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The balance between microbial arsenic methylation and demethylation in paddy soils underpins global arsenic risk and straighthead disease in rice

Jun Dai, Chuan Chen, Zhiqiang Zhai, Axiang Gao, David R. Johnson, Peter M. Kopittke, Fang‐Jie Zhao, Peng Wang

2025Proceedings of the National Academy of Sciences9 citationsDOIOpen Access PDF

Abstract

Arsenic contamination in rice poses a global challenge to food safety and agricultural productivity, as toxic methylated arsenic species-dimethylarsinic acid (DMA) and its highly toxic derivative, methylated monothioarsenate (DMMTA)-accumulate in rice grains. These arsenic species endanger human health and trigger rice straighthead disease, a crop disorder that drastically reduces yields. However, the microbial ecological processes driving arsenic speciation in paddy soils, and their link to striking geographic disparities in rice arsenic speciation profiles and disease prevalence, remain poorly understood. Here, we integrate soil chronosequences spanning 1 to 2,000 y of rice cultivation, a global metagenomic survey of 801 paddy soils, controlled incubations, and field surveys to demonstrate that the balance between arsenic-methylating and arsenic-demethylating microbes is the key determinant of rice grain arsenic speciation and straighthead disease susceptibility. We show that young and moderate-age paddy soils (<700 y), common in regions such as the Americas and Europe, are enriched in arsenic-methylating bacteria, leading to elevated DMA and DMMTA in soils and rice grains. In contrast, ancient paddies in Southeast Asia harbor robust populations of DMA-demethylating methanogenic archaea that effectively mitigate the buildup of these toxic arsenic species. We identify core microbial taxa whose abundances serve as predictive biomarkers and construct a global risk map linking a high methylator-to-demethylator ratio in soils with increased straighthead disease incidence. These findings advance our understanding of arsenic biogeochemistry in agroecosystems and establish a predictive framework for identifying regions at elevated risk of arsenic-induced crop disorders and food contamination.

Topics & Concepts

ArsenicAgroecosystemGenetic algorithmPaddy fieldRhizosphereArsenic contamination of groundwaterAgronomyCropBiogeochemistryMetagenomicsBiologyArsenobetaineSoil waterAgricultureEnvironmental chemistryEnvironmental scienceArsenic toxicityOryza sativaEcologyMetalloidFood securityStaple foodSoil microbiologyTrophic levelFood safetyOryzaBiotechnologyContaminationMethylmercuryEcosystemArsenic contamination and mitigationHeavy metals in environmentHeavy Metal Exposure and Toxicity