Abstract
Contamination of rice by the potent neurotoxin methylmercury (MeHg) originates from microbe-mediated Hg methylation in soils. However, the high diversity of Hg methylating microorganisms in soils hinders the prediction of MeHg formation and challenges the mitigation of MeHg bioaccumulation via regulating soil microbiomes. Here we explored the roles of various cropland microbial communities in MeHg formation in the potentials leading to MeHg accumulation in rice and reveal that Geobacteraceae are the key predictors of MeHg bioaccumulation in paddy soil systems. We characterized Hg methylating microorganisms from 67 cropland ecosystems across 3,600 latitudinal kilometres. The simulations of a rice-paddy biogeochemical model show that MeHg accumulation in rice is 1.3–1.7-fold more sensitive to changes in the relative abundance of Geobacteraceae compared to Hg input, which is recognized as the primary parameter in controlling MeHg exposure. These findings open up a window to predict MeHg formation and accumulation in human food webs, enabling more efficient mitigation of risks to human health through regulations of key soil microbiomes.
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Data availability
The authors declare that the main data supporting the findings of this study are available within the article and its Supplementary Information file and the Source data. The data of rice-planting area for each country was obtained from the Food and Agriculture Organization of the United Nations (FAO) website (https://www.fao.org/faostat/en/#data/QCL). The per-capita rice consumption rates were retrieved from China Statistical Yearbook 2011 (https://www.stats.gov.cn/sj/ndsj/2011/indexch.htm). The shape data for the map of China is provided by the Resource and Environment Science and Data Center (https://www.resdc.cn/Datalist1.aspx?FieldTyepID=20,0, accessed on 18 October 2022). Source data are provided with this paper.
Code availability
Code of rice-paddy model are available in the Source data.
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Acknowledgements
We appreciate G. Qiu (Institute of Geochemistry, Chinese Academy of Sciences) for providing data on site-specific rice MeHg concentrations for the model evaluation. W. Tang appreciates the financial support from National Natural Science Foundation of China (42107223), Fundamental Research Funds for the Central Universities (0211-14380175) and Natural Science Foundation of Jiangsu Province (BK 20230082 and BK 20190319). H.Z. gets supports from National Natural Science Foundation of China (41673075). Y.-R.L. acknowledges financial support from National Natural Science Foundation of China (42177022), and L.C. appreciates National Natural Science Foundation of China (41701589). H.R. is supported by the financial support from National Natural Science Foundation of China (52388101).
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H.Z., Y.-R.L., L.C. and W. Tang conceived and designed this study. W. Tang, Z.L. and Y.H. conducted all the experiments. Y.-R.L. led the analysis of microbial community. Z.L. and X.Z. modified the RPBM with the help from S.Y.K. L.C. and L.M.N. did the sensitivity analysis. H.Z., Y.-R.L., L.C. and W. Tang led the manuscript writing. C.S. and M.W. significantly contributed to the writing of the manuscript, and other authors participated in the writing.
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Nature Food thanks Shaojie Song, Yuebing Sun, Anil Somenahally and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Location, rice yield (annual provincial yield, a), and Hg concentrations (b and c) in 67 sites of paddy soil across Asia.
The background colors in panel (a) indicate the national rice planting areas (million ha, 2017) in these countries and these data were obtained from the FAO website (accessed on Oct. 14 2022). The global map is provided by Natural Earth (https://www.naturalearthdata.com/downloads/10m-cultural-vectors/, accessed on Oct. 18 2022). The base map of China is provided by the Resource and Environment Science and Data Center (https://www.resdc.cn/Datalist1.aspx?FieldTyepID=20,0, accessed on 18 October 2022).
Extended Data Fig. 2 Microbial community of hgcA+ microbes in rice paddy soils during rice cultivation (a) and the relative abundance of hgcA-containing Geobacteraceae in rhizospheric and non-rhizospheric paddy soils (b).
NS indicates there are no significant (p > 0.05; one-way ANOVA) differences between the two groups. N = 15 for each type of soils. For box plot, the box, whisker, line, square, and diamond indicate a 25−75% range, 10−90% range, median, mean, and outliers, respectively.
Extended Data Fig. 3 The relationship between Hg methylation potential (the ratio of soil MeHg to soil THg, MeHg/THg, %) and hgcA-containing Deltaproteobacteria (a) or Archaea (b).
P values are obtained based on a two-sided test.
Extended Data Fig. 4 Changes in dissolved THg concentration in the overlying water in the metabolism inhibition assays with Se addition.
