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Quantitative analysis of global protein stability rates in tissues.

ABSTRACT: Protein degradation is an essential mechanism for maintaining proteostasis in response to internal and external perturbations. Disruption of this process is implicated in many human diseases. We present a new technique, QUAD (Quantification of Azidohomoalanine Degradation), to analyze the global degradation rates in tissues using a non-canonical amino acid and mass spectrometry. QUAD analysis reveals that protein stability varied within tissues, but discernible trends in the data suggest that cellular environment is a major factor dictating stability. Within a tissue, different organelles and protein functions were enriched with different stability patterns. QUAD analysis demonstrated that protein stability is enhanced with age in the brain but not in the liver. Overall, QUAD allows the first global quantitation of protein stability rates in tissues, which will allow new insights and hypotheses in basic and translational research.

SUBMITTER: McClatchy DB

PROVIDER: S-EPMC7524747 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Quantitative analysis of global protein stability rates in tissues.

McClatchy Daniel B DB Martínez-Bartolomé Salvador S Gao Yu Y Lavallée-Adam Mathieu M Yates John R JR

Scientific reports 20200929 1

Protein degradation is an essential mechanism for maintaining proteostasis in response to internal and external perturbations. Disruption of this process is implicated in many human diseases. We present a new technique, QUAD (Quantification of Azidohomoalanine Degradation), to analyze the global degradation rates in tissues using a non-canonical amino acid and mass spectrometry. QUAD analysis reveals that protein stability varied within tissues, but discernible trends in the data suggest that ce ...[more]

PMID: 32994440

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Project description:Plasmid loss rate measurements are standard in microbiology and key to understanding plasmid stabilization mechanisms. The conventional assays eliminate selection for plasmids at the beginning of the experiment and screen for the appearance of plasmid-free cells over long-term population growth. However, it has been long appreciated in plasmid biology that the growth rate differential between plasmid-free and plasmid-containing cells at some point overshadows the effect of primary loss events, such that the assays can greatly over-estimate inherent loss rates. The standard solutions to this problem are to either consider the very early phase of loss where the fraction of plasmid-free cells increases linearly, or to measure the growth rate difference either by following the population for longer time or by measuring growth rates separately. Here we mathematically show that in all these cases, seemingly small experimental errors in the growth rate estimates can overshadow the estimates of the loss rates. For many plasmids, loss rates may thus be much lower than previously thought, and for some plasmids, the estimated loss rate may have nothing to do with actual loss rates. We further modify two independent experimental methods to separate inherent losses from growth differences and apply them to the same plasmids. First we use a high-throughput microscopy-based approach to screen for plasmid-free cells at extremely short time scales--tens of minutes rather than tens of generations--and apply it to a par⁻ version of mini-R1. Second we modify a counterselection-based plasmid loss assay inspired by the Luria-Delbrück fluctuation test that completely separates losses from growth, and apply it to various R1 and pSC101 derivatives. Concordant results from the two assays suggest that plasmids are lost at a lower frequency than previously believed. In fact, for par⁻ mini-R1 the observed loss rate of about 10⁻³ per cell and generation seems to be so low as to be inconsistent with what we know about the R1 stabilization mechanisms, suggesting these well characterized plasmids may have some additional and so far unknown stabilization mechanisms, for example improving copy number control or partitioning at cell division.

Dataset Information

Quantitative analysis of global protein stability rates in tissues.

Publications

Quantitative analysis of global protein stability rates in tissues.

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