Towards a mechanistic understanding of microalgae-bacteria interactions: integration of metabolomic analysis and computational models

The intricate relationships between microalgae and bacteria are best understood by integrated, multistep, and interdisciplinary approaches that have metabolites at their core. Interactions amongst marine microalgae and heterotrophic bacteria drive processes underlying major biogeochemical cycles and are important for many artificial systems. These dynamic and complex interactions span the range from cooperative to competitive, and it is the diverse and intricate networks of metabolites and chemical mediators that are predicted to principally dictate the nature of the relationship at any point in time. Recent advances in technologies to identify, analyze, and quantify metabolites have allowed for a comprehensive view of the molecules available for exchange and/or reflective of organismal interactions, setting the stage for development of mechanistic understanding of these systems. Here, we (i) review the current knowledge landscape of microalgal-bacterial interactions by focusing on metabolomic studies of selected, simplified model systems; (ii) describe the state of the field of metabolomics, with specific focus on techniques and approaches developed for microalga-bacterial interaction studies; and (iii) outline the main approaches for development of mathematical models of these interacting systems, which collectively have the power to enhance interpretation of experimental data and generate novel testable hypotheses. We share the viewpoint that a comprehensive and integrated series of -omics approaches that include theoretical formulations are necessary to develop predictive and mechanistic understanding of these biological entities.

Automated summary

The complex relationship between microalgae and bacteria is best understood through integrated approaches that focus on metabolites. Metabolomic studies have allowed for a comprehensive understanding of these interactions, while mathematical models enhance interpretation of experimental data and generate testable hypotheses.