Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-10-17 eCollection Date: 2024-10-01 DOI:10.1371/journal.pcbi.1012533

Natchapon Srinak, Porntip Chiewchankaset, Saowalak Kalapanulak, Pornpan Panichnumsin, Treenut Saithong

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Abstract

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.

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代谢交叉进食相互作用调节了有机负荷可变废水中微生物燃料电池的动态群落结构。

微生物燃料电池（MFC）在工业废水处理中的效率受到微生物群落的深刻影响，而多变的工业运行会破坏微生物群落。虽然已经确定了在特定条件下与 MFC 性能相关的微生物群落，但还缺乏对趋同群落结构和适应途径的全面了解。在此，我们开发了一种基于代谢交叉进食的微生物-微生物相互作用基因组尺度代谢模型（mmGEM），用于研究处理菠萝罐头厂含硫化物废水的 MFC 中微生物群落的适应性。该代谢模型包括三个主要的微生物群落：硫酸盐还原菌（SRB）、甲烷菌（MET）和硫化物氧化菌（SOB）。我们的研究结果表明，随着有机负荷率（OLR）的增加，群落从 SOB 主导型转变为 MET 主导型，同时 MFC 的性能也在下降。mmGEM 准确预测了低 OLRs（L-OLRs）时的微生物相对丰度以及对高 OLRs（H-OLRs）的适应性。模拟结果表明，在 H-OLRs 条件下，由于硫酸盐-硫化物（S）循环减少以及与 SRB 的醋酸盐交叉馈入，SOB 的生长受到了限制。更多来自 SRB 的交叉进食代谢物被转移到 MET，从而促进其竞争优势。通过评估不同 OLR 条件下的交叉供料动态，可以进行基于实际情况的模拟，探索酸度升高对 SOB 生长和 MFC 性能的潜在影响。这项工作强调了代谢交叉进食在形成微生物群落结构以应对高OLRs方面的作用。所获得的洞察力将为在实际工业环境中实施 MFC 技术的有效策略的开发提供参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

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