Elucidation of complexity and prediction of interactions in microbial communities

Abstract

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

Figure 1

Milestones in microbiology and computational modelling for the study of microbial communities.

Figure 2

Interactions amongst microorganisms. Within a complex community, the members of the population may engage in multiple interactions at the same time. 0 refers to neutral interaction, while + and – depicts a positive or a negative outcome of interaction.

Figure 3

Overview of different omics techniques. Different experimental approaches and computational strategies are applied for the study of microbial communities. During gene amplicon analysis, SSUs or ITS sequences are clustered into operational taxonomic units (OTUs) and the taxonomic identity is assigned for each OTU based on sequence homology against known sequences in a database. The resulting OTUs are used to calculate the relative abundance of each organism and quantify the population diversity between samples. In shotgun metagenomics, genomic DNA sequences can either be mapped to a reference database or used for de novo assembly of genomes. The recovered genomes can be used to assign phylogeny, calculate the relative abundance of the identified genome and assess the functional capability. In metatranscriptomics, messenger RNA (mRNA) is used to generate complementary DNA libraries that can either be mapped to reference genomes to generate gene expression profiles. These expression profiles are used to identify active pathways, genes and organisms. In metaproteomics, mass spectrometry and fragmentation are used to reveal the amino acid sequence of peptides. The identified peptides are associated with full‐length proteins by sequence homology searches against a reference database. Similar to metatranscriptomic analysis, protein expression profiles can be used to identify active pathways as well as active organisms. In metabolomics, metabolites are separated using chromatography techniques and identified and quantified using mass spectrometry. Similar to metaproteomics, the comparison between fragmentation profiles and reference databases is used to annotate the metabolic compound. Enrichment and clustering analysis can be applied to reveal patterns between sets of samples or to identify condition‐dependent compounds.

Figure 4

Integration of experimental data and in silico analysis in a community systems biology approach. The first step in the iterative workflow encompasses the identification of individual community members, followed by the creation and validation of single metabolic networks. Subsequently, a predictive community model is constructed and validated. Various experimental and computational methods are interspersed along the design–build–test–learn cycle to unravel community interactions and predict community dynamics.