LuYuxing/MetaBench huggingface dataset:
Question: Write a study description for the study titled 'Serum metabolomics identifies unique inflammatory signatures to distinguish rheumatoid arthritis responders and non-responders to TNF inhibitor therapy'.
Answer: Rheumatoid arthritis (RA) is an auto-immune disease which causes irreversible damage to tissue and cartilage within synovial joints. Rapid diagnosis and treatment with disease-modifying therapies is essential to reduce inflammation and prevent destruction of the joint. RA is a heterogeneous disease, and many patients do not respond to front-line therapies, requiring escalation of treatment onto biologics, of which TNF inhibitors (TNF-i) are the most common. In this study we determined whether serum metabolomics, using nuclear magnetic resonance (NMR) and Fourier transform infarred (FTIR) spectroscopy, could discriminate RA blood sera from healthy human controls and whether serum metabolomics could be used to predict response or non-response to TNF inhibitor (TNF-i) therapy. NMR spectroscopy identified 35 metabolites in RA sera, with acetic acid being significantly lower in RA sera compared to healthy controls (HC, FDR1). FTIR spectroscopy identified a significant difference between RA and HC sera in the 1000-1200 cm-1 spectral area, representing the mixed region of carbohydrates and nucleic acids (FDR<0.05). Sera from RA patients who responded to TNF-i were significantly different from TNF-i non-responder sera in the 1600-1700 cm-1 region (FDR<0.05). We propose that NMR and FTIR serum metabolomics could be used as a diagnostic tool alongside current clinical parameters to diagnose RA and to predict whether a patient with severe RA will respond to TNF-i.
Question: Write a study description for the study titled '1H NMR metabolomic and transcriptomic analyses reveal urinary metabolites as biomarker candidates in response to protein undernutrition in adult rats'.
Answer: BACKGROUND: Protein undernutrition is prevalent mainly among elderly people, and it could develop diseases such as sarcopenia. Urinary metabolites may become biomarkers reflecting protein undernutrition, but this has been insufficiently investigated.OBJECTIVE: The purpose of this study was to identify novel urinary metabolites as biomarker candidates responsive to protein undernutrition.METHODS: Adult male Wistar rats were fed an AIN-93 M diet [14% casein, control (CT) diet] or an AIN-93 M-based isocaloric diet [5% casein, low protein (LP) diet] for 4 weeks. 1H nuclear magnetic resonance metabolomic analysis was performed on urine samples weekly and on plasma and liver samples at week 4 to identify metabolites that respond to protein undernutrition. Liver samples were also subjected to mRNA microarray and quantitative PCR analyses at week 4 to investigate alterations of the gene expressions responsive to protein undernutrition.RESULTS: Urinary taurine levels were significantly lower in the LP group than in the CT group (79.7% lower) at week 1, and remained constant until week 4. Hepatic taurine level and gene expression level of cysteine dioxygenase type 1, the rate-limiting enzyme for taurine biosynthesis, were also significantly lower in the LP group than in the CT group (81.1% and 32.5% lower, respectively). Urinary trimethylamine N-oxide (TMAO) levels were significantly higher in the LP group than in the CT group (49.7% higher) at week 2, and remained constant until week 4. Hepatic TMAO level was also higher in the LP group than in the CT group (81.2% higher). Hepatic gene expression levels of flavin-containing monooxygenase 1 and 5, the enzymes for TMAO biosynthesis, were also significantly higher in the LP group than in the CT group (61.6% and 311.8% higher, respectively).CONCLUSIONS: Urinary taurine and TMAO levels substantially respond to LP diet ingestion, and may act as non-invasive biomarker candidates to detect protein undernutrition.
Question: Write a study description for the study titled 'Normalization techniques for PARAFAC modeling of urine metabolomics data'.
Answer: One of the body fluids often used in metabolomics studies is urine. The peak intensities of metabolites in urine are affected by the urine history of an individual resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity. Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. In this approach special transformations are defined to deal with the ratio problem. In essence, it comes down to using another distance measure than the Euclidian Distance that is used in the conventional analysis of metabolomics data. We will illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) to be used in cases where samples of some biological material are measured at multiple time points. Aim of the paper is to develop PARAFAC modeling of three-way metabolomics data in the context of compositional data and compare this with standard normalization techniques for the specific case of urine metabolomics data.
Question: Write a study description for the study titled 'Absolute quantification of serum cytokines and metabolome reveals the glycylproline regulating antibody-fading in convalescent COVID-19 patients'.
