Biology subjects

Noda, S.

Publications and source records attributed to Noda, S..

6 recordsLinked to original sources

bioRxiv2026

Threonine Supplementation Reduces Methylglyoxal Overflow by Increasing Glycolysis Flux at the Payoff Phase: A Metabolic Modeling Analysis

Methylglyoxal (MGO) is a metabolic byproduct of sugar metabolism involved in the formation of advanced glycation end products (AGEs) and its accumulation disrupts protein function, redox balance and cellular viability. Yet the metabolic rewiring required to divert MGO overflow and prevent these cytotoxic effects remains largely unknown. To address these gaps, we used flux sampling and flux balance analysis in genome scale metabolic model iJO1366 of E. coli. High glucose increased MGO flux from 0.16 mmol/gDCW/h to 1.87 mmol/gDCW/h in simulation. Large scale in silico screening of metabolic reactions identified that increasing threonine uptake, thereby increasing ethanol flux, reduced glucose induced increase in MGO flux from 1.87 mmol/gDCW/h to 0.405 mmol/gDCW/h. In silico inhibition of ethanol production (NAD+ regeneration) inhibited the MGO lowering potential of threonine. Threonine supplementation actively drives the acetaldehyde to ethanol flux to provide localized NAD+ relief, which subsequently enhances the flux of payoff phase in glycolysis (glyceraldehyde 3-phosphate), thereby efficiently draining the stagnated DHAP pool and shutting down the overflow towards MGO production. Unexpectedly, under reduced oxygen conditions, high glucose caused only a minimal increase in MGO flux, which was not reduced by threonine supplementation. Our study also decoded the mechanistic details behind this paradox. Though this model driven hypothesis requires further validation in vivo, these stoichiometric predictions have applications in metabolic engineering, for commercial MGO production. Furthermore, in vivo studies focused on bacterial stress, gut microbiome imbalances and AGE related diseases might benefit from these simulations

systems biology↗

bioRxiv2026

Machine-learning-guided enzyme discovery and redox-system engineering for efficient production of the nylon monomer methyl 12-aminododecanoate in Escherichia coli

Methyl 12-aminododecanoate (ADAME) is a key precursor for Nylon 12 synthesis and an attractive target for sustainable microbial production. However, efficient biosynthesis of ADAME requires the coordinated oxidation and transamination of methyl dodecanoate (DAME), and identifying compatible enzymes for this multistep pathway remains challenging. In this study, we applied a machine learning-based bioinformatic screening approach to identify alternative enzymes for DAME-to-ADAME bioconversion. Among 18 selected distantly related AlkB homologs, AlkBGp01 from Alcanivorax sp. P2S70 showed activity toward DAME, producing 12-hydroxydodecanoic acid methyl ester (HDAME) and 12-oxododecanoic acid methyl ester (ODAME) despite low sequence identity to Pseudomonas putida AlkB. Further screening identified compatible redox partners, AlkG60 from the same species as AlkBGp01 and AlkTp04, whose combination with AlkBGp01 resulted in almost sixfold higher ODAME production than that achieved with P. putida AlkBFGJLT. ODAME production was further increased by more than threefold by conjugating these three newly identified proteins. The optimized single-plasmid system comprising conjugated AlkBGp01, AlkG60, and AlkTp04, together with AlaD and newly identified omega-transaminases EAV41574 produced 0.28 mM gDCW-1 ADAME, representing a 5.6-fold improvement over the previously reported P. putida AlkBFGJLT-based system with AlaD-CV2025. These results demonstrate that machine learning-guided enzyme discovery can identify functional distantly related homologs and compatible enzyme combinations for constructing efficient synthetic pathways. This study also highlights the importance of optimizing pathway architecture, including enzyme conjugation and plasmid configuration, for improving microbial production of bio-based monomer precursors.

bioengineering↗

bioRxiv2026

Constitutive sporulation in wild fission yeast enhances insect-mediated survival

Sexual reproduction in yeast typically occurs under nutrient-limited conditions, producing stress-resistant spores that promote survival. However, wild Schizosaccharomyces japonicus strains isolated from fruit flies exhibit constitutive sporulation even under nutrient-rich conditions. Here we demonstrate that this nutrient-independent sporulation enhances survival by enabling spores to resist digestion by Drosophila melanogaster, and that although such strains are disadvantageous under nutrient-rich conditions, they are positively selected during insect predation. Genetic analyses reveal that multiple mutations in six meiotic regulatory genes, acting via epistasis, underlie this phenotype. This trait is widely distributed across natural populations throughout Japan. These findings suggest that constitutive sexual reproduction functions as an adaptive strategy facilitating insect-mediated dispersal, reflecting an ecological context where sexual reproduction is maintained despite its costs. This work provides new insights into yeast-insect symbiosis, evolutionary dynamics of sexual adaptation, and potential applications in yeast breeding.

