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

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.

Keywords: Polypedates megacephalus; artificial hibernation; gut microbiota; network; probiotics.

Figure 1

Time dependent taxonomic composition spanning 15 days over AH. Taxonomic composition of fecal microbiota over 12 time points including pre- and post-artificial hibernation (AH) at the phylum level. Each bar represents one individual.

Figure 2

Inferred interaction partners of Bacillus, Corynebacterium, and Lactococcus. Complex relationships were inferred from gut bacterial communities in the 15-day time series data. Each node represents an inferred interaction partner (IIP) and each edge represents an inferred interaction relation between them. The edges in green represent commensal or mutual interactions, and the edges in red represent amensal or competitive interactions. Only the IIPs that contained two inferred interaction relationships are shown.

Figure 3

Relative abundance of IIPs of Bacillus, Corynebacterium, and Lactococcus after two-week oral trials. Five oral administrations included (A) G₁: L. garvieae (10⁷ CFU g⁻¹) in 0.9% saline, (B) G₂: C. variabile (10⁷ CFU g⁻¹) in 0.9% saline, (C) G₃: B. coagulans (10⁷ CFU g⁻¹) in 0.9% saline, (D) G₄: A combination of B. coagulans, C. variabile, and L. garvieae (each contains 10⁷ CFU g⁻¹) in 0.9% saline, and (E) G₅: Control (0.9% saline). Each node represents an IIP that is correlated with Bacillus, Corynebacterium, or Lactococcus, and each edge represents an inferred interaction relation between them. The edges in green represent commensal or mutual interactions, and the edges in red represent amensal or competitive interactions. To better visualize the distribution, the size of each node represents the relative abundance of gut microbes in logarithmic scale.