Inferring Microbial Interactions in the Gut of the Hong Kong Whipping Frog (Polypedates megacephalus) and a Validation Using Probiotics.

Francis Cheng-Hsuan Weng,Grace Tzun-Wen Shaw,Chieh-Yin Weng,Yi-Ju Yang,Daryi Wang

doi:10.3389/fmicb.2017.00525

Abstract

The concerted activity of intestinal microbes is crucial to the health and development of their host organisms. Investigation of microbial interactions in the gut should deepen our understanding of how these micro-ecosystems function. Due to advances in Next Generation Sequencing (NGS) technologies, various bioinformatic strategies have been proposed to investigate these microbial interactions. However, due to the complexity of the intestinal microbial community and difficulties in monitoring their interactions, at present there is a gap between the theory and biological application. In order to construct and validate microbial relationships, we first induce a community shift from simple to complex by manipulating artificial hibernation (AH) in the treefrog Polypedates megacephalus. To monitor community growth and microbial interactions, we further performed a time-course screen using a 16S rRNA amplicon approach and a Lotka-Volterra model. Lotka-Volterra models, also known as predator–prey equations, predict the dynamics of microbial communities and how communities are structured and sustained. An interaction network of gut microbiota at the genus level in the treefrog was constructed using Metagenomic Microbial Interaction Simulator (MetaMIS) package. The interaction network obtained had 1,568 commensal, 1,737 amensal, 3,777 mutual, and 3,232 competitive relationships, e.g., Lactococcus garvieae has a commensal relationship with Corynebacterium variabile. To validate the interacting relationships, the gut microbe composition was analyzed after probiotic trials using single strain (L. garvieae, C. variabile, and Bacillus coagulans, respectively) and a combination of L. garvieae, C. variabile, and B. coagulans, because of the cooperative relationship among their respective genera identified in the interaction network. After a 2 week trial, we found via 16S rRNA amplicon analysis that the combination of cooperative microbes yielded significantly higher probiotic concentrations than single strains, and the immune response (interleukin-10 expression) also significantly changed in a manner consistent with improved probiotic effects. By taking advantage of microbial community shift from simple to complex, we thus constructed a reliable microbial interaction network, and validated it using probiotic strains as a test system.

Highlights

Gut microbes and their hosts exist in a symbiotic relationship
Our results showed that the combination of three representatives of these genera, L. garvieae, C. variabile, and B. coagulans, works more efficiently than any single strain, reflecting the reliability of the inferred microbial interaction
At the numbers of reads generated for each sample, the numbers of genera were in the saturation phase (Supplementary Figure 1), indicating that genera from each sample had been sufficiently recovered in MiSeq sequencing

Summary

Introduction

Gut microbes and their hosts exist in a symbiotic relationship. Gut microbes contribute to important host functions, including fermenting unused energy substrates, training the immune system, preventing growth of pathogenic bacteria, and regulating gut development (Hooper et al, 2002; Xu and Gordon, 2003; Li et al, 2008; Perez et al, 2010; Ye et al, 2014). The gLV equations have been used successfully to predict temporal dynamics of microbiota in the mouse intestine (Stein et al, 2013), and within a cheese-making environment (Mounier et al, 2008), by analyzing microbiome time-series data. Time-series data inherently contain information including the statistical dependency of observations as a function of time. When these features of time-series data are properly modeled, it is possible to gain substantial new insights into the behavior of the system under study. Some studies even suggested that the distribution of interaction pairs ( obtained using a gLV dynamic model) in an ecological system can be used to predict microbiota stability (Coyte et al, 2015)

Methods

Results

Conclusion