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ABSTRACT Treponema pallidum, the causative agent of syphilis, poses a significant global health threat. Its strict reliance on host-derived nutrients and difficulties inin vitrocultivation have impeded detailed metabolic characterization. In this study, we present iTP251, the first genome-scale metabolic model ofT. pallidum, reconstructed and extensively curated to capture its unique metabolic features. These refinements included the curation of key reactions such as pyrophosphate-dependent phosphorylation and pathways for nucleotide synthesis, amino acid synthesis, and cofactor metabolism. The model demonstrated high predictive accuracy, validated by a MEMOTE score of 92%. To further enhance its predictive capabilities, we developed ec-iTP251, an enzyme-constrained version of iTP251, incorporating enzyme turnover rate and molecular weight information for all reactions having gene-protein-reaction associations. Ec-iTP251 provides detailed insights into protein allocation across carbon sources, showing strong agreement with proteomics data (Pearson’s correlation of 0.88) in the central carbon pathway. Moreover, the thermodynamic analysis revealed that lactate uptake serves as an additional ATP-generating strategy to utilize unused proteomes, albeit at the cost of reducing the driving force of the central carbon pathway by 27%. Subsequent analysis identified glycerol-3-phosphate dehydrogenase as an alternative electron sink, compensating for the absence of a conventional electron transport chain while maintaining cellular redox balance. These findings highlightT. pallidum’s metabolic adaptations for survival and redox balance in nutrient-limited, extracellular host environments, providing a foundation for future research into its unique bioenergetics. IMPORTANCEThis study advances our understanding ofTreponema pallidum, the syphilis-causing pathogen, through the reconstruction of iTP251, the first genome-scale metabolic model for this organism, and its enzyme-constrained version, ec-iTP251. The work addresses the challenges of studyingT. pallidum, an extracellular, host-adapted pathogen, due to its strict dependence on host-derived nutrients and challenges inin vitrocultivation. Validated with strong agreement to proteomics data, the model demonstrates high predictive reliability. Key insights include unique metabolic adaptations such as lactate uptake for ATP production and alternative redox-balancing mechanisms. These findings provide a robust framework for future studies aimed at unraveling the pathogen's survival strategies and identifying potential metabolic vulnerabilities. 

Abstract Rhodopseudomonas palustris, a versatile bacterium with diverse biotechnological applications, can effectively breakdown lignin, a complex and abundant polymer in plant biomass. This study investigates the metabolic response ofR. palustriswhen catabolizing various lignin breakdown products (LBPs), including the monolignolsp-coumaryl alcohol, coniferyl alcohol, sinapyl alcohol,p-coumarate, sodium ferulate, and kraft lignin. Transcriptomics and proteomics data were generated for those specific LBP breakdown conditions and used as features to train machine learning models, with growth rates as the target. Three models—Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machine (SV)—were compared, with ANN achieving the highest predictive accuracy for both transcriptomics (94%) and proteomics (96%) datasets. Permutation feature importance analysis of the ANN models identified the top twenty genes and proteins influencing growth rates. Combining results from both transcriptomics and proteomics, eight key transport proteins were found to significantly influence the growth ofR. palustrison LBPs. Re-training the ANN using only these eight transport proteins achieved predictive accuracies of 86% and 76% for proteomics and transcriptomics, respectively. This work highlights the potential of ANN-based models to predict growth-associated genes and proteins, shedding light on the metabolic behavior ofR. palustrisin lignin degradation under aerobic and anaerobic conditions. ImportanceThis study is significant as it addresses the biotechnological potential ofRhodopseudomonas palustrisin lignin degradation, a key challenge in converting plant biomass into commercially important products. By training machine learning models with transcriptomics and proteomics data, particularly Artificial Neural Networks (ANN), the work achieves high predictive accuracy for growth rates on various lignin breakdown products (LBPs). Identifying top genes and proteins influencing growth, especially eight key transport proteins, offers insights into the metabolic niche ofR. palustris. The ability to predict growth rates using just these few proteins highlights the efficiency of ANN models in distilling complex biological systems into manageable predictive frameworks. This approach not only enhances our understanding of lignin derivative catabolism but also paves the way for optimizingR. palustrisfor sustainable bioprocessing applications, such as bioplastic production, under varying environmental conditions. 

Abstract Neisseria gonorrhea (Ngo) is a major concern for global public health due to its severe implications for reproductive health. Understanding its metabolic phenotype is crucial for comprehending its pathogenicity. Despite Ngo’s ability to encode tricarboxylic acid (TCA) cycle proteins, GltA and AcnB, their activities are notably restricted. To investigate this phenomenon, we used the iNgo_557 metabolic model and incorporated a constraint on total cellular protein content. Our results indicate that low cellular protein content severely limits GltA and AcnB activity, leading to a shift toward acetate overflow for Adenosine triphosphate (ATP) production, which is more efficient in terms of protein usage. Surprisingly, increasing cellular protein content alleviates this restriction on GltA and AcnB and delays the onset of acetate overflow, highlighting protein allocation as a critical determinant in understanding Ngo’s metabolic phenotype. These findings underscore the significance of Ngo’s metabolic adaptation in light of optimal protein allocation, providing a blueprint to understand Ngo’s metabolic landscape. 

ABSTRACT During aerobic growth,S. aureusrelies on acetate overflow metabolism, a process where glucose is incompletely oxidized to acetate, for its bioenergetic needs. Acetate is not immediately captured as a carbon source and is excreted as waste by cells. The underlying factors governing acetate overflow inS. aureushave not been identified. Here, we show that acetate overflow is favored due to a thermodynamic bottleneck in the TCA cycle specifically involving the oxidation of succinate to fumarate by succinate dehydrogenase. This bottleneck reduces flux through the TCA cycle, making it more efficient forS. aureusto generate ATP via acetate overflow metabolism. Additionally, the protein allocation cost of maintaining ATP flux through the restricted TCA cycle is greater than that of acetate overflow metabolism. Finally, we show that the TCA cycle bottleneck providesS. aureusthe flexibility to redirect carbon toward maintaining redox balance through lactate overflow when oxygen becomes limiting, albeit at the expense of ATP production through acetate overflow. Overall, our findings suggest that overflow metabolism offersS. aureusdistinct bioenergetic advantages over a thermodynamically constrained TCA cycle, potentially supporting its commensal–pathogenic lifestyle.