Methane Cycling Microbes are Important Predictors of Methylmercury Accumulation in Rice Paddies

Abstract

Microbial production of methylmercury from inorganic mercury in rice paddies poses health risks to consumers of this essential dietary staple. Although mercury-methylating communities are well characterized, the microbial guilds contributing to methylmercury accumulation in rice paddies remain unclear. Here, we collected paddy soils across a mercury concentration gradient throughout the rice growing season to identify microbial and environmental factors influencing methylmercury dynamics. We show that hgcA gene abundance, the key gene required for methylation, was not a significant predictor of methylmercury concentration in paddy soils. We also show that merB gene abundance correlated with methylmercury in mercury-polluted rhizosphere samples. Methane cycling genes were actively expressed, and their beta-diversity was significantly associated with methylmercury levels. Methanogen abundance correlated with higher methylmercury under elevated total mercury concentrations. Analysis of the methanotroph-associated mbnT gene, implicated in demethylation, revealed an unexpected positive correlation with methylmercury. Multiple regression and machine learning models converged on mercury bioavailability and methanogen/methanotroph abundances as key predictors of methylmercury, with methanogen-associated hgcA gene abundance and methanogen-methanotroph interactions highlighted under flooded, low-redox conditions. These findings suggest that methane-cycling microbes play key roles in methylmercury cycling dynamics and point to management strategies that could simultaneously mitigate mercury pollution and greenhouse gas emissions. ImportanceMethylmercury is a microbially-derived neurotoxin that accumulates in rice, which is a global food staple. Predicting mercurys fate in rice paddies is challenging because of the interplay between microbes responsible for methylmercury cycling and variables that control mercury availability. Our study coupled genomic and geochemical measurements with machine learning to identify the key predictors of methylmercury accumulation in paddy soils. We demonstrate that methanogen and methanotroph abundance, and mercury bioavailability, are major predictors of methylmercury variability in paddies. We show that considering interactions between methane cycling guilds improves our capacity to predict methylmercury accumulation in soils compared to approaches that rely solely on mercury cycling genes. This work can inform remediation strategies for mercury in rice paddies but also wetlands and permafrost where methane and mercury cycling are tightly coupled. Such strategies could provide a solution to simultaneously mitigate methylmercury exposure and reduce greenhouse gas emissions amid global environmental change.