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  Long-lived proteins and DNA as candidate predictive biomarkers for tissue associated diseases

Liu, X., Novak, B., Namendorf, C., Steigenberger, B., Zhang, Y., & Turck, C. W. (2024). Long-lived proteins and DNA as candidate predictive biomarkers for tissue associated diseases. iScience, 27(4): 109642. doi:10.1016/j.isci.2024.109642.

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 Creators:
Liu, Xiaosong1, Author
Novak, Bozidar1, Author
Namendorf, Christian1, Author
Steigenberger, Barbara2, Author           
Zhang, Yaoyang1, Author
Turck, Christoph W.1, Author
Affiliations:
1external, ou_persistent22              
2Scientific Service Groups, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565170              

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Free keywords: TURNOVER; PROTEASOME; PEPTIDES; REVEALS; NUCLEAR; OSTEOCLASTOGENESIS; CELLS; BIRTH; ACIDScience & Technology - Other Topics;
 Abstract: Protein turnover is an important mechanism to maintain proteostasis. Long-lived proteins (LLPs) are vulnerable to lose their function due to time -accumulated damages. In this study we employed in vivo stable isotope labeling in mice from birth to postnatal day 89. Quantitative proteomics analysis of ten tissues and plasma identified 2113 LLPs, including widespread and tissue -specific ones. Interestingly, a significant percentage of LLPs was detected in plasma, implying a potential link to age -related cardiovascular diseases. LLPs identified in brains were related to neurodegenerative diseases. In addition, the relative quantification of DNA -derived deoxynucleosides from the same tissues provided information about cellular DNA renewal and showed good correlation with LLPs in the brain. The combined data reveal tissue -specific maps of mouse LLPs that may be involved in pathology due to a low renewal rate and an increased risk of damage. Tissue -derived peripheral LLPs hold promise as biomarkers for aging and age -related diseases.

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Language(s): eng - English
 Dates: 2024-03-282024-04-09
 Publication Status: Issued
 Pages: 19
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: iScience
Source Genre: Journal
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Publ. Info: Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis : Elsevier
Pages: - Volume / Issue: 27 (4) Sequence Number: 109642 Start / End Page: - Identifier: ISSN: 2589-0042
CoNE: https://pure.mpg.de/cone/journals/resource/2589-0042