Project description:Seafood coloration is typically considered an indicator of quality and nutritional value by consumers. One such seafood is the Xishi abalone (Haliotis gigantea), which displays muscle color polymorphism wherein a small subset of individuals display orange coloration of muscles due to carotenoid enrichment. However, the metabolic basis for carotenoid accumulation has not been thoroughly investigated in marine mollusks. Here, GC-TOF-MS-based untargeted metabolite profiling was used to identify key pathways and metabolites involved in differential carotenoid accumulation in abalones with variable carotenoid contents. Cholesterol was the most statistically significant metabolite that differentiated abalones with orange muscles against those with common white muscles. This observation is likely due to the competitive interactions between cholesterol and carotenoids during cellular absorption. In addition, the accumulation of carotenoids was also related to fatty acid contents. Overall, this study indicates that metabolomics can reflect physiological changes in organisms and provides a useful framework for exploring the mechanisms underlying carotenoid accumulation in abalone types.
Project description:Nothapodytes nimmoniana is a natural source of camptothecin, a known anticancer drug. Stem and leaf extracts of Nothapodytes nimmoniana were studied to assess its in-vitro action on cancer progression events in a cervical cancer cell line, the HeLa. As a well-studied ideal model system, the HeLa has been chosen and the effects of extracts on cancer progression events have been traced out. HeLa cells and media samples were analyzed for differentially expressed proteins and metabolites respectively on RRLC-ESI-QTOFMS. The CBFA2T1, cysteine-rich protein 2-binding protein, Zinc finger protein 788, transcription factor RFX3 and angiomotin-like protein 1 were significantly expressed proteins while 3-Hydroxysuberic acid, Indole-3-carboxylic acid, N8-Acetylspermidine, L-Octanoylcarnitine were significantly expressed metabolites. Aminoacyl-tRNA biosynthesis, purine metabolism, and valine, leucine, arginine, proline metabolism were the most prominent pathways. The multi-omics approach has the greatest metabolites and proteins profiling potential hence was applied here to find signatures of N. nimmoniana extracts treatment of cervical cancer.
Project description:Diagnosis of ovarian cancer is difficult due to the lack of clinical symptoms and effective screening algorithms. In this study, we aim to develop models for ovarian cancer diagnosis by detecting metabolites in urine and plasma samples. Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive ion mode was used for metabolome quantification in 235 urine samples and 331 plasma samples. Then, Urine and plasma metabolomic profiles were analyzed by univariate and multivariate statistics. Four groups of samples: normal control, benign, borderline and malignant ovarian tumors were enrolled in this study. A total of 1330 features and 1302 features were detected from urine and plasma samples respectively. Based on two urine putative metabolites, five plasma putative metabolites and five urine putative metabolites, three models for distinguishing normal-ovarian tumors, benign-malignant (borderline + malignant) and borderline-malignant ovarian tumors were developed respectively. The AUC (Area Under Curve) values were 0.987, 0876 and 0.943 in discovery set and 0.984, 0.896 and 0.836 in validation set for three models. Specially, the diagnostic model based on 5 plasma putative metabolites had better early-stage diagnosis performance than CA125 alone. The AUC values of the model were 0.847 and 0.988 in discovery and validation set respectively. Our results showed that normal and ovarian tumors have unique metabolic signature in urine and plasma samples, which shed light on the ovarian cancer diagnosis and classification.
Project description:Our lab has previously shown that treatment of murine heart valve explants with ATRA promotes calcification in vitro. In order to fully explore gene expression changes in the valve in response to ATRA or LE540, a high throughput microarray was performed. In this data set, we include expression data from dissected murine post natal aortic valve explants treated with ATRA or DMSO as a vehicle control. These data are used to obtain 432 probe sets that are differentially expressed in response to ATRA treatment, with 118 being downregulated and 314 increased.
Project description:ObjectiveSepsis is a life-threatening condition secondary to infection that evolves into a dysregulated host response and is associated with acute organ dysfunction. Sepsis-induced cardiac dysfunction is one of the most complex organ failures to characterize. This study performed comprehensive metabolomic profiling that distinguished between septic patients with and without cardiac dysfunction.MethodPlasma samples collected from 80 septic patients were analysed by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyse the metabolic model between septic patients with and without cardiac dysfunction. The screening criteria for potential candidate metabolites were as follows: variable importance in the projection (VIP) >1, P < 0.05, and fold change (FC) > 1.5 or < 0.7. Pathway enrichment analysis further revealed associated metabolic pathways. In addition, we constructed a subgroup metabolic analysis between the survivors and non-survivors according to 28-day mortality in the cardiac dysfunction group.ResultsTwo metabolite markers, kynurenic acid and gluconolactone, could distinguish the cardiac dysfunction group from the normal cardiac function group. Two metabolites, kynurenic acid and galactitol, could distinguish survivors and non-survivors in the subgroup analysis. Kynurenic acid is a common differential metabolite that could be used as a candidate for both diagnosis and prognosis for septic patients with cardiac dysfunction. The main associated pathways were amino acid metabolism, glucose metabolism and bile acid metabolism.ConclusionMetabolomic technology could be a promising approach for identifying diagnostic and prognostic biomarkers of sepsis-induced cardiac dysfunction.