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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.
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Unique Adaptations of a Photosynthetic Microbe Rhodopseudomonas palustris to the Toxicological Effects of Perfluorooctanoic Acid
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.
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- Award ID(s):
- 1943310
- PAR ID:
- 10595636
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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