Responses of maize roots and the rhizosphere microbiome to varying precipitation during a long-term field experiment. Zea mays strain:B73

Understanding how annual weather variation, including droughts, affect plant roots and rhizosphere microbial dynamics in different years is essential for predicting plant responses to climate fluctuations. This study aimed to investigate the effects of alternating dry and moist years on maize root gene expression and the rhizosphere microbiome composition, and to reveal interactions between the two. Zea mays B73 wild type (WT) and a root hair deficient mutant (rth3) were grown on two substrates during a three-year field experiment with alternating precipitation, designated as dry, moist, dry. Root gene expression was analyzed between the two dry years and the moist year, supported by the enzymatic activity of superoxide dismutase. The rhizosphere was analyzed by measuring the enzyme kinetic parameters of beta-glucosidase, acid phosphatase, leucine aminopeptidase and N-acetylglucosaminidase, accompanied by the 16S rRNA- and acdS gene carrying (16S, acdS+) microbiome. Year was identified as the main driver of root gene expression and the 16S community, with a distinct pattern of drought-responsive genes between dry years and the moist year. Substrate was the main driver of the acdS+ community and influenced root gene expression and the 16S community composition, indicating interactive effects between maize roots and the rhizosphere microbiome. The presence of root hairs had a marginal effect. This study highlights the role of annual weather variation in shaping root gene expression, rhizosphere microbiome and enzyme kinetics and underlines the role of substrate in structuring acdS+ communities. Our results suggest that plant-microbe interactions are highly sensitive to precipitation variability and might be influenced by repeated maize planting. They emphasize the importance of precipitation history in shaping plant-microbe interactions, which can serve as a basis for drought resilience strategies in agriculture.