contribute to the diverse profiles of short-chain fatty acids in vivo. Additionally, metabolome analysis demonstrated that acetylcholine and indole-3-acetic acid were the main differentiating metabolites of the two strains. Therefore, the strains of L. sakei exhibited varying degrees of effectiveness in alleviating IBD-related symptoms, and the possible reasons for these variations were attributed to discrepancies in the carbohydrate-active enzymes and metabolites among the strains.PMID:38868211 | PMC:PMC10989848 | DOI:10.1002/imt2.136
Profound perturbations are found in the proteome and metabolome in children with obesity after weight loss intervention
Heliyon. 2024 May 25;10(11):e31917. doi: 10.1016/j.heliyon.2024.e31917. eCollection 2024 Jun 15.ABSTRACTBACKGROUND AND AIMS: The mechanisms occur in children with obesity after lifestyle intervention remain poorly explained. Here, we investigated the serum proteomes and metabolomes of children with obesity who had undergone 30 days of weight loss intervention.METHODS AND RESULTS: Serum samples and clinical parameters were collected before and after lifestyle alteration interventions. Proteomic and metabolomic profiling was used to identify the differentially expressed proteins and differentially abundant metabolites in response to weight loss intervention. Lifestyle alteration interventions significantly decreased BMI, waist circumference, hip circumference and body fat, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL) and high non-HDL cholesterol, but not TG and high-density lipoprotein cholesterol (HDL), in children with obesity. By comparing the multiomics data, we identified 43 proteins and 165 metabolites that were significantly differentially expressed in children with obesity before and after lifestyle alteration interventions. Using integrated -omics analysis, we obtained 7 KEGG pathways that were organically integrated based on the correlations between differentially expressed proteins (DEPs) and metabolites (DMs). Further interaction analysis identified 7 proteins as candidate DEPs and 9 metabolites as candidate DMs. Interestingly, we found that some of these candidate DEPs and candidate DMs were significantly correlated with clinical parameters.CONCLUSION: Our results provide valuable proteome and metabolome data resources for better understanding weight loss-associated responses in children with obesity. In addition, we analyzed the number of significantly differentially expressed proteins and metabolites, shed new light on weight loss pathogenesis in children with obesity, and added potential therapeutic agents for obese children.PMID:38867950 | PMC:PMC11167357 | DOI:10.1016/j.heliyon.2024.e31917
Taxonomic identification of bile salt hydrolase-encoding lactobacilli: Modulation of the enterohepatic bile acid profile
Imeta. 2023 Jul 16;2(3):e128. doi: 10.1002/imt2.128. eCollection 2023 Aug.ABSTRACTBile salt hydrolases (BSHs) are enzymes that are essential for the enterohepatic metabolism of bile acids (BAs). BSHs catalyze the production of unconjugated BAs and regulate the homeostasis of BA pool. This study identified Lactobacillus as a crucial BSH-encoding genus, and 16 main species were obtained using metagenomic data from publicly available human gut microbiome databases. Then, the 16 species of lactobacilli were classified into four typical categories by BSH phylotypes, including five species encoding BSH-T0, six species encoding BSH-T2, four species encoding BSH-T3, and Ligilactobacillus salivarius encoding both BSH-T0 and BSH-T3. The lactobacilli with the highest in vitro deconjugation activities against seven conjugated BAs were the BSH-T3-encoding strains. Furthermore, in vivo studies in mice administered four representative lactobacilli strains encoding different BSH phylotypes showed that treatment with BSH-T3-encoding Limosilactobacillus reuteri altered the structure of the gut microbiome and metabolome and significantly increased the levels of unconjugated BAs and total BA excretion. Our findings facilitated the taxonomic identification of crucial BSH-encoding lactobacilli in human gut microbiota and shed light on their contributions toward modulation of the enterohepatic circulation of BAs, which will contribute to future therapeutic applications of BSH-encoding probiotics to improve human health.PMID:38867937 | PMC:PMC10989828 | DOI:10.1002/imt2.128
