Evidence map›Paper›PMID 38426629›Full record

ArticleAging cell2024

Transcriptomic and intervention evidence reveals domestic dogs as a promising model for anti-inflammatory investigation.

Min Zeng, Tong Zhou, Zhiyu Li, Guimei Li, Shurun Zhang, Lu Wang, Qing-Guo Huang, Ju-Dong Li, P Nadeeshika Samarawickrama, Yonghan He and 1 more

Open access · goldAbstract read
In one paragraph

Article in Aging cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. 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

11 authors at 4 institutions in 1 country.

Min ZengKey Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
Tong ZhouKey Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
Zhiyu LiState Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China.
Guimei LiKey Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
Shurun ZhangKey Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
Lu WangState Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China.
Qing-Guo HuangKunming Police Dog Base of the Chinese Ministry of Public Security, Kunming, China.
Ju-Dong LiKunming Police Dog Base of the Chinese Ministry of Public Security, Kunming, China.
P Nadeeshika SamarawickramaKey Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
Yonghan HeKey Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.ORCID 0000-0003-4409-4590
Guo-Dong WangKey Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.ORCID 0000-0002-9407-4363
Kunming Institute of Zoology · CNKunming Institute of Botany · CNMinistry of Public Security of the People's Republic of China · CNYunnan University · CN

Funding

National Key R&D Program of China 2019YFA0707101National Key R&D Program of China 2023YFC3603300National Natural Science Foundation of China 82171558Pioneer Hundred Talents Program of the Chinese Academy of SciencesSpring City Plan: The High-level Talent Promotion and Training Project of Kunming 2022SCP001State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University 2021KF006STI2030-Major Projects 2021ZD0203900Youth Innovation Promotion Association of CASYunnan Fundamental Research Projects 202201AS070038Yunnan Fundamental Research Projects 202201AV070011Yunnan Fundamental Research Projects 202305AH340006Yunnan Revitalization Talent Support Program Young Talent Project
6 · The paper itself

Abstract

Domestic dogs have great potential to expand our understanding of the determinants of aging. To understand the aging pattern of domestic dogs and evaluate whether they can be used as an aging model, we performed RNA sequencing on white blood cells from domestic dogs aged 1-9 years and treated aged dogs with classical antiaging approaches. We obtained 30 RNA sequencing libraries and identified 61 age-associated genes with dynamic changes, the majority of which were related to metabolism and immune function, which may be predominant biomarkers for aging in dogs. We next treated aged dogs with canine mesenchymal stem cells (cMSCs), nicotinamide mononucleotide, and rapamycin to determine whether and how they responded to the antiaging interventions. The results showed that these treatments can significantly reduce the level of inflammatory factors (IL-6 and TNF-α). MSCs effectively improved the heart functions of aged dogs. Three key potential age-related genes (PYCR1, CCRL2, and TOX) were reversed by MSC treatment, two of which (CCRL2 and TOX) are implicated in immunity. Overall, we profiled the transcriptomic pattern of domestic dogs and revealed that they may be a good model of aging, especially in anti-inflammatory investigations.

Indexed as

TranscriptomeAgingAnimalsAnti-Inflammatory AgentsDogsInflammationMesenchymal Stem CellsAnti-Inflammatory Agentsage‐associated genesaginganti‐inflammatorycanine mesenchymal stem cellsdog models

Identifiers

PMID38426629
PMCPMC11113267
OpenAlexW4392357816

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.