This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Plants are home to a diverse array of microorganisms that support their growth and help them adapt to environmental stress—of which there has been an abundance lately. High temperatures and drought caused by human-induced climate change have led to reduced crop yields and the use of biofertilizers such as rhizosphere microorganisms, which help plants absorb nutrients and increase their resistance to disease and stress.
Yet individual microorganisms have difficulty sustaining themselves in crops and remaining functional in the fluctuating environment of an agricultural field. This is where microbial communities can help. A recent approach involves creating defined microbial communities—DMCs—by combining multiple microorganisms, which in turn influence each other and can thereby strengthen their resilience.
However, the composition and function of such a community change depending on plant-specialized metabolites and environmental conditions, making them impractical to use in conventional trial-and-error experiments aimed at finding optimal microbial communities from among a vast number of combinations. This motivated a team of researchers from Kyoto University, with help from colleagues at Tohoku University and RIKEN, to integrate microorganism data and establish a rational, predictable method for designing DMCs. The team chose the humble tomato as its plant subject.
The study is published in The ISME Journal. "I became particularly interested in moving beyond simple one-to-one relationships to understand how these metabolite-mediated interactions unfold within the diverse microbial communities found in soil, and how they influence plant growth," says KyotoU's co-corresponding author Akifumi Sugiyama. The team isolated bacteria from tomato plant roots, then combined nine types of bacteria and two types of plant metabolites to create microbial communities in various patterns.
They inoculated tomato roots with these communities and varied cultivation temperatures. Using the resulting data set, the scientists employed the elastic net regression algorithm to construct a machine-learning model that could predict the aboveground fresh weight of tomatoes. This allowed the team to design a new microbial community predicted to have a positive effect on tomato growth.
Laboratory testing indicated that the DMC G2 microbial community, composed of six bacteria and the tomatine metabolite, promoted tomato growth and improved tolerance to high-temperature stress. Finally, outdoor cultivation trials showed that DMC G2 significantly increased the aboveground fresh weight of tomatoes, and gene analysis revealed the plants' enhanced high-temperature tolerance. "We needed to cultivate tomatoes under a wide variety of conditions with high precision, so I would like to thank the team members who carried out these repetitive yet exacting experiments over approximately two years," says Sugiyama.
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