Biology subjects

Kaneko, K.

Publications and source records attributed to Kaneko, K..

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bioRxiv2021

Temporal Analyses of Postnatal Liver Development and Maturation by Single Cell Transcriptomics

Liver is the major metabolic organ, although its postnatal development and maturation are inadequately understood. We analyzed 52,834 single cell transcriptomes and identified 31 cell types or states in mouse livers at postnatal day 1, 3, 7, 21 and 56. We observed unexpectedly high levels of hepatocyte heterogeneity in the developing liver and progressive construction of the zonated metabolic functions from pericentral to periportal hepatocytes, which was orchestrated with development of sinusoid endothelial, stellate and Kupffer cells. Trajectory and gene regulatory analyses captured 36 transcription factors, including a circadian regulator Bhlhe40, in programming liver development. Remarkably, we identified a special group of macrophages enriched at day 7 with a hybrid phenotype of macrophages and endothelial cells, which may regulate sinusoidal construction and Treg cell function. This study provides a comprehensive atlas that covers all hepatic cell types instrumental for further dissection of liver development, metabolic functions and diseases. In BriefO_LISingle cell transcriptomics of all hepatic cell types in neonatal and adult livers C_LIO_LIConcerted development of zonated metabolic functions in hepatocytes and NPCs C_LIO_LITransient emergence of a distinct group of macrophages at postnatal day 7 C_LIO_LIHepatic cell-cell communications that program postnatal liver development C_LI

developmental biology↗

bioRxiv2021

Corticohippocampal circuit dysfunction in a mouse model of Dravet syndrome

Dravet syndrome (DS) is a neurodevelopmental disorder defined by treatment-resistant epilepsy, autism spectrum disorder, and sudden death, due to pathogenic variants in SCN1A encoding the Nav1.1 sodium channel subunit. Convergent data suggest hippocampal dentate gyrus (DG) pathology. We found that optogenetic stimulation of entorhinal cortex was ictogenic in DS (Scn1a+/-) but not wild-type mice in vivo. Two-photon calcium imaging in brain slice demonstrated profound impairment in filtering of perforant path input by DG in young adult Scn1a+/- mice due to enhanced excitatory input to granule cells. Excitability of parvalbumin interneurons (PV-INs) was near-normal and selective activation of PV-INs rescued circuit impairments. This demonstrates developmental reorganization of hippocampal circuitry that can be modulated by recruitment of functional PV-INs, suggesting potential therapeutic approaches towards seizure modulation. The identified circuit abnormality mirrors that seen in models of chronic temporal lobe epilepsy, suggesting convergent mechanisms linking genetic and acquired causes of temporal lobe-onset seizures.

neuroscience↗

bioRxiv2021

Evolution of Phenotypic Plasticity under Host-Parasite Interactions

Robustness and plasticity are essential features that allow biological systems to cope with complex and variable environments. Through the evolution of a given environment, the former, the insensitivity of phenotypes, is expected to increase, whereas the latter, the changeability of phenotypes, tends to diminish. However, in nature, plasticity is preserved to a certain degree. One possible cause for this is environmental variation, with one of the most important "environmental " factors being inter-species interactions. As a first step toward investigating phenotypic plasticity in response to an ecological interaction, we present the study of a simple two-species system consisting of hosts and parasites. Hosts are expected to evolve to achieve a phenotype that optimizes fitness and increases the robustness of the corresponding phenotype by reducing phenotypic fluctuations. Conversely, plasticity evolves in order to avoid certain phenotypes being attacked by parasites. By simulating evolution using the host gene-expression dynamics model, we analyze the evolution of genotype-phenotype mapping. If the interaction is weak, the fittest phenotype of the host evolves to reduce phenotypic variances. In contrast, if a sufficient degree of interaction occurs, the phenotypic variances of hosts increase to escape parasite attacks. For the latter case, we found two strategies: if the noise in the stochastic gene expression is below a certain threshold, the phenotypic variance increases via genetic diversification, whereas above the threshold, it is increased due to noise-induced phenotypic plasticity. We examine how the increase in the phenotypic variances due to parasite interactions influences the growth rate of a single host, and observed a trade-off between the two. Our results help elucidate the roles played by noise and genetic mutations in the evolution of phenotypic plasticity and robustness in response to host-parasite interactions. Author summaryPlasticity and phenotypic variability induced by internal or external perturbations are common features of biological systems. However, certain environmental conditions initiate evolution to increase fitness and, in such cases, phenotypic variability is not advantageous, as has been demonstrated by previous laboratory and computer experiments. As a possible origin for such plasticity, we investigated the role of host-parasite interactions, such as those between bacteria and phages. Different parasite types attack hosts of certain phenotypes. Through numerical simulations of the evolution of host genotype-phenotype mapping, we found that, if the interaction is sufficiently strong, hosts increase phenotypic plasticity by increasing phenotypic fluctuations. Depending on the degree of noise in gene expression dynamics, there are two distinct strategies for increasing the phenotypic variances: via stochasticity in gene expression or via genetic variances. The former strategy, which can work over a faster time scale, leads to a decline in fitness, whereas the latter reduces the robustness of the fitted state. Our results provide insights into how phenotypic variances are preserved and how hosts can escape being attacked by parasites whose genes mutate to adapt to changes in parasites. These two host strategies, which depend on internal and external conditions, can be verified experimentally, for example, via the transcriptome analysis of microorganisms.

