Project description:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using four tissues of A. japonicum.
Project description:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using seven tissues of Magnolia obovata
Project description:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using seven tissues of M. japonicus.
Project description:Esophageal squamous cell carcinoma (ESCC) is characterized as a metabolic disorder characterized by lipid metabolic reprogramming. To investigate the regional characteristics of ESCC patients in Xinjiang Province, China, and lipid metabolism, in this study, we described the characteristics of the serum lipid composition in Kazakh ESCC patients by performing an integrated analysis of the transcriptome and lipidomic data. Serum samples from 30 Kazakh ESCC patients and 30 healthy individuals were subjected to targeted lipid metabolomics analysis via UPLC‒MS/MS, while 3 tumor samples and matched adjacent normal tissues from 30 ESCC patients were subjected to transcriptome analysis. Compared with those in the healthy group, we observed obvious changes in the serum lipid subclass content, chain length and unsaturation in the ESCC patients. Integrated lipidomic and transcriptomic analyses revealed that unsaturated fatty acid biosynthesis, fatty acid metabolism, lipid degradation, cholesterol metabolism and the AMPK signaling pathway were enriched in tumor tissues. In addition, RT–qPCR results demonstrated that genes closely related to these pathways were differentially expressed between the ESCC group and the healthy control group. Considering the key role of AMPK in lipid metabolism, we conducted a targeted lipid metabolomics analysis on AMPK-knockdown esophageal cancer cells by UPLC‒MS/MS. These findings suggested that AMPK might be correlated with lipid metabolism in Kazakh ESCC patients, identifying potential therapeutic targets of AMPK and other lipid metabolism-related markers against the progression of ESCC.
Project description:Females typically outlive males, a disparity mitigated by castration, yet the molecular underpinnings remain elusive. Our study leverages untargeted metabolomics and RNA sequencing to uncover the pivotal compounds and genes influencing healthy aging post-castration, examining serum, kidney, and liver biospecimens from 12-week and 18-month old castrated male mice and their unaltered counterparts. Behavioral tests and LC-MS/MS metabolomics reveal that castrated males exhibit altered steroid hormones, superior cognitive performance, and higher levels of anti-oxidative compounds like taurine, despite identical diets. Integrated metabolome-transcriptome analysis confirms reduced lipid peroxidation and oxidative stress in female and castrated male mice, suggesting a protective mechanism against aging. Histological examinations post-cisplatin treatment highlight the model’s applicability in studying drug toxicity and reveal varying susceptibility in organ-specific toxicities, underlining the crucial role of sex hormones in physiological defenses. In essence, our castration model unveils a feminized metabolic and transcriptomic intermediary, serving as a robust tool for studying gender-specific aspects of healthy aging and exploring sex hormone-induced differences in diverse biomedical domains.
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
Project description:RNAseq and LC/MS metabolomics analysis of C. difficile strain 630 grown in BHIS media with 50% (vol/vol) faecal water added, compared with control BHIS containing only the additional PBS used for prep of Faecal water. Cells grown in biological triplicates to late log phase (T=6h) prior to harvest. Goal was to determine changes in gene expression caused by exposure to Faecal water, and changes in the metabolite profile of faecal water containing medium when incubated with actively growing C. difficile cells
