Towards an Enhanced Understanding of Plant–Microbiome Interactions to Improve Phytoremediation: Engineering the Metaorganism

Phytoremediation is a promising technology to clean-up contaminated soils based on the synergistic actions of plants and microorganisms. However, to become a widely accepted, and predictable remediation alternative, a deeper understanding of the plant–microbe interactions is needed. A number of studies link the success of phytoremediation to the plant-associated microbiome functioning, though whether the microbiome can exist in alternative, functional states for soil remediation, is incompletely understood. Moreover, current approaches that target the plant host, and environment separately to improve phytoremediation, potentially overlook microbial functions and properties that are part of the multiscale complexity of the plant-environment wherein biodegradation takes place. In contrast, in situ studies of phytoremediation research at the metaorganism level (host and microbiome together) are lacking. Here, we discuss a competition-driven model, based on recent evidence from the metagenomics level, and hypotheses generated by microbial community ecology, to explain the establishment of a catabolic rhizosphere microbiome in a contaminated soil. There is evidence to ground that if the host provides the right level and mix of resources (exudates) over which the microbes can compete, then a competitive catabolic and plant-growth promoting (PGP) microbiome can be selected for as long as it provides a competitive superiority in the niche. The competition-driven model indicates four strategies to interfere with the microbiome. Specifically, the rhizosphere microbiome community can be shifted using treatments that alter the host, resources, environment, and that take advantage of prioritization in inoculation. Our model and suggestions, considering the metaorganism in its natural context, would allow to gain further knowledge on the plant–microbial functions, and facilitate translation to more effective, and predictable phytotechnologies.