Identifying Keystone Species In The Microbial Community Based On Cross-sectional Data.

Identifying Keystone Species In The Microbial Community Based On Cross-sectional Data.
Curr Gene Ther. 2018 Oct 08;:
Authors: Li M, Zhang J, Wu B, Zhou Z, Xu Y
Abstract
In microbial communities, the keystone species have a greater impact on the performance and dynamics of ecosystem than that of other species, in which we can see from the results losing gut microbiome causes some specific diseases. A mass of ongoing studies aim at identifying links between microbial community structure and human diseases. In this paper, we are introducing a valid keystone species identification method, in which a new Spread Intensity (SI) algorithm is used. Because the accuracies of current keystone species identification algorithms are difficult to evaluate for the high diversity and uncultivated status of microbial communities, we simulated cross-sectional data of microbial communities with known interactions and set up standard keystoneness rankings using Generalized Lotka-Volterra(GLV) model. Subsequently, we compared SI algorithm with existing methods by using simulated data and got an obvious better performance of SI algorithm than other methods. Also, we applied this method to gut microbiota datasets and identified some microbes having potential association with body weight. We first assembled three correlation metrics to calculate the interspecies correlation. Then we applied network deconvolution to remove indirect correlations. Finally, we u...

Source: Current Gene Therapy - October 8, 2018 Category: Genetics & Stem Cells Authors: Li M, Zhang J, Wu B, Zhou Z, Xu Y Tags: Curr Gene Ther Source Type: research