LuYuxing/MetaBench huggingface dataset:
Question: Write a study description for the study titled 'New aspects characterizing nonobese NAFLD by the analysis of the intestinal flora and metabolites using a mouse model'.
Answer: Nonalcoholic fatty liver disease (NAFLD) is a major public health problem due to the high incidence affecting approximately one-third of the world’s population. NAFLD is usually linked to obesity and excessive weight. A subset of patients with NAFLD express normal or low body mass index; thus, the condition is called nonobese NAFLD or lean NAFLD. However, patients and healthcare professionals have little awareness and understanding of NAFLD in nonobese individuals. Furthermore, preclinical results from nonobese animal models with NAFLD are unclear. Gut microbiota and their metabolites in nonobese/lean NAFLD patients differ from those in obese NAFLD patients. Therefore, we analyzed the biochemical indices, intestinal flora and intestinal metabolites in a nonobese NAFLD mouse model established using a methionine-choline deficient (MCD) diet. The significantly lean MCD mice had a remarkable fatty liver with lower serum triglyceride and free fatty acid levels as well as higher alanine transaminase and aspartate transaminase levels than normal mice. 16s RNA sequencing of fecal DNA showed that the overall richness and diversity of the intestinal flora decreased in MCD mice, whereas the Firmicutes/Bacteroidota ratio was increased. g_Tuzzerella, s_Bifidobacterium pseudolongum and s_Faecalibaculum rodentium were the predominant species in nonobese NAFLD mice. Fecal metabolomics using LC-MS/MS revealed the potential biomarkers for the prognosis and diagnosis of nonobese NAFLD, including high levels of tyramine glucuronide, 9,12,13-TriHOME and pantetheine 4'-phosphate, and low levels of 3-carbamoyl-2-phenylpropionaldehyde, N-succinyl-L,L-2,6-diaminopimelate, 4-methyl-5-thiazoleethanol, homogentisic acid and estriol. Our findings could be useful to identify and develop drugs to treat nonobese NAFLD and lean NAFLD.
Question: Write a study description for the study titled 'Gut microbiome dysbiosis drives metabolic dysfunction in Familial dysautonomia'.
Answer: Familial dysautonomia (FD) is a rare genetic neurologic disorder caused by impaired neuronal development and progressive degeneration of both the peripheral and central nervous systems. FD is monogenic, with >99.4% of patients sharing an identical point mutation in the elongator acetyltransferase complex subunit 1 (ELP1) gene, providing a relatively simple genetic background in which to identify modifiable factors that influence pathology. Gastrointestinal symptoms and metabolic deficits are common among FD patients, which supports the hypothesis that the gut microbiome and metabolome are altered and dysfunctional compared to healthy individuals. Here we show significant differences in gut microbiome composition (16 S rRNA gene sequencing of stool samples) and NMR-based stool and serum metabolomes between a cohort of FD patients (~14% of patients worldwide) and their cohabitating, healthy relatives. We show that key observations in human subjects are recapitulated in a neuron-specific Elp1-deficient mouse model, and that cohousing mutant and littermate control mice ameliorates gut microbiome dysbiosis, improves deficits in gut transit, and reduces disease severity. Our results provide evidence that neurologic deficits in FD alter the structure and function of the gut microbiome, which shifts overall host metabolism to perpetuate further neurodegeneration.
Question: Write a study description for the study titled 'The metabolic background is a global player in Saccharomyces gene expression epistasis'.
