Systematic analysis of protein turnover in primary cells

Mathieson, Toby; Franken, Holger; Kosiński, Jan; Kurzawa, Nils; Zinn, Nico; Sweetman, Gavain; Poeckel, Daniel; Ratnu, Vikram S.; Schramm, Maike; Becher, Isabelle; Steidel, Michael; Noh, Kyung‐Min; Bergamini, Giovanna; Beck, Martin; Bantscheff, Marcus; Savitski, Mikhail M.

doi:10.1038/s41467-018-03106-1

Cited by 325 publications

(373 citation statements)

References 43 publications

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“…Notably, these significant proteostasis differences are derived from relative correlation analysis and are unlikely to be tightly dependent on gene expression levels. Several reports have indicated differential stability of the two proteasome subunits (Mathieson et al , ). Becher et al () used a thermal proteome profiling strategy by MS and reported that during the cell cycle the proteasome showed a different stability and abundance variation in a protein subset of the 19S regulatory sub‐complex.…”

Section: Discussionmentioning

confidence: 99%

Isoform‐resolved correlation analysis between mRNA abundance regulation and protein level degradation

Šalovská

Zhu

Gandhi

et al. 2020

Molecular Systems Biology

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Profiling of biological relationships between different molecular layers dissects regulatory mechanisms that ultimately determine cellular function. To thoroughly assess the role of protein post‐translational turnover, we devised a strategy combining pulse stable isotope‐labeled amino acids in cells (pSILAC), data‐independent acquisition mass spectrometry (DIA‐MS), and a novel data analysis framework that resolves protein degradation rate on the level of mRNA alternative splicing isoforms and isoform groups. We demonstrated our approach by the genome‐wide correlation analysis between mRNA amounts and protein degradation across different strains of HeLa cells that harbor a high grade of gene dosage variation. The dataset revealed that specific biological processes, cellular organelles, spatial compartments of organelles, and individual protein isoforms of the same genes could have distinctive degradation rate. The protein degradation diversity thus dissects the corresponding buffering or concerting protein turnover control across cancer cell lines. The data further indicate that specific mRNA splicing events such as intron retention significantly impact the protein abundance levels. Our findings support the tight association between transcriptome variability and proteostasis and provide a methodological foundation for studying functional protein degradation.

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Section: Discussionmentioning

confidence: 99%

Isoform‐resolved correlation analysis between mRNA abundance regulation and protein level degradation

Šalovská

Zhu

Gandhi

et al. 2020

Molecular Systems Biology

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“…have the shortest half-life in all cell types while mitochondrial proteins have the longest, an observation also made by 75 when classifying their data set with GO terms for endoplasmic reticulum, Golgi apparatus, cytoplasm, mitochondrion and nucleus. It is possible that in neurons, the extended half-lives of M6P-Lyso proteins may relate to the fact that the M6P modification persists after delivery of newly synthesized lysosomal proteins to the lysosome 78 while in most other cell types, the M6P modification is removed by acid phosphatase 5, an enzyme that is low in brain 79 .…”

Section: It Is Not Clear Why the Half-lives Of The Luminal Lysosomal mentioning

confidence: 74%

“…To determine whether this apparent disconnect is specific to lysosomal proteins, we compared protein thermal stability and half-life on an organelle-specific basis. Estimates of the melting point of proteins in the environment in which they function were obtained from cellular thermal protein profiling studies on three different cell lines [72][73][74] while protein half-lives were obtained from stable isotope labeling by amino acids in cell culture (SILAC) studies of protein turnover in five non-dividing cell types 75 . We compared lysosomal proteins with proteins located in other organelles using two different classification schemes.…”

Section: Discussionmentioning

confidence: 99%

Protein thermal stability does not correlate with cellular half-life: Global observations and a case study of tripeptidyl-peptidase 1

