Identifying Keystone Species in the Microbial Community Based on Cross- Sectional Data

Area of Science:

• Microbial ecology
• Systems biology
• Bioinformatics

Background:

• Keystone species disproportionately influence microbial community structure and ecosystem function.
• Disruptions in the gut microbiome are linked to various human diseases.
• Accurate identification of keystone species is challenging due to microbial community complexity.

Purpose of the Study:

• Introduce and validate a novel keystone species identification method, the Spread Intensity (SI) algorithm.
• Evaluate the performance of the SI algorithm against existing methods using simulated data.
• Apply the SI algorithm to identify potential keystone species in the human gut microbiome associated with body weight.

Main Methods:

• Simulated cross-sectional microbial community data using the Generalized Lotka-Volterra (GLV) model to establish standard keystoneness rankings.
• Comparison of the SI algorithm with existing identification methods using simulated data.
• Application of the SI algorithm to gut microbiota datasets, involving correlation metrics, network deconvolution, and Molecular Ecological Network Analysis (MENA).

Main Results:

• The SI algorithm demonstrated superior performance in identifying keystone species compared to existing methods on simulated data.
• The SI algorithm successfully identified microbes potentially associated with body weight in human gut microbiota datasets.
• The method effectively identified highly correlated species within the gut microbiome linked to body weight.

Conclusions:

• The Spread Intensity (SI) algorithm provides a robust and accurate method for keystone species identification in microbial communities.
• This approach offers a valuable tool for understanding the role of specific microbes in host-associated diseases.
• The findings enable the development of targeted interventions, such as gene therapy, for gut-associated conditions like obesity.