Summary of Study ST002431
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 PR001564. The data can be accessed directly via it's Project DOI: 10.21228/M8J41M This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002431 |
| Study Title | MS profiling of the Long Term Evolution Experiment |
| Study Summary | The metabolome of a cell is the integration point of an organism's environment, genetics, and gene expression pattern. The metabolic phenotype can be under selection and is known to contribute to adaption. However, the metabolome's inherent networked and convoluted nature makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the Long Term Evolution Experiment (LTEE) as a model to understand how mutations can transduce themselves through a cellular network, eventually affecting metabolism and perhaps fitness. We used mass-spectropscopy to broadly survey the metabolomes of both ancestors and all 12 evolved lines and combined this with genomic and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work brings the field closer to a complete genotype-phenotype map for the LTEE and a better understanding of how mutations might affect fitness through the metabolome. We used mass-spectroscopy to profile metabolic changes in the Long Term Evolution Experiment and link these change to upstream changes in gene expression and mutations. |
| Institute | Rutgers University |
| Department | Genetics |
| Laboratory | Shah |
| Last Name | Favate |
| First Name | John |
| Address | 145 Bevier Road, Piscataway, NJ, 08854 |
| john.favate@rutgers.edu | |
| Phone | 7325894642 |
| Submit Date | 2022-12-01 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzXML |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-02-16 |
| Release Version | 1 |
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Subject:
| Subject ID: | SU002520 |
| Subject Type: | Bacteria |
| Subject Species: | Escherichia coli |
| Taxonomy ID: | 562 |
