Abstract Metabolic phenotypes vary within microbial species, yet how such variation is organized remains unclear. Diversification in carbon–source utilization, in particular, often appears idiosyncratic, showing weak correspondence to phylogeny or simple gene content. Here, we combine quantitative growth phenotyping of natural Escherichia coli isolates across 32 carbon sources with diversification observed during de novo laboratory evolution, together with a reaction-level description of metabolic similarity. Despite deep phylogenetic divergence, growth–rate profiles varied independently of lineage. Instead, growth rates across carbon sources covaried in recurrent modular patterns aligned with similarities in required metabolic reactions. Closely analogous modular relationships re-emerged during de novo evolution, indicating parallel diversification across evolutionary contexts. Growth-rate variation in natural and experimentally evolved datasets collapsed onto a shared low-dimensional variance structure. Together, our results indicate that quantitative metabolic phenotypes vary along a limited set of recurring, module-linked axes, providing an organizational perspective on intraspecific metabolic diversity despite weak phylogenetic signal.

The evolution of mating signals drives reproductive isolation and speciation across diverse lineages. However, how short peptide pheromones, typically subject to strong structural constraints, achieve functional diversification remain unclear. In the fission yeast Schizosaccharomyces pombe, a previously established library of 153 single-amino acid variants of the mating pheromone M-factor was applied to large-scale competition assays under varied mating conditions. Mutations deleterious under standard conditions became advantageous at specific environmental pH levels, demonstrating context-dependent pheromone function activation. Synthetic peptide assays confirmed that certain substitutions act as environmental molecular switches. Comparative analysis with the closely related Schizosaccharomyces octosporus species further identified a permissive mutation that mitigates the effects of otherwise inactivating changes, enabling an evolutionary route without intermediate fitness loss. Our findings reveal how short peptide signals can evolve via environmentally contingent activation and compensatory interactions, offering a mechanistic framework for understanding the ecological and evolutionary dynamics of mating communication.

ABSTRACT Accurate shell shape quantification is critical for studying biodiversity and evolution, yet intraspecific variability in bivalves makes morphology-based identification difficult. Traditional methods, including landmark-based analyses and elliptic Fourier descriptors, suffer either from subjectivity in homologous point selection or from limited use of contour information. Here, we introduce Morpho-VAE, a deep generative framework integrating a variational autoencoder with a supervised classifier, to analyze shell images of five Anadara species. Morpho-VAE outperforms conventional approaches in species classification by embedding morphological variation into a low-dimensional space where species cluster distinctly. To highlight species-specific morphological patterns, we develop a patch masking assay, revealing the hinge line as a shared morphological marker across species and species-specific regions near the umbo and anterior ventral margin. The decoder further enables morphological visualization via image reconstruction and interpolation. Our results show that Morpho-VAE can automatically extract species-defining morphological patterns from raw images, providing complementary or novel insights beyond traditional morphometric methods.

Embryonic development in multicellular organisms exhibits diverse morphogenetic patterns, which can generally be categorized into fundamental types such as monolayer and multilayer spheres, as well as cell masses. Furthermore, we identify two distinct processes for the formation of spherical structures. These basic patterns are thought to be governed by the microscopic properties of intercellular adhesion. However, the specific mechanisms linking the microscopic factors to the emergence of distinct macroscopic morphogenetic patterns remain poorly understood. In this study, we explore how different morphogenetic patterns arise by employing a computational model that incorporates intercellular adhesion and polarity. Our results demonstrate that all fundamental morphogenetic patterns can be generated through the interplay of two key parameters: the polarity strength of the cell and the regulation of polarity via mechanical signals. Furthermore, analytical considerations reveal key mechanisms underlying the formation of these patterns. These findings highlight the critical role of physical constraints in morphogenesis and suggest potential applications to the design of artificial tissues and organoids.

