NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data

Abstract

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

Functional analysis of the apple fruit microbiome based on shotgun metagenomic sequencing of conventional and organic orchard samples

“…The description of the predicted source metabolites (metabolic inputs) in the Israeli organic and conventional orchards, together with the metabolic potential (the enzymes), allows us to simulate the metabolic activity in both environments (Tal et al, 2020) and explore the influence of environmental resources on metabolic capacities in a given environment . Simulations generate a set of all possible metabolites that can be produced (representing “function”), given (1) a set of possible reactions identified in the metagenome (representing “community”) and (2) sets of compounds representing the organic/conventional environmental resources (Tal et al, 2021) (Figure 1F). Here, expansion simulations were carried out in the two predicted environments representing the conventional and organic Israeli orchards (Figure 5B).…”

“…The environmental proxy is a list of metabolites predicted to be externally consumed from the environment (‘environmental resources’; Figure 1D). Predictions are based on the implantation of Tarjan's SCC (Widder et al, 2016) using the EdgeRtoSeeds algorithm in NetCom (Tal et al, 2021). Since the environment‐specific networks were constructed based on DA enzymes, they were highly fragmented, leading to the prediction of artificial source metabolites (Ofaim et al, 2017).…”

“…We used the expansion algorithm to predict metabolic activities in each environment through its implantation in NetCom (Tal et al, 2021; Berihu et al, 2023). The algorithm predicts feasible reactions in the metabolic network (expanded) given a pre‐defined set of substrates and reactions.…”

A framework for the targeted recruitment of crop-beneficial soil taxa based on network analysis of metagenomics data

“…1 D). Predictions are based on the implantation of Tarjan’s SCC [ 25 ] through its implantation in NetCom [ 28 ]. Since the treatment-specific sub-networks were constructed based on differentially abundant enzymes only, they are highly fragmented, leading to a prediction of artificial source-metabolites [ 29 ].…”

“…Similar to genomic approaches, where species-specific metabolic networks are constructed based on the content of enzyme coding genes [ 27 , 28 ], community networks can be constructed based on the functional annotations of metagenomic data [ 29 ]. Network-based simulations allow one to address the influence of changing environmental inputs (e.g., root-specific secreted metabolome, seed meal composition) or the functional repertoire of the community (genomic content in the sample) on the network structure and composition.…”