Metabolic implications for dual substrate growth in "Candidatus Accumulibacter"
Paez-Watson, T.; Jansens, C.; van Loosdrecht, M. C. M.; Roy, S.
Show abstract
This study explores the metabolic implications of dual substrate uptake in "Candidatus Accumulibacter", focusing on the co-consumption of volatile fatty acids and amino acids under conditions typical of enhanced biological phosphorus removal (EBPR) systems. Combining batch tests from highly enriched "Ca. Accumulibacter" cultures with conditional flux balance analysis (cFBA) predictions, we demonstrated that co-consumption of acetate and aspartate leads to synergistic metabolic interactions, lowering ATP loss compared to individual substrate consumption. The metabolic synergy arises from the complementary roles of acetate and aspartate uptake: acetate uptake provides acetyl-CoA to support aspartate metabolism, while aspartate conversion generates NADH, reducing the need for glycogen degradation during acetate uptake. We termed this type of metabolic interaction as reciprocal synergy. We further expanded our predictions to uncover three types of interactions between catabolic pathways when substrates are co-consumed by "Ca. Accumulibacter": (i) neutral, (ii) one-way synergistic and (iii) reciprocal synergistic interactions. Our results highlight the importance of network topology in determining metabolic interactions and optimizing resource use. These findings provide new insights into the metabolism "Ca. Accumulibacter" and suggest strategies for improving EBPR performance in wastewater treatment plants, where the influent typically contains a mixture of organic carbon compounds. SynopsisThis research demonstrates how dual substrate uptake by "Ca. Accumulibacter" enhances metabolic efficiency in EBPR by reducing global ATP losses through optimization of storage polymer usage.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Diagnosing and predicting mixed culture fermentations with unicellular and guild-based metabolic models 97%
- Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions 96%
- Integrative genome-scale metabolic modeling reveals versatile metabolic strategies for methane utilization in Methylomicrobium album BG8 96%
Similar papers in this journal
- Modeling Versatile and Dynamic Anaerobic Metabolism for PAOs/GAOs Competition Using Agent-based Model and Verification via Single Cell Raman Micro-spectroscopy 97%
- An integrated meta-omics approach for identifying candidate organic micropollutant degraders in complex microbial communities 96%
- Temporal Dynamics of Microbial Communities in Anaerobic Digestion: Influence of Temperature and Feedstock Composition on Reactor Performance and Stability. 96%
Similar papers in this journal
- Genome-Scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation 97%
- The potential for polyphosphate metabolism in Archaea and anaerobic polyphosphate formation in Methanosarcina mazei. 94%
- Sustained organic loading disturbance favors nitrite accumulation in bioreactors with variable resistance, recovery and resilience of nitrification and nitrifiers 94%
Similar papers in this journal
- COSMOS: COmmunity and Single Microbe Optimisation System 96%
- Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models 96%
- An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism 94%
Similar papers in this journal
- High Throughput Fitness Profiling Reveals Loss Of GacS-GacA Regulation Improves Indigoidine Production In Pseudomonas putida 96%
- Novel two-stage processes for optimal chemical production in microbes 95%
- Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.