Identifying keystone species in microbial communities using deep learning
Por:
Wang, XW, Sun, Z, Jia, HJ, Michel-Mata, S, Angulo, MT, Dai, L, He, XS, Weiss, ST, Liu, YY
Publicada:
1 ene 2024
Ahead of Print:
1 nov 2023
Resumen:
Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.
Using deep learning to identify the assembly rules of microbial communities from different habitats, the authors develop a framework to quantify and predict the community-specific keystoneness of each species in any microbiome sample.
Filiaciones:
Wang, XW:
Brigham & Womens Hosp, Channing Div Network Med, Dept Med, Boston, MA 02115 USA
Harvard Med Sch, Boston, MA 02115 USA
Sun, Z:
Brigham & Womens Hosp, Channing Div Network Med, Dept Med, Boston, MA 02115 USA
Harvard Med Sch, Boston, MA 02115 USA
Jia, HJ:
Fudan Univ, Sch Life Sci, Shanghai, Peoples R China
Fudan Univ, Inst Precis Med Greater Bay Area Guangzhou, Guangzhou, Peoples R China
Michel-Mata, S:
Princeton Univ, Dept Ecol & Evolutionary Biol, Princeton, NJ USA
Angulo, MT:
Univ Nacl Autonoma Mexico, Inst Math, Juriquilla, Mexico
Dai, L:
Shenzhen Inst Adv Technol, Shenzhen Inst Synthet Biol, CAS Key Lab Quantitat Engn Biol, Shenzhen, Peoples R China
Univ Chinese Acad Sci, Beijing, Peoples R China
He, XS:
Forsyth Inst, Dept Microbiol, Cambridge, MA USA
Harvard Sch Dent Med, Dept Oral Med Infect & Immun, Boston, MA USA
Weiss, ST:
Brigham & Womens Hosp, Channing Div Network Med, Dept Med, Boston, MA 02115 USA
Harvard Med Sch, Boston, MA 02115 USA
Liu, YY:
Brigham & Womens Hosp, Channing Div Network Med, Dept Med, Boston, MA 02115 USA
Harvard Med Sch, Boston, MA 02115 USA
Univ Illinois, Carl R Woese Inst Genom Biol, Ctr Artificial Intelligence & Modeling, Champaign, IL 61820 USA
Green Submitted
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