Molecular ecological network analyses

Abstract

Background: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.

Results: Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).

Conclusions: The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Figure 1

Overview of the Random Matrix Theory (RMT)-based molecular ecological network analysis. Two major parts are included, network construction and network analyses. In each of them, several key steps are outlined.

Figure 2

Process of random matrix theory-based approach for automatically detecting threshold to construct molecular ecological networks.

Figure 3

The robustness to noise of RMT-based MEN construction. Ineasing levels of Gaussian noise were added to the pyrosequencing datasets under experimental warming. The mean of noise was zero and standard deviation (σ_noise) was set to 5, 10, 20, 30 to 100 % of the average of relative abundance of whole dataset. The thresholds (S_t) of all permutated datasets were set to 0.76 that was consistent with original dataset.

Figure 4

The submodules of the warming pMEN. (A) The network graph with submodule structure by the fast greedy modularity optimization method. Each node signifies an OTU, which could correspond to a miobial population. Colors of the nodes indicate different major phyla. A blue edge indicates a positive interaction between two individual nodes, while a red edge indicates a negative interaction. (B) The correlations and heatmap to show module eigengenes of warming pMEN. The upper part is the hierarchical clustering based on the Pearson correlations among module eigengenes and the below heatmap shows the coefficient values (r). Red color means higher correlation whereas green color signified lower correlation. (C) ZP-plot showing distribution of OTUs based on their module-based topological roles. Each dot represents an OTU in the dataset of warming (red), or unwarming (green). The topological role of each OTU was determined according to the scatter plot of within-module connectivity (z) and among-module connectivity (P) [55,60].

Figure 5

The correlations between module eigengenes and environmental traits in the warming pMEN. The color of each plot indicates the correlation between corresponding module eigengene and environmental trait. Red color means highly positive correlation and green color means highly negative correlation. The numbers in each plot are the correlation coefficient (r) and significance (p) in parentheses. The environmental traits include soil pH value (pH), NO₃-nitrogen content (NO₃N), soil carbon content (SC) and average soil temperature (avgT).

Figure 6

An overview of molecular ecological network analysis pipeline (MENAP).