Optimizing resource use is essential for the survival and fitness of species in microbial communities ubiquitous in natural and engineered ecosystems. These ecosystems are often characterized by the simultaneous presence of multiple substrates such as volatile fatty acids, amino acids and sugars. Yet, the evaluation of metabolic potential for these microbial community members is predominantly based on single substrate utilisation. Metabolic and ecological implications of the interactions of multiple substrates, particularly in environments with changes in redox conditions and substrate availability, remain poorly understood. In this study, we investigate the metabolic interactions resulting from co-substrate utilization in polyphosphate-accumulating organisms within wastewater treatment systems. We combined experimental analysis of highly enriched “Ca. Accumulibacter” mixed cultures with genome-resolved metagenomics and conditional flux balance analysis (cFBA) to quantify the physiological relevance of co-substrate uptake. We observe that anaerobic co-substrate utilisation of acetate and aspartate result in metabolic interactions leading to optimized redox balance, reduced ATP losses and increased biomass yields by up to 8% compared to individual substrate use. Metabolic modelling revealed that these benefits emerge from the network topology, where the interaction of different metabolic routes gives rise to synergistic effects. Extending our analysis to additional substrate pairs, we classify metabolic interactions into three general types: (i) neutral, (ii) one-way synergistic and (iii) reciprocal synergistic. Our findings highlight the importance of metabolic interactions and cellular resource allocation strategies in dynamic microbial ecosystems. This study provides a broader ecological framework for understanding competitive metabolic strategies in environmental organisms. Co-substrate utilization can have direct implications for improving the yield or productivity of bioprocesses.

With the rapid growing availability of metagenome assembled genomes (MAGs) and associated metabolic models, the identification of metabolic potential in individual community members has become possible. However, the field still lacks an unbiassed systematic evaluation of the generated metagenomic information to uncover not only metabolic potential, but also feasibilities of these models under specific environmental conditions. In this study, we present a systematic analysis of the metabolic potential in species of "Candidatus Accumulibacter", a group of polyphosphate-accumulating organisms (PAOs). We constructed a metabolic model of the central carbon metabolism and compared the metabolic potential among available MAGs for “Ca. Accumulibacter” species. By combining Elementary Flux Modes Analysis (EFMA) with max-min driving force (MDF) optimization, we obtained all possible flux distributions of the metabolic network and calculated their individual thermodynamic feasibility. Our findings reveal significant variations in the metabolic potential among “Ca. Accumulibacter” MAGs, particularly in the presence of anaplerotic reactions. EFMA revealed 700 unique flux distributions in the complete metabolic model that enable the anaerobic uptake of acetate and its conversion into polyhydroxyalkanoates (PHAs), a well-known phenotype of “Ca. Accumulibacter”. However, thermodynamic constraints narrowed down this solution space to 146 models that were stoichiometrically and thermodynamically feasible (MDF > 0 kJ/mol), of which only 8 were strongly feasible (MDF > 7 kJ/mol). Notably, several novel flux distributions for the metabolic model were identified, suggesting putative, yet unreported, functions within the PAO communities. Overall, this work provides valuable insights into the metabolic variability among "Ca. Accumulibacter" species and redefines the anaerobic metabolic potential in the context of phosphate removal. More generally, the integrated workflow presented in this paper can be applied to any metabolic model obtained from a MAG generated from microbial communities to objectively narrow the expected phenotypes from community members.

Biological wastewater treatment relies on microorganisms that grow as flocs, biofilms, or granules for efficient separation of biomass from cleaned water. This biofilm structure emerges from the interactions between microbes that produce, and are embedded in, extracellular polymeric substances (EPS). The true composition and structure of the EPS responsible for dense biofilm formation are still obscure. We conducted a bottom-up approach utilizing advanced glycomic techniques to explore the glycan diversity in the EPS from a highly enriched “Candidatus Accumulibacter” granular sludge. Rare novel sugar monomers such as N-Acetylquinovosamine (QuiNAc) and 2-O-Methylrhamnose (2-OMe-Rha) were identified to be present in the EPS of both enrichments. Further, a high diversity in the glycoprotein structures of said EPS was identified by means of lectin based microarrays. We explored the genetic potential of “Ca. Accumulibacter” high quality metagenome assembled genomes (MAGs) to showcase the shortcoming of top-down bioinformatics based approaches at predicting EPS composition and structure, especially when dealing with glycans and glycoconjugates. This work suggests that more bottom-up research is necessary to understand the composition and complex structure of EPS in biofilms since genome based inference cannot directly predict glycan structures and glycoconjugate diversity.

We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments.

The understanding of microbial communities and the biological regulation of its members is crucial for implementation of novel technologies using microbial ecology. One poorly understood metabolic principle of microbial communities is resource allocation and biosynthesis. Resource allocation theory in polyphosphate accumulating organisms (PAOs) is limited as a result of their slow imposed growth rate (typical sludge retention times of at least 4 days) and limitations to quantify changes in biomass components over a 6 hours cycle (less than 10% of their growth). As a result, there is no direct evidence supporting that biosynthesis is an exclusive aerobic process in PAOs that alternate continuously between anaerobic and aerobic phases. Here, we apply resource allocation metabolic flux analysis to study the optimal phenotype of PAOs over a temperature range of 4 °C to 20 °C. The model applied in this research allowed to identify optimal metabolic strategies in a core metabolic model with limited constraints based on biological principles. The addition of a constraint limiting biomass synthesis to be an exclusive aerobic process changed the metabolic behaviour and improved the predictability of the model over the studied temperature range by closing the gap between prediction and experimental findings. The results validate the assumption of limited anaerobic biosynthesis in PAOs, specifically “Candidatus Accumulibacter” related species. Interestingly, the predicted growth yield was lower, suggesting that there are mechanistic barriers for anaerobic growth not yet understood nor reflected in the current models of PAOs. Moreover, we identified strategies of resource allocation applied by PAOs at different temperatures as a result of the decreased catalytic efficiencies of their biochemical reactions. Understanding resource allocation is paramount in the study of PAOs and their currently unknown complex metabolic regulation, and metabolic modelling based on biological first principles provides a useful tool to develop a mechanistic understanding.