Evidence map›Paper›PMID 42905550›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Validation of targets linked to weight gain in C57BL/6 mice and translational aspects in humans.

Yingga Wu, Agata M Rudolf, Lu Wang, Zhengjie Zhang, Chaoqun Niu, Fan Xia, Min Li, Anyongqi Wang, Sharon E Mitchell, Haoran Zhang and 3 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Yingga Wu *Institutes of Biomedical Sciences, Inner Mongolia University, Hohhot 010020, China.
Agata M Rudolf *Laboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Lu Wang *Laboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0002-1654-3259
Zhengjie Zhang *Laboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Chaoqun NiuShenzhen Key laboratory of Metabolic Health, Center for Energy Metabolism and Reproduction, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Fan XiaLaboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Min LiLaboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Anyongqi WangLaboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Sharon E MitchellInstitute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen AB24 2TZ, Scotland, United Kingdom.ORCID 0000-0003-3941-0309
Haoran ZhangInstitutes of Biomedical Sciences, Inner Mongolia University, Hohhot 010020, China.
Yalin HeInstitutes of Biomedical Sciences, Inner Mongolia University, Hohhot 010020, China.
Chunyu ChenLaboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
John R SpeakmanLaboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0002-2457-1823

Funding

| Natural Science Foundation of Shenzhen Municipality (Shenzhen Natural Science Foundation) ZDSYS20210427152400001NSFC Young Scientists Fund Type C 32501029NSF of China 2150710519NSF of China 92357304NSF of China W2411022troduction Project of High-Level Talents in Inner Mongolia University Biology 10000-23122101/027
6 · The paper itself

Abstract

Obesity is linked to complex interactions between an organism's genetic background and its environment. Mouse models have been invaluable research tools to study obesity, with C57BL/6N being widely studied, given its propensity for weight gain on a high-fat diet (HFD). Despite this, the genetic basis of its susceptibility is poorly understood. In a partner paper we have identified SNP variants in 11 genes causing susceptibility to, or protection against, HFD-induced obesity in male C57BL/6N mice explaining 87% of the individual variation in fat gain. Surprisingly, most of them were maximally expressed outside the brain. In this study, we further validate some of these targets. We demonstrate that tissue-specific inhibition of Nova1 and Mchr1 in muscle, Nr1i3 in liver, and Ahr in lung and liver led to resistance to HFD-induced obesity or glucose intolerance. Further RNAseq analysis revealed that mTOR, Acox1, IL-15, and eIF2a signals are positioned downstream of these genes, potentially mediating their effects on obesity susceptibility. Importantly, variants in eight of the genes identified here have been previously implicated in human obesity, suggesting that a deeper understanding of the genetic/environment interactions of model systems can inform this complex human disease. Our work confirms that body fat regulation is likely more complex than current models which focus on the brain-adipose axis suggest.

Indexed as

ObesityWeight GainAnimalsDiet, High-FatDisease Models, AnimalGenetic Predisposition to DiseaseHumansLiverMaleMiceMice, Inbred C57BLPolymorphism, Single NucleotideTranslational Research, Biomedicalenergy balancefood intakegeneticsmouseobesity

Identifiers

PMID42905550
PMCPMC13715730

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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.