Answer: The COVID-19 pandemic has incurred tremendous costs worldwide and is still threatening public health in the 'new normal'. The association between neutralizing antibody levels and metabolic alterations in convalescent patients with COVID-19 is still poorly understood. In the present work, we conducted absolutely quantitative profiling to compare the plasma cytokines and metabolome of ordinary convalescent patients with antibodies (CA), convalescents with rapidly faded antibodies (CO) and healthy subjects. As a result, we identified that cytokines such as M-CSF and IL-12p40 and plasma metabolites such as glycylproline (gly-pro) and long-chain acylcarnitines could be associated with antibody fading in COVID-19 convalescent patients. Following feature selection, we built machine-learning-based classification models using 17 features (6 cytokines and 11 metabolites). Overall accuracies of more than 90% were attained in at least 6 machine-learning models. Of note, the dipeptide gly-pro, a product of enzymatic peptide cleavage catalyzed by dipeptidyl peptidase 4 (DPP4), strongly accumulated in CO individuals compared with the CA group. Furthermore, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination experiments in healthy mice demonstrated that supplementation of gly-pro down-regulates SARS-CoV-2-specific receptor-binding domain antibody levels and suppresses immune responses, whereas the DPP4 inhibitor sitagliptin can counteract the inhibitory effects of gly-pro upon SARS-CoV-2 vaccination. Our findings not only reveal the important role of gly-pro in the immune responses to SARS-CoV-2 infection but also indicate a possible mechanism underlying the beneficial outcomes of treatment with DPP4 inhibitors in convalescent COVID-19 patients, shedding light on therapeutic and vaccination strategies against COVID-19.
Question: Write a study description for the study titled 'Metabolomics-based and functional validation to explore the effect of the gut microbe-associated metabolite guanidinoacetic acid on rectal adenocarcinoma'.
Answer: Objective: The gut microbiota plays a crucial role in rectal adenocarcinoma (READ). Nonetheless, little is known about corresponding changes in gut microbiota–host interactions. This study aimed to identify the structure of the gut microbiota and the characteristics of faecal metabolites in patients with READ.Methods: Faecal samples were collected from 33 individuals with READ and 34 healthy controls (HC), with post-neoadjuvant treatment faecal samples also collected from the 5 READ patients, and 16S rRNA gene amplification sequencing and untargeted liquid chromatography-mass spectrometry metabolomics analysis were performed. Methods such as weighted correlation network analysis (WGCNA) and molecular docking to determine target gene–related information, and to explore the effects of guanidinoacetic acid (GAA) on colorectal cancer cell lines through in vitro experiments.Results: Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes were the most abundant phyla in patients with READ and healthy controls (HC). At the genus level, Peptostreptococcus exhibited marked enrichment in the READ cohorts, whereas Faecalibacterium was dominant in the HC group. The predicted microbial functional analysis indicated a significant increase in the metabolism of betaine, guanidinoacetic acid (GAA), L-tryptophan, arachidonic acid, and arachidic acid. Moreover, by comparing the faecal metabolites between the HC and READ groups,Pearson’s correlation analysis demonstrated significant interactions between six microbiota taxa and nine metabolites, suggesting specific human metabolic pathways. The cellular function results demonstrated that GAA promoted the proliferation, invasion, and migration of colorectal cells while inhibiting apoptosis. Further analysis revealed that GAA may might promote CRC progression of colorectal cancer by affecting molecules such as KLB, CA2, CSTG, CYP4F12, and GZBM.Conclusion: This study provides a foundational guide for the systematic and multidimensional evaluation of the contribution of the gut microbiota and its metabolites to READ and identifies a metabolite (namely, GAA) that may be of important in the occurrence, development, and treatment of READ, offering a new perspective for further research on READ.
Question: Write a study description for the study titled 'Limited oxygen availability in standard cell culture alters metabolism and function in terminally differentiated cells'.
Answer: Cell culture is generally considered to be hyperoxic. However, the importance of cellular oxygen consumption is often underappreciated, with rates of oxygen consumption often sufficient to cause hypoxia at cell monolayers. We initially focused on cultured adipocytes as a terminally differentiated cell-type with substantial oxygen consumption rates to support diverse cellular functions. Under standard conditions, cultured adipocytes are hypoxic and highly glycolytic. Increasing oxygen diverted glucose flux toward mitochondria and resulted in thousands of gene expression changes that pointed toward alleviated physiological transcriptional responses to hypoxia. Phenotypically, providing more oxygen increased adipokine secretion and rendered adipocytes more sensitive to insulin and lipolytic stimuli. The functional benefits of increasing pericellular oxygen were transferable to other cellular systems including hPSC-derived hepatocytes and cardiac organoids. Our findings suggest that oxygen is limiting in many terminally-differentiated cell culture systems, and that controlling oxygen availability can improve the quality and translatability of cell models.
