Deciphering the origin of developmental stability: The role of intracellular expression variability in evolutionary conservation.

Progress in evolutionary developmental biology (evo-devo) has deepened our understanding of how intrinsic properties of embryogenesis, along with natural selection and population genetics, shape phenotypic diversity. A focal point of recent empirical and theoretical research is the idea that highly developmentally stable phenotypes are more conserved in evolution. Previously, we demonstrated that in Japanese medaka (Oryzias latipes), embryonic stages and genes with high stability, estimated through whole-embryo RNA-seq, are highly conserved in subsequent generations. However, the precise origin of the stability of gene expression levels evaluated at the whole-embryo level remained unclear. Such stability could be attributed to two distinct sources: stable intracellular expression levels or spatially stable expression patterns. Here we demonstrate that stability observed in whole-embryo RNA-seq can be attributed to stability at the cellular level (low variability in gene expression at the cellular levels). We quantified the intercellular variations in expression levels and spatial gene expression patterns for seven key genes involved in patterning dorsoventral and rostrocaudal regions during early development in medaka. We evaluated intracellular variability by counting transcripts and found its significant correlation with variation observed in whole-embryo RNA-seq data. Conversely, variation in spatial gene expression patterns, assessed through intraindividual left-right asymmetry, showed no correlation. Given the previously reported correlation between stability and conservation of expression levels throughout embryogenesis, our findings suggest a potential general trend: the stability or instability of developmental systems-and the consequent evolutionary diversity-may be primarily anchored in intrinsic fundamental elements such as the variability of intracellular states.

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 hierarchical organization and irreversibility can manifest independently of direct selection pressures associated with these characteristics, instead they appear to evolve as byproducts of selective forces favoring a diversity of differentiated cell types. 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.

Laboratory Evolution of Antimicrobial Resistance in Bacteria to Develop Rational Treatment Strategies.

Laboratory evolution studies, particularly with Escherichia coli, have yielded invaluable insights into the mechanisms of antimicrobial resistance (AMR). Recent investigations have illuminated that, with repetitive antibiotic exposures, bacterial populations will adapt and eventually become tolerant and resistant to the drugs. Through intensive analyses, these inquiries have unveiled instances of convergent evolution across diverse antibiotics, the pleiotropic effects of resistance mutations, and the role played by loss-of-function mutations in the evolutionary landscape. Moreover, a quantitative analysis of multidrug combinations has shed light on collateral sensitivity, revealing specific drug combinations capable of suppressing the acquisition of resistance. This review article introduces the methodologies employed in the laboratory evolution of AMR in bacteria and presents recent discoveries concerning AMR mechanisms derived from laboratory evolution. Additionally, the review outlines the application of laboratory evolution in endeavors to formulate rational treatment strategies.

Loss of function of Hog1 improves glycerol assimilation in Saccharomyces cerevisiae.

We previously isolated a mutant of Saccharomyces cerevisiae strain 85_9 whose glycerol assimilation was improved through adaptive laboratory evolution. To investigate the mechanism for this improved glycerol assimilation, genome resequencing of the 85_9 strain was performed, and the mutations in the open reading frame of HOG1, SIR3, SSB2, and KGD2 genes were found. Among these, a frameshift mutation in the HOG1 open reading frame was responsible for the improved glycerol assimilation ability of the 85_9 strain. Moreover, the HOG1 gene disruption improved glycerol assimilation. As HOG1 encodes a mitogen-activated protein kinase (MAPK), which is responsible for the signal transduction cascade in response to osmotic stress, namely the high osmolarity glycerol (HOG) pathway, we investigated the effect of the disruption of PBS2 gene encoding MAPK kinase for Hog1 MAPK on glycerol assimilation, revealing that PBS2 disruption can increase glycerol assimilation. These results indicate that loss of function of Hog1 improves glycerol assimilation in S. cerevisiae. However, single disruption of the SSK2, SSK22 and STE11 genes encoding protein kinases responsible for Pbs2 phosphorylation in the HOG pathway did not increase glycerol assimilation, while their triple disruption partially improved glycerol assimilation in S. cerevisiae. In addition, the HOG1 frameshift mutation did not improve glycerol assimilation in the STL1-overexpressing RIM15 disruptant strain, which was previously constructed with high glycerol assimilation ability. Furthermore, the effectiveness of the HOG1 disruptant as a bioproduction host was validated, indicating that the HOG1 CYB2 double disruptant can produce L-lactic acid from glycerol.

A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape.