Answer: The regulation of gene expression in response to nutrient availability is fundamental to the genotype-phenotype relationship. The metabolic-genetic make-up of the cell, as reflected in auxotrophy, is hence likely to be a determinant of gene expression. Here, we address the importance of the metabolic-genetic background by monitoring transcriptome, proteome and metabolome in a repertoire of 16 Saccharomyces cerevisiae laboratory backgrounds, combinatorially perturbed in histidine, leucine, methionine and uracil biosynthesis. The metabolic background affected up to 85% of the coding genome. Suggesting widespread confounding, these transcriptional changes show, on average, 83% overlap between unrelated auxotrophs and 35% with previously published transcriptomes generated for non-metabolic gene knockouts. Background-dependent gene expression correlated with metabolic flux and acted, predominantly through masking or suppression, on 88% of transcriptional interactions epistatically. As a consequence, the deletion of the same metabolic gene in a different background could provoke an entirely different transcriptional response. Propagating to the proteome and scaling up at the metabolome, metabolic background dependencies reveal the prevalence of metabolism-dependent epistasis at all regulatory levels. Urging a fundamental change of the prevailing laboratory practice of using auxotrophs and nutrient supplemented media, these results reveal epistatic intertwining of metabolism with gene expression on the genomic scale.
Question: Write a study description for the study titled 'Determination of Abundant Metabolite Matrix Adducts Illuminates the Dark Metabolome of MALDI-Mass Spectrometry Imaging Datasets'.
Answer: Spatial metabolomics using mass spectrometry imaging (MSI) is a powerful tool to map hundreds to thousands of metabolites in biological systems. One major challenge in MSI is the annotation of m/z values, which is substantially complicated by background ions introduced throughout the chemicals and equipment used during experimental procedures. Among many factors, the formation of adducts with sodium or potassium ions, or in case of matrix-assisted laser desorption ionization (MALDI)-MSI, the presence of abundant matrix clusters strongly increases total m/z peak counts. Currently, there is a limitation to identify the chemistry of the many unknown peaks to interpret their biological function. We took advantage of the co-localization of adducts with their parent ions and the accuracy of high mass resolution to estimate adduct abundance in 20 datasets from different vendors of mass spectrometers. Metabolites ranging from lipids to amines and amino acids form matrix adducts with the commonly used 2,5-dihydroxybenzoic acid (DHB) matrix like [M + (DHB-H2O) + H]+ and [M + DHB + Na]+. Current data analyses neglect those matrix adducts and overestimate total metabolite numbers, thereby expanding the number of unidentified peaks. Our study demonstrates that MALDI-MSI data are strongly influenced by adduct formation across different sample types and vendor platforms and reveals a major influence of so far unrecognized metabolite-matrix adducts on total peak counts (up to one third). We developed a software package, mass2adduct, for the community for an automated putative assignment and quantification of metabolite-matrix adducts enabling users to ultimately focus on the biologically relevant portion of the MSI data.
Question: Write a study description for the study titled 'Alterations in the amino acid profile in patients with papillary thyroid carcinoma with and without Hashimoto’s thyroiditis'.
Answer: PURPOSE: Amino acids (AAs) play important physiological roles in living cells. Some amino acid changes in blood are specific for autoimmune disorders, and some are specific for thyroid cancer. The aims of this study were to profile AA metabolites in the serum of patients with papillary thyroid carcinoma (PTC0) without Hashimoto’s thyroiditis (HT) and patients with PTC with HT (PTC1) and predict whether AA metabolites are associated with thyroid disease, thyroid hormone and thyroid autoantibodies. METHODS: A total of 93 serum samples were collected, including 28 healthy controls (HCs), 28 PTC0 patients and 37 PTC1 patients. Serum samples were analysed by high-performance liquid chromatography-triple stage quadrupole-mass spectrometry (HPLC-TSQ-MS), and 21 amino acids (AAs) were detected.RESULTS: The serum concentration of glutamic acid was significantly elevated in PTC1 patients compared with PTC0 patients. Lysine was the second amino acid that differentiated these two groups of PTC patients. In addition, the serum concentrations of glycine, alanine and tyrosine were significantly reduced in both PTC patient groups compared to the HC group. These AAs were also correlated with thyroid hormones and antibodies. Five amino acid markers, namely, glycine, tyrosine, glutamic acid, glutamine and arginine, separated/distinguished PTC0 patients from healthy subjects, and eight AA markers, the same AAs as above without arginine but with alanine, leucine, valine and histidine, separated/distinguished PTC1 patients from healthy subjects based on ROC analysis.CONCLUSION: Compared with the HCs, changes in AAs in PTC0 and PTC1 patients showed similar patterns, suggesting the possibility of a common pathophysiological basis, which confirms preliminary research that PTC is significantly associated with pathologically confirmed HT. We found two AAs, lysine and alanine, that can perform diagnostic functions in distinguishing PTC1 from PTC0.
