Biology subjects

Saha, R.

Publications and source records attributed to Saha, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Random mtDNA mutagenesis maps genotype-phenotype associations across metabolic contexts and lineages

Mitochondrial DNA (mtDNA) mutations cause inherited disease, accumulate with age, and recur in tumors, yet their effects remain unresolved because existing models interrogate one engineered allele at a time. We developed mtDIVE, a random C[->]T mutagenesis platform for pooled mitochondrial forward genetics in human epithelial cells and zebrafish embryos. mtDIVE generates genotypes ranging from single substitutions to >100 mutations per molecule, impairs respiration, and causes lineage-specific developmental delay. Coupling single-cell transcriptomics with mtDNA genotyping revealed that selection on mutant genomes and their transcriptional consequences depend on metabolic state and developmental lineage. Obligate OXPHOS eliminates highly mutated cells by apoptosis, whereas glycolytic conditions promote their competitive displacement. Individual loci exert modest but non-redundant effects that predict cell state beyond aggregate mutation burden, and cancer-recurrent variants are enriched among mutations linked to reduced mitochondrial transcription. Thus, mtDIVE links mtDNA genotypes to cellular phenotypes, separating the consequences of mutation identity from mutational load.

genomics↗

bioRxiv2026

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

systems biology↗

bioRxiv2026

Natural Variation in Maize Shikimate Dehydrogenase Alters Enzyme Activity and Kernel Homoserine Accumulation.

Metabolic diversity in maize kernels determines nutritional quality and end-use value. Therefore, understanding its genetic basis is essential for crop improvement and elucidating plant metabolic regulation. Here, we integrated metabolite profiling with metabolite-based genome-wide association studies (mGWAS), structural modeling, enzyme kinetics, and genome-scale metabolic simulations to identify genetic determinants of kernel metabolite variation in 265 maize inbred lines. Profiling of 57 metabolites revealed inter-genotypic variation, with homoserine among the most variable metabolites. mGWAS identified 62 locus-trait associations implicating 788 candidate genes, including 154 encoding metabolic enzymes. A major association for homoserine mapped to the shikimate dehydrogenase gene Sad1 on chromosome 10. Four tightly linked coding-region SNPs, including three non-synonymous variants, defined two Sad1 haplotypes associated with differential homoserine accumulation, independent of gene expression variation. Structural analysis and recombinant enzyme assays showed that these substitutions occur within catalytic and cofactor-binding domains and alter catalytic efficiency. Genome-scale metabolic modeling indicated that variation in SAD1 activity influences plastidial oxaloacetate availability for aspartate and homoserine biosynthesis through redox-coupled flux via the malate-oxaloacetate shuttle. Together, our results indicate that Sad1 allelic variation alters enzyme function and amino acid accumulation, linking the shikimate pathway, redox metabolism, and amino acid biosynthesis in maize kernels.

plant biology↗

bioRxiv2026

Dissolution-Controlled Nanocrystalline Rifapentine Formulation for Tuberculosis Treatment

Current tuberculosis (TB) treatment suffers from drawbacks such as long regimens, high pill burden and side effects leading to non-adherence and poor treatment outcomes. Dissolution-controlled drug depot formulation with high drug loading is a clinically successful drug delivery strategy. Such depots reduce the dosing frequency for treatments requiring daily administration, thereby improving treatment adherence and compliance. However, dissolution-controlled depots for first-line TB drugs have not been demonstrated due to their high solubility and high dose requirements. In this study, we overcame this challenge by developing injectable, extended-release, dissolution-controlled depots of nanocrystalline rifapentine (NCRPT), microcrystalline rifapentine (MCRPT) and amorphous rifapentine microparticles (ARPT) with more than 75% loading. Crystalline formulations resulted in much slower depot dissolution compared to amorphous formulations. A single intramuscular (IM) injection of NCRPT in mice resulted in therapeutic serum concentrations for over a week. We then demonstrated the efficacy of NCRPT in both pre-exposure prophylaxis and therapeutic models of mice TB. NCRPT administered at 60 mg/kg once every two weeks demonstrated excellent efficacy in a mouse model of TB infection. In each case, a [~] 4-log-fold reduction in lung bacterial load compared to untreated mice was observed. These results open new avenues for developing LAI formulations of TB drugs and could improve patient compliance and TB management.

bioengineering↗

bioRxiv2026

Spatial Metabolic Modeling Reveals Zinc–Citrate Rewiring and Therapeutic Vulnerabilities in Prostate Cancer

