Evidence map›Paper›PMID 27894254›Full record

ArticleGenetics, selection, evolution : GSE2016

Divergent selection-induced obesity alters the composition and functional pathways of chicken gut microbiota.

Jinmei Ding, Lele Zhao, Lifeng Wang, Wenjing Zhao, Zhengxiao Zhai, Li Leng, Yuxiang Wang, Chuan He, Yan Zhang, Heping Zhang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
3.1field-weighted citation impact, top 8% 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, 48 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

12 authors at 3 institutions in 1 country.

Jinmei DingDepartment of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, 200240, People's Republic of China.
Lele ZhaoDepartment of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, 200240, People's Republic of China.
Lifeng WangCollege of Food Science and Engineering, Inner Mongolia Agricultural University, Key Laboratory of Dairy Biotechnology and Engineering, Hohhot, 010018, People's Republic of China.
Wenjing ZhaoDepartment of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, 200240, People's Republic of China.
Zhengxiao ZhaiDepartment of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, 200240, People's Republic of China.
Li LengCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
Yuxiang WangCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
Chuan HeDepartment of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, 200240, People's Republic of China.
Yan ZhangShanghai Personal Biotechnology Limited Company, Shanghai, 200231, People's Republic of China.
Heping ZhangCollege of Food Science and Engineering, Inner Mongolia Agricultural University, Key Laboratory of Dairy Biotechnology and Engineering, Hohhot, 010018, People's Republic of China.
Hui LiCollege of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
He MengDepartment of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, 200240, People's Republic of China. menghe@sjtu.edu.cn.
Shanghai Jiao Tong University · CNNortheast Agricultural University · CNInner Mongolia Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe gastrointestinal tract is populated by a complex and vast microbial network, with a composition that reflects the relationships of the symbiosis, co-metabolism, and co-evolution of these microorganisms with their host. The mechanism that underlies such interactions between the genetics of the host and gut microbiota remains elusive.

resultsTo understand how genetic variation of the host shapes the gut microbiota and interacts with it to affect the metabolic phenotype of the host, we compared the abundance of microbial taxa and their functional performance between two lines of chickens (fat and lean) that had undergone long-term divergent selection for abdominal fat pad weight, which resulted in a 4.5-fold increase in the fat line compared to the lean line. Our analysis revealed that the proportions of Fusobacteria and Proteobacteria differed significantly between the two lines (8 vs. 18% and 33 vs. 24%, respectively) at the phylum level. Eight bacterial genera and 11 species were also substantially influenced by the host genotype. Differences between the two lines in the frequency of host alleles at loci that influence accumulation of abdominal fat were associated with differences in the abundance and composition of the gut microbiota. Moreover, microbial genome functional analysis showed that the gut microbiota was involved in pathways that are associated with fat metabolism such as lipid and glycan biosynthesis, as well as amino acid and energy metabolism. Interestingly, citrate cycle and peroxisome proliferator activated receptor (PPAR) signaling pathways that play important roles in lipid storage and metabolism were more prevalent in the fat line than in the lean line.

conclusionsOur study demonstrates that long-term divergent selection not only alters the composition of the gut microbiota, but also influences its functional performance by enriching its relative abundance in microbial taxa. These results support the hypothesis that the host and gut microbiota interact at the genetic level and that these interactions result in their co-evolution.

Indexed as

Body CompositionGastrointestinal MicrobiomeSelection, GeneticAnimalsBiodiversityChickensCluster AnalysisComputational BiologyFemaleGene Expression ProfilingGenetic VariationMaleMetagenomeMetagenomicsMolecular Sequence AnnotationObesityRNA, BacterialRNA, Ribosomal, 16S

Identifiers

PMID27894254
PMCPMC5127100
OpenAlexW2558351005

What OpenQuestion holds

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LicenceCC BY
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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.