Question: Write a study description for the study titled 'A machine learning model for the prediction and verification of the protective intestinal microbiota-derived L-valine in sepsis'.
Answer: The intestinal microflora and metabolites produced by these microbes serve as important regulators of the development of sepsis. Accordingly, this study was designed to systematically explore the relationships between the regulation of septicemia and both the intestinal flora and fecal metabolites by examining the functional roles of metabolites in the protection against sepsis-associated intestinal damage. To that end, fecal and peripheral blood mononuclear cell (PBMC) samples were collected from sepsis patients and healthy controls. A series of longitudinal multi-omics analyses were then used to assess the links between the intestinal flora or associated metabolites and PBMCs in sepsis patients, while animal model studies were further used to probe the protective effects of intestinal flora-derived metabolites on intestinal damage and immunity in the context of sepsis. These analyses revealed that intestinal dysbiosis was a common finding in sepsis patients, which commonly exhibited higher levels of deleterious bacteria and/or reductions in beneficial bacteria. A machine learning approach was used to identify samples from sepsis patients, revealing that at the genus level, sepsis samples could be distinguished by the presence of Bifidobacterium, Bacteroides, Porphyromonas, Prevotell, Enterococcus, Anaerococcus and Veillonella species. Metabolomics analyses indicated that there were significant differences in the levels of intestinal flora-derived metabolites including L-serine, L-valine and L-tyrosine when comparing samples from the sepsis and control groups, while corresponding transcriptomic analyses of PBMC samples using an ImmunecellAI analytical approach revealed a significant sepsis-related increase in the abundance of T cells and Th17 cells. Single-cell sequencing data from sepsis-associated PBMCs was also downloaded from the GEO database, confirming the observation that Th17 cell levels and those of other immune cells rose significantly in the context of septicemia. Animal model experiments revealed that intestinal microbiota-derived L-valine was able to alleviate inflammation and protest against sepsis-induced intestinal damage by inhibiting Th17 cell activation. Overall, these results thus highlight the successful application of machine learning to distinguish between sepsis and control samples based on the composition of the intestinal flora while demonstrating the potential therapeutic benefits of L-valine as an inhibitor of Th17 cell activity that may offer value as a means of alleviating or preventing intestinal damage in treated individuals.
Question: Write a study description for the study titled 'CHML regulates migration and invasion in hepatocellular carcinoma via transcriptional and metabolic reprogramming'.
Answer: Hepatocellular carcinoma (HCC), a prevalent malignant neoplasm, presents significant therapeutic challenges. However, the key factors and mechanisms driving HCC metastasis remain incompletely understood. This study aimed to elucidate the mechanism through which CHML regulates the migration and invasion of HCC cells. Following CHML knockout or overexpression, we assessed the proliferative capacity of HCC cells using the Cell Counting Kit-8 (CCK-8) assay, 5-ethynyl-2′-deoxyuridine (EdU) incorporation, colony formation assay, and subcutaneous xenograft tumor models in nude mice. Cell migration and invasion were evaluated using wound healing and Transwell assays. Results demonstrated that CHML knockout significantly inhibited the migration and invasion of HCC cells in vitro, whereas CHML overexpression promoted these phenotypes. Transcriptomic sequencing revealed CHML-mediated regulation of migration-associated pathways, whereas untargeted metabolomics identified choline metabolism as a key significantly altered pathway. Notably, the integration of transcriptomics and untargeted metabolomics identified choline metabolism as a pivotal pathway in CHML-regulated migration and invasion. The subsequent mechanistic analysis demonstrated that CHML upregulated the Solute carrier family 44 member 3 (SLC44A3) to enhance choline uptake, thereby increasing phosphatidic acid (PA) production. This metabolic shift activated MAPK and PI3K-AKT signaling cascades, ultimately driving HCC cell migration and invasion. Our findings provide novel insights into metabolic dependencies in HCC metastasis and position CHML as a promising therapeutic target.
