Researcher
研究者名
Sugiyama, Akifumi
Overview
Kyoto, Japan -- 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 of late. 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 stresses. Yet single microorganisms have difficulties 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 changes depending on plant-specialized metabolites and environmental conditions, rendering these rather impractical to use in conventional trial-and-error experiments aiming to find optimal microbial communities from amongst 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 their plant subject.
"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 these communities on tomato roots and varied cultivation temperatures. With the resulting dataset, the scientists employed the elastic net regression algorithm to construct a machine-learning model with the ability to predict the aboveground fresh weight of tomatoes. This allowed the team to then 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, both promoted tomato growth and improved tolerance to high-temperature stress. Finally, outdoor cultivation trials proved 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 a period of approximately two years," says Sugiyama.
This study demonstrates the effectiveness of data-driven microbial community design in improving crop stress tolerance. The team's versatile methods are expected to broadly extend to other crops and therefore hold significant potential in enabling sustainable agriculture.
