Evidence map›Paper›PMID 36769128›Full record

ArticleInternational journal of molecular sciences2023

Modeling SILAC Data to Assess Protein Turnover in a Cellular Model of Diabetic Nephropathy.

Barbara Di Camillo, Lucia Puricelli, Elisabetta Iori, Gianna Maria Toffolo, Paolo Tessari, Giorgio Arrigoni

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Barbara Di CamilloDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.ORCID 0000-0001-8415-4688
Lucia PuricelliDepartment of Medicine, University of Padova, 35128 Padova, Italy.
Elisabetta IoriDepartment of Medicine, University of Padova, 35128 Padova, Italy.
Gianna Maria ToffoloDepartment of Information Engineering, University of Padova, 35131 Padova, Italy.
Paolo TessariDepartment of Medicine, University of Padova, 35128 Padova, Italy.
Giorgio ArrigoniProteomics Center, University of Padova and Azienda Ospedaliera di Padova, 35128 Padova, Italy.ORCID 0000-0002-4103-2733

Funding

University of Padua CPDA101844
6 · The paper itself

Abstract

Protein turnover rate is finely regulated through intracellular mechanisms and signals that are still incompletely understood but that are essential for the correct function of cellular processes. Indeed, a dysfunctional proteostasis often impacts the cell's ability to remove unfolded, misfolded, degraded, non-functional, or damaged proteins. Thus, altered cellular mechanisms controlling protein turnover impinge on the pathophysiology of many diseases, making the study of protein synthesis and degradation rates an important step for a more comprehensive understanding of these pathologies. In this manuscript, we describe the application of a dynamic-SILAC approach to study the turnover rate and the abundance of proteins in a cellular model of diabetic nephropathy. We estimated protein half-lives and relative abundance for thousands of proteins, several of which are characterized by either an altered turnover rate or altered abundance between diabetic nephropathic subjects and diabetic controls. Many of these proteins were previously shown to be related to diabetic complications and represent therefore, possible biomarkers or therapeutic targets. Beside the aspects strictly related to the pathological condition, our data also represent a consistent compendium of protein half-lives in human fibroblasts and a rich source of important information related to basic cell biology.

Indexed as

Diabetes MellitusDiabetic NephropathiesFibroblastsHumansProtein BiosynthesisProteinsProteolysisProteinsdiabetesfibroblastsnephropathyprotein half-lifeprotein turnover rateproteomicsSILAC

Identifiers

PMID36769128
PMCPMC9917874

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.