Question: Write a study description for the study titled 'Metabolomic approaches to dissect dysregulated metabolism in the progression of pre-diabetes to T2DM'.
Answer: Most of pre-diabetes were eventually progress to diabetes, thereby profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate internal motivation of the progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of serum samples (20 subjects per group) according to fasting blood glucose (FBG) concentration were collected for metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with healthy group (FBG < 5.6 mmol/L), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol/L ≤ FBG < 6.1 mmol/L), 237 in the late stage of pre-diabetes (6.1 mmol/L ≤ FBG < 7.0 mmol/L) and 245 in T2DM group (FBG ≥ 7.0 mmol/L). A total of 27 DEMs were identified shared in all comparisons. Among them, L-norleucine was down-regulated whereas ethionamide, glutathione oxidized, 5-Methylcytosine and alpha-D-Glucopyranoside beta-D-fructofuranosyl were increased with the rising of FBG. Surprisingly, 15 (11 LPCs, L-norleucine, glutathione oxidized, arachidonic acid and 5-Oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and pathway enrichment analysis of DEMs showed a significant annotation to insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including L-norleucine, 8 of which were L-arginine or its metabolites. L-norleucine and L-arginine could be served as biomarkers for pre-diabetes. The inventory of metabolites provides by our serum metabolome offer insights into T2DM physiology metabolism.
Question: Write a study description for the study titled 'A Divergent Selection on Breast Meat Ultimate pH, a Key Factor for Chicken Meat Quality, is Associated With Different Circulating Lipid Profiles'.
Answer: BACKGROUND: Chicken meat has become a major source of protein for human consumption. However, the quality of the meat is not yet under control, especially since pH values that are too low or too high are often observed. In an attempt to get a better understanding of the genetic and biochemical determinants of the ultimate pH, two genetic lines of broilers were divergently selected for low (pHu−) or high (pHu+) breast meat pHu. In this study, the serum lipidome of 17-day-old broilers from both lines was screened for pHu markers using liquid-chromatography coupled with mass spectrometry (LC-HRMS).RESULTS: A total of 185 lipids belonging to 4 groups (glycerolipids, glycerophospholipids, sterols, sphingolipids) were identified in the sera of 268 broilers from the pHu lines by targeted lipidomics. The glycerolipids, which are involved in energy storage, were in higher concentration in the blood of pHu− birds. The glycerophospholipids (phosphatidylcholines, phosphatidylethanolamines) with long and polyunsaturated acyl chains were more abundant in pHu+ than in pHu− while the lysophosphatidylcholines and lysophosphatidylethanolamines, known to be associated with starch, were observed in higher quantity in the serum of the pHu− line. Finally, the concentration of the sterols and the ceramides, belonging to the sphingolipids class, were higher in the pHu+ and pHu−, respectively. Furthermore, orthogonal partial least-squares analyses highlighted a set of 68 lipids explaining 77% of the differences between the two broilers lines (R2Y = 0.77, Q2 = 0.67). Among these lipids, a subset of 40 predictors of the pHu value was identified with a Root Mean Squared Error of Estimation of 0.18 pH unit (R2Y = 0.69 and Q2 = 0.62). The predictive model of the pHu value was externally validated on 68 birds with a Root Mean Squared Error of Prediction of 0.25 pH unit.CONCLUSION: The sets of molecules identified will be useful for a better understanding of relationship between serum lipid profile and meat quality, and will contribute to define easily accessible pHu biomarkers on live birds that could be useful in genetic selection.