Horizontal gene transfer is a major driver of bacterial evolution and the global dissemination of antibiotic resistance genes (ARGs). Conjugative plasmids play a crucial role in ARG spread across hosts within their host range, yet the genetic and functional determinants shaping plasmid host range remain poorly understood. Here, we systematically analyzed the gene content of conjugative/mobilizable plasmids derived from Enterobacterales from public databases and found that two distinct survival strategies were enriched in different host-range groups: a "stealth" strategy, which actively represses its own transcription by employing a global regulator hns, was particularly enriched in broad-host-range plasmids, whereas a "manipulative" strategy, which promotes its establishment by manipulating host machineries including SOS response and defense systems, was more common in narrow-host-range plasmids. Plasmids employing either strategy constituted the majority of conjugative plasmids analyzed, and accumulated significantly more ARGs than plasmids with neither strategy. Our data further suggested that stealth plasmids facilitate the acquisition of emerging ARGs, while manipulative plasmids amplify the copy number of established ARGs. This "stealth-first" model successfully recapitulated historical ARG dissemination patterns. These findings provide critical insights into the relationship between plasmid survival strategies and host range, advancing our understanding of the global patterns underlying plasmid-mediated ARG transmission.

Protein synthesis in cell-free protein synthesis systems often exhibits nonintuitive input-output relationships. In the PURE system, a reconstituted cell-free system, protein production peaked at low elongation factor Tu (EF-Tu) concentrations and decreased at higher concentrations, resulting in a characteristic bell-shaped profile. Here, we investigated the origin of this behavior using a detailed mechanistic model of translation in the PURE system, designated as ePURE, which describes the reaction dynamics of hundreds of molecular species and reactions. Our computational analysis suggested that excess EF-Tu sequesters the initiator tRNA (tRNAfMet) into nonproductive EF-Tu·GTP·Met-tRNAfMet complexes, thereby depleting the pool of initiator tRNA available for translation initiation. This suppression arises from competition for a limited molecular resource rather than from direct inhibition. Based on this mechanism, we predicted that increasing the concentrations of tRNAfMet and methionyl-tRNA formyltransferase would eliminate the bell-shaped dependence, and experimentally confirmed this prediction. Under these modified conditions, the bell-shaped response disappeared and protein production was enhanced. These findings demonstrate how mechanistic computational models can reveal hidden constraints underlying nonintuitive input-output relationships in complex biochemical networks and guide the rational optimization of cell-free protein synthesis systems.

Metabolic phenotypes vary within microbial species, yet how such variation is organized remains unclear. Diversification in carbon‑source utilization, in particular, often appears idiosyncratic, showing weak correspondence to phylogeny or simple gene content. Here, we combine quantitative growth phenotyping of natural Escherichia coli isolates across 32 carbon sources with diversification observed during de novo laboratory evolution, together with a reaction-level description of metabolic similarity. Despite deep phylogenetic divergence, growth‑rate profiles varied independently of lineage. Instead, growth rates across carbon sources covaried in recurrent modular patterns aligned with similarities in required metabolic reactions. Closely analogous modular relationships re-emerged during de novo evolution, indicating parallel diversification across evolutionary contexts. Growth-rate variation in natural and experimentally evolved datasets collapsed onto a shared low-dimensional variance structure. Together, our results indicate that quantitative metabolic phenotypes vary along a limited set of recurring, module-linked axes, providing an insight into why similar metabolic states can repeatedly arise across independent evolutionary contests.

Gene expression responds to various types of perturbations, such as mutations, environmental changes, and stochastic molecular noises. These different types of variability are often interdependent, where genes sensitive to one perturbation tend to be sensitive to others. However, the relationship between plasticity (variability in response to environmental changes) and noise (variability among cells under the same conditions) in gene expression remains debatable. Previous studies predicted a positive correlation between plasticity and noise in nonessential genes, but these were often measured at different levels: plasticity at the mRNA level and noise at the protein level. This methodological discrepancy complicates the understanding of their relationship. We addressed this by measuring protein expression in Escherichia coli, quantifying both plasticity and noise from the same dataset using flow cytometry. Essential genes exhibited lower noise and plasticity than nonessential genes. Nonessential genes showed a positive correlation between noise and plasticity, while essential genes did not. This study provides empirical evidence of essentiality-dependent coupling between noise and plasticity in protein expression, highlighting the organization of different types of variabilities.