Shape measurements are crucial for evolutionary and developmental biology; however, they present difficulties in the objective and automatic quantification of arbitrary shapes. Conventional approaches are based on anatomically prominent landmarks, which require manual annotations by experts. Here, we develop a machine-learning approach by presenting morphological regulated variational AutoEncoder (Morpho-VAE), an image-based deep learning framework, to conduct landmark-free shape analysis. The proposed architecture combines the unsupervised and supervised learning models to reduce dimensionality by focusing on morphological features that distinguish data with different labels. We applied the method to primate mandible image data. The extracted morphological features reflected the characteristics of the families to which the organisms belonged, despite the absence of correlation between the extracted morphological features and phylogenetic distance. Furthermore, we demonstrated the reconstruction of missing segments from incomplete images. The proposed method provides a flexible and promising tool for analyzing a wide variety of image data of biological shapes even those with missing segments.

Inference of transcriptome signatures of Escherichia coli in long-term stationary phase.

"Non-growing" is a dominant life form of microorganisms in nature, where available nutrients and resources are limited. In laboratory culture systems, Escherichia coli can survive for years under starvation, denoted as long-term stationary phase, where a small fraction of cells manages to survive by recycling resources released from nonviable cells. Although the physiology by which viable cells in long-term stationary phase adapt to prolonged starvation is of great interest, their genome-wide response has not been fully understood. In this study, we analyzed transcriptional profiles of cells exposed to the supernatant of 30-day long-term stationary phase culture and found that their transcriptome profiles displayed several similar responses to those of cells in the 16-h short-term stationary phase. Nevertheless, our results revealed that cells in long-term stationary phase supernatant exhibit higher expressions of stress-response genes such as phage shock proteins (psp), and lower expressions of growth-related genes such as ribosomal proteins than those in the short-term stationary phase. We confirmed that the mutant lacking the psp operon showed lower survival and growth rate in the long-term stationary phase culture. This study identified transcriptional responses for stress-resistant physiology in the long-term stationary phase environment.

Rare-event sampling analysis uncovers the fitness landscape of the genetic code.

The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains unclear why this universal code has arisen and been maintained during evolution, it may have been preserved under selection pressure. Theoretical studies comparing the SGC and numerically created hypothetical random genetic codes have suggested that the SGC has been subject to strong selection pressure for being robust against translation errors. However, these prior studies have searched for random genetic codes in only a small subspace of the possible code space due to limitations in computation time. Thus, how the genetic code has evolved, and the characteristics of the genetic code fitness landscape, remain unclear. By applying multicanonical Monte Carlo, an efficient rare-event sampling method, we efficiently sampled random codes from a much broader random ensemble of genetic codes than in previous studies, estimating that only one out of every 1020 random codes is more robust than the SGC. This estimate is significantly smaller than the previous estimate, one in a million. We also characterized the fitness landscape of the genetic code that has four major fitness peaks, one of which includes the SGC. Furthermore, genetic algorithm analysis revealed that evolution under such a multi-peaked fitness landscape could be strongly biased toward a narrow peak, in an evolutionary path-dependent manner.

Wickerhamiella bidentis sp. nov., a novel yeast species isolated from flowers and insects in Japan.

Two strains were isolated from flowers and insects in Japan, namely NBRC 115686T and NBRC 115687, respectively. Based on sequence analysis of the D1/D2 domain of the 26S large subunit (LSU) rRNA gene and the internal transcribed spacer (ITS) region and physiological characteristics, these strains were found to represent a novel yeast species of the genus Wickerhamiella. Considering pairwise sequence similarity, NBRC 115686T and NBRC 115687 differ from the type strain of the most closely related species, Wickerhamiella galacta NRRL Y-17645T, by 65-66 nucleotide substitutions with 12 gaps (11.65-11.83 %) in the D1/D2 domain of the LSU rRNA gene. The novel species differ from the closely related Wickerhamiella species in some physiological characteristics. For example, compared with Wickerhamiella galacta JCM 8257T, NBRC 115686T and NBRC 115687 assimilated d-galactose, and could grow at 35 and 37 °C. Hence, the name Wickerhamiella bidentis sp. nov. is proposed to accommodate this species in the genus Wickerhamiella. The holotype is NBRC 115686T (ex-type strain JCM 35540=CBS 18008).

Stability in gene expression and body-plan development leads to evolutionary conservation.

