Evidence map›Paper›PMID 33159023›Full record

ArticleAging2020

Comparative proteomic analysis identifies biomarkers for renal aging.

Meiqi Yi, Yingying Ma, Songbiao Zhu, Chengting Luo, Yuling Chen, Qingtao Wang, Haiteng Deng

Open access · greenAbstract readComparative Study
In one paragraph

Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.4field-weighted citation impact, top 18% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

9 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Determining the age of single cells using scMLEAge.bioRxiv : the preprint server for biology · 2026
    Article
  3. Kidney Disease as a Driver of Immunosenescence: Mechanisms and Potential Interventions.Journal of the American Society of Nephrology : JASN · 2026
    Review
  4. Article
  5. Article
  6. The peroxisome: an update on mysteries 3.0.Histochemistry and cell biology · 2024
    Review
  7. Review
  8. Review
  9. Review
4 · The record

Corrections and comments

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

7 authors at 3 institutions in 1 country.

Meiqi YiMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Yingying MaMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Songbiao ZhuMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Chengting LuoMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Yuling ChenMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Qingtao WangBeijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing, China.
Haiteng DengMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Tsinghua University · CNBeijing Chao-Yang Hospital · CNCapital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteomics have long been applied into characterization of molecular signatures in aging. Due to different methods and instrumentations employed for proteomic analysis, inter-dataset validation needs to be performed to identify potential biomarkers for aging. In this study, we used comparative proteomics analysis to profile age-associated changes in proteome and glutathionylome in mouse kidneys. We identified 108 proteins that were differentially expressed in young and aged mouse kidneys in three different datasets; from these, 27 proteins were identified as potential renal aging biomarkers, including phosphoenolpyruvate carboxykinase (Pck1), CD5 antigen-like protein (Cd5l), aldehyde dehydrogenase 1 (Aldh1a1), and uromodulin. Our results also showed that peroxisomal proteins were significantly downregulated in aged mice, whereas IgGs were upregulated, suggesting that peroxisome deterioration might be a hallmark for renal aging. Glutathionylome analysis demonstrated that downregulation of catalase and glutaredoxin-1 (Glrx1) significantly increased protein glutathionylation in aged mice. In addition, nicotinamide mononucleotide (NMN) administration significantly increased the number of peroxisomes in aged mouse kidneys, indicating that NMN enhanced peroxisome biogenesis, and suggesting that it might be beneficial to reduce kidney injuries. Together, our data identify novel potential biomarkers for renal aging, and provide a valuable resource for understanding the age-associated changes in kidneys.

Indexed as

ProteomeProteomicsAge FactorsAgingAnimalsBiomarkersChromatography, Reverse-PhaseDatabases, ProteinKidneyMiceMice, Inbred C57BLNicotinamide MononucleotidePeroxisomesProteostasisTandem Mass SpectrometryBiomarkersNicotinamide MononucleotideProteomebiomarkersglutathionylationNMNproteomicsrenal aging

Identifiers

PMID33159023
PMCPMC7695359
OpenAlexW3097973013

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.