LuYuxing/MetaBench huggingface dataset:
Question: Write a study description for the study titled 'Gut Microbiome and Metabolome Alterations in Mice with Arsenic Exposure and Antibiotic Treatment: Potential Role of Arsenic Biotransformation Genes'.
Answer: Arsenic is the most prevalent toxic metalloid in environment. Evidence of mouse models (germ-free or antibiotic treatment) indicates the gut microbiota’s involvement in the metabolism of ingested arsenic. Linking genes to gut microbial arsenic metabolism with altered toxicity and lifetime in the human body remains challenging. To investigate the role of arsenic biotransformation genes (ABGs) in gut microbial arsenic metabolism, conventional and antibiotic-treated mice (gut microbiota depletion by cefoperazone) were exposed to arsenic via drinking water for 2 weeks. A total of 17 ABGs were detected in the mouse gut, and their abundances (e.g., arsC, arsH and arsB genes) were markedly reduced in antibiotic-treated mice exposed to arsenic in comparison with conventional mice. Arsenic accumulation in liver, spleen and ileum were decreased as well. Consistently, both antibiotic treatment and arsenic exposure significantly induced gut microbiome perturbations including Erysipelotrichaceae and Bacteroidaceae, and metabolic alterations with up-regulated metabolites classified as flavonoids and amino acids. The ABGs were remarkably correlated with specific gut bacteria and metabolic profiles, exemplified by Prevotellaceae carrying arsC gene involved in amino acid metabolism, and Erysipelotrichaceae as aoxA gene carrier alleviating histamine accumulation. These findings provide a strong basis for studying arsenic induced toxicity and disease risk.
Question: Write a study description for the study titled 'Bioaccumulation of therapeutic drugs by human gut bacteria: secreted metabolite analysis (FIA-MS) (C.saccharolyticum;E.coli;L.plantarum;L.lactis;S.salivarius assays)'.
Answer: Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. Interactions at this level have only recently started to being systematically mapped and the main underlying mechanism proposed is chemical transformation of drugs by microbes (biotransformation). Here, we investigated the depletion of 15 structurally diverse drugs by 25 representative gut bacterial strains. This revealed 70 bacteria-drug interactions, 29 of which had not been reported before.Over half of the new interactions can be ascribed to bioaccumulation, that is bacteria storing the drug without chemically modifying it, and in most cases without their growth being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click-chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the community composition through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioral response of Caenorhabditis elegans to duloxetine.Taken together, bioaccumulation by gut bacteria might be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, likely in an individual manner.Linked studies;UPLC-MS/MS assays of Bifidobacterium animalis subsp. lactis are reported in MTBLS1264.UPLC-MS/MS assay of Lacrimispora saccharolytica and Escherichia coli IAI1 is reported in MTBLS1319.UPLC-MS/MS assays of Escherichia coli ED1a, Clostridium saccharolyticum, Escherichia coli IAI1, Streptococcus salivarius, Eubacterium rectale, Lactobacillus plantarum WCFS1 and Lactococcus lactis subsp. lactis Il1403 are reported in MTBLS1627.UPLC-MS/MS assay of Bacteroides uniformis and Clostridium saccharolyticum is reported in MTBLS1757.UPLC-MS/MS assay of Eubacterium rectale and Streptococcus salivarius is reported in MTBLS1792.UPLC-MS/MS assay of Lacrimispora saccharolytica and standards is reported in MTBLS2885.
Question: Write a study description for the study titled 'Kidney Tissue Targeted Metabolic Profiling of Unilateral Ureteral Obstruction Rats by NMR'.
Answer: Renal interstitial fibrosis is a common pathological process in the progression of kidney disease. A nuclear magnetic resonance (NMR) based metabolomics approach was used to analyze the kidney tissues of renal interstitial fibrosis (RIF) rats, induced by unilateral ureteral obstruction (UUO). The combination of a variety of statistical methods was used to screen out 14 significantly changed potential metabolites related with multiple biochemical processes including amino acid metabolism, adenine metabolism, energy metabolism, osmolyte change and induced oxidative stress. The exploration of the contralateral kidneys enhanced the understanding of the disease, which was also supported by serum biochemistry and kidney histopathology results. In addition, the pathological parameters (clinical chemistry, histological and immunohistochemistry results) were correlated with the significantly changed differential metabolites related with RIF. This study shows that target tissue metabolomics analysis can be used as a useful tool to understand the mechanism of the disease and provide a novel insight in the pathogenesis of RIF.
