Functional molecular ecological networks

Abstract

Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on "species" richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO(2) (eCO(2)) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO(2) enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO(2) and ambient CO(2) (aCO(2)) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO(2) and aCO(2), at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO(2) dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change.

FIG 1

Overview of RMT-based MEN analysis. Six key steps are outlined here for molecular ecological analysis. A typical figure is placed in each step to highlight the main characteristics of such types of analysis.

FIG 2

Distributions of major functional genes in the network under aCO₂ (blue) and eCO₂ (red). The distribution of genes varies substantially among different functional groups. The gene designations are explained in the legend to Fig. 3.

FIG 3

Impact of eCO₂ on the network interactions of key functional genes. (A) Network interactions of the top six functional genes with the highest connectivities under eCO₂. (B) Network interactions of the corresponding functional genes under aCO₂. Each node signifies a functional gene. Colors of the nodes indicate different functional genes. A blue line indicates a positive interaction between two individual nodes, while a red line indicates a negative interaction. The networks were constructed by the RMT-based approach with the GeoChip data. The network interactions for these microbial functional genes were complex under eCO₂ but simple under aCO₂, suggesting that eCO₂ has a significant impact on the network interactions among key functional genes/populations in the grassland soil microbial communities. The gene are chi (endochitinase), bcsG (endoglucanase), chi36 (exochitinase), exg (exoglucanase), lip (lignin peroxidase), mnp (manganese peroxidase), pglA (pectinase), phox (phenol oxidase), xyn (xylananase), CODH (carbon monoxide dehydrogenase), FTHFS (tetrahydrofolate formylase), pcc (propionyl coenzyme A carboxylase), rbcL (ribulose-1,5-bisphosphate carboxylase oxygenase), mcrA (methyl coenzyme M reductase), pmoA (methane monooxygenase), nifH (nitrogenase reductase), nirK (nitrite reductase), nirS (nitrite reductase), nrfA (c-type cytochrome nitrite reductase), ppk (polyphosphate kinase), ppx (exopolyphosphatase), dsrA (dissimilatory sulfite reductase), and sox (sulfite oxidase).

FIG 4

Network interactions of microorganisms containing nifH genes under eCO₂ (A) and aCO₂ (B). Microorganisms containing nifH genes formed complex network interactions with other functional groups, and some nifH-containing populations serve as central hubs in this community. The networks were constructed by the RMT-based approach with the GeoChip data from eCO₂ and aCO₂ and only shared nifH nodes, and their nearest neighbors in the network are shown here. The nifH genes detected in both fMENs of aCO₂ and eCO₂ are displayed with a bigger node size. The gene designations are explained in the legend to Fig. 3. The numbers represent the GenBank protein IDs to differentiate different nifH genes because most of them represent uncultivated microorganisms.

FIG 5

Network interactions of a nifH hub under both eCO₂ and aCO₂. The nifH-containing uncultivated microorganism had intensive positive interactions with many functional groups of diverse phylogenetic origins under eCO₂ (A) but very simple interactions with other functional groups under aCO₂ (B). Only this nifH gene node (110630622) and its nearest neighbors are shown. The direct interactions with this nifH gene are labeled with thick lines, whereas the indirect interactions are marked with thin lines.