Prostate cancer exhibits a metabolic phenotype distinct from the Warburg paradigm, characterized by reprogramming of the zinc-citrate secretory axis that normally drives citrate accumulation and secretion in prostatic epithelium. How this metabolic rewiring is organized across the spatial architecture of tumors and linked to androgen receptor (AR) signaling remains poorly understood. We integrated spatial transcriptomics data from twelve regions spanning normal, inflamed, and Gleason-graded prostate tissues with genome-scale metabolic modeling to quantify metabolic activity across tissue states. The analysis identified the zinc-citrate-aconitase axis as a central feature of prostate cancer metabolic reprogramming, linking increased aconitase activity to enhanced citrate export, acetyl-CoA generation, and de novo lipogenesis. Metabolic similarity was more strongly associated with spatial proximity than with histological classification, suggesting field-level metabolic reprogramming beyond visible pathological boundaries. AR activity displayed grade-dependent associations with lipid metabolic pathways, indicating heterogeneous coupling between androgen signaling and metabolism across tumor regions. Consistent with the established biology of prostate cancer, predicted flux distributions did not support a classical Warburg phenotype. A flux-based target prioritization framework identified HMGCR, FASN, and SLC25A1 as candidate therapeutic targets, with independent support from TCGA-PRAD expression profiles and DepMap CRISPR essentiality data. Together, these findings provide a spatially resolved view of prostate cancer metabolism, establish the zinc-citrate axis as a dominant feature of metabolic organization, and identify candidate metabolic vulnerabilities for therapeutic intervention.

Cancer Biology↗

bioRxiv2026

Solute Carrier Transporter Family Modulates Neutrophil Metabolism During Health and Disease

Neutrophils are the most abundant leukocytes in humans and play a central role in immune regulation. Although traditionally viewed as terminally differentiated cells with limited plasticity, growing evidence indicates that neutrophils exhibit substantial functional heterogeneity in response to stress. To date, however, most studies have focused on transcriptional and signaling changes, while metabolic heterogeneity, especially beyond central carbon metabolism, remains poorly characterized. Here, we systematically investigate metabolic reprogramming in neutrophils under three stress conditions: granulocyte colony-stimulating factor (G-CSF) treatment, hematopoietic stem cell transplantation (HSCT), and pancreatic ductal adenocarcinoma (PDAC). Using condition-specific genome-scale metabolic (GSM) models, we identify distinct metabolic vulnerabilities across neutrophil states. Vitamin metabolism emerged as a key differentiating feature between G-CSF- and HSCT-treated neutrophils, whereas PDAC-associated neutrophils displayed globally enhanced metabolic activity coupled with restricted metabolite exchange fluxes. Furthermore, solute carrier (SLC) family transporters were identified as major metabolic regulators underlying stress-induced neutrophil reprogramming. Together, our findings demonstrate that neutrophil heterogeneity extends beyond transcriptional programs to encompass profound metabolic specialization, highlighting metabolism as a critical dimension of neutrophil plasticity in health and disease.

immunology↗

bioRxiv2026

β-strand complementation within tip initiation complexes licenses assembly of diverse type IV filaments

PilC/PilY1 proteins are tip-located adhesins of type IV pili (T4P) that are critical for T4P function in diverse behaviors including twitching motility, DNA uptake, and host cell adhesion. PilC and PilY1 adhesins are proposed to interact with initiation complexes composed of minor pilins (PilIJK family proteins) to aid in initiation of T4P polymerization, but it has been unclear how PilC/PilY1 proteins promote fiber assembly. We combined structural modeling, genetic, and biochemical experiments using Neisseria gonorrhoeae and Acinetobacter baylyi to delineate how PilC/PilY1 control T4P assembly: a short peptide at the C-terminus of PilC/PilY1 initiates T4P assembly via {beta}-strand complementation with PilK-family minor pilins. This {beta}-strand is necessary and partially sufficient to trigger fiber assembly. In a working model, the PilK-PilC/PilY1 complex is recognized by a preformed PilI-PilJ heterodimer to form a quaternary "licensing complex" that then templates and initiates fiber assembly. In type II secretion systems (T2SS) lacking PilC/PilY1, PilK homologs directly incorporate the terminal {beta}-strand provided by PilC/PilY1 in T4P. Moreover, phylogenetically distinct Tad T4P lack a canonical PilK homolog and instead contain a structurally similar minor pilin-like protein called TadG/CpaL that is important for fiber assembly. We show that CpaL of Caulobacter crescentus Tad T4P acts similarly to the T2SS PilK homolog to provide the C-terminal {beta}-strand required for assembly. Our results explain how PilC/PilY1 can be retained on the fiber tip under enormous tensile loads generated during mechanical shear and T4P retraction and demonstrate how diverse T4P systems employ {beta}-strand complementation to license fiber assembly. SIGNIFICANCEProkaryotic type IV filaments are ancient, diverse, and broadly distributed nanomachines that assemble and retract to execute diverse microbial functions. They include type IV pili and type II secretion systems, mediating toxin secretion, motility, surface adhesion, biofilm formation, DNA uptake, and other functions. Here, we show that two widely conserved subunits of the tip, PilI and PilJ, form a module that recognizes the folding of a {beta}-sheet in a third subunit, PilK. The final {beta}-strand in this sheet can be supplied in trans by the last [~]10 aminoacyl residues of large PilC/PilY1 adhesins, or in cis by PilK itself. In a working model, this recognition results in formation of a PilIJK trimer, which then licenses fiber polymerization through a templating mechanism.