BACKGROUND: Phenotypic evolution is mainly explained by selection for phenotypic variation arising from factors including mutation and environmental noise. Recent theoretical and experimental studies have suggested that phenotypes with greater developmental stability tend to have a constant phenotype and gene expression level within a particular genetic and environmental condition, and this positively correlates with stronger evolutionary conservation, even after the accumulation of genetic changes. This could reflect a novel mechanism that contributes to evolutionary conservation; however, it remains unclear whether developmental stability is the cause, or whether at least it contributes to their evolutionary conservation. Here, using Japanese medaka lines, we tested experimentally whether developmental stages and gene expression levels with greater stability led to their evolutionary conservation. RESULTS: We first measured the stability of each gene expression level and developmental stage (defined here as the whole embryonic transcriptome) in the inbred F0 medaka population. We then measured their evolutionary conservation in the F3 generation by crossing the F0 line with the distantly related Japanese medaka line (Teradomori), followed by two rounds of intra-generational crossings. The results indicated that the genes and developmental stages that had smaller variations in the F0 generation showed lower diversity in the hybrid F3 generation, which implies a causal relationship between stability and evolutionary conservation. CONCLUSIONS: These findings suggest that the stability in phenotypes, including the developmental stages and gene expression levels, leads to their evolutionary conservation; this most likely occurs due to their low potential to generate phenotypic variation. In addition, since the highly stable developmental stages match with the body-plan-establishment stage, it also implies that the developmental stability potentially contributed to the strict conservation of animal body plan.

Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape.

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 by 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 phenotype-based fitness landscape for antibiotic resistance evolution by quantifying the multidimensional phenotypic changes, i.e., time-series data of resistance for eight different drugs. We show that different peaks of the landscape correspond to different drug resistance mechanisms, thus supporting the validity of the inferred phenotype-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.

Experimental evidence for the correlation between RNA structural fluctuations and the frequency of beneficial mutations.

RNA has been used as a model molecule to understand the adaptive evolution process owing to the simple relationship between the structure (i.e., phenotype) and sequence (i.e., genotype). RNA usually forms multiple substructures with similar thermodynamic stabilities, called structural fluctuations. Ancel and Fontana theoretically proposed that structural fluctuation is directly related to the ease of change in structures by mutations and thus works as a source of adaptive evolution; however, experimental verification is limited. Here, we analyzed 76 RNA genotypes that appeared in our previous in vitro evolution to examine whether (i) RNA fluctuation decreases as adaptive evolution proceeds and (ii) RNAs that have larger fluctuations tend to have higher frequencies of beneficial mutations. We first computationally estimated the structural fluctuations of all RNAs and observed that they tended to decrease as their fitness increased. We next measured the frequency of beneficial mutations for 10 RNA genotypes and observed that the total number of beneficial mutations was correlated with the size of the structural fluctuations. These results consistently support the idea that the structural fluctuation of RNA, at least those evaluated in our study, works as a source of adaptive evolution.

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 2 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 levels on molecular evolution and test the relevance of the possible underlying mechanisms, we analyzed the genome sequences of 99 strains of Escherichia coli for evolution within species in nature. We also analyzed genomic mutations accumulated under laboratory conditions as a model of de novo evolution within species. Here, we show that E-R anticorrelation is significant in both past and de novo evolution within species in E. coli. Our data also confirmed ongoing purifying selection on highly expressed genes. Ongoing selection included codon-level purifying selection, supporting the relevance of the underlying mechanisms. However, the impact of codon-level purifying selection on the constraints in evolution within species might be smaller than previously expected from evolution between species.

Single mutation makes Escherichia coli an insect mutualist.

Microorganisms often live in symbiosis with their hosts, and some are considered mutualists, where all species involved benefit from the interaction. How free-living microorganisms have evolved to become mutualists is unclear. Here we report an experimental system in which non-symbiotic Escherichia coli evolves into an insect mutualist. The stinkbug Plautia stali is typically associated with its essential gut symbiont, Pantoea sp., which colonizes a specialized symbiotic organ. When sterilized newborn nymphs were infected with E. coli rather than Pantoea sp., only a few insects survived, in which E. coli exhibited specific localization to the symbiotic organ and vertical transmission to the offspring. Through transgenerational maintenance with P. stali, several hypermutating E. coli lines independently evolved to support the host's high adult emergence and improved body colour; these were called 'mutualistic' E. coli. These mutants exhibited slower bacterial growth, smaller size, loss of flagellar motility and lack of an extracellular matrix. Transcriptomic and genomic analyses of 'mutualistic' E. coli lines revealed independent mutations that disrupted the carbon catabolite repression global transcriptional regulator system. Each mutation reproduced the mutualistic phenotypes when introduced into wild-type E. coli, confirming that single carbon catabolite repression mutations can make E. coli an insect mutualist. These findings provide an experimental system for future work on host-microbe symbioses and may explain why microbial mutualisms are omnipresent in nature.

Development of a device that generates a temperature gradient in a microtiter plate for microbial culture.

