Evidence map›Paper›PMID 39710721›Full record

ArticleAnimal microbiome2024

Metagenomic insights into the resistome, mobilome, and virulome of dogs with diverse lifestyles.

Nan Zhou, Weiye Chen, Luming Xia, Min Li, Huiping Ye, Chao Lv, Yiwen Chen, Zile Cheng, Tae-Jin Park, Pak-Leung Ho and 5 more

Abstract read
In one paragraph

Article in Animal microbiome, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Investigating the Sharing ofPathogens (Basel, Switzerland) · 2026
    Article
  2. 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

15 authors.

Nan Zhou *School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Weiye Chen *School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Luming Xia *Shanghai Center for Animal Disease Prevention and Control, Shanghai, China.
Min LiSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Huiping YeChongming Center for Animal Disease Prevention and Control, Shanghai, China.
Chao LvSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yiwen ChenSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zile ChengSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tae-Jin ParkHME Healthcare Co., Ltd., Suwon-Si, Gyeonggi-Do, Republic of Korea.
Pak-Leung HoCarol Yu Center for Infection and Department of Microbiology, University of Hong Kong, Hong Kong, China.
Xin GaoComputational Biosciences Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Xiaokui GuoSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China. xkguo@shsmu.edu.cn.
Hongjin ZhaoShanghai Center for Animal Disease Prevention and Control, Shanghai, China. hongjin945@163.com.
Huiluo CaoCarol Yu Center for Infection and Department of Microbiology, University of Hong Kong, Hong Kong, China. hcao@hku.hk.
Yongzhang ZhuSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China. yzhzhu@sjtu.edu.cn.

Funding

China Medical Board No. 20-365Health and Medical Research Fund No.19201901National Key Research and Development Program of China 2024YFE0199000National Natural Science Foundation of China No. 32170141Shanghai Jiao Tong University Integrated Innovation Fund No. 2020-01
6 · The paper itself

Abstract

backgroundDogs-whether pets, rural, or stray-exhibit distinct living styles that influence their fecal microbiota and resistomes, yet these dynamics remain underexplored. This study aimed to analyze and compare the fecal microbiota and resistomes of three groups of dogs (37 pets, 20 rural, and 25 stray dogs) in Shanghai, China.

resultsMetagenomic analysis revealed substantial differences in fecal microbial composition and metabolic activities among the dog groups. Pet dogs displayed the lowest microbial diversity. Using Shapley Additive Explanations (SHAP), an interpretable machine learning approach, Ligilactobacillus emerged as the most diverse genus, with significantly higher SHAP values in stray dogs, suggesting enhanced adaptability to more variable and less controlled environments. Across all samples, 587 antibiotic resistance genes (ARGs) were identified, conferring resistance to 14 antibiotic classes. A striking observation was the detection of mcr-1 in one pet dog, indicating a potential public health risk. The floR gene was identified as a key differentiator in resistance profiles, particularly in pet and rural dogs, likely due to antibiotic exposure in their environments.

conclusionThis study provides the first comprehensive assessment of fecal microbiota and resistome variations among dogs with different lifestyles, revealing a less resilient microbiome and heightened antimicrobial resistance in pet dogs, which could have public health implications.

Indexed as

Antimicrobial resistanceFecal microbiotaPet dogsRural dogsStray dogs

Identifiers

PMID39710721
PMCPMC11663309

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.