Question: Write a study description for the study titled 'Myeloid PFKFB3-mediated glycolysis promotes kidney fibrosis'.
Answer: Excessive renal fibrosis is a common pathology in progressive chronic kidney diseases. Inflammatory injury and aberrant repair processes contribute to the development of kidney fibrosis. Myeloid cells, particularly monocytes/macrophages, play a crucial role in kidney fibrosis by releasing their proinflammatory cytokines and extracellular matrix components such as collagen and fibronectin into the microenvironment of the injured kidney. Numerous signaling pathways have been identified in relation to these activities. However, the involvement of metabolic pathways in myeloid cell functions during the development of renal fibrosis remains understudied. In our study, we initially reanalyzed single-cell RNA sequencing data of renal myeloid cells from Dr. Denby’s group and observed an increased glycolytic pathway in myeloid cells that are critical for renal inflammation and fibrosis. To investigate the role of myeloid glycolysis in renal fibrosis, we utilized a model of unilateral ureteral obstruction in mice deficient of Pfkfb3, an activator of glycolysis, in myeloid cells (Pfkfb3ΔMϕ) and their wild type littermates (Pfkfb3WT). We observed a significant reduction in fibrosis in the obstructive kidneys of Pfkfb3ΔMϕ mice compared to Pfkfb3WT mice. This was accompanied by a substantial decrease in myeloid cell infiltration, as well as a decrease in the numbers of M1 and M2 macrophages and suppression of macrophage to obtain myofibroblast phenotype in the obstructive kidneys of Pfkfb3ΔMϕ mice. Mechanistic studies indicate that glycolytic metabolites stabilize HIF1α, leading to alterations in macrophage phenotypes that contribute to renal fibrosis. In conclusion, our study implicates that targeting myeloid glycolysis represents a novel approach to inhibit renal fibrosis.
Question: Write a study description for the study titled 'Inferring metabolic mechanisms of interaction within a defined gut microbiota'.
Answer: The diversity and number of species present within microbial communities create the potential for a multitude of interspecies metabolic interactions. Here, we develop, apply, and experimentally test a framework for inferring metabolic mechanisms associated with interspecies interactions. We perform pairwise growth and metabolome profiling of co-cultures of strains from a model mouse microbiota. We then apply our framework to dissect emergent metabolic behaviors that occur in co-culture. Based on one of the inferences from this framework, we identify and interrogate an amino acid cross-feeding interaction and validate that the proposed interaction leads to a growth benefit in vitro. Our results reveal the type and extent of emergent metabolic behavior in microbial communities composed of gut microbes. We focus on growth-modulating interactions, but the framework can be applied to interspecies interactions that modulate any phenotype of interest within microbial communities.
Question: Write a study description for the study titled 'Gut metabolome of Trachemys scripta elegans after fecal microbiota transplantation and supplement GABA,Arg in feed.'.
Answer: Extract the intestinal contents of Trachemys scripta elegans subjected to environmental stress at a salinity of ten PSU for 6 months, isolate the gut microbiota, and transplant them into other healthy Trachemys scripta elegans to observe their gut microbiota metabolite. Add GAGA and Arg to the feed and observe changes in gut metabolites after 2 months of feeding.
Question: Write a study description for the study titled 'A metabolic switch orchestrated by IL-18 and the cyclic dinucleotide cGAMP programs intestinal tolerance'.
Answer: Tissues are exposed to diverse inflammatory challenges that shape future inflammatory responses. While cellular metabolism regulates immune function, how metabolism programs and stabilizes immune states within tissues and tunes susceptibility to inflammation is poorly understood. Here, we describe an innate immune metabolic switch that programs long-term intestinal tolerance. Intestinal interleukin-18 (IL-18) stimulation elicited tolerogenic macrophages by preventing their proinflammatory glycolytic polarization via metabolic reprogramming to fatty acid oxidation (FAO). FAO reprogramming was triggered by IL-18 activation of SLC12A3 (NCC), leading to sodium influx, release of mitochondrial DNA, and activation of stimulator of interferon genes (STING). FAO was maintained in macrophages by a bistable switch that encoded memory of IL-18 stimulation and by intercellular positive feedback that sustained the production of macrophage-derived 2'3'-cyclic GMP-AMP (cGAMP) and epithelial-derived IL-18. Thus, a tissue-reinforced metabolic switch encodes durable immune tolerance in the gut and may enable reconstructing compromised immune tolerance in chronic inflammation.
Question: Write a study description for the study titled 'Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts'.
