Skip to content
#protein folding Open access

Biochar mitigates dimethylarsenate-stimulated methane emissions mediated by DOM-microbe interactions in paddy soils

Sep 2026 · Frontiers in Microbiology · 0 citations · 48 references
Arsenic contamination and mitigation

Abstract

Paddy soils represent key biogeochemical interfaces linking arsenic (As) transformation and carbon (C) cycling. Dimethylarsenate (DMAs), a widespread organoarsenic contaminant in rice paddies, can alter methane (CH 4 ) dynamics, but the underlying mechanisms based on dissolved organic matter (DOM)-microbe interaction and the mitigation potential of biochar still remained insufficiently understood. Here, we investigated As biogeochemistry, CH 4 emissions, DOM quantity and composition, microbial community assembly and functional gene variations through a 98-day microcosm incubation experiment using paddy soil amended with DMAs alone and DMAs combined with biochar. We found that DMAs greatly promoted CH₄ emissions in a dose-dependent manner, with the DMAs120 treatment increasing cumulative CH₄ emissions by 60.4% relative to the control. DMAs underwent significant biotransformation, declining from 98.7% on the 7th day to 26.2% of the total As by day 98. Porewater DOC concentrations were significantly increased by up to 1.81-fold (DMAs120, day 28). PARAFAC analysis revealed that DMAs shifted DOM composition toward labile protein-like fractions, with tryptophan-like (C2) and tyrosine-like (C3) components increasing while microbial humic-like (C1) and terrestrial humic-like (C4) fractions decreased. Such labile DOM precisely matched substrate requirements of enriched anaerobic fermentative microbes, establishing synergistic metabolic networks to supply methanogenic precursors. DMAs markedly increased mcrA gene abundance by 4.2–7.6 times with negligible change in pmoA , confirming stimulated methanogenesis rather than suppressed oxidation. By contrast, biochar effectively decreased % DMAs in porewater, reducing CH 4 emissions by 54.3% after 2% biochar amendment. Biochar also decreased DOC concentrations and protein-like DOM (C2, C3), reducing bioavailable substrates for methanogens. Furthermore, biochar suppressed mcrA gene abundances by 12.7–81.3%, while increased that of pmoA gene by 7.1–7.5 times, as well as increased porewater As(V) and enriched ammonia-oxidizing Candidatus_Nitrosopelagicus to potentially or indirectly stimulate anaerobic CH₄ oxidation. These results establish mechanistic connections between DMAs biotransformation, DOM turnover and CH 4 production in paddy soils, and verify that biochar restructures DOM-microbe interactions to mitigate DMAs-stimulated CH 4 emissions. This study deepens insights into As-C biogeochemical coupling and offers practical support for simultaneous As remediation and greenhouse gas control in paddy ecosystems.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.