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Gupta, Meera

Publications and source records attributed to Gupta, Meera.

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Global protein turnover quantification in Escherichia coli reveals cytoplasmic recycling under nitrogen limitation

Protein turnover is critical for proteostasis, but turnover quantification is challenging, and even in well-studied E. coli, proteome-wide measurements remain scarce. Here, we quantify the turnover rates of ~3200 E. coli proteins under 13 conditions by combining heavy isotope labeling with complement reporter ion quantification and find that cytoplasmic proteins are recycled when nitrogen is limited. We use knockout experiments to assign substrates to the known cytoplasmic ATP-dependent proteases. Surprisingly, none of these proteases are responsible for the observed cytoplasmic protein degradation in nitrogen limitation, suggesting that a major proteolysis pathway in E. coli remains to be discovered. Lastly, we show that protein degradation rates are generally independent of cell division rates. Thus, we present broadly applicable technology for protein turnover measurements and provide a rich resource for protein half-lives and protease substrates in E. coli, complementary to genomics data, that will allow researchers to study the control of proteostasis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

wuhrlab/BACIQ

Multiplexed proteomics has emerged as a powerful tool to measure relative protein expression levels across multiple conditions. The relative protein abundances are inferred by comparing the signals generated by isobaric tags, which encode the samples’ origins. Intuitively, the trust associated with a protein measurement depends on the similarity of ratios from the protein’s peptides and the signal-strength of these measurements. However, typically the average peptide ratio is reported as the estimate of relative protein abundance, which is only the most likely ratio with a very naive model. Moreover, there is no sense on the confidence in these measurements. Here, we present a mathematically rigorous approach that integrates peptide signal strengths and peptide-measurement agreement into an estimation of the true protein ratio and the associated confidence (BACIQ). The main advantages of BACIQ are: It removes the need to threshold reported peptide signal based on an arbitrary cut-off, thereby reporting more measurements from a given experiment; Confidence can be assigned without replicates; For repeated experiments BACIQ provides confidence intervals for the union, not the intersection, of quantified proteins; and For repeated experiments, BACIQ confidence intervals are more predictive than confidence intervals based on protein measurement agreement. To demonstrate the power of BACIQ, we reanalyzed previously published data on subcellular protein movement upon treatment with an Exportin-1 inhibiting drug. We detect ~2x more highly significant movers, down to subcellular localization changes of ~1% . Thus, our method drastically increases the value obtainable from quantitative proteomics experiments helping researchers to interpret their data and prioritize resources. To make our approach easily accessible we distribute it via a Python/Stan package.

Peshkin, Leonid↗