Answer: Metabolomics, understood as the science that manages the study of compounds from the metabolism, is an essential tool for deciphering metabolic changes in disease. The experiments rely on the use of high-throughput analytical techniques such as liquid chromatography coupled to mass spectrometry (LC-ToF MS). This hyphenation has brought positive aspects such as higher sensitivity, specificity and the extension of the metabolome coverage in a single run. The analysis of a high number of samples in a single batch is currently not always feasible due to technical and practical issues (i.e., a drop of the MS signal) which result in the MS stopping during the experiment obtaining more than a single sample batch. In this situation, careful data treatment is required to enable an accurate joint analysis of multi-batch data sets. This paper summarizes the analytical strategies in large-scale metabolomic experiments; special attention has been given to QC preparation troubleshooting and data treatment. Moreover, labeled internal standards analysis and their aim in data treatment, and data normalization procedures (intra- and inter-batch) are described. These concepts are exemplified using a cohort of 165 patients from a study in asthma.
Question: Write a study description for the study titled 'Untargeted metabolomics workshop report: quality control considerations from sample preparation to data analysis'.
Answer: The Metabolomics workshop on experimental and data analysis training for untargeted metabolomics was hosted by the Proteomics Society of India in December 2019. The Workshop included six tutorial lectures and hands-on data analysis training sessions presented by seven speakers. The tutorials and hands-on data analysis sessions focused on workflows for liquid chromatography-mass spectrometry (LC-MS) based on untargeted metabolomics. We review here three main topics from the workshop which were uniquely identified as bottlenecks for new researchers: a) experimental design, b) quality controls during sample preparation and instrumental analysis and c) data quality evaluation. Our objective here is to present common challenges faced by novice researchers and present possible guidelines and resources to address them. We provide resources and good practices for researchers who are at the initial stage of setting up metabolomics workflows in their labs.Complete detailed metabolomics/lipidomics protocols are available online at EMBL-MCF protocol including video tutorials.
Question: Write a study description for the study titled 'Prenatal exposure to trace elements impacts mother-infant gut microbiome, metabolome and resistome during the first year of life'.
Answer: Infancy is a critical period for the colonization of the gut microbiome. However, xenobiotic effects on gut microbiome development in early life remain poorly understood. Here, we recruit 146 mother-infant pairs and collect stool samples at 3, 6, and 12 months after delivery for amplicon sequencing (N = 353), metagenomics (N = 65), and metabolomics (N = 198). Trace elements in maternal hair samples (N = 119) affect the alpha diversity of the infant gut microbiome. Shannon diversity in 3-month-old infants is correlated positively with selenium and negatively with copper, and the relative abundance of Bifidobacterium increases under high exposure to aluminum and manganese. During the first year of life, infants and their paired mothers have distinct microbial diversity and composition, and their bacterial community structures gradually approach. There are 56 differential metabolites between the first and second postpartum visits and 515 differential metabolites between the second and third visits. The typical profile of antibiotic resistance genes (ARGs) differs significantly between infants and their mothers. High copper and arsenic exposure may induce the enrichment of ARGs in the infant gut. Our findings highlight the dynamics of the gut microbiome, metabolites, and ARG profiles of mother-infant pairs after delivery, associated with prenatal exposure to trace elements.
Question: Write a study description for the study titled 'Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study'.
Answer: INTRODUCTION: Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods. OBJECTIVES: To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study. METHODS: We applied 14 different strategies of normalization to 40 urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models. RESULTS: Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination.CONCLUSIONS: This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples.
Question: Write a study description for the study titled 'Aspartate potentiates tobramycin against multidrug-resistant Edwardsiella tarda through enhancing proton motive force and membrane permeability'.
Answer: The antibiotic susceptibility/resistance of bacteria is influenced by their metabolic states. In tobramycin-resistant Edwardsiella tarda (LTB4-RToB), a dynamic metabolic state is observed, characterized by a general reduction in most metabolites as the minimum inhibitory concentration (MIC) increases. Among these, aspartate downregulation emerges as the most critical biomarker. Exogenous aspartate enhances the efficacy of tobramycin, enabling it to effectively kill both lab-evolved and clinically isolated multidrug-resistant (MDR) E. tarda, as demonstrated in an animal model. Aspartate increases intracellular tobramycin levels in a concentration- and time-dependent manner by promoting drug uptake. Metabolic reprogramming analysis of LTB4-RToB and clinically isolated MDR strain WY-28 reveals that exogenous aspartate restores downregulated metabolites, primarily enhancing alanine, aspartate, and glutamate metabolism; the pyruvate cycle; and glycine, serine, and threonine metabolism. These changes elevate the proton motive force and membrane permeability, thereby increasing tobramycin uptake. The resulting intracellular drug concentration surpasses the bactericidal threshold, effectively overcoming resistance in both LTB4-RroB and clinical MDR E. tarda. These findings identify aspartate as a potent metabolic reprogramming agent that potentiates aminoglycosides by boosting intracellular drug accumulation, offering a strategy to combat antibiotic-resistant E. tarda.
