Summary of Study ST001382
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 PR000946. The data can be accessed directly via it's Project DOI: 10.21228/M8DM63 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Study ID | ST001382 |
| Study Title | Distinct metabolic states of a cell guide alternate fates of mutational buffering through altered proteostasis |
| Study Summary | Changes in metabolism can alter the cellular milieu; can this also change intracellular proteostasis? Since proteostasis can modulate mutational buffering, if change in metabolism has the ability to change proteostasis, arguably, it should also alter mutational buffering. Building on this, we find that altered cellular metabolic states in E. coli buffer distinct mutations. Buffered-mutants had folding problems in vivo and were differently chaperoned in different metabolic states. Notably, this assistance was dependent upon the metabolites and not on the increase in canonical chaperone machineries. Additionally, we were able to reconstitute the folding assistance afforded by metabolites in vitro and propose that changes in metabolite concentrations have the potential to alter proteostasis. Collectively, we unravel that the metabolite pools are bona fide members of proteostasis and aid in mutational buffering. Given the plasticity in cellular metabolism, we posit that metabolic alterations may play an important role in the positive or negative regulation of proteostasis. |
| Institute | CSIR-National Chemical Laboratory |
| Last Name | Shanmugam |
| First Name | Dhanasekaran |
| Address | Dr. Homi Bhabha Road, Pune, maharashtra, 411008, India |
| d.shanmugam@ncl.res.in | |
| Phone | 2025902719 |
| Submit Date | 2020-05-14 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzXML |
| Analysis Type Detail | LC-MS |
| Release Date | 2020-06-01 |
| Release Version | 1 |
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Combined analysis:
| Analysis ID | AN002303 |
|---|---|
| Analysis type | MS |
| Chromatography type | Reversed phase |
| Chromatography system | Thermo Accela 1250 |
| Column | Thermo Accucore C18 (100 x 2.1mm,2.6um) |
| MS Type | ESI |
| MS instrument type | Orbitrap |
| MS instrument name | Thermo Q Exactive Orbitrap |
| Ion Mode | NEGATIVE |
| Units | Peak Intensity |
MS:
| MS ID: | MS002146 |
| Analysis ID: | AN002303 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | LC-MS analysis was done using a Exactive Orbitrap mass spectrometer, coupled to an Accela U-HPLC and HTC PAL autosampler. The mass spectrometer was run in negative mode, scanning a mass-charge ratio (m/z) range of 85-1000. The RAW file output from the mass spectrometer was converted from the profile mode into centroid mode using the ReAdW or Proteowizard program and further analyzed using the ElMAVEN program. Data from replicate samples for each time point was aligned within MAVEN and ion chromatograms were extracted for each compound to within a 10 PPM window of the expected m/z value. Peaks were detected from these ion chromatograms and their quality was ascertained using default settings available in MAVEN. Metabolites were identified by matching the retention times as well as the m/z values to >99% pure commercial standards for which in-house calibration was done. Grouped peaks from replicate samples for all time points were matched to the expected retention time of standards, and the peaks with a quality score of at least 0.5 were hand picked for metabolites of interest. Signals obtained from blank runs were used for noise correction and only peaks with a signal intensity of at least 1000 counts were considered. |
| Ion Mode: | NEGATIVE |
