Biology subjects

Furusawa, C.

Publications and source records attributed to Furusawa, C..

31 records · Page 2Linked to original sources

bioRxiv2023

Non-monotonic Relationship between Mutation Rate and Speed of adaptation under Antibiotic Exposure in Escherichia coli

The mutation is a fundamental source of biological evolution that create genetic variation in populations. Mutations can create new advantageous traits, but also potentially interfere with pre-existing organismal functions. Therefore, organisms may have evolved their mutation rates to appropriate levels to maintain or improve their fitness. In this study, we aimed to experimentally quantify the relationship between mutation rate and the speed of antibiotic resistance evolution. We conducted experimental evolution using twelve Escherichia coli mutator strains with increased mutation rates and five antibiotics. Our results showed that the highest mutation rate did not necessarily lead to the highest speed of adaptation, indicating a non-monotonic relationship between the speed of drug resistance evolution and mutation rate as expected. Moreover, this relationship was observed to differ among drugs, with significant differences in peak size observed between bacteriostatic and bactericidal antibiotics. We also successfully reproduced the mutation-rate dependence of the speed of adaptation using numerical simulation of a population dynamics model. These findings offer significant insights into the mutation rates evolution concomitant with the development of antibiotic resistance.

evolutionary biology↗

bioRxiv2023

Evolution of hierarchy and irreversibility in theoretical cell differentiation model

The process of cell differentiation in multicellular organisms is characterized by hierarchy and irreversibility in many cases. However, the conditions and selection pressures that give rise to these characteristics remain poorly understood. By using a mathematical model, here we show that the network of differentiation potency (differentiation diagram) becomes necessarily hierarchical and irreversible by increasing the number of terminally differentiated states under certain conditions. The mechanisms generating these characteristics are clarified using geometry in the cell state space. The results demonstrate that the appearance of these characteristics can be driven without assuming the adaptive significance. The study also provides a new perspective on the structure of gene regulatory networks that produce hierarchical and irreversible differentiation diagrams. These results indicate some constraints on cell differentiation, which are expected to provide a starting point for theoretical discussion of the implicit limits and directions of evolution in multicellular organisms.

biophysics↗

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↗

bioRxiv2022

A method for morphological feature extraction based on variational auto-encoder : an application to mandible shape

Shape analysis of biological data is crucial for investigating the morphological variations during development or evolution. However, conventional approaches for quantifying shapes are difficult as exemplified by the ambiguity in the landmark-based method in which anatomically prominent "landmarks" are manually annotated. In this study, a morphological regulated variational autoencoder (Morpho-VAE) is proposed that conducts image-based shape analysis using imaging processing through a deep-learning framework, thereby removing the need for defining landmarks. The proposed architecture comprises a VAE combined with a classifier module. This integration of unsupervised and supervised learning models (i.e., VAE and classifier modules) is designed to reduce dimensionality by focusing on the morphological features in which the differences between data with different labels are best distinguished. The proposed method is applied to the image dataset of the primate mandible to extract morphological features, which allow us to distinguish different families in a low dimensional latent space. Furthermore, the visualization analysis of decision-making of Morpho-VAE clarifies the area of the mandibular joint that is important for family-level classification. The generative nature of the proposed model is also demonstrated to complement a missing image segment based on the remaining structure. Therefore, the proposed method, which flexibly performs landmark-free feature extraction from complete and incomplete image data is a promising tool for analyzing morphological datasets in biology. AUTHOR SUMMARYShape is the most intuitive visual characteristic; however, shape is generally difficult to measure using a small number of variables. Specifically, for biological data, shape is sometimes highly diverse as it has been acquired through a long evolutionary process, adaptation to environmental factors, etc., which limits the straightforward approach to shape measurement. Therefore, a systematic method for quantifying such a variety of shapes using a low-dimensional quantity is needed. To this end, we propose a novel method that extracts low-dimensional features to describe shapes from image data using machine learning. The proposed method is applied to the primate mandible image data to extract morphological features that reflect the characteristics of the groups to which the organisms belong and then those features are visualized. This method also reconstructs a missing image segment from an incomplete image based on the remaining structure. To summarize, this method is applicable to the shape analysis of various organisms and is a useful tool for analyzing a wide variety of image data, even those with a missing segment.

