Evidence map›Paper›PMID 37770480›Full record

ArticleNature communications2023

Metabolic crosstalk between skeletal muscle cells and liver through IRF4-FSTL1 in nonalcoholic steatohepatitis.

Shanshan Guo, Yonghao Feng, Xiaopeng Zhu, Xinyi Zhang, Hui Wang, Ruwen Wang, Qiongyue Zhang, Yiming Li, Yan Ren, Xin Gao and 4 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
7.1field-weighted citation impact, top 2% 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

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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

14 authors at 4 institutions in 1 country.

Shanshan Guo *Department of Endocrinology and Metabolism, State Key Laboratory of Genetic Engineering, School of Life Sciences, Huashan Hospital, Fudan University, Shanghai, 200040, China.
Yonghao Feng *Department of Endocrinology and Metabolism, State Key Laboratory of Genetic Engineering, School of Life Sciences, Huashan Hospital, Fudan University, Shanghai, 200040, China.ORCID http://orcid.org/0000-0002-2585-5203
Xiaopeng Zhu *Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Xinyi Zhang *Human Phenome Institute, Fudan University, Shanghai, 201203, China.
Hui WangShanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism & Integrative Biology, Fudan University, Shanghai, 200438, China.
Ruwen WangSchool of Kinesiology, Shanghai University of Sport, Shanghai, 200438, China.
Qiongyue ZhangDepartment of Endocrinology and Metabolism, Huashan Hospital, Fudan University, Shanghai, 200040, China.
Yiming LiDepartment of Endocrinology and Metabolism, Huashan Hospital, Fudan University, Shanghai, 200040, China.
Yan RenExperiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.ORCID http://orcid.org/0000-0002-4007-8625
Xin GaoDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Hua BianDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. zhongshan_bh@126.com.
Tiemin LiuDepartment of Endocrinology and Metabolism, State Key Laboratory of Genetic Engineering, School of Life Sciences, Huashan Hospital, Fudan University, Shanghai, 200040, China. tiemin_liu@fudan.edu.cn.
Huanqing GaoDepartment of Endocrinology and Metabolism, State Key Laboratory of Genetic Engineering, School of Life Sciences, Huashan Hospital, Fudan University, Shanghai, 200040, China. gao_hq@fudan.edu.cn.
Xingxing KongDepartment of Endocrinology and Metabolism, State Key Laboratory of Genetic Engineering, School of Life Sciences, Huashan Hospital, Fudan University, Shanghai, 200040, China. kongxingxing@fudan.edu.cn.ORCID http://orcid.org/0000-0002-8395-0268
Fudan University · CNSun Yat-sen University · CNShanghai University of Sport · CNShanghai University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inter-organ crosstalk has gained increasing attention in recent times; however, the underlying mechanisms remain unclear. In this study, we elucidate an endocrine pathway that is regulated by skeletal muscle interferon regulatory factor (IRF) 4, which manipulates liver pathology. Skeletal muscle specific IRF4 knockout (F4MKO) mice exhibited ameliorated hepatic steatosis, inflammation, and fibrosis, without changes in body weight, when put on a nonalcoholic steatohepatitis (NASH) diet. Proteomics analysis results suggested that follistatin-like protein 1 (FSTL1) may constitute a link between muscles and the liver. Dual luciferase assays showed that IRF4 can transcriptionally regulate FSTL1. Further, inducing FSTL1 expression in the muscles of F4MKO mice is sufficient to restore liver pathology. In addition, co-culture experiments confirmed that FSTL1 plays a distinct role in various liver cell types via different receptors. Finally, we observed that the serum FSTL1 level is positively correlated with NASH progression in humans. These data indicate a signaling pathway involving IRF4-FSTL1-DIP2A/CD14, that links skeletal muscle cells to the liver in the pathogenesis of NASH.

Indexed as

Follistatin-Related ProteinsNon-alcoholic Fatty Liver DiseaseAnimalsHumansLiverLiver CirrhosisMiceMice, Inbred C57BLMuscle, SkeletalSignal TransductionFollistatin-Related ProteinsFSTL1 protein, human

Identifiers

PMID37770480
PMCPMC10539336
OpenAlexW4387136802

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.