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:GC-MS metabolite profiling of C. reinhardtii cultures under different trophic conditions and acclimations at different times after inoculation.
Project description:Arbuscular mycorrhiza (AM) interactions between plants and Glomeromycota fungi primarily support phosphate aquisition of most terrestrial plant species. To unravel gene expression in Medicago truncatula root colonization by AM fungi, we used genome-wide transcriptome profiling based on whole mycorrhizal roots. We used GeneChips to detail the global programme of gene expression in response to colonization by arbuscular mycorrhizal fungi and in response to a treatment with phosphate and identified genes differentially expressed during this process. Medicago truncatula roots were harvested at 28 days post inoculation with the two different arbuscular mycorrhizal fungi Glomus intraradices (Gi-Myc) and Glomus mosseae (Gm-Myc) under low phosphate conditions (20 µM phosphate) or after a 28 days treatment with 2 mM phosphate in the absence of arbuscular mycorrhizal fungi (2mM-P). As a control, uninfected roots grown under low phosphate conditions (20 µM phosphate) were used (20miM-P). Three biological replicates consisting of pools of five roots were used for RNA extraction and hybridization on Affymetrix GeneChips.
Project description:Nontargeted and targeted metabolomics measurements of abiotic stress responses in three-week-old Arabidopsis thaliana plants' rosette leaf tissue for Col-0 wild type plants and double/triple knockout mutants of aquaporins (pip2;1 pip2;2 and pip2;1 pip2;2 pip2;4) treated with drought, heat at different air humidities, or combined drought-heat stress at different air humidities. This experiment contains FT-ICR-MS measurements for 103 Arabidopsis thaliana rosette leaf samples covering three genotypes under six different environmental conditions. The three genotypes comprise the Col-0 wildtype and two loss-of-function mutants of aquaporins, a pip2;1 pip2;2 double mutant and a pip2;1 pip2;2 pip2;4 triple mutant (respective AGI locus identifiers: AT3G53420, AT2G37170, AT5G60660). The six conditions include control condition (well-watered, 22 °C, 70% relative air humidity), drought stress (one week without watering), heat stress without changing the absolute humidity of the ambient air (6 hours at 33 °C, 37% relative air humidity), heat stress with supplemented air humidity to maintain a constant vapor pressure deficit before and during the heat episode (6 hours at 33 °C, 84% relative air humidity), and the combinations of drought pretreatment with each of the two heat stress variants (one week of drought followed by 6 hours of heat stress). Samples from all conditions were harvested at the same time (within 15 min starting at 5 pm). For validation, GC-TOF-MS measurements were done for two genotypes (wildtype, double mutant) and two conditions (drought, control) on partially overlapping samples.
Project description:Light is a key environmental factor in the growth, development, and metabolism of the edιble mushroom Pleurotus ostreatus. In this study, the effect of white, blue, green, yellow, red light, and darkness on the global protein expression profile of P. ostreatus LGAM 1123 grown in submerged cultivation was investigated. No inhibition of the growth of the fungus, in all the light conditions tested, compared to the dark, was observed. However, the mycelial protein content was reduced by 10% in blue and white light. From the proteomic analysis, a different proteome was expressed for each light wavelength, with red and blue light presenting the most distinctive proteome profiles. Blue light induces pathways such as the citrate cycle (TCA cycle), glycolysis/gluconeogenesis and biosynthesis of amino acids, while red light induces mRNA-related pathways. GC-MS analysis of biomass revealed differences in the produced amino acids, sugars, and lipids. The distinct regulation of proteins and bioactive compounds under different light wavelengths growth indicate that using a specific light wavelength, the metabolism of P. ostreatus could turn into particular biochemical pathways. These strategies could be useful for the food industry because specific nutrients could be increased in the fermentation of edible fungi without the need for genetic engineering of the strain.
Project description:Cold stress is one of the most severe environmental conditions which cause huge losses in crop production worldwide. We identified a DEAD box RNA helicase, RCF1 (Regulator of CBF gene expression 1) that controls pre-mRNA splicing of cold-stress-responsive genes including positive and negative regulators of CBF genes. We used whole genome tiling array analysis under normal growth conditions to identify transcripts which are mis-spliced/intron-retained in rcf1-1. Fourteen-day-old wild type and rcf1-1 seedlings grown on MS agar medium (1x MS salts, 2% sucrose, 0.6% agar, pH 5.7) under normal conditions were harvested for total RNA isolation. RNA was extracted with Trizol reagent (Invitrogen) and synthesized into double-stranded DNA that was hybridized to whole genome tiling arrays (Affymetrix Arabidopsis Tiling1.0R). All hybridizations were performed with 3 biological replicates.