Summary of Study ST001199
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 PR000807. The data can be accessed directly via it's Project DOI: 10.21228/M8CD73 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 | ST001199 |
| Study Title | Non-targeted LC-MS Analysis of Soluble Metabolites in the Non-Polar MTBE Phase (part-V) |
| Study Summary | Cyanobacteria are a model photoautotroph and a chassis for the sustainable production of fuels and chemicals. Yet, knowledge of photoautotrophic metabolism in the natural environment of day/night cycles is lacking yet has implications for improved yield from plants, algae, and cyanobacteria. Here, a thorough approach to characterizing diverse metabolites—including carbohydrates, lipids, amino acids, pigments, co-factors, nucleic acids and polysaccharides—in the model cyanobacterium Synechocystis sp. PCC 6803 (S. 6803) under sinusoidal diurnal light-dark cycles was developed and applied. A custom photobioreactor and novel multi-platform mass spectrometry workflow enabled metabolite profiling every 30-120 minutes across a 24-hour diurnal sinusoidal LD (“sinLD”) cycle peaking at 1,600 mol photons m 2 s-1. We report widespread oscillations across the sinLD cycle with 90%, 94%, and 40% of the identified polar/semi-polar, non-polar, and polymeric metabolites displaying statistically significant oscillations, respectively. Microbial growth displayed distinct lag, biomass accumulation, and cell division phases of growth. During the lag phase, amino acids (AA) and nucleic acids (NA) accumulated to high levels per cell followed by decreased levels during the biomass accumulation phase, presumably due to protein and DNA synthesis. Insoluble carbohydrates displayed sharp oscillations per cell at the day-to-night transition. Potential bottlenecks in central carbon metabolism are highlighted. Together, this report provides a comprehensive view of photosynthetic metabolite behavior with high temporal resolution, offering insight into the impact of growth synchronization to light cycles via circadian rhythms. Incorporation into computational modeling and metabolic engineering efforts promises to improve industrially-relevant strain design. |
| Institute | Colorado State University |
| Department | Chemical and Biological Engineering |
| Last Name | Peebles |
| First Name | Christie |
| Address | 700 Meridian Ave, Fort Collins, CO 80523 |
| christie.peebles@colostate.edu | |
| Phone | 970-491-6779 |
| Submit Date | 2019-03-02 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | cdf |
| Analysis Type Detail | LC-MS |
| Release Date | 2019-07-17 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN001995 |
|---|---|
| Analysis type | MS |
| Chromatography type | Reversed phase |
| Chromatography system | Waters Xevo G2 |
| Column | Waters Acquity UPLC CSH Phenyl Hexyl ( 100 x 1.0mm,1.7um) |
| MS Type | ESI |
| MS instrument type | QTOF |
| MS instrument name | Waters Xevo QS |
| Ion Mode | POSITIVE |
| Units | spectral abundance per cell |
MS:
| MS ID: | MS001848 |
| Analysis ID: | AN001995 |
| Instrument Name: | Waters Xevo QS |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Compounds were created by clustering features using RAMClustR (Broeckling et al. 2014). RAMClustR uses a similarity matric which calculates feature correlation across samples and retention time correlation between features. Hierarchical clustering of the similarity matrix was computed via the fastcluter package (Müllner 2013). The resulting clustered dendrogram is cut using DynamicTreeCut and spectra are created with clusters and features abundances from input data (Langfelder, Zhang, and Horvath 2008). The abundance for each mass in spectra is a weighted mean of feature intensity. The RAMClustR outputs are compounds (clusters of correlated features) and intensities for each sample; spectral abundance intensities reflect weighted mean of all features within the compound. |
| Ion Mode: | POSITIVE |
