Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference

被引:0
作者
Lei, Yinlei [1 ,2 ]
Li, Min [3 ]
Zhang, Han [4 ,5 ]
Deng, Yu [1 ,2 ]
Dong, Xinyu [6 ]
Chen, Pengyu [2 ]
Li, Ye [7 ]
Zhang, Suhua [4 ]
Li, Chengtao [4 ]
Wang, Shouyu [8 ]
Tao, Ruiyang [1 ]
机构
[1] Minist Justice, Shanghai Forens Serv Platform, Shanghai Key Lab Forens Med, Key Lab Forens Sci,Acad Forens Sci, Shanghai, Peoples R China
[2] Zunyi Med Univ, Dept Forens Med, Zunyi, Peoples R China
[3] Shandong First Med Univ & Shandong Acad Med Sci, Sch Clin & Basic Med Sci, Jinan, Peoples R China
[4] Fudan Univ, Inst Forens Sci, Shanghai, Peoples R China
[5] Guizhou Med Univ, Dept Forens Med, Guiyang, Peoples R China
[6] Minhang Branch Shanghai Publ Secur Bur, Shanghai, Peoples R China
[7] Xinjiang Med Univ, Sch Basic Med Sci, Dept Forens Med, Urumqi, Peoples R China
[8] Fudan Univ, Shanghai Med Coll, Dept Forens Med, Shanghai, Peoples R China
关键词
human microbiome; 16S rRNA; geographic regions; machine learning; PREDICTION;
D O I
10.1128/msphere.00672-24
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
The human microbiome, the community of microorganisms that reside on and inside the human body, is critically important for health and disease. However, it is influenced by various factors and may vary among individuals residing in distinct geographic regions. In this study, 220 samples, consisting of sterile swabs from palmar skin and oral and nasal cavities were collected from Chinese Han individuals living in Shanghai, Chifeng, Kunming, and Urumqi, representing the geographic regions of east, northeast, southwest, and northwest China. The full-length 16S rRNA gene of the microbiota in each sample was sequenced using the PacBio single-molecule real-time sequencing platform, followed by clustering the sequences into operational taxonomic units (OTUs). The analysis revealed significant differences in microbial communities among the four regions. Cutibacterium was the most abundant bacterium in palmar samples from Shanghai and Kunming, Psychrobacter in Chifeng samples, and Psychrobacillus in Urumqi samples. Additionally, Streptococcus and Staphylococcus were the dominant bacteria in the oral and nasal cavities. Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. The prediction accuracy using hypervariable regions (V3-V4 and V4-V5) was comparable with that of using the entire 16S rRNA. Overall, our study highlights the distinctiveness of the human microbiome in individuals living in these four regions. Furthermore, the microbiome can serve as a biomarker for geographic origin inference, which has immense application value in forensic science. IMPORTANCE Microbial communities in human hosts play a significant role in health and disease, varying in species, quantity, and composition due to factors such as gender, ethnicity, health status, lifestyle, and living environment. The characteristics of microbial composition at various body sites of individuals from different regions remain largely unexplored. This study utilized single-molecule real-time sequencing technology to detect the entire 16S rRNA gene of bacteria residing in the palmar skin, oral, and nasal cavities of Han individuals from four regions in China. The composition and structure of the bacteria at these three body sites were well characterized and found to differ regionally. The results elucidate the differences in bacterial communities colonizing these body sites across different regions and reveal the influence of geographical factors on human bacteria. These findings not only contribute to a deeper understanding of the diversity and geographical distribution of human bacteria but also enrich the microbiome data of the Asian population for further studies.
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页数:16
相关论文
共 48 条
  • [1] The human skin microbiome
    Byrd, Allyson L.
    Belkaid, Yasmine
    Segre, Julia A.
    [J]. NATURE REVIEWS MICROBIOLOGY, 2018, 16 (03) : 143 - 155
  • [2] Optimisation of methods for bacterial skin microbiome investigation: primer selection and comparison of the 454 versus MiSeq platform
    Castelino, Madhura
    Eyre, Stephen
    Moat, John
    Fox, Graeme
    Martin, Paul
    Ho, Pauline
    Upton, Mathew
    Barton, Anne
    [J]. BMC MICROBIOLOGY, 2017, 17
  • [3] Geography and Location Are the Primary Drivers of Office Microbiome Composition
    Chase, John
    Fouquier, Jennifer
    Zare, Mahnaz
    Sonderegger, Derek L.
    Knight, Rob
    Kelley, Scott T.
    Siegel, Jeffrey
    Caporaso, J. Gregory
    [J]. MSYSTEMS, 2016, 1 (02)
  • [4] Diversity of nasal microbiota and its interaction with surface microbiota among residents in healthcare institutes
    Chen, Chang-Hua
    Liou, Ming-Li
    Lee, Cheng-Yang
    Chang, Ming-Chuan
    Kuo, Han-Yueh
    Chang, Tzu-Hao
    [J]. SCIENTIFIC REPORTS, 2019, 9 (1)
  • [5] Impact of Preservation Method and 16S rRNA Hypervariable Region on Gut Microbiota Profiling
    Chen, Zigui
    Hui, Pak Chun
    Hui, Mamie
    Yeoh, Yun Kit
    Wong, Po Yee
    Chan, Martin C. W.
    Wong, Martin C. S.
    Ng, Siew C.
    Chan, Francis K. L.
    Chan, Paul K. S.
    [J]. MSYSTEMS, 2019, 4 (01)
  • [6] Chintalapati S, 2004, CELL MOL BIOL, V50, P631
  • [7] Choi BG, 2019, YONSEI MED J, V60, P191
  • [8] Clarke T, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-21779-z
  • [9] Some like it cold: understanding the survival strategies of psychrophiles
    De Maayer, Pieter
    Anderson, Dominique
    Cary, Craig
    Cowan, Don A.
    [J]. EMBO REPORTS, 2014, 15 (05) : 508 - 517
  • [10] Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes
    Earl, Joshua P.
    Adappa, Nithin D.
    Krol, Jaroslaw
    Bhat, Archana S.
    Balashov, Sergey
    Ehrlich, Rachel L.
    Palmer, James N.
    Workman, Alan D.
    Blasetti, Mariel
    Sen, Bhaswati
    Hammond, Jocelyn
    Cohen, Noam A.
    Ehrlich, Garth D.
    Mell, Joshua Chang
    [J]. MICROBIOME, 2018, 6