bioinformatics↗

bioRxiv2022

Purifying selection enduringly acts on the sequence evolution of highly expressed proteins in Escherichia coli

The evolutionary speed of a protein sequence is constrained by its expression level, with highly expressed proteins evolving relatively slowly. This negative correlation between expression levels and evolutionary rates (known as the E-R anticorrelation) has already been widely observed in past macroevolution between species from bacteria to animals. However, it remains unclear whether this seemingly general law also governs recent evolution, including past and de novo, within a species. However, the advent of genomic sequencing and high-throughput phenotyping, particularly for bacteria, has revealed fundamental gaps between the two evolutionary processes and has provided empirical data opposing the possible underlying mechanisms which are widely believed. These conflicts raise questions about the generalization of the E-R anticorrelation and the relevance of plausible mechanisms. To explore the ubiquitous impact of expression level on molecular evolution, and to test the relevance of the possible underlying mechanisms, we analyzed the genome sequences of 99 strains of Escherichia coli for microevolution in nature. We also analyzed genomic mutations accumulated under laboratory conditions as a model of de novo microevolution. Here, we show that the E-R anticorrelation is significant in both past and de novo microevolution in E. coli. Our data also confirmed ongoing purifying selection acting on highly expressed genes. Ongoing selection included codon-level purifying selection, supporting the relevance of the underlying mechanisms. However, their contributions to the constraints in recent evolution might be smaller than previously expected from past macroevolution.

evolutionary biology↗

bioRxiv2022

Single mutation makes Escherichia coli an insect mutualist

We report an experimental system in which Escherichia coli evolves into an insect mutualist. When the essential gut symbiont of the stinkbug Plautia stali was replaced by E. coli, a few survivor insects exhibited specific localization and vertical transmission of E. coli. Through trans-generational maintenance with P. stali, several hyper-mutating E. coli lines independently evolved hosts high adult emergence and improved body color. Such "mutualistic" E. coli lines exhibited independent mutations disrupting the carbon catabolite repression (CCR) global transcriptional regulator. Each of the mutations reproduced the mutualistic phenotypes when introduced into wild-type E. coli, confirming that the single CCR mutations instantly make E. coli an insect mutualist. Our discovery uncovers that evolution of elaborate mutualism can proceed more easily and rapidly than conventionally envisaged.

evolutionary biology↗

bioRxiv2021

Operon formation by insertion sequence IS3 in Escherichia coli

Operons are a hallmark of the genomic and regulatory architecture of prokaryotes. However, the mechanism by which two genes placed far apart gradually come close and form operons remains to be elucidated. Here, we propose a new model of the origin of operons: Mobile genetic elements called insertion sequences can facilitate the formation of operons by consecutive insertion-deletion-excision reactions. This mechanism barely leaves traces of insertion sequences and is difficult to detect in evolution in nature. We performed, to the best of our knowledge, the first experimental demonstration of operon formation, as a proof of concept. The insertion sequence IS3 and the insertion sequence excision enhancer are genes found in a broad range of bacterial species. We introduced these genes into insertion sequence-less Escherichia coli and found that, supporting our hypothesis, the activity of the two genes altered the expression of genes surrounding IS3, closed a 2.7 kilobase pair gap between a pair of genes, and formed new operons. This study shows how insertion sequences can facilitate the rapid formation of operons through locally increasing the structural mutation rates and highlights how coevolution with mobile elements may shape the organization of prokaryotic genomes and gene regulation.

genomics↗

bioRxiv2021

Identification of bacterial drug-resistant cells by the convolutional neural network in transmission electron microscope images