microbiology↗

bioRxiv2026

AI-designed nuclease performs robust knock out, base editing and prime editing in plants

Genome-editing applications in biological organisms rely on a limited set of naturally occurring enzymes. Artificial intelligence (AI) is transforming protein design. Here, building on the AI-designed nuclease OpenCRISPR-1, we establish the plant-optimized AI-designed editors (PAiD) as a versatile genome-editing platform in plants. PAiD supports NHEJ-mediated indel generation, adenine and cytosine base editing, and prime editing, with robust performance across multiple loci comparable to SpCas9. These results highlight the translational potential of AI-designed nucleases for plant genome engineering.

plant biology↗

bioRxiv2026

A Closed Computational - Experimental Loop Identifies Metabolic Collapse at the Root of Macrophage Dysfunction due to Zinc Dyshomeostasis

Zinc plays a crucial role in immune regulation, the oxidative stress response, and epithelial barrier integrity, yet zincs precise role in regulating metabolic and immunological functions in myeloid cells remains poorly understood. Here, we employ a systems biology approach using constraint-based modeling to elucidate the consequences of myeloid-specific loss of ZIP8 on macrophage metabolic function and antibacterial capabilities. We demonstrate that macrophage populations in the lung of ZIP8 knockout (Zip8KO) mice exhibit widespread metabolic disruption, spanning glycolysis, butanoate metabolism, amino acid metabolism, and mitochondrial function. Specifically, Zip8KO macrophages exhibit impaired nutrient uptake and dysregulated energy metabolism, which is exacerbated following Streptococcus pneumoniae infection. Genome-scale metabolic modeling and flux analysis revealed a paradoxical pattern of metabolic suppression prior to infection, followed by overcompensation post-infection, potentially driving immune dysfunction. Consistent with these predictions Zip8KO bone marrow-derived macrophages displayed increased ATP demand and disrupted mitochondrial energetics, compromising their ability to control infection. Importantly, we identified succinate, and kynurenic acid as metabolites capable of restoring immune responses and validated their ability to enhance bacterial clearance in Zip8KO BMDMs. Together, these findings establish ZIP8 as a central regulator of immune-metabolic homeostasis and suggest potential therapeutic avenues to restore immune function in settings of zinc deficiency.

immunology↗

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

Diffusion Models vs. DCGANs for Class-Imbalanced Lung Cancer CT Classification: A Comparative Study

