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 the 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. IMPORTANCE Methylmercury is a microbially derived neurotoxin that accumulates in the food staple rice ( Oryza sativa ). Mitigating the health effects of methylmercury exposure requires predicting mercury cycling dynamics in rice paddies. This task is challenging because of the complex interplay of microbial and environmental factors. Our study coupled genomic and geochemical measurements with machine learning models to identify the key biological indicators of methylmercury accumulation. We demonstrated that the abundance of methanogens and methanotrophs is a major microbial predictor of methylmercury variability. This predictive framework, which considers the interactions between these coupled microbial guilds, offers greater power than methods relying only on mercury methylation genes. These findings inform better management and remediation strategies for rice paddies, offering a path to reduce methylmercury exposure and mitigate greenhouse gas emissions.
Zhang et al. (Tue,) studied this question.