Annotating physiological, morphological, or genomic features to diverse bacterial and archaeal species is essential for the trait-based interpretations of complex microbiomes in community ecology. One promising way for high-throughput trait annotation is taxonomic marker gene-based trait prediction, where traits are predicted from 16S rRNA gene sequences based on their homology, taxonomy, or phylogeny. Many tools have been developed using different prediction methods and trait datasets; however, this situation lacks a unified framework of different prediction methods and their systematic evaluation, resulting in low accessibility to preferred methods. Here, we proposed Bac2Feature, an easy-to-use web tool that integrates different prediction methods in standardized trait dataset and systematically evaluated the prediction performance. We found that the prediction method based on 16S rRNA gene phylogenetic tree displayed superior prediction accuracy than that of other methods. The prediction accuracy of each trait was strongly associated with its phylogenetic signal. Furthermore, we set prediction thresholds for phylogenetic distance to avoid spurious trait prediction. Applied to the infant gut microbiome, Bac2Feature could reproduce trait-based shifts during primary succession. Our unified framework widens access to the trait-based approach and its applications to other fields, such as medical research. Bac2Feature is available at https://github.com/fuyo780/Bac2Feature (command line interface) and https://bac2feature.k.u-tokyo.ac.jp/ (Web interface).

Microbial populations exhibit a broad spectrum of nutrient utilization strategies, ranging from strategies utilizing diverse nutrients, called "generalists," to those being highly adapted to specific nutrients, called "specialists." The mathematical conditions for the diversification of nutrient utilization strategies are central questions in theoretical ecology. Previous studies have shown that trade-offs among different resource utilization functions that cells cannot utilize broad types of substrates at near-maximum speed are crucial for the emergence of diverse strategies. However, in natural settings, nutrient availability often fluctuates over time, imposing additional trade-offs on cells. Cells that grow rapidly under nutrient-rich conditions will suffer a higher death rate under nutrient-poor conditions, creating a growth-death trade-off that intersects with the classical resource-use trade-off. Here, we introduce a unified mathematical model that simultaneously incorporates the resource-use trade-off and the growth-death trade-off. The nutrient supply was modeled as discrete stochastic events, capturing realistic temporal fluctuations. We show that the relative balance between growth and death rates critically influences the dominance of either generalist or specialist strategies. Specifically, under conditions of high average growth rates among different environments and a weak trade-off between growth and death rates, generalists prevail. In contrast, when the growth-death trade-off is intense, specialists emerge as the dominant strategy. Our findings reveal that accounting for the growth-death trade-off is crucial for understanding how microbial communities adapt and evolve in temporally varying environments.

In this Journal Club, Chikara Furusawa reflects on a 1991 publication by Tom Ray that presented Tierra, an evolvable computer program that pioneered the use of artificial life to study biological phenomena.

Kurthia intestinigallinarum was isolated from the oral cavity of a deer. In this article, we report the complete genome sequence of K. intestinigallinarum. This is the first complete genome report for this species.

Bacterial communities exhibit various classes of interspecies interactions, ranging from synergistic to competitive. As these interaction classes play a crucial role in determining characteristics of bacterial communities, including species composition and community stability, understanding the mechanisms that shape them is important. Whereas several studies have suggested that synergistic interactions are rare, a study focused on single-carbon-source environments reported them to be relatively common. This discrepancy highlights the potential role of carbon source diversity in shaping interaction classes, although the quantitative relationship remains unclear. To elucidate this relationship, we examined 896 interspecies interactions amongst 28 synthetic bacterial pairs, isolated from various environments, under 32 conditions with varying levels of carbon source diversity. As a result, we frequently observed synergistic interactions in single-carbon-source environments, with the interactions shifting to competitive as the carbon source diversity increased. Further analyses suggested that this shift was driven by processes occurring in environments with an increased diversity of carbon sources, such as resource competition. Our findings provide new insights into how environmental factors, particularly carbon source diversity, shape interspecies interactions in bacterial communities.

