Metabolic modelling of anaerobic amino acid uptake and storage by fermentative polyphosphate accumulating organisms

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-03-18 DOI:10.1016/j.watres.2025.123512

R. Thomson , K. Close , A. Riley , D.J. Batstone , A. Oehmen

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Abstract

Existing enhanced biological phosphorus removal (EBPR) models do not fully describe the metabolism of fermentative polyphosphate accumulating organisms (fPAOs), particularly under mixed substrate conditions representative of fermentation-enhanced EBPR (F-EBPR) processes. This study presents a steady-state metabolic model integrating anaerobic amino acid (AA) fermentation and storage processes in fPAOs. The model identifies key metabolic interactions underlying fPAO metabolism, prioritising substrate accumulation over fermentation. This results in significant changes to ATP and reduction-oxidation (redox) flows as compared to when relying on previous AA fermentation models typically used to describe fPAO metabolism, with medium-chain-length (MCL) polyhydroxyalkanoate (PHA) formation and polyphosphate (polyP) consumption acting as important electron and energy management mechanisms, respectively. Succinate, rather than volatile fatty acids (VFAs), was identified as the more likely synergetic substrate between fermentative and conventional PAOs (cPAOs) under these conditions. Moreover, conditions favourable of VFA efflux by fPAOs may also favour a shift away from a polyP accumulation to a fermentation dominant metabolism. Further work is required to verify the role of MCL-PHA fractions, alongside the contribution of free intracellular AA accumulation as compared to polymers such as cyanophycin or polyglutamate on fPAO metabolism. This metabolic model provides a framework for better understanding the role of fPAOs and their interactions with cPAOs within EBPR processes, informing future modelling and optimisation of F-EBPR systems.

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发酵聚磷酸盐积累生物厌氧氨基酸吸收和储存的代谢模型

现有的强化生物除磷（EBPR）模型并不能完全描述发酵性聚磷酸盐积累生物（fPAOs）的新陈代谢，尤其是在混合底物条件下的发酵强化 EBPR（F-EBPR）过程。本研究提出了一个整合了厌氧氨基酸（AA）发酵和 fPAOs 储存过程的稳态代谢模型。该模型确定了支撑 fPAO 新陈代谢的关键代谢相互作用，即底物积累优先于发酵。与以前通常用于描述 fPAO 新陈代谢的 AA 发酵模型相比，这导致 ATP 和还原氧化（氧化还原）流发生了重大变化，中链长度（MCL）聚羟基烷酸（PHA）的形成和聚磷酸盐（polyP）的消耗分别成为重要的电子和能量管理机制。在这些条件下，琥珀酸而非挥发性脂肪酸（VFAs）更有可能成为发酵型 PAOs 和传统 PAOs（cPAOs）之间的协同底物。此外，有利于 fPAOs 外排挥发性脂肪酸的条件也可能有利于从聚磷酸酯积累向发酵主导代谢的转变。还需要开展进一步的工作来验证 MCL-PHA 组分的作用，以及细胞内游离 AA 的积累对 fPAO 新陈代谢的贡献（与氰藻苷或聚谷氨酸等聚合物相比）。该代谢模型为更好地理解 EBPR 过程中 fPAO 的作用及其与 cPAO 的相互作用提供了一个框架，为未来 F-EBPR 系统的建模和优化提供了信息。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.