Elucidation of complexity and prediction of interactions in microbial communities

Abstract

SummaryMicroorganisms 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.

The social network of microorganisms — how auxotrophies shape complex communities

“…Genome-based prediction of auxotrophies can indicate potential cooperative or competitive interactions 79 . In silico analysis of metabolic pathways, confirmed by experimental data, can yield models of carbon, energy and nutrient flow in microbial communities 78,80 .…”

“…Temporal profiling at a strain-level resolution is fundamental to study longitudinal dynamics of microbial communities 76 . Shotgun metagenomics in combination with differential binning permits the retrieval of nearly complete genomes from complex microbial communities and enables identification of auxotrophies and nutrient requirements 78 .…”

“…Functional profiling using metatranscriptomics, metaproteomics and metabolomics: high-throughput functional profiles of microbial communities can be obtained by sequence-based and mass spectrometry-based omic methods 76,78 . Metatranscriptomics, metaproteomics and metabolomics measure mRNA, protein or metabolite levels, respectively.…”

Interkingdom microbial consortia mechanisms to guide biotechnological applications

“…mineral precipitation and dissolution) and climate change (Warren and Kauffman, ; Jorgensen et al ., ; Prokopenko et al ., ; Wagner, ; Wegener et al ., ; Dang and Lovell, ; Zhang et al ., ). Furthermore, interkingdom consortia have been widely found and extensively studied in the human body, such as biofilms in the oral cavity and human gut commensal microbiota (Thakur et al ., ; Zuñiga et al ., ).…”

“…mineral precipitation and dissolution) and climate change (Warren and Kauffman, 2003;Jorgensen et al, 2012;Prokopenko et al, 2013;Wagner, 2015;Wegener et al, 2015;Dang and Lovell, 2016;Zhang et al, 2017). Furthermore, interkingdom consortia have been widely found and extensively studied in the human body, such as biofilms in the oral cavity and human gut commensal microbiota (Thakur et al, 2016;Zuñiga et al, 2017). Important interactions, mechanisms and structures determined in studies of natural interkingdom consortia are critical first steps in the design of an artificial interkingdom consortium.…”