Convergent evolution of proteins provides insights into repeatability of genetic adaptation. While local convergence of proteins at residue or domain level has been characterized, global structural convergence by inter-domain/molecular interactions remains largely unknown. Here we present structural convergent evolution on fusion enzymes of aldehyde dehydrogenases (ALDHs) and alcohol dehydrogenases (ADHs). We discover BdhE (bifunctional dehydrogenase E), an enzyme clade that emerged independently from the previously known AdhE family through distinct gene fusion events. AdhE and BdhE show shared enzymatic activities and non-overlapping phylogenetic distribution, suggesting common functions in different species. Cryo-electron microscopy reveals BdhEs form donut-like homotetramers, contrasting AdhE's helical homopolymers. Intriguingly, despite distinct quaternary structures and < 30% amino acid sequence identity, both enzymes forms resemble dimeric structure units by ALDH-ADH interactions via convergently elongated loop structures. These findings suggest convergent gene fusions recurrently led to substrate channeling evolution to enhance two-step reaction efficiency. Our study unveils structural convergence at inter-domain/molecular level, expanding our knowledges on patterns behind molecular evolution exploring protein structural universe.

The rate and direction of phenotypic evolution depend on the availability of phenotypic variants induced genetically or environmentally. It is widely accepted that organisms do not display uniform phenotypic variation, with certain variants arising more frequently than others in response to genetic or environmental perturbations. Previous studies have suggested that gene regulatory networks channel both environmental and genetic influences. However, how the gene regulatory networks influence phenotypic variation remains unclear. To address this, we characterize transcriptional variations in Escherichia coli under environmental and genetic perturbations. Based on the current understanding of transcriptional regulatory networks, we identify genetic properties that explain gene-to-gene differences in transcriptional variation. Our findings highlight the role of gene regulatory networks in shaping the shared phenotypic variability across different perturbations.

Cells robustly maintain metabolic functions despite environmental fluctuations that broadly alter reaction kinetics. However, kinetic models of cellular metabolism often exhibit fragility, losing stability with minor parameter changes. This discrepancy suggests that cells possess metabolic regulations that preserve the stable state under environmental perturbations. To understand the principles of metabolic robustness, we investigated the effects of temperature changes on a kinetic model of Escherichia coli central metabolism. We found that a gradual temperature decrease destabilized the metabolic state, leading to an abrupt shift to a new state in which glycolytic and tricarboxylic acid cycle (TCA cycle) fluxes decreased, and ATP production efficiency dropped. This shift was triggered by an elevated ATP/ADP ratio, which created a bottleneck in the glycolytic pathway. To assess the relationship between the destabilization and the ATP/ADP ratio, we introduced a rapid ATP-ADP exchange reaction and prevented this surge in the ATP/ADP ratio. Under the ATP/ADP ratio homeostasis, ATP production efficiency remained high across temperatures. Furthermore, we demonstrated that the destabilization was also avoided by altering enzyme abundances through sampling multiple stable steady states under cold conditions. The predicted enzyme regulation to maintain high ATP production efficiency was consistent with experimental observations of E. coli at low temperatures. Our findings indicate that balancing key cofactors, particularly the ATP/ADP ratio, is crucial for preserving metabolic stability under environmental perturbations.