Effective lung cancer detection from CT scans remains critically challenged by class imbalance where benign and normal cases are underrepresented, leading to biased machine learning models with reduced sensitivity for minority classes and potentially missed diagnoses in cancer screening applications. We present a comprehensive comparative analysis of Diffusion Models and Deep Convolutional Generative Adversarial Networks (DCGANs), both incorporating modern architectural enhancements including spectral normalization, self-attention mechanisms, and conditional generation, for addressing class imbalance in lung cancer CT classification. Using the IQ-OTH/NCCD dataset comprising 1,097 CT images across normal, benign, and malignant categories with statistical validation across 10 independent runs, we evaluated both approaches through quantitative image quality metrics (Frechet Inception Distance, Kullback-Leibler divergence, Kernel Inception Distance, and Inception Score) and downstream classification performance. While Diffusion models consistently outperformed DCGANs across most image quality measures, the clinical significance was confirmed through task-based validation. Both generative approaches successfully addressed class imbalance: DCGAN-augmented datasets achieved overall accuracy of 0.9760 {+/-} 0.0116 with benign recall improvement from 0.833 to 0.933, while Diffusion-augmented datasets reached superior performance of 0.9959 {+/-} 0.0068 with perfect benign recall (1.000 {+/-} 0.000). Critically for cancer screening where false negatives carry severe consequences, Diffusion maintained the highest malignant detection sensitivity (0.997 {+/-} 0.008) with substantially lower performance variance, demonstrating more consistent synthetic data quality. These findings establish that while both modern architectures can mitigate class imbalance, Diffusion models superior recall performance and lower variability position them as the preferred approach for high-stakes clinical applications, demonstrating that ultimate validation must prioritize downstream clinical task performance over image quality metrics alone.

cancer biology↗

bioRxiv2025

Dietary fatty acids as determinants of cuticular wax profiles in Tribolium castaneum

Cuticular waxes are complex mixtures of hydrocarbons and other lipophilic compounds that form an outer protective layer over the insect cuticle playing a role in ecological interactions as well as physiological processes. While CHC profiles are largely genetically determined, several studies have linked diet to cuticular hydrocarbons and limited research indicates a potential influence of dietary fatty acids. Considering this, the current work investigates the effect of nine cereal flours, i.e., wheat, rice, maize, barley, oat, ragi, sorghum, soybean and gram, each with a distinct fatty acid composition, on the cuticular wax profile of Tribolium castaneum (Coleoptera: Tenebrionidae), a globally significant stored grain insect. Fatty acid compositions of the nine flours and the cuticular waxes from the beetle populations reared on each diet were characterized using gas chromatography mass spectrometry (GC-MS). In all the beetle populations, the most abundant compound was 1-pentadecene, followed by cis-9-tetradecen-1-ol and 1-heptadecene, irrespective of the diet. While no qualitative differences in cuticular wax composition were found, significant quantitative variation in cuticular wax profile was observed across dietary groups. Similarity percentage (SIMPER) analysis revealed a cumulative Bray- Curtis dissimilarity of 47.72 among the flour samples based on their fatty acid profiles, and a dissimilarity of 18.51 among T. castaneum populations reared on these flours based on their cuticular wax composition. Non-metric multidimensional scaling (NMDS) further indicated substantial variation in the cuticular wax profiles of beetle populations raised on different dietary sources. Pearsons correlation analysis between the concentrations of individual cuticular wax components and the fatty acid content of the respective diets demonstrated strong correlations between specific cuticular compounds and dietary fatty acids. Notably, levels of 1-pentadecene in the beetle cuticle were strongly associated with the concentrations of hexadecanoic and octadecanoic acids in the flours. These results support the hypothesis that, alongside endogenous conserved biosynthetic pathways, dietary fatty acid composition contributes to the modulation of insect cuticular chemistry. This dietary influence may have important implications for understanding insect ecological plasticity and diet-mediated changes in surface chemistry.

zoology↗

bioRxiv2025

GAN-Enhanced Machine Learning and Metabolic Modeling Identify Reprogramming in Pancreatic Cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is one of the deadliest forms of cancer and presents a significant clinical challenge due to poor prognosis and limited treatment options. In this study, we developed a novel framework integrating genome-scale metabolic modeling (GSM) with machine learning to identify metabolic biomarkers and vulnerabilities in PDAC. We addressed the inherent class imbalance in cancer datasets by generating synthetic healthy samples using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), implementing a three-step biological filtration process to ensure their validity. Our approach achieved 94.83% accuracy in distinguishing between healthy and cancerous metabolic states. Systems-level analysis revealed three key dysregulated pathways: heparan sulfate degradation, O-glycan metabolism, and heme degradation. We identified impaired lysosomal degradation of heparan sulfate proteoglycans as a potential contributor to PDAC pathogenesis, providing a mechanistic explanation for the previously observed association between lysosomal storage disorders and pancreatic cancer. Additionally, we found that nervonic acid transport (MAR00336) was the most discriminative reaction between healthy and cancerous states, with gene-level analysis highlighting FABPs, SLC27As, ACSLs, and ACSBGs as key molecular drivers of metabolic reprogramming in PDAC. Overall, our multi-level approach connected genetic drivers to functional metabolic consequences, revealing coordinated upregulation of fatty acid transport and activation processes. These findings enhance our understanding of PDAC metabolism and present potential therapeutic targets, demonstrating the value of integrated computational approaches in cancer research.

