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联合网络分析与可解释机器学习揭示了一万多个反刍动物微生物基因组的环境适应性。

Combined network analysis and interpretable machine learning reveals the environmental adaptations of more than 10,000 ruminant microbial genomes.

作者信息

Yan Yueyang, Shi Tao, Bao Xin, Gai Yunpeng, Liang Xingxing, Jiang Yu, Li Qiushi

机构信息

Key Laboratory for Zoonoses Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.

College of Animal Science and Technology, Northwest A&F University, Yangling, China.

出版信息

Front Microbiol. 2023 Sep 20;14:1147007. doi: 10.3389/fmicb.2023.1147007. eCollection 2023.

DOI:10.3389/fmicb.2023.1147007
PMID:37799596
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10548237/
Abstract

BACKGROUND

The ruminant gastrointestinal contains numerous microbiomes that serve a crucial role in sustaining the host's productivity and health. In recent times, numerous studies have revealed that variations in influencing factors, including the environment, diet, and host, contribute to the shaping of gastrointestinal microbial adaptation to specific states. Therefore, understanding how host and environmental factors affect gastrointestinal microbes will help to improve the sustainability of ruminant production systems.

RESULTS

Based on a graphical analysis perspective, this study elucidates the microbial topology and robustness of the gastrointestinal of different ruminant species, showing that the microbial network is more resistant to random attacks. The risk of transmission of high-risk metagenome-assembled genome (MAG) was also demonstrated based on a large-scale survey of the distribution of antibiotic resistance genes (ARG) in the microbiota of most types of ecosystems. In addition, an interpretable machine learning framework was developed to study the complex, high-dimensional data of the gastrointestinal microbial genome. The evolution of gastrointestinal microbial adaptations to the environment in ruminants were analyzed and the adaptability changes of microorganisms to different altitudes were identified, including microbial transcriptional repair.

CONCLUSION

Our findings indicate that the environment has an impact on the functional features of microbiomes in ruminant. The findings provide a new insight for the future development of microbial resources for the sustainable development in agriculture.

摘要

背景

反刍动物胃肠道含有大量微生物群落,对维持宿主的生产力和健康起着至关重要的作用。近年来,大量研究表明,包括环境、饮食和宿主在内的影响因素的变化,有助于塑造胃肠道微生物对特定状态的适应性。因此,了解宿主和环境因素如何影响胃肠道微生物将有助于提高反刍动物生产系统的可持续性。

结果

基于图形分析视角,本研究阐明了不同反刍动物物种胃肠道的微生物拓扑结构和稳健性,表明微生物网络对随机攻击更具抗性。基于对大多数类型生态系统微生物群中抗生素抗性基因(ARG)分布的大规模调查,还证明了高风险宏基因组组装基因组(MAG)的传播风险。此外,还开发了一个可解释的机器学习框架来研究胃肠道微生物基因组的复杂高维数据。分析了反刍动物胃肠道微生物对环境的适应性进化,并确定了微生物对不同海拔高度的适应性变化,包括微生物转录修复。

结论

我们的研究结果表明,环境对反刍动物微生物群落的功能特征有影响。这些发现为农业可持续发展的微生物资源未来发展提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/befe49510b2f/fmicb-14-1147007-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/86e625f46621/fmicb-14-1147007-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/448c9f32a713/fmicb-14-1147007-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/d32fe345b722/fmicb-14-1147007-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/6b49eb7f3d8a/fmicb-14-1147007-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/2c0c8885f6ad/fmicb-14-1147007-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/befe49510b2f/fmicb-14-1147007-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/86e625f46621/fmicb-14-1147007-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/448c9f32a713/fmicb-14-1147007-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/d32fe345b722/fmicb-14-1147007-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/6b49eb7f3d8a/fmicb-14-1147007-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/2c0c8885f6ad/fmicb-14-1147007-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f1f/10548237/befe49510b2f/fmicb-14-1147007-g006.jpg

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