Collier

Nemtsova

Kuber

et al. 2019

Preprint

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Late-infantile neuronal ceroid lipofuscinosis (LINCL) is a neurodegenerative lysosomal storage disorder caused by mutations in the gene encoding the protease tripeptidyl-peptidase 1 (TPP1). Progression of LINCL can be slowed or halted by enzyme replacement therapy, where recombinant human TPP1 is administered to patients. In this study, we utilized protein engineering techniques to increase the stability of recombinant TPP1 with the rationale that this may lengthen its lysosomal half-life, potentially increasing the potency of the therapeutic protein. Utilizing multiple structure-based methods that have been shown to increase the stability of other proteins, we have generated and evaluated over 70 TPP1 variants. The most effective mutation, R465G, increased the melting temperature of TPP1 from 55.6°C to 64.4°C and increased its enzymatic half-life at 60°C from 5.4 min to 21.9 min. However, the intracellular half-life of R465G and all other variants tested in cultured LINCL-patient derived lymphoblasts was similar to that of WT TPP1. These results provide structure/function insights into TPP1 and indicate that improving in vitro thermal stability alone is insufficient to generate TPP1 variants with improved physiological stability. This conclusion is supported by a proteome-wide analysis that indicates that lysosomal proteins have higher melting temperatures but also higher turnover rates than proteins of other organelles. These results have implications for similar efforts where protein engineering approaches, which are frequently evaluated in vitro, may be considered for improving the physiological properties of proteins, particularly those that function in the lysosomal environment.

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“…These were mapped against protein sequence data from UniProt [29] and Ensembl [30], protein structural data from the Protein Data Bank (biounit database, downloaded 28/04/2017), and protein interaction data from a large non-redundant protein-protein interaction network (UniPPIN) [31], which incorporates various interaction databases [32,33,34,35,36] and recent large-scale experimental studies [37,38,39]. Protein thermal stability and half-life data were obtained from separate large-scale studies [21,23]. Transcriptomic data were taken from GTEx [24], while protein abundance data (protein per million [ppm]) for each tissue/sample type were obtained from PaxDb [22].…”

Section: Data Sourcesmentioning

confidence: 99%

“…Second, we also make use of recently available large-scale proteomic measurements, including protein half-life [21], abundance [22], thermal stability [23] and transcriptomics data [24], to uncover biophysical and biochemical principles governing the impact of variants. Our analyses highlight a striking difference in the enrichment of pathogenic and population variants, which depends upon their localisation to protein domain and structural features.…”

mentioning

confidence: 99%

Missense variants in health and disease affect distinct functional pathways and proteomics features

Laddach

Fraternali

2019

Preprint

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Missense variants are present amongst the healthy population, but some of them are causative of human diseases. Therefore, a classification of variants associated with "healthy" or "diseased" states is not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences, is essential to better distinguish pathogenic from population variants. Here we quantify variant enrichment across full-length proteins, their domains and 3D-structure defined regions. We integrate this with available transcriptomic and proteomic (protein half-life, thermal stability, abundance) data. Using this approach we have mined a rich set of molecular features which enable us to understand the differences underlying pathogenic and population variants: pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing, localise to protein cores and interaction interfaces, and are enriched in more abundant proteins. In terms of their molecular properties, we find that common population variants and pathogenic variants show the greatest contrast. Additionally, in contrary to other studies, we find that rare population variants display features closer to common than pathogenic variants. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants. Such details could be harnessed to predict variant deleteriousness, and prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at http://fraternalilab. kcl.ac.uk/ZoomVar. It allows users to programmatically annotate a large number of missense variants with protein structural information, and to calculate variant enrichment in different protein structural regions. Significance StatementOne of the greatest challenges in understanding the genetic basis of diseases is to discriminate between likely harmless and potentially disease-causing sequence variants. To better evaluate the pathogenic potential of missense variants, we developed a strategy to quantitatively measure the enrichment of both disease and non disease-related variants within a protein based on its structural and domain organisation. By integrating available transcriptomics and proteomics data, our approach distinguishes pathogenic from population variants far more clearly than previously possible, and reveals hitherto unknown details of how different proteins exhibit resilience and/or sensitivity towards genetic variants. Our results will help to prioritise variant-enriched proteins for therapeutic targeting; we have created the ZoomVar database, accessible at http://fraternalilab.kcl.ac.uk/ZoomVar, for programmatic mapping of user-defined variants to protein structural and domain information.

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Systematic analysis of protein turnover in primary cells

Cited by 325 publications

References 43 publications

Isoform‐resolved correlation analysis between mRNA abundance regulation and protein level degradation

Isoform‐resolved correlation analysis between mRNA abundance regulation and protein level degradation

Protein thermal stability does not correlate with cellular half-life: Global observations and a case study of tripeptidyl-peptidase 1

Missense variants in health and disease affect distinct functional pathways and proteomics features

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