evolutionary biology↗

bioRxiv2021

Evolution of Mendelian dominance in gene regulatory networks associated with phenotypic robustness

Mendelian inheritance is a fundamental law of genetics. Considering two alleles in a diploid, a phenotype of a heterotype is dominated by a particular homotype according to the law of dominance. This picture is usually based on simple genotype-phenotype mapping in which one gene regulates one phenotype. However, in reality, some interactions between genes can result in deviation from Mendelian dominance. Here, by using the numerical evolution of diploid gene regulatory networks (GRNs), we discuss whether Mendelian dominance evolves beyond the classical case of one-to-one genotype-phenotype mapping. We examine whether complex genotype-phenotype mapping can achieve Mendelian dominance through the evolution of the GRN with interacting genes. Specifically, we extend the GRN model to a diploid case, in which two GRN matrices are added to give gene expression dynamics, and simulate evolution with meiosis and recombination. Our results reveal that Mendelian dominance evolves even under complex genotype-phenotype mapping. This dominance is achieved via a group of genotypes that differ from each other but have a common phenotype given by the expression of target genes. Calculating the degree of dominance shows that it increases through the evolution, correlating closely with the decrease in phenotypic fluctuations and the increase in robustness to initial noise. This evolution of Mendelian dominance is associated with phenotypic robustness against meiosis-induced genome mixing, whereas sexual recombination arising from the mixing of chromosomes from the parents further enhances dominance and robustness. Owing to this dominance, the robustness to genetic differences increases, while the optimal fitness is sustained up to a large difference between the two genomes. In summary, Mendelian dominance is achieved by groups of genotypes that are associated with the increase in phenotypic robustness to noise. Author summaryMendelian dominance is one of the most fundamental laws in genetics. When two conflicting characters occur in a single diploid, the dominant character is always chosen. Assuming that one gene makes one character, this law is simple to grasp. However, in reality, phenotypes are generated via interactions between several genes, which may alter Mendels dominance law. The evolution of robustness to noise and mutations has been investigated extensively using complex expression dynamics with gene regulatory networks. Here, we applied gene-expression dynamics with complex interactions to the case of a diploid and simulated the evolution of the gene regulatory network to generate the optimal phenotype given by a certain gene expression pattern. Interestingly, after evolution, Mendelian dominance is achieved via a group of genes. This group-based Mendelian dominance is shaped by phenotype insensitivity to genome mixing by meiosis and evolves concurrently with the robustness to noise. By focusing on the influence of phenotypic robustness, which has received considerable attention recently, our result provides a novel perspective as to why Mendels law of dominance is commonly observed.

evolutionary biology↗

bioRxiv2020

Entangled gene regulatory networks with cooperative expression endow robust adaptive responses to unforeseen environmental changes

Living organisms must respond to environmental changes. Generally, accurate and rapid responses are provided by simple, unidirectional networks that connect inputs with outputs. Besides accuracy and speed, however, biological responses should also be robust to environmental or intracellular noise and mutations. Furthermore, cells must also respond to unforeseen environmental changes that have not previously been experienced, to avoid extinction prior to the evolutionary rewiring of their networks, which takes numerous generations. To address the question how cells can make robust adaptation even to unforeseen challenges, we have investigated gene regulatory networks that mutually activate or inhibit, and have demonstrated that complex entangled networks can make appropriate input-output relationships that satisfy such adaptive responses. Such entangled networks function when the expression of each gene shows sloppy and unreliable responses with low Hill coefficient reactions. To compensate for such sloppiness, several detours in the regulatory network exist. By taking advantage of the averaging over such detours, the network shows a higher robustness to environmental and intracellular noise as well as to mutations in the network, when compared to simple unidirectional circuits. Furthermore, it is demonstrated that the appropriate response to unforeseen environmental changes, allowing for functional outputs, is