MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles

Abstract

Background: The complexity and dynamics of microbial communities are major factors in the ecology of a system. With the NGS technique, metagenomics data provides a new way to explore microbial interactions. Lotka-Volterra models, which have been widely used to infer animal interactions in dynamic systems, have recently been applied to the analysis of metagenomic data.

Results: In this paper, we present the Lotka-Volterra model based tool, the Metagenomic Microbial Interacticon Simulator (MetaMIS), which is designed to analyze the time series data of microbial community profiles. MetaMIS first infers underlying microbial interactions from abundance tables for operational taxonomic units (OTUs) and then interprets interaction networks using the Lotka-Volterra model. We also embed a Bray-Curtis dissimilarity method in MetaMIS in order to evaluate the similarity to biological reality. MetaMIS is designed to tolerate a high level of missing data, and can estimate interaction information without the influence of rare microbes. For each interaction network, MetaMIS systematically examines interaction patterns (such as mutualism or competition) and refines the biotic role within microbes. As a case study, we collect a human male fecal microbiome and show that Micrococcaceae, a relatively low abundance OTU, is highly connected with 13 dominant OTUs and seems to play a critical role. MetaMIS is able to organize multiple interaction networks into a consensus network for comparative studies; thus we as a case study have also identified a consensus interaction network between female and male fecal microbiomes.

Conclusions: MetaMIS provides an efficient and user-friendly platform that may reveal new insights into metagenomics data. MetaMIS is freely available at: https://sourceforge.net/projects/metamis/ .

Keywords: Lotka-Volterra; Metagenomics; Network dynamics.

Fig. 1

The rationale behind MetaMIS. a The input of MetaMIS consists of microbial abundance profiles, and after its implementation there are two possible outcomes, success or failure of the interaction network. b In a microbial community, abundance-ranking OTUs appeared sequentially in different network

Fig. 2

The interface of MetaMIS. A typical analytic workflow proceeds through four steps: (a) uploading formulated data file(s), (b) specifying the parameters, (c) performing the calculations for the network, and (d) visualizing the outputs, which comprise five panels, (I) to (V). See Fig. 3 for a detailed description of these panels

Fig. 3

The analytic schema of MetaMIS. Panel I contains the original (a) and predicted (b) abundance profiles. Inferred microbial interactions are displayed in tabular form (c) and topologically (d), as shown by the global (D-1) and specific views (D-2) in Panel II. Panel III summarizes the distribution of interaction patterns (e) and their interactive strength (f) for each microbe. The PCA plot is intended to help users to identify key microbes (g). Panel IV provides a systematic diagram (h) to monitor and compare the performance from diverse interaction networks. Panel V displays a consensus network (i) in which interactions have more consensus directions among interaction networks

Fig. 4

Predicted microbial interactions show biological connections. a Positive interactions (black circles) were rich in metabolic complementarity. Negative interactions (white circles) generally showed lower levels of metabolic complementarity. b There were no differences of metabolic complementarity between the two groups in which positive or negative interactions were randomly selected. The error bar represented the standard error of metabolic complementarities for each group

Fig. 5

A consensus interaction network of male and female intestinal community. The red (or blue) arrow represents the activation (or repression)