Biology subjects

Kathol, M.

Publications and source records attributed to Kathol, M..

4 recordsLinked to original sources

bioRxiv2025

Machine Learning Reveals Proteome-Encoded Growth Predictors of Rhodopseudomonas palustris CGA009 on Lignin Aromatics

Microbial utilization of lignin-derived aromatics requires extensive metabolic flexibility, yet growth outcomes vary sharply with substrate chemistry and oxygen availability. Whether this variability reflects distinct growth programs or alternative realizations of shared biochemical constraints remains unclear. Here, we combine quantitative proteomics with cross-condition machine learning to test whether growth-rate variation in Rhodopseudomonas palustris can be predicted directly from proteome composition and to identify the proteomic features that consistently encode growth potential across environments. Using OmniProt, a cross-condition neural modeling and interpretive framework, we predicted growth rates across 16 lignin-derived substrate-oxygen combinations, including held-out conditions, demonstrating that growth-relevant information is encoded in the proteome. Interpreting model reliance using Monte Carlo SHAP values and dependence-aware perturbation analyses revealed a compact, hierarchical organization of growth determinants. Despite pronounced oxygen-driven bifurcation in proteome abundance, the features required for accurate growth prediction were largely regime-invariant, defining a conserved biochemical core overlaid by adaptive, condition-specific modulators. These findings reconcile metabolic versatility with constrained growth control and show that diverse lignin-derived substrates converge onto a limited set of proteome-encoded growth bottlenecks accessed through flexible regulatory programs.

systems biology↗

bioRxiv2025

Artificial Neural Network Reveals the Role of Transport Proteins in Rhodopseudomonas palustris CGA009 During Lignin Breakdown Product Catabolism

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 of R. palustris when catabolizing various lignin breakdown products (LBPs), including the monolignols p-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 of R. palustris on 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 of R. palustris in lignin degradation under aerobic and anaerobic conditions. ImportanceThis study is significant as it addresses the biotechnological potential of Rhodopseudomonas palustris in 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 of R. 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 optimizing R. palustris for sustainable bioprocessing applications, such as bioplastic production, under varying environmental conditions.

systems biology↗

bioRxiv2025

High Enzyme Promiscuity in Lignin Degradation Mechanisms in Rhodopseudomonas palustris CGA009

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 palustris is 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 in R. palustris, to investigate the lignin consumption mechanisms of R. palustris, the essentiality of redox homeostasis to lignin consumption, elucidate a potential lignin catabolic superpathway, and enable more economically viable sustainable lignin valorization processes.

bioengineering↗

bioRxiv2021

Synthetic Biology Tool Development Advances Predictable Gene Expression in the Metabolically Versatile Soil Bacterium Rhodopseudomonas palustris

Harnessing the unique biochemical capabilities of non-model microorganisms would expand the array of biomanufacturing substrates, process conditions, and products. There are non-model microorganisms that fix nitrogen and carbon dioxide, derive energy from light, catabolize methane and lignin-derived aromatics, are tolerant to physiochemical stresses and harsh environmental conditions, store lipids in large quantities, and produce hydrogen. Model microorganisms often only break down simple sugars and require low stress conditions, but they have been engineered for the sustainable manufacture of numerous products, such as fragrances, pharmaceuticals, cosmetics, surfactants, and specialty chemicals, often by using tools from synthetic biology. Transferring complex pathways with all of the needed cofactors, energy sources, and cellular conditions from a non-model microorganism to a common chassis has proven to be exceedingly difficult. Utilization of unique biochemical capabilities could also be achieved by engineering the host; although, synthetic biology tools developed for model microbes often do not perform as designed in other microorganisms. The metabolically versatile Rhodopseudomonas palustris CGA009, a purple non-sulfur bacterium, catabolizes aromatic compounds derived from lignin in both aerobic and anaerobic conditions and can use light, inorganic, and organic compounds for its source of energy. R. palustris utilizes three nitrogenase isozymes to fulfill its nitrogen requirements while also generating hydrogen. Furthermore, the bacterium produces two forms of RuBisCo in response to carbon dioxide/bicarbonate availability. While this potential chassis harbors many beneficial traits, stable heterologous gene expression has been problematic due to its intrinsic resistance to many antibiotics and the lack of synthetic biology parts investigated in this microbe. To address these problems, we have characterized gene expression and plasmid maintenance for different selection markers, started a synthetic biology toolbox specifically for the photosynthetic R. palustris, including origins of replication, fluorescent reporters, terminators, and 5 untranslated regions, and employed the microbes endogenous plasmid for exogenous protein production. This work provides essential synthetic biology tools for engineering R. palustris many unique biochemical processes and has helped define the principles for expressing heterologous genes in this promising microbe through a methodology that could be applied to other non-model microorganisms.

bioengineering↗