Biology subjects

Kawada, M.

Publications and source records attributed to Kawada, M..

3 recordsLinked to original sources

bioRxiv2024

Programming aliphatic polyester degradation by engineered bacterial spores

Enzymatic degradation of plastics is a sustainable approach to addressing the growing issue of plastic accumulation. The primary challenges for using enzymes as catalysts are issues with their stability and recyclability, further exacerbated by their costly production and delicate structures. Here, we demonstrate an approach that leverages engineered spores that display target enzymes in high density on their surface to catalyze aliphatic polyester degradation and create self-degradable materials. Engineered spores display recombinant enzymes on their surface, eliminating the need for costly purification processes. The intrinsic physical and biological characteristics of spores enable easy separation from the reaction mixture, repeated reuse, and renewal. Engineered spores displaying lipases completely degrade aliphatic polyesters and retain activity through four cycles, with full activity recovered through germination and sporulation. Directly incorporating spores into polyesters results in robust materials that are completely degradable. Our study offers a straightforward and sustainable biocatalytic approach to plastic degradation.

bioengineering↗

bioRxiv2022

Multi-dimensional antibiotic resistance time series reveals the phenotype-based fitness landscape for Escherichia coli

The fitness landscape represents the complex relationship between genotype or phenotype and fitness under a given environment, the structure of which allows the explanation and prediction of evolutionary trajectories. Although previous studies have constructed fitness landscapes based on comprehensively studying the mutations in specific genes, the high dimensionality of genotypic changes prevents us from developing a fitness landscape capable of predicting evolution for the whole cell. Herein, we address this problem by inferring the fitness landscape for antibiotic resistance evolution by quantifying the phenotypic changes, that is, multi-dimensional time-series measurements of the drug resistance profile. Using the time-series data of drug resistance for multiple drugs, we inferred that the fitness landscape underlies the evolution dynamics of resistance. We showed that different peaks of the landscape correspond to different drug resistance mechanisms, thus supporting the validity of the inferred fitness landscape. We further discuss how inferred phenotype-fitness landscapes could contribute to the prediction and control of evolution. This approach bridges the gap between phenotypic/genotypic changes and fitness while contributing to a better understanding of drug resistance evolution.

evolutionary biology↗

bioRxiv2020

High-throughput laboratory evolution and evolutionary constraints in Escherichia coli

Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation for evolutionary constraints is lacking. Here, we performed a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Using interpretable machine learning techniques, we analyzed the phenotype-genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis revealed the underlying biological processes responsible for these distinct states, leading to the identification of novel trade-off relationships associated with drug resistance. We also report a novel constraint that leads to decelerated evolution. These findings bridge the genotypic, gene expression, and drug resistance space and lead to a better understanding of evolutionary constraints for antibiotic resistance.

evolutionary biology↗