Hanseniaspora, a genus of yeasts in which many species reproduce sexually, has attracted the attention of researchers because of its prevalence in diverse ecological niches. Building on our extensive collection efforts since 2020, three previously unknown yeast strains from wild Drosophila species trapped in ripe bananas in Okinawa, Japan, were isolated. Using a multifaceted approach, including physiological assessments and sequence analysis of the D1/D2 domain of the 26S LSU rRNA gene and the internal transcribed spacer (ITS) region, it was revealed that these strains are novel members of the genus Hanseniaspora. The three strains, JCM 36741T, JCM 36742 and JCM 36748, had identical sequences in their respective D1/D2 and ITS regions, justifying their classification as a single species. Moreover, the new species exhibited a remarkable degree of sequence divergence from its closest relatives, differing by 7 nucleotide substitutions (1.28%) in the D1/D2 domain, 29 nucleotide substitutions and 4 gaps (4.08%) in the ITS regions. These substantial sequence differences highlight the distinctiveness of this novel species in the genus Hanseniaspora. Further analysis revealed physiological characteristics that distinguished the new species from its closest relative, Hanseniaspora hatyaiensis (nom. inval.). These findings culminated in the proposed name Hanseniaspora drosophilae sp. nov., which recognizes the unique ecological niche within the Drosophila microbiota. By uncovering this novel species, this study not only adds to the growing body of knowledge on yeast diversity but also sheds light on the intricate ecological relationships that shape microbial communities. The implications of this discovery extend beyond taxonomic boundaries, inviting further exploration of the evolutionary dynamics and ecological significance of yeast-fly interactions. We propose accommodating H. drosophilae sp. nov. in the genus Hanseniaspora with JCM 36741T as the holotype. The MycoBank accession number is MB 853822.

The genome structure fundamentally shapes bacterial physiology, ecology, and evolution. Though insertion sequences (IS) are known drivers of drastic evolutionary changes in the genome structure, the process is typically slow and challenging to observe in the laboratory. Here, we developed a system to accelerate IS-mediated genome structure evolution by introducing multiple copies of a high-activity IS in Escherichia coli. We evolved the bacteria under relaxed neutral conditions, simulating those leading to IS expansion in host-restricted endosymbionts and pathogens. Strains accumulated a median of 24.5 IS insertions and underwent over 5% genome size changes within ten weeks, comparable to decades-long evolution in wild-type strains. The detected interplay of frequent small deletions and rare large duplications updates the view of genome reduction under relaxed selection from a simple consequence of the deletion bias to a nuanced picture including transient expansions. The high IS activity resulted in structural variants of IS and the emergence of composite transposons, illuminating potential evolutionary pathways for ISs and composite transposons. The extensive genome rearrangements we observed establish a baseline for assessing the fitness effects of IS insertions, genome size changes, and rearrangements, advancing our understanding of how mobile elements shape bacterial genomes.

Homeostasis is a fundamental characteristic of living systems. Unlike rigidity, homeostasis necessitates that systems respond flexibly to diverse environments. Understanding the dynamics of biochemical systems when subjected to perturbations is essential for the development of a quantitative theory of homeostasis. In this study, we analyze the response of bacterial metabolism to externally imposed perturbations using kinetic models of Escherichia coli’s central carbon metabolism in nonlinear regimes. We found that three distinct kinetic models consistently display strong responses to perturbations; In the strong responses, minor initial discrepancies in metabolite concentrations from steady-state values amplify over time, resulting in significant deviations. This pronounced responsiveness is a characteristic feature of metabolic dynamics, especially since such strong responses are seldom seen in toy models of the metabolic network. Subsequent numerical studies show that adenyl cofactors consistently influence the responsiveness of the metabolic systems across models. Additionally, we examine the impact of network structure on metabolic dynamics, demonstrating that as the metabolic network becomes denser, the perturbation response diminishes—a trend observed commonly in the models. To confirm the significance of cofactors and network structure, we constructed a simplified metabolic network model, underscoring their importance. By identifying the structural determinants of responsiveness, our findings offer implications for bacterial physiology, the evolution of metabolic networks, and the design principles for robust artificial metabolism in synthetic biology and bioengineering.