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:Methylomicrobium buryatense 5GB1 is an obligate methylotroph, which grows on methane or methanol with similar growth rates. Core metabolic pathways are similar on both substrates, but recent studies of methane metabolism suggest that growth on methanol might have significant differences from growth on methane. In this study, both a targeted metabolomics approach as well as a 13C tracer approach have been taken to understand core carbon metabolism in M. buryatense 5GB1 during methanol growth, to determine whether such differences occur. Targeted metabolomics analyses were performed on both methane and methanol cultures to identify metabolic nodes with altered fluxes. Several key metabolites showed significant differences in pool size. Noticeably, 2-keto-3-deoxy-6-phosphogluconate (KDPG) showed much larger pools under methanol culture, suggesting the Entner-Doudoroff (ED) pathway was more active. Intermediates in other parts of metabolism also showed differences in pool sizes under methanol growth. A systematic shift of active core metabolism is proposed to explain the changes. In order to distinguish flux partition differences at the C3-C4 node, 13C tracer analysis was also applied to methanol-grown cultures. Using the experimental results as constraints, we applied flux balance analysis to determine the metabolic flux phenotype of M. buryatense 5GB1 growing on methanol. The resulting new insights into core metabolism of this methanotroph provide an improved basis for future strain design.
Project description:Untargeted multi-omics analysis of plasma is an emerging tool for the identification of novel biomarkers for evaluating disease prognosis and for a better understanding of molecular mechanisms underlying human disease. The successful application of metabolomic and pro-teomic approaches relies on reproducibly quantifying a wide range of metabolites and proteins. Herein, we report the results of untargeted metabolomic and proteomic analyses from blood plasma samples following analyte extraction by two frequently used solvent systems: chloro-form/methanol and methanol-only. Whole blood samples were collected from participants (n=6) at University Hospital Sharjah (UHS) hospital, then plasma was separated and extracted by two methods i. methanol precipitation and, ii. 4:3 methanol:chloroform extraction. The coverage and reproducibility of the two methods were assessed by ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). The study revealed that metabolite extraction by methanol-only showed greater reproducibility for both metabolomic and proteomic quantifications than did methanol/chloroform, while yielding similar peptide coverage. However, coverage of extracted metabolites was higher with the methanol/chloroform precipitation.
Project description:The field of metabolomics generally lacks standardized methods for the preparation of samples prior to analysis. This is especially true for metabolomics of reef-building corals, where the handful of studies that have been published employ a range of sample preparation protocols. The utilization of metabolomics may prove essential in understanding coral biology in the face of increasing environmental threats, and an optimized method for preparing coral samples for metabolomics analysis would aid this cause. The current study evaluates three important steps during samples processing of stony corals: (i) metabolite extraction, (ii) metabolism preservation and (iii) subsampling. Results indicate that a modified Bligh and Dyer extraction is more reproducible across multiple coral species compared to methyl tert-butyl ether and methanol extractions, while a methanol extraction is superior for feature detection. Additionally, few differences are detected between spectra from frozen or lyophilized coral samples. Finally, extraction of entire coral nubbins increases feature detection, but decreases throughput and is more susceptible to subsampling error compared to a novel tissue powder subsampling method. Overall, we recommend the use of a modified Bligh and Dyer extraction, lyophilized samples, and analysis of brushed tissue powder for the preparation of reef-building coral samples for 1H NMR metabolomics.
Project description:understanding the biology of methicillin resistant staphylococcus aureus (MRSA) is crucialto unlocking insights for new targets in the fight against this pathogen, there is howeverlimited reports of methadological approaches for carrying proteomic and metabolomic profiling in S.aureus. Therefore, we describe the use of a dual-functionality methanol extraction method for the concurrent extraction of protein and metabolites from S.aureus and reporton the comparative analysis of the proteomic and metabolomic profiles of MRSA versus methicillin sensitive S. aureus (MSSA). Using a reference strain from MRSA and MSSA, we first compared the MRSA proteome extracted using the methanol method to the one from the traditionally used urea method. Then using the methanol extraction method, we compared the proteome and metabolome of MRSA versus MSSA. Through this study, we demonstrated the effectivnessof the methanol-based-dual-extraction method, providing simultaneous insights into the proteomic and metabolomic landscapes of S.aureus strains, demonstrating the utility of proteomic and metabolomic profiling for elucidating the biological basis of antimicrobial resistance