evolutionary biology↗

bioRxiv2024

Explainable AI-guided identification of a novel protein-RNA interactive frame for selective siRNA accumulation in plants

Small interference RNA (siRNA) selectively accumulates and acts in RNA interference (RNAi). Although the components involved in siRNA production have long been the focus of studies to elucidate RNAi processes, the mechanism(s) for selectivity of siRNA (or RNAi effectivity) remains unclear. In a novel approach, we developed a progressive deep learning (DL) framework integrating Transformer and convolutional neural networks to predict the sequences of selectively accumulated siRNAs across various land plant species. These approaches achieved high-accuracy prediction of selectively accumulated 21-nt siRNAs and further identified their key signals, which are positionally and linguistically flexible sequences surrounding the target siRNA. We experimentally validated the contribution of these flexible key signal sequences to siRNA accumulation selectivity using virus-induced gene silencing (VIGS) in Nicotiana benthamiana, and identified RNA-binding proteins that directly recognize the key signal sequences to act for selective siRNA accumulation. These insights provide a novel framework for investigating RNAi mechanisms in plants. One sentence summaryWe discovered a novel mechanism centered on RNA-protein interactions involving the selective accumulation of small-RNAs in plants by applying advanced deep learning frames.

plant biology↗

bioRxiv2024

Alterations of PINK1-PRKN signaling in mice during normal aging

The ubiquitin kinase-ligase pair PINK1-PRKN identifies and selectively marks damaged mitochondria for elimination via the autophagy-lysosome system (mitophagy). While this cytoprotective pathway has been extensively studied in vitro upon acute and complete depolarization of mitochondria, the significance of PINK1-PRKN mitophagy in vivo is less well established. Here we used a novel approach to study PINK1-PRKN signaling in different energetically demanding tissues of mice during normal aging. We demonstrate a generally increased expression of both genes and enhanced enzymatic activity with aging across tissue types. Collectively our data suggest a distinct regulation of PINK1-PRKN signaling under basal conditions with the most pronounced activation and flux of the pathway in mouse heart compared to brain or skeletal muscle. Our biochemical analyses complement existing mitophagy reporter readouts and provide an important baseline assessment in vivo, setting the stage for further investigations of the PINK1-PRKN pathway during stress and in relevant disease conditions.

neuroscience↗

bioRxiv2023

Integration of shoot-derived polypeptide signals by root TGA transcription factors is essential for survival under fluctuating nitrogen environments

Unlike plants in the field, which experience significant temporal fluctuations in environmental conditions, plants in the laboratory are typically grown in controlled, stable environments. Therefore, signaling pathways evolved for survival in continuously fluctuating environments often remain functionally latent in laboratory settings. Here, we show that TGA1 and TGA4 act as hub transcription factors through which the expression of genes involved in high-affinity nitrate uptake are regulated in response to shoot-derived phloem mobile polypeptides, CEP DOWNSTREAM 1 (CEPD1), CEPD2 and CEPD-like 2 (CEPDL2) as nitrogen (N) deficiency signals, and Glutaredoxin S1 (GrxS1) to GrxS8 as N sufficiency signals. CEPD1/2/CEPDL2 and GrxS1-S8 competitively bind to TGA1/4 in roots, with the former acting as transcription coactivators that enhance the uptake of nitrate, while the latter function as corepressor complexes together with TOPLESS to limit nitrate uptake. Arabidopsis plants deficient in TGA1/4 maintain basal nitrate uptake and exhibit growth similar to wild-type plants in a stable N environment, but were impaired in regulation of nitrate acquisition in response to shoot N demand, leading to defective growth under continuously fluctuating N environments where rhizosphere nitrate ions switch periodically between deficient and sufficient states. TGA1/4 are crucial transcription factors that enable plants to survive under fluctuating and challenging N environmental conditions.

plant biology↗