Metabolite profiling of human-originated Lachnospiraceae at the strain level
Imeta. 2022 Oct 13;1(4):e58. doi: 10.1002/imt2.58. eCollection 2022 Dec.ABSTRACTThe human gastrointestinal (GI) tract harbors diverse microbes, and the family Lachnospiraceae is one of the most abundant and widely occurring bacterial groups in the human GI tract. Beneficial and adverse effects of the Lachnospiraceae on host health were reported, but the diversities at species/strain levels as well as their metabolites of Lachnospiraceae have been, so far, not well documented. In the present study, we report on the collection of 77 human-originated Lachnospiraceae species (please refer hLchsp, https://hgmb.nmdc.cn/subject/lachnospiraceae) and the in vitro metabolite profiles of 110 Lachnospiraceae strains (https://hgmb.nmdc.cn/subject/lachnospiraceae/metabolites). The Lachnospiraceae strains in hLchsp produced 242 metabolites of 17 categories. The larger categories were alcohols (89), ketones (35), pyrazines (29), short (C2-C5), and long (C > 5) chain acids (31), phenols (14), aldehydes (14), and other 30 compounds. Among them, 22 metabolites were aromatic compounds. The well-known beneficial gut microbial metabolite, butyric acid, was generally produced by many Lachnospiraceae strains, and Agathobacter rectalis strain Lach-101 and Coprococcus comes strain NSJ-173 were the top 2 butyric acid producers, as 331.5 and 310.9 mg/L of butyric acids were produced in vitro, respectively. Further analysis of the publicly available cohort-based volatile-metabolomic data sets of human feces revealed that over 30% of the prevailing volatile metabolites were covered by Lachnospiraceae metabolites identified in this study. This study provides Lachnospiraceae strain resources together with their metabolic profiles for future studies on host-microbe interactions and developments of novel probiotics or biotherapies.PMID:38867908 | PMC:PMC10989990 | DOI:10.1002/imt2.58
Decoding the microbiome and metabolome of the Panchagavya-An indigenous fermented bio-formulation
Imeta. 2022 Nov 15;1(4):e63. doi: 10.1002/imt2.63. eCollection 2022 Dec.ABSTRACTFor the first time, updated molecular techniques were used to validate and elucidate the effect of the Panchagavya. Metagenomics was used to decipher the bacterial microbiome structure, which showed promising results for their existence and abundance in the Panchagavya.PMID:38867902 | PMC:PMC10989784 | DOI:10.1002/imt2.63
MetOrigin: Discriminating the origins of microbial metabolites for integrative analysis of the gut microbiome and metabolome
Imeta. 2022 Mar 21;1(1):e10. doi: 10.1002/imt2.10. eCollection 2022 Mar.ABSTRACTThe interactions between the gut microbiome and metabolome play an important role in human health and diseases. Current studies mainly apply statistical correlation analysis between the gut microbiome and all the identified metabolites to explore their relationship. However, it remains challenging to identify the specific metabolic functions of microbes without in vitro culture experiments for validation. Discriminating the microbial metabolites from others (e.g., host, food, or environment) and exploring their metabolic functions and correlations with microbiome specifically may improve the efficiency and accuracy of biomarker discovery. So far, there have been no such bioinformatics tools available. Herein, we developed MetOrigin, an interactive web server that discriminates metabolites originating from the microbiome, performs the origin-based metabolic pathway enrichment analysis, and integrates the statistical correlations and biological relationships in the database using Sankey network visualization. MetOrigin not only enables the quick identification of microbial metabolites and their metabolic functions but also facilitates the discovery of specific bacterial species that are closely associated with metabolites statistically and biologically. MetOrigin is freely available at http://metorigin.met-bioinformatics.cn/.PMID:38867728 | PMC:PMC10989983 | DOI:10.1002/imt2.10