Evidence map›Paper›PMID 40224297›Full record

ArticleLife medicine2025

Unraveling the metabolic heterogeneity and commonality in senescent cells using systems modeling.

Gong-Hua Li, Yu-Hong Li, Qin Yu, Qing-Qing Zhou, Run-Feng Zhang, Chong-Jun Weng, Ming-Xia Ge, Qing-Peng Kong

Abstract read
In one paragraph

Article in Life medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
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

8 authors.

Gong-Hua LiState Key Laboratory of Genetic Evolution & Animal Models, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China.
Yu-Hong LiCollege of Biological Resources and Food Engineering, Qujing Normal University, Qujing 655000, China.
Qin YuState Key Laboratory of Genetic Evolution & Animal Models, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China.
Qing-Qing ZhouState Key Laboratory of Genetic Evolution & Animal Models, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China.
Run-Feng ZhangState Key Laboratory of Genetic Evolution & Animal Models, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China.
Chong-Jun WengState Key Laboratory of Genetic Evolution & Animal Models, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China.
Ming-Xia GeState Key Laboratory of Genetic Evolution & Animal Models, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China.
Qing-Peng KongState Key Laboratory of Genetic Evolution & Animal Models, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China.ORCID https://orcid.org/0000-0002-4996-3655

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellular senescence is a key contributor to aging and aging-related diseases, but its metabolic profiles are not well understood. Here, we performed a systematic analysis of the metabolic features of four types of cellular senescence (replication, irradiation, reactive oxygen species [ROS], and oncogene) in 12 cell lines using genome-wide metabolic modeling and meta-analysis. We discovered that replicative and ROS-induced senescence share a common metabolic signature, marked by decreased lipid metabolism and downregulated mevalonate pathway, while irradiation and oncogene-induced senescence exhibit more heterogeneity and divergence. Our genome-wide knockout simulations showed that enhancing the mevalonate pathway, by administrating mevalonate for instance, could reverse the metabolic alterations associated with senescence and human tissue aging, suggesting a potential anti-aging or lifespan-extending effect. Indeed, the experiment in

Indexed as

cellular senescencemeta-analysismetabolic modelingmetabolic profile

Identifiers

PMID40224297
PMCPMC11992571

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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.