The emergence of bacteria that are resistant to antibiotics is common in areas where antibiotics are used widely. The current standard procedure for detecting bacterial drug resistance is based on bacterial growth under antibiotic treatments. Here we describe the morphological changes in enoxacin-resistant Escherichia coli cells and the computational method used to identify these resistant cells in transmission electron microscopy (TEM) images without using antibiotics. Our approach was to create patches from TEM images of enoxacin-sensitive and enoxacin-resistant E. coli strains, use a convolutional neural network for patch classification, and identify the strains on the basis of the classification results. The proposed method was highly accurate in classifying cells, achieving an accuracy rate of 0.94. Using a gradient-weighted class activation mapping to visualize the region of interest, enoxacin-resistant and enoxacin-sensitive cells were characterized by comparing differences in the envelope. Moreover, Pearsons correlation coefficients suggested that four genes, including lpp, the gene encoding the major outer membrane lipoprotein, were strongly associated with the image features of enoxacin-resistant cells.

systems biology↗

bioRxiv2020

Distinct specificity of two pheromone G-protein coupled receptors, Map3 and Mam2, in fission yeast species.

Most sexually reproducing organisms have the ability to recognize individuals of the same species. In ascomycetes including yeasts, potential mating between cells of opposite mating-type depends on the molecular recognition of two peptidyl mating pheromones by their corresponding G-protein coupled receptors (GPCRs). Although such pheromone/receptor systems are likely to function in both mate choice and prezygotic isolation, very few studies have focus on the differences in pheromone/receptor system between mating types. The fission yeast Schizosaccharomyces pombe has two mating types (sexes), Plus (P) and Minus (M). Here we investigated the specificity of the two GPCRs, Mam2 and Map3, for their respective pheromones, P-factor and M-factor, in fission yeast. First, we switched GPCRs between S. pombe and the closely related species Schizosaccharomyces octosporus, which showed that SoMam2 (Mam2 of S. octosporus) is partially functional in S. pombe, whereas SoMap3 (Map3 of S. octosporus) is not interchangeable. Next, we swapped individual domains of Mam2 and Map3 with the respective domains in SoMam2 and SoMap3, which revealed differences between the receptors both in the intracellular regions that regulate the downstream signaling of pheromones and in the molecular recognition for pheromone binding. In particular, we demonstrated that two amino acid residues of Map3, F214 and F215, are essential for the specificity of M-factor recognition. Thus, the differences in these two GPCRs are likely to reflect the significantly distinct specificities of their respective pheromone/receptor systems; that is, the specificity of Map3 is more stringent than that of Mam2. We speculate that this sexual asymmetry might allow ascomycete fungi to generate novel prezygotic barriers within a population, while maintaining strong mate choice. Our genetic analyses also contribute to our understanding of the receptors that comprise the Class D GPCRs belonging to the fungal pheromone receptor family. Author summaryCourtship signals play a key role in the fertilization processes of living beings from animals to microorganisms and, in particular, sex pheromones are involved in differentiating among species. On the other hand, changes in a pheromone/receptor system might also alter species-specificity during the selection of a mating partner, which is likely to facilitate prezygotic isolation. Here we demonstrate a distinct difference in the specificity of GPCRs for pheromones between cells of opposite mating-type in the fission yeast Schizosaccharomyces pombe, by conducting a comprehensive genetic analysis and comparison of these proteins between closely related fission yeast species. Our finding of sexual asymmetry due to the relative strictness of receptors in S. pombe suggests that it might be one of the driving forces behind the formation of new species. Such differences in pheromone/receptor interactions between the sexes may occur in a variety of life-forms such as insects and amphibians; hence, the findings of this study might be extended to other organisms. In addition, this study of S. pombe pheromone receptors sheds light on the functions of the unique Class D GPCRs.

genetics↗

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↗

bioRxiv2017

High-Throughput Laboratory Evolution Of Escherichia coli Under Multiple Stress Environments