Question: Write a study description for the study titled 'NMR-based metabolomics identifies patients at high risk of death within two years after acute myocardial infarction in the AMI-Florence II cohort'.
Answer: Risk stratification and management of acute myocardial infarction patients continue to be challenging despite considerable efforts made in the last decades by many clinicians and researchers. The aim of this study was to investigate the metabolomic fingerprint of acute myocardial infarction using nuclear magnetic resonance spectroscopy on patient serum samples and to evaluate the possible role of metabolomics in the prognostic stratification of acute myocardial infarction patients. In total, 978 acute myocardial infarction patients were enrolled in this study; of these, 146 died and 832 survived during 2 years of follow-up after the acute myocardial infarction. Serum samples were analyzed via high-resolution 1H-nuclear magnetic resonance spectroscopy and the spectra were used to characterize the metabolic fingerprint of patients. Multivariate statistics were used to create a prognostic model for the prediction of death within 2 years after the cardiovascular event. In the training set, metabolomics showed significant differential clustering of the two outcomes cohorts. A prognostic risk model predicted death with 76.9% sensitivity, 79.5% specificity, and 78.2% accuracy, and an area under the receiver operating characteristics curve of 0.859. These results were reproduced in the validation set, obtaining 72.6% sensitivity, 72.6% specificity, and 72.6% accuracy. Cox models were used to compare the known prognostic factors (for example, Global Registry of Acute Coronary Events score, age, sex, Killip class) with the metabolomic random forest risk score. In the univariate analysis, many prognostic factors were statistically associated with the outcomes; among them, the random forest score calculated from the nuclear magnetic resonance data showed a statistically relevant hazard ratio of 6.45 (p = 2.16×10-16). Moreover, in the multivariate regression only age, dyslipidemia, previous cerebrovascular disease, Killip class, and random forest score remained statistically significant, demonstrating their independence from the other variables. For the first time, metabolomic profiling technologies were used to discriminate between patients with different outcomes after an acute myocardial infarction. These technologies seem to be a valid and accurate addition to standard stratification based on clinical and biohumoral parameters.
Question: Write a study description for the study titled 'Alterations in fecal metagenomics and serum metabolomics indicate management strategies for patients with Budd-Chiari syndrome'.
Answer: OBJECTIVES: We investigated the effects of gut microbiota and serum metabolic levels on the patients with Budd-Chiari syndrome (B-CS), and their guidance for clinical management strategies.DESIGN: 214 B-CS subjects including untreated case (n=93) and treated case (n=121), were enrolled in this study. Gut microbiota and serum metabolome were analyzed using shotgun metagenomics and liquid chromatography mass spectrometry (LC-MS). 41 controls were included.RESULTS: The gut microbiota in B-CS patients showed greater abundance of Campylobacter and lower Saccharomyces, Deinococcus and Thiomonas (P < 0.05). 13 differential metabolites such as taurocholate and (R)−3−Hydroxybutyric acid were identified in B-CS patients. The random forest (RF) models showed that serum metabolome could effectively identify B-CS from healthy controls and RF-metabolomics exhibited perfect discrimination (AUC = 100%, 95% CI: 100% – 100%), which was significantly higher than that achieved by RF-metagenomics (AUC =58.48%, 95% CI: 38.46% – 78.5%). The results of association analysis showed that Campylobacter concisus and taurocholate were significantly positively correlated in B-CS patients with clinical manifestations (P < 0.05). Moreover, Actinobacteria was significantly increased in untreated B-CS patients vs. treated B-CS patients (P < 0.05). Campylobacter and Veillonella were significantly increased in treated B-CS patients vs. health controls (P < 0.05). In addition, Fewer discrepant microbes were found between treated and untreated groups for the patients without clinical manifestation.CONCLUSIONS: We identified major alterations in the gut microbiota and serum metabolome of B-CS patients. Alterations in fecal metagenomics and serum metabolomics guided management strategies for the patients with Budd-Chiari syndrome.