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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. 

Developing permanent magnets with fewer critical elements requires understanding hysteresis effects and coercivity through visualizing magnetization reversal. Here, we numerically investigate the effect of the geometry of nanoscale ferromagnetic inclusions in a paramagnetic/nonmagnetic matrix to understand the key factors that maximize the magnetic energy product of such nanocomposite systems. Specifically, we have considered a matrix of “3 μm × 3 μm × 40 nm” dimension, which is a sufficiently large volume, two-dimensional representation considering that the ferromagnetic inclusions' thickness is less than 3.33% of the lateral dimensions simulated. Using this approach, which minimizes edge effects to approximate bulk-like magnetic behavior while remaining computationally tractable for simulation, we systematically studied the effect of the thickness of ferromagnetic strips, separation between the ferromagnetic strips due to the nonmagnetic matrix material, different saturation magnetization values, and the length of these ferromagnetic strips on magnetic coercivity and remanence by simulating the hysteresis loop plots for each geometry. Furthermore, we study the underlying micromagnetic mechanism for magnetic reversal to understand the factors that could help attain the maximum magnetic energy densities for ferromagnetic nanocomposite systems in a paramagnetic/nonmagnetic material matrix. In this study, we have used material parameters of an exemplary Alnico alloy system, a rare-earth-free, thermally stable nanocomposite, which could potentially replace high-strength NdFeB magnets in applications that do not require large energy products. However, we project the energy density (BH)max of materials with higher saturation magnetization to have an ideal theoretical limit of (BH)max ∼94 kJ/m3 (∼12 MGOe), which is ∼(35%–40%) of the energy density of Rare-Earth Free Magnets. This energy density could be higher if exchange bias from antiferromagnets, defects, and pinning is included and could stimulate further experimental work on the fabrication and large-scale manufacturing of RE-free PMs with different nanocomposite systems. 

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 Understanding how photosynthetic organisms including plants and microbes respond to their environment is crucial for optimizing agricultural practices and ensuring food and energy security, particularly in the context of climactic change and sustainability. This perspective embeds back-of-the-envelope calculations across a photosynthetic organism design and scale up workflow. Starting from the whole system level, we provide a recipe to pinpoint key genetic targets, examine the logistics of detailed computational modeling, explore environmentally driven phenotypes, and feasibility as an industrial biofuel production chassis. While complex computer models or high throughput in vivo studies often dominate scientific inquiry, this perspective highlights the power of simple calculations as a valuable tool for initial exploration and evaluating study feasibility. Fermi calculations are defined as quick, approximate estimations made using back-of-the-envelope calculations and straightforward reasoning to achieve order-of-magnitude accuracy, named after the physicist Enrico Fermi. We show how Fermi calculations, based on fundamental principles and readily available data, can offer a first pass understanding of metabolic shifts in plants and microbes in response to environmental and genetic changes. We also discuss how Fermi checks can be embedded in data-driven advanced computing workflows to enable bio-aware machine learning. Lastly, an understanding of state-of-the-art is necessary to guide study feasibility and identifying key levers to maximize cost to return ratios. Combining biology- and resource- aware Fermi calculations, this proposed approach enables researchers to prioritize resource allocation, identify gaps in predictions and experiments, and develop intuition about how observed responses of plants differ between controlled laboratory environments and industrial conditions. 

ABSTRACT In this study, we investigate the PFOA removal capabilities ofRhodopseudomonas palustris(R. palustris), a fluoroacetate dehalogenase containing microbe as a potential candidate for achieving bioremediation. In the 50-day PFOA uptake experiment, R. palustris removed 44 ± 6.34 % PFOA after 20 days of incubation, which was then reduced to a final removal of 6.23 ± 12.75 %. Results indicate PFOA was temporarily incorporated into the cell membrane before being released partially into the media after cell lysis. This incorporation might be attributed to the combined effect of hydrophobic interaction between PFOA and the cell membrane and the reduced electrostatic repulsion from the high ion presence in the growth medium. The growth ofR. palustrisduring the PFOA uptake experiment was 9-fold slower than their growth without PFOA. This study also completely defines the toxicity range of PFOA forR. palustristhrough a toxicity assay. Increasing PFOA concentration reduced the microbe growth, with complete inhibition around 200 ppm. For various concentrations of PFOA, R. palustris exhibits interesting diauxic growth behavior. An accelerated growth phase was followed by a temporary death phase in the first 24 hours in the presence of 12.5-100 ppm PFOA, implying a unique adaptation mechanism to PFOA. 

JF2 08-2F Crusty is a novel melanized polyextremotolerant fungus isolated from a biological soil crust, which we believe harborsMethylobacteriumspp. endosymbionts, called Pinky. Crusty is capable of utilizing many sources of carbon and nitrogen and is resistant to multiple metals and UV-C due to its melanized cell wall. We were unable to recover a Pinky-free culture of Crusty via usage of antibiotics. However, when exposed to antibiotics that kill or stop the growth of the Pinky, growth of Crusty is significantly stunted, implying that actively growing Pinky symbionts are needed for Crusty’s optimal growth. The Crusty-Pinky symbiosis also seems to be able to perform active metabolism in carbonless and nitrogenless medium, which we believe is due to Pinky’s ability to perform aerobic anoxygenic photosynthesis. Finally, Pinky was identified as being capable of growth stimulation of the algaeChlorella sorokiniana, indicating that Pinky likely produces cytokinins or auxins whichMethylobacteriumare known for. Features of this symbiosis provide us insight into the ecological roles of these microbes within the biological soil crust. 

Abstract Lignin is a universal waste product of the agricultural industry and is currently seen as a potential feedstock for more sustainable manufacturing. While it is the second most abundant biopolymer in the world, most of it is currently burned as it is a very recalcitrant material. Many recent studies, however, have demonstrated the viability of biocatalysis to improve the value of this feedstock and convert it into more useful chemicals, such as polyhydroxybutyrate, and clean fuels like hydrogen and n-butanol.Rhodopseudomonas palustrisis a gram-negative bacterium which demonstrates a plethora of desirable metabolic capabilities, including aromatic catabolism useful for lignin degradation. This study uses a multi-omics approach, including the first usage of CRISPRi inR. palustris, to investigate the lignin consumption mechanisms ofR. palustris, the essentiality of redox homeostasis to lignin consumption, elucidate a potential lignin catabolic superpathway, and enable more economically viable sustainable lignin valorization processes.