Bacterial cells have a remarkable capacity to adapt and to evolve to environmental changes. Although many mutations contributing to adaptive evolution have been identified, the relationship between the mutations and the phenotypic changes responsible for fitness gain has yet to be fully elucidated. For a better understanding of phenotype-genotype relationship in evolutionary dynamics, we performed high-throughput laboratory evolution of Escherichia coli under various stress conditions using an automated culture system. One measure of phenotype, transcriptome analysis, revealed that the expression changes which occurred during the evolution were generally similar among the strains evolved in the same stress environment. We also found several genes and gene functions for which mutations were commonly fixed in the strains resistant to the same stress, and whose effects on resistance were verified experimentally. We demonstrated that the integration of transcriptome and genome data enables us to extract the mechanisms for stress resistance.\n\nAuthor summaryUnderstanding the relationship between phenotypic and genetic changes is a fundamental goal in evolutionary biology, which can provide insights into the past and future evolutionary trajectories. Evolution of microorganisms in a laboratory has been the primary approach to clarify the mappings of phenotypic and genotypic changes. Here, we performed high-throughput laboratory evolution with bacteria using an automated culture system, to quantify phenotypic and genotypic changes occurred under various stress conditions. We identified various stress-specific gene expression changes and mutations, and contributions of them to fitness gain were validated. These results demonstrated that the integration of phenotypic and genotypic changes makes it possible to extract the mechanisms for stress resistance evolution, which will contribute to bioengineering applications.

evolutionary biology↗

bioRxiv2017

Formation Of Dominant Mode By Evolution In Biological Systems

A reduction in high-dimensional phenotypic states to a few degrees of freedom is essential to understand biological systems. One possible origin of such a reduction (as recently discussed) is the steady growth of cells that constrains each components replication rate. Here, in contrast, our aim is to investigate consequences of evolutionary robustness, which is shown to cause a stronger dimensional reduction in possible phenotypic changes in response to a variety of environmental conditions. First, we examined global protein expression changes in Escherichia coli after various environmental perturbations. Remarkably, they were proportional across components, across different types of environmental conditions, while the proportion coefficient corresponded to the change in growth rate. Because such global proportionality is not generic to all systems under a condition of steady growth, a new conceptual framework is then needed. We hypothesized that such proportionality is a result of evolution. To test this hypothesis, we analyzed a cell model--with a huge number of components, that reproduces itself via a catalytic reaction network--and confirmed that common proportionality in the concentrations of all components is shaped through evolutionary processes to maximize cell growth (and therefore fitness) under a given environmental condition. Furthermore, we found that the changes in concentration across all components in response to environmental and evolutionary changes are constrained to the changes along a one-dimensional major axis within a huge-dimensional state space. On the basis of these observations, we propose a theory in which high-dimensional phenotypic changes after evolution are constrained to the points near a one-dimensional major axis that correlates with the growth rate, to achieve both evolutionary robustness and plasticity. By formulating this proposition in terms of dynamical systems, broad experimental and numerical results on phenotypic changes caused by evolution and adaptation are coherently explained.\n\nSummaryCells generally consist of thousands of components whose abundance levels change through adaptation and evolution. Accordingly, each steady cell state can be represented as a point in a high-dimensional space of component concentrations. In the context of equilibrium statistical thermodynamics, even though the state space is high-dimensional, macroscopic description only by a few degrees of freedom is possible for equilibrium systems; however, such characterization by a few degrees of freedom has not yet been achieved for cell systems. Given that they are not in equilibrium, we need some other constraint to be imposed. Here, by restricting our focus to a cellular state with steady growth that is achieved after evolution,we examine how the expression levels of its several components change under different environmental conditions. On the basis of analysis of protein expression levels in recent bacterial experiments as well as the results of simulations using a toy cell model consisting of thousands of components that are reproduced by catalytic reactions, we found that adaptation and evolutionary paths in high-dimensional state space are constrained to changes along a one-dimensional curve, representing a major axis for all the observed changes. Moreover, this one-dimensional structure emerges only after evolution and is not applicable to any system showing steady growth. This curve is determined by the growth rate of a cell, and thus it is possible to describe an evolved system by means of a growth rate function. All the observed results are consistent with the hypothesis that changes in high-dimensional states are nearly confined to the major axis in response to environmental, evolutionary, and stochastic perturbations. This description opens up the possibility to characterize a cell state as a macroscopic growth rate, as is the case for the thermodynamic potential. This approach can provide estimates of which phenotypic changes are theoretically more evolvable, as predicted simply from their observed environmental responses.

biophysics↗

bioRxiv2016

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.\n\nIn this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.\n\nThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.

bioinformatics↗