Although temperature is a fundamental parameter in biology, testing various temperature conditions simultaneously is often difficult. In the present study, we developed a device for generating a temperature gradient in arrays of wells on a microtiter plate. This device consists of a pair of Peltier elements and temperature sensors placed on both ends of a flat aluminum bar to generate a linear temperature gradient. The device loads a microtiter plate at the center of the aluminum bar and transfers the temperature gradient to the bottom of the wells in the plate. This device successfully maintained a temperature gradient of 38.2 to 43.1°C on the horizontal axis of a 96-well microtiter plate in an incubator at 31°C. Furthermore, using this device, we demonstrated a laboratory evolution experiment of Escherichia coli, which was selected on the basis of its ability to grow at high temperatures. The developed device also facilitates a two-dimensional assay to determine the effects of temperature and drug concentrations on cellular growth.

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, Pearson's 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.

Potential contribution of intrinsic developmental stability toward body plan conservation.

BACKGROUND: Despite the morphological diversity of animals, their basic anatomical patterns-the body plans in each animal phylum-have remained highly conserved over hundreds of millions of evolutionary years. This is attributed to conservation of the body plan-establishing developmental period (the phylotypic period) in each lineage. However, the evolutionary mechanism behind this phylotypic period conservation remains under debate. A variety of hypotheses based on the concept of modern synthesis have been proposed, such as negative selection in the phylotypic period through its vulnerability to embryonic lethality. Here we tested a new hypothesis that the phylotypic period is developmentally stable; it has less potential to produce phenotypic variations than the other stages, and this has most likely led to the evolutionary conservation of body plans. RESULTS: By analyzing the embryos of inbred Japanese medaka embryos raised under the same laboratory conditions and measuring the whole embryonic transcriptome as a phenotype, we found that the phylotypic period has greater developmental stability than other stages. Comparison of phenotypic differences between two wild medaka populations indicated that the phylotypic period and its genes in this period remained less variational, even after environmental and mutational modifications accumulated during intraspecies evolution. Genes with stable expression levels were enriched with those involved in cell-cell signalling and morphological specification such as Wnt and Hox, implying possible involvement in body plan development of these genes. CONCLUSIONS: This study demonstrated the correspondence between the developmental stage with low potential to produce phenotypic variations and that with low diversity in micro- and macroevolution, namely the phylotypic period. Whereas modern synthesis explains evolution as a process of shaping of phenotypic variations caused by mutations, our results highlight the possibility that phenotypic variations are readily limited by the intrinsic nature of organisms, namely developmental stability, thus biasing evolutionary outcomes.

Experimental demonstration of operon formation catalyzed by insertion sequence.

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 thus difficult to detect in nature. In this study, as a proof-of-concept, we reproducibly demonstrated operon formation in the laboratory. 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 kb 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.

Acceleration of target production in co-culture by enhancing intermediate consumption through adaptive laboratory evolution.

Co-culture is a promising way to alleviate metabolic burden by dividing the metabolic pathways into several modules and sharing the conversion processes with multiple strains. Since an intermediate is passed from the donor to the recipient via the extracellular environment, it is inevitably diluted. Therefore, enhancing the intermediate consumption rate is important for increasing target productivity. In the present study, we demonstrated the enhancement of mevalonate consumption in Escherichia coli by adaptive laboratory evolution and applied the evolved strain to isoprenol production in an E. coli (upstream: glucose to mevalonate)-E. coli (downstream: mevalonate to isoprenol) co-culture. An engineered mevalonate auxotroph strain was repeatedly sub-cultured in a synthetic medium supplemented with mevalonate, where the mevalonate concentration was decreased stepwise from 100 to 20 µM. In five parallel evolution experiments, all growth rates gradually increased, resulting in five evolved strains. Whole-genome re-sequencing and reverse engineering identified three mutations involved in enhancing mevalonate consumption. After introducing nudF gene for producing isoprenol, the isoprenol-producing parental and evolved strains were respectively co-cultured with a mevalonate-producing strain. At an inoculation ratio of 1:3 (upstream:downstream), isoprenol production using the evolved strain was 3.3 times higher than that using the parental strain.

Decoding gut microbiota by imaging analysis of fecal samples.

The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis.

The sixth transmembrane region of a pheromone G-protein coupled receptor, Map3, is implicated in discrimination of closely related pheromones in Schizosaccharomyces pombe.

Most sexually reproducing organisms have the ability to recognize individuals of the same species. In ascomycete fungi including yeasts, 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 focused on the stringency of pheromone receptors. The fission yeast Schizosaccharomyces pombe has two mating types, Plus (P) and Minus (M). Here, we investigated the stringency 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 activation by the pheromone. In particular, we demonstrated that two amino acid residues of Map3, F214 and F215, are key residues important for discrimination of closely related M-factors. Thus, the differences in these two GPCRs might reflect the significantly distinct stringency/flexibility of their respective pheromone/receptor systems; nevertheless, species-specific pheromone recognition remains incomplete.