cancer biology↗

bioRxiv2025

Minimal Gene Signatures Enable High-Accuracy Prediction of Antibiotic Resistance in Pseudomonas aeruginosa

Antimicrobial resistance (AMR) in Pseudomonas aeruginosa poses a critical global health challenge, with current diagnostics relying on slow, culture-based methods. Here, we present a ML framework leveraging transcriptomic data to predict antibiotic resistance with high accuracy. We applied a genetic algorithm to 414 clinical isolates to identify minimal, highly predictive gene sets ([~]35-40 genes) distinguishing resistant from susceptible strains for meropenem, ciprofloxacin, tobramycin, and ceftazidime. Automated ML classifiers trained on these sets achieved accuracies of 96-99% on test data (F1 scores: 0.93-0.99), surpassing clinical deployment thresholds. Multiple distinct, non-overlapping gene subsets exhibited comparable performance, indicating that resistance acquisition broadly impacts the expression of diverse regulatory and metabolic genes. Comparison with known resistance markers from CARD and operon annotations revealed a substantial number of previously unannotated clusters, highlighting significant knowledge gaps in current AMR understanding. Mapping these genes onto independently modulated gene sets (iModulons) revealed transcriptional adaptations across diverse genetic regions. Overall, this study presents a streamlined machine-learning workflow for transcriptomic data and offers a pathway toward rapid diagnostics and personalized treatment strategies against AMR.

systems biology↗

bioRxiv2025

Genome-scale metabolic modeling and machine learning unravel metabolic reprogramming and mast cell role in lung cancer through a multi-level analysis

Lung cancer remains a leading cause of cancer-related deaths worldwide, with immune interactions, particularly involving mast cells, playing a crucial role. Mast cells contribute to both pro- and anti-tumorigenic activities, influencing immune modulation, angiogenesis, and tissue remodeling. This study provides a comprehensive multi-level analysis of metabolic alterations in lung cancer through genome-scale metabolic modeling (GSM) and machine learning. Using 43 paired lung tissue samples, we developed metabolic models of lung cancer and mast cells, revealing a significant reduction in resting mast cells in cancerous tissues. Our Random Forest classifier accurately distinguished between healthy and cancerous states, identifying key metabolic signatures. We found that lung cancer cells selectively upregulate valine, isoleucine, histidine, and lysine metabolism in the aminoacyl-tRNA pathway to support their elevated energy demands. Mast cell metabolism exhibited enhanced histamine transport and increased glutamine consumption in the tumor microenvironment, suggesting a shift towards immunosuppressive activity. Additionally, our novel Metabolic Thermodynamic Sensitivity Analysis (MTSA) showed impaired biomass production in cancerous mast cells across physiological temperatures (36 to 40C), indicating metabolic vulnerabilities. By elucidating the metabolic adaptations of mast cells and lung cancer cells, our study highlights their interplay in tumor progression and identifies potential therapeutic targets and diagnostic markers for future investigation. Author SummaryOur research examines the intricate relationship between lung cancer and mast cells, a type of immune cell, using advanced computational methods. We developed detailed metabolic models of lung tissue and mast cells through a multi-level approach to understand how their metabolism changes in cancer. Our findings reveal that lung cancer cells modify their metabolic pathways to meet increased energy demands, including enhanced utilization of four specific amino acids within the aminoacyl-tRNA pathway. In addition, mast cells in lung cancer environments show increased histamine release and glutamine consumption, suggesting they become more active in ways that might promote tumor growth. Additionally, we found that cancerous mast cells are less able to adapt to temperature changes compared to healthy ones, which could impact how they respond during fever or inflammation. These insights provide new perspectives on how lung cancer affects the immune system and could lead to novel approaches for diagnosis and treatment. By understanding these complex interactions, we aim to contribute to the development of more effective strategies for combating lung cancer.

cancer 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

Unique Adaptations of a Photosynthetic Microbe Rhodopseudomonas palustris to the Toxicological Effects of Perfluorooctanoic Acid

In this study, we investigate the PFOA removal capabilities of Rhodopseudomonas 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 of R. palustris during the PFOA uptake experiment was 9-fold slower than their growth without PFOA. This study also completely defines the toxicity range of PFOA for R. palustris through 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.

ecology↗

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↗