Summary of Study ST001956
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001243. The data can be accessed directly via it's Project DOI: 10.21228/M81T40 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Study ID | ST001956 |
| Study Title | Timecourse exometabolome analysis of glucose grown Rubrivivax benzoatilyticus cells |
| Study Type | Timecourse experiment |
| Study Summary | Bacterial cells were grown on glucose under photoheterotrophic conditions for 18 days. Spent media of cells, harvested at 3rd, 9th and 18th day of growth, was vacuum dried and the metabolome was extracted in methanol. The extracted metabolites were derivatized and analyzed using GC-MS. |
| Institute | University of Hyderabad |
| Last Name | Gupta |
| First Name | Deepshikha |
| Address | Dept. of Plant Sciences, |
| deepshikha@uohyd.ac.in | |
| Phone | +918985420802 |
| Submit Date | 2021-10-13 |
| Analysis Type Detail | LC-MS |
| Release Date | 2021-10-31 |
| Release Version | 1 |
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Project:
| Project ID: | PR001243 |
| Project DOI: | doi: 10.21228/M81T40 |
| Project Title: | Footprint dynamics study |
| Project Type: | GC-MS quantitative analysis |
| Project Summary: | Data analysis at three time points- exponential, early and late stationary phase uncovered dynamic metabolite abundance implying metabolic rewiring of Rubrivivax benzoatilyticus JA2 cells, in response to glucose. To study dynamic changes in the metabolome, footprint analysis (exometabolome extracted from the spent media of glucose grown Rubrivivax benzoatilyticus cells), using GC-MS, was carried out at three time points- exponential phase (G3), early (G9) and late (G18) stationary phase. Metabolites were extracted in methanol, derivatized by adding BSTFA-TCMS to protect the functional groups and analysed by GC-MS. The analysis listed metabolic features at each time point, of which 149 metabolites were identified, based on the mass spectra comparison in the database (NIST similarity >700, Golm database), at one and/or other time point, while other metabolites remained unidentified. Identified metabolic features along with their respective peak area at G3, G9 and G18 were recorded and submitted to MetaboAnalyst 4.0 online software to identify significant metabolic pattern and variation. The result of the Hierarchical Clustering Analysis (HCA) shows that metabolites clustered into five groups based on the response pattern specifying the metabolic dissimilarity between the three samples. Group I, II and V comprises metabolites with high concentration in G18, G9 and G3 samples respectively, group III and IV includes metabolites whose concentration was high in two of the three samples. Pairwise score plot of principal component analysis (PCA) provided an overview of the separation pattern amongst the most significant principal components (PCs). To assess the significance of class discrimination, partial least squares - discriminant analysis (PLS-DA) was performed. The exometabolome samples were seen clearly separated by PLS-DA analysis with the R2 and Q2 value of 0.95 and 0.4 respectively indicating the goodness of fit and predictability, suggesting representative model for the difference in metabolomes. The Variable Importance in Projection (VIP scores) derived from PLS-DA model was used to ascertain key metabolic features significant for group separation. Metabolites with VIP score >1 were considered to have statistically contributed to the model. Forty metabolites were identified as statistically significant contributors to the model and were mainly accountable for group separation in the model. Metabolites were classified based on their chemical structure as alkanes (20%), sugars (28%), organic acid (17%), amino acid (10%), fatty acid (8%), nucleotide (3%) and others (5%). Amongst these forty metabolites, a total of 19, 25 and 33 were detected in G3, G9 and G18 samples respectively. |
| Institute: | University of Hyderabad |
| Department: | Department of Plant sciences |
| Laboratory: | Bacterial discovery and metabolomics laboratory |
| Last Name: | Gupta |
| First Name: | Deepshikha |
| Address: | Dept. of Plant Sciences, University of Hyderabad, Hyderabad, India. |
| Email: | deepshikha@uohyd.ac.in |
| Phone: | +918985420802 |
| Funding Source: | Department of Science and Technology, Government of India |