Extended Data Fig. 4 Data are presented as mean ± SD, n = 3 replicates.
Extended Data Fig. 5 The concentrations of sulfate in metabolism inhibition assay I.
Asterisk indicates the significant difference (*, p < 0.05; ***, p < 0.001; one-way ANOVA) between two treatments. Data are presented as mean ± SD. For high FeRB soils, n = 9 soils × 3 replicates for each soil. For high SRB and high methanogens soil, n = 4 soils × 3 replicates for each soil. The value obtained from each replicate is plotted as a dot on the bar.
Extended Data Fig. 6 The evaluation of the THg concentration in surface water (a, b, c) and pore water (d, e, f).
In panels (a) and (d), data are presented as mean ± SD, n = 7 (Anhui), 10 (Guangdong), 8 (Guangxi), 16 (Guizhou), 4 (Hainan), 5 (Heilongjiang), 3 (Hubei), 8 (Hunan), 5 (Jiangsu), 3 (Jiangxi), 7 (Liaoning), and 2 sites (Sichuan) for the observed values, and n = 5, 5, 3, 6, 2, 8, 8, 7, 8, 7, 3, 5 sites for the modeled values for above provinces. Each dot on the bar indicates the value for a site. In panels (b) and (e), each dot represents a provincial average. P values are obtained based on a two-sided test. Error bands are 95% confidence intervals of the regression lines. In panels (c) and (f), the box, whisker, line, square, and diamond indicate a 25−75% range, 1.5 IQR range, median, mean, and outliers, respectively. The sample size is 78 and 67 sites for the Observed and Modeled, respectively.
Extended Data Fig. 7 The evaluation of the THg (a, b, c) and MeHg concentration in soils (d, e, f).
In panels (a) and (d), data are presented as mean ± SD, n = 7 (Anhui), 10 (Guangdong), 8 (Guangxi), 16 (Guizhou), 4 (Hainan), 5 (Heilongjiang), 3 (Hubei), 8 (Hunan), 5 (Jiangsu), 3 (Jiangxi), 7 (Liaoning), and 2 sites (Sichuan) for the observed values, and n = 5, 5, 3, 6, 2, 8, 8, 7, 8, 7, 3, 5 sites for the modeled values for above provinces. Each dot on the bar indicates the value for a site. Asterisk indicates the significant difference (*, p < 0.05; **, p < 0.01; one-way ANOVA) between the observed and modeled values. In panels (b) and (e), each dot represents a provincial average. P values are obtained based on a two-sided test. Error bands are 95% confidence intervals of the regression lines. In panels (c) and (f), the box, whisker, line, square, and diamond indicate a 25−75% range, 1.5 IQR range, median, mean, and outliers, respectively. The sample size is 78 and 67 sites for the Observed and Modeled, respectively.
Extended Data Fig. 8 The responses of rice MeHg to changes in the relative abundance of FeRB, SRB, or methanogens.
(a) increase of 10% in the relative abundance of microbial methylators. (b) decrease of 10% in the relative abundance of microbial methylators. Asterisk indicates the significant difference (***, p < 0.001; one-way ANOVA). The box, whisker, line, and square indicate a 25−75% range, 1.5 IQR range, median, and mean values, respectively. The sample size is 67 sites for model simulations.
Extended Data Fig. 9 The responses of rice MeHg (a), changes in IQ decrement (b) and HQ (c) to a 10% decrease in deposition, irrigation, or FeRB abundance.
The box, whisker, line, square, and diamond indicate a 25−75% range, 1.5 IQR range, median, mean, and outliers, respectively. The sample size is 67 sites for model simulations.
Extended Data Fig. 10 Relationships between Hg methylation potential in soil and the relative abundance of Syntrophs.
P values are obtained based on a two-sided test.
Supplementary information
Supplementary Information
Supplementary Methods 1 and 2, Discussion 1–7, Figs. 1–5, Tables 1–8 and references.
Source data
Source Data Figs. 1–4 and 5
Source data for figures in the main article.
Source Data Fig. 4a
Matlab code for the rice-paddy biogeochemistry model.
Source Data Extended Data Figs. 1–10
Source data for all the extended data figures.
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Zhong, H., Tang, W., Li, Z. et al. Soil Geobacteraceae are the key predictors of neurotoxic methylmercury bioaccumulation in rice. Nat Food 5, 301–311 (2024). https://doi.org/10.1038/s43016-024-00954-7
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DOI: https://doi.org/10.1038/s43016-024-00954-7
