Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Abstract

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

A Metataxonomic Analysis of Maple Sap Microbial Communities Reveals New Insights Into Maple Syrup Complexity

“…Microorganisms occur in communities and can influence each other's activity directly using targeted mechanisms or indirectly by modify their environments (Tyc et al, 2014;Grandclement et al, 2016;Ratzke and Gore, 2018;Gu et al, 2020;Gupta et al, 2021). In some cases, microbial interactions may be as important as the presence of an individual species for community function (Gould et al, 2018) and microorganisms can depend on metabolite production by other community members for survival (Ponomarova et al, 2017;Ratzke et al, 2020).…”

Interactive and Dynamic Effects of Rootstock and Rhizobiome on Scion Nutrition in Cacao Seedlings

“…We interpret the insignificant nutrient-microbe correlations and Mantel tests and the results of stepwise regression as a call for more complex understanding of plant-soil-microbe interactions at intermediate scales, e.g., between binary associations and entire communities. Identification of functional guilds, statistical methods to identify the most pertinent features of highdimensionality datasets, and synthetic-community-based studies could prove useful here among other methods (recently reviewed by Gupta et al, 2021). Plant growth promotion or enhanced plant nutrient content mediated by these intermediate-scale groups of microorganisms could result from direct mechanisms (e.g., direct nutrient transfer by symbionts) or indirect mechanisms (e.g., increased bioavailability of soil nutrients), but in either case would be an emergent property of the group rather than attributable to a single organism.…”

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

“…The method of choice depends on the parameters to be explored. Some parameters include directionality (positive, negative, or neutral), reciprocity (unidirectional or bidirectional), strength, mode of action, and spatiotemporal variation (hours, weeks, order of colonization) [ 23 , 24 ]. A combination of qualitative strategies and quantitative models will enable discovery of interdependencies in networks with several interconnected partners such as those in syntrophic communities [ 25 ].…”