Question: Write a study description for the study titled 'Influence of ultrasound treatment on the physical characteristics and metabolites of tempe'.
Answer: Tempe is a traditional fermented soybean food product that is rich in proteins. For centuries, the tempe production method has remained unchanged; however, modification of the tempe production process may improve its nutritional quality. In this study, we introduced and optimized ultrasound treatment via sonication to tempe production processes and measured its physical characteristics (moisture content, color and texture) and metabolite profile. Based on our results, 30 min sonication at 10 °C was the best treatment to increase the amino acid content in tempe when compared to the control. There were no differences in terms of the texture and color intensity between the optimized sonicated and control tempe. However, the moisture content was significantly increased in the optimized sonicated tempe compared to that in the control. To our knowledge, this is the first study to report the use of sonication for increasing the total amino acid content in tempe.
Question: Write a study description for the study titled 'L-fucose and fucoidan alleviate high-salt diet-promoted acute inflammation'.
Answer: Excessive salt intake is a widespread health issue observed in almost every country around the world. A high salt diet (HSD) has a strong correlation with numerous diseases, including hypertension, chronic kidney disease and autoimmune disorders. However, the mechanisms underlying HSD-promotion of inflammation and exacerbation of these diseases are not fully understood. In this study, we observed that HSD consumption reduced the abundance of the gut microbial metabolite L-fucose, leading to a more substantial inflammatory response in mice. A HSD led to increased peritonitis incidence in mice, as evidenced by the increased accumulation of inflammatory cells and elevated levels of inflammatory cytokines, such as TNF-α, IL-6 and MCP-1, in peritoneal lavage fluid. Following the administration of broad-spectrum antibiotics, HSD-induced inflammation was abolished, indicating that the proinflammatory effects of HSD were not due to the direct effect of sodium, but rather to HSD-induced alterations in the composition of the gut microbiota. By using untargeted metabolomics techniques, we determined that the levels of the gut microbial metabolite L-fucose were reduced by a HSD. Moreover, the administration of L-fucose or fucoidan, a compound derived from brown that is rich in L-fucose, normalized the level of inflammation in mice following HSD induction. In addition, both L-fucose and fucoidan inhibited LPS-induced macrophage activation in vitro. In summary, our research showed that reduced L-fucose levels in the gut contributed contributes to HSD-exacerbated acute inflammation in mice; these results indicate that L-fucose and fucoidan could interfere with HSD-promotion of the inflammatory response.
Question: Write a study description for the study titled 'Metabolomic Signature of Early Vascular Aging (EVA) in Hypertension'.
Answer: Arterial stiffening is a hallmark of early vascular aging (EVA) syndrome and an independent predictor of cardiovascular morbidity and mortality. In this case-control study we sought to identify plasma metabolites associated with EVA syndrome in the setting of hypertension. An untargeted metabolomic approach was used to identify plasma metabolites in an age-, BMI- and sex-matched groups of EVA (n = 79) and non-EVA (n = 73) individuals with hypertension. After raw data processing and filtration, 497 putative compounds were characterized, out of which 4 were identified as lysophosphaditylcholines (LPCs) [LPC (18:2), LPC (16:0), LPC (18:0) and LPC (18:1)]. A main finding of this study shows that identified LPCs were independently associated with EVA status. Although LPCs have been shown previously to be positively associated with inflammation and atherosclerosis, we observed that hypertensive individuals characterized by 4 down-regulated LPCs had 3.8 times higher risk of EVA compared to those with higher LPC levels (OR = 3.8, 95% CI 1.7 – 8.5, P < .001). Our results provide new insights into a metabolomic phenotype of vascular aging and warrants further investigation of negative association of LPCs with EVA status. This study suggests that LPCs are potential candidates to be considered for further evaluation and validation as predictors of EVA in patients with hypertension