Question: Write a study description for the study titled 'Rumen Fluid Metabolomics Analysis of Sheep Under Different Grazing Intensities with Supplementary Feeding'.
Answer: This dataset comprises untargeted LC-MS metabolomics profiles of rumen samples from 8 sheep under moderate grazing with supplementary feeding and 8 sheep under intensive grazing with supplementary feeding. Samples were analyzed using Thermo Fisher Scientific mass spectrometers in positive/negative ionization modes. Data were processed with Progenesis QI and are intended for identifying differential metabolites in the rumen between the two grazing intensities with supplementation.
Question: Write a study description for the study titled 'Legume rhizodeposition promotes nitrogen fixation by soil microbiota under crop diversification'.
Answer: Biological nitrogen fixation by free-living bacteria and rhizobial symbiosis with legumes plays a key role in sustainable crop production. Here, we study how different crop combinations influence the interaction between peanut plants and their rhizosphere microbiota via metabolite deposition and functional responses of free-living and symbiotic nitrogen-fixing bacteria. Based on a long-term (8 year) diversified cropping field experiment, we find that peanut co-cultured with maize and oilseed rape lead to specific changes in peanut rhizosphere metabolite profiles and bacterial functions and nodulation. Flavonoids and coumarins accumulate due to the activation of phenylpropanoid biosynthesis pathways in peanuts. These changes enhance the growth and nitrogen fixation activity of free-living bacterial isolates, and root nodulation by symbiotic Bradyrhizobium isolates. Peanut plant root metabolites interact with Bradyrhizobium isolates contributing to initiate nodulation. Our findings demonstrate that tailored intercropping could be used to improve soil nitrogen availability through changes in the rhizosphere microbiome and its functions.
Question: Write a study description for the study titled 'Metabolomic landscape of fetal organs development during late gestation in mice'.
Answer: Metabolism is the critical basis for mammalian physiological functions. The systematic metabolic characteristics of major organ development during late gestation to adapt to postnatal environmental changes are still absent. Here, we detected metabolic patterns of the mouse fetal organs, including the heart, stomach, liver, brain, and placenta, from embryonic days (E)15.5 to 19.5 using liquid chromatography-mass spectrometry combined with RNA sequencing and proteomics data. Our metabolic and multi-omics data showed that organs exhibited their unique metabolic characteristics during late gestation, and significant metabolic pattern transitions, especially the enhancement of digesting the fatty acids and proteins, occurred at the E16.5 to E18.5 stage. Additionally, we found the abundance of carnosine and histidine in the placenta may serve as a way to test their levels in the newborn brain in vitro. Our dataset provides a comprehensive metabolic landscape of mammalian organ development in late gestation.
Question: Write a study description for the study titled 'LipidCreator workbench to probe the lipidomic landscape - Targeted lipidomics analysis of S. cerevisiae to determine true and false identification'.
Answer: Here we present LipidCreator, a software that fully supports targeted lipidomics assay development. LipidCreator offers a comprehensive framework to compute MS/MS fragment masses for over 60 lipid classes. LipidCreator provides all functionalities needed to define fragments, manage stable isotope labeling, optimize collision energy and generate in silico spectral libraries. We validate LipidCreator assays computationally and analytically and prove that it is capable to generate large targeted experiments to analyze blood and to dissect lipid-signaling pathways such as in human platelets.In MTBLS1376, to validate the performance and accuracy of the transition lists provided by LipidCreator, we validated our theoretical calculations and the transition lists computed by LipidCreator experimentally, monitoring true and false targets on the model organism Saccharomyces cerevisiae. The lipidome of the yeast S. cerevisiae is well described and has been thoroughly investigated before. It has a limited number of fatty acyls and double bonds, and certain fatty acyls and lipid classes do not occur in it, which is important for our consecutive false identification calculations. Therefore, the yeast matrix from the theoretical calculation, 21 target lipids and 21 decoy lipids from commercially available lipid standards that are known to not be present in the yeast lipidome were selected. Based on our previous results, none of the lipid species was redundant among all three sets.Studies linked to this manuscript include;MTBLS1333; LipidCreator: Collision Energy Optimization and Fragment Intensity Prediction for Lipid Mediators - Thermo QExactive HFMTBLS1334; LipidCreator: Collision Energy Optimization and Fragment Intensity Prediction for Lipid Mediators - Agilent QTofMTBLS1369; LipidCreator: Platelet isolation and stimulation - Targeted Phospho- and Glycerolipid ProfilingMTBLS1375; LipidCreator: Targeted lipidomics analysis of Human Plasma samples and comparison with NIST SRM 1950 standardMTBLS1376; LipidCreator: Targeted lipidomics analysis of S. cerevisiae to determine true and false identificationMTBLS1381; LipidCreator: Platelet isolation and stimulation - Targeted Lipid Mediator ProfilingMTBLS1382; LipidCreator: Platelet isolation and stimulation - DIA Lipid Mediator Validation
Question: Write a study description for the study titled 'Metabolic profiling of Candida clinical isolates of different species and infection sources'.
Answer: Candida species are the most common cause of opportunistic fungal infections. Rapid identification and novel approaches for the characterization of these fungi are of great interest to improve the diagnosis and the knowledge about their pathogenic properties. This study aimed to characterize clinical isolates of Candida spp. by proteomics (MALDI-TOF MS) and metabolomics (1H-NMR), and to correlate their metabolic profiles with Candida species, source of infection and different virulence associated parameters. In particular, 49 Candida strains from different sources (blood, n = 15; vagina, n = 18; respiratory tract, n = 16), belonging mainly to C. albicans complex (61%), C. glabrata (20%) and C. parapsilosis (12%) species were used. Several extracellular and intracellular metabolites showed significantly different concentrations among isolates recovered from different sources of infection, as well as among different Candida species. These metabolites were mainly related to the glycolysis or gluconeogenesis, tricarboxylic acid cycle, nucleic acid synthesis and amino acid and lipid metabolism. Moreover, we found specific metabolic fingerprints associated with the ability to form biofilm, the antifungal resistance (i.e. caspofungin and fluconazole) and the production of secreted aspartyl proteinase. In conclusion, 1H-NMR-based metabolomics can be useful to deepen Candida spp. virulence and pathogenicity properties.
Question: Write a study description for the study titled 'Multi-Omic Analysis and Platelet Function Distinguish Treatment Responses to Hydroxytyrosol in Cardiovascular Risk'.
Answer: Many polyphenols have been recognized for their antioxidant and health benefits. The present study attempted, for the first time, to determine the effects of daily oral intake of β-cyclodextrin-encapsulated hydroxytyrosol (HT) on plasma platelet reactivity and proteomic and metabolomic profiles of patients at cardiovascular risk and correlated it with biomarkers of cardiovascular (CV) status. The results showed that 28-day regular HT intake improved the lipid profile and lowered platelet function ex vivo (n=26). The suppression of platelet reactivity and agonist-induced platelet activation observed after diet intervention were measured by CD61/CD62P expression and apoptotic microparticles. Microbiota analysis revealed HT treatment significantly increased and decreased the abundance of Ruminiclostridium sp. and Desulfovidrio sp., respectively. Proteomic and metabolic responses were heterogeneous within two distinct ̶ responder and non-responder ̶ subgroups, associated mainly with thrombotic and hemostatic signals. Metabolomic analysis further confirmed the differentially expressed metabolites within the two subgroups, highlighting an improvement in glutathione metabolism after HT treatment. The correlations between biomarkers, proteins, and metabolites offer a more comprehensive representation of the glutathione and coagulation pathways affected by HT treatment. These findings have important implications for the development of novel therapeutic strategies aimed at preventing CV diseases by targeting HT-mediated platelet dysfunction and lipid changes, which are important for the primary prevention of CV diseases. The current approach has the potential to improve the diagnosis and categorization of therapeutic responses in the medical field.
Question: Write a study description for the study titled 'J-Resolved 1H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis'.
Answer: 1H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D 1H NMR spectra, we have investigated the use of 2D J-resolved (JRES) 1H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.
Question: Write a study description for the study titled 'LC-MS analysis of faecal extracts obtained during colorectal cancer resectional surgery'.
Answer: A prospective observational pilot study from right-sided colorectal cancer (RCRC) patients was performed at Imperial College London NHS Trust, UK. Treatment naïve patients undergoing elective resectional surgery for primary RCRCs were recruited. Fresh intra-luminal fecal samples were obtained at three anatomical sites: 5 cm pre-, tumour, and 5 cm post-tumour. Samples were analysed by liquid chromatography-mass spectrometry.  [1][1] Petra Paizs, Duncan Roberts, Nathan Danckert, James Lewis, Maria Sani, Marta Jamroziak, Robert Goldin, Julian Marchesi, Lauren Ford, James Alexander, Zoltan Takats, James Kinross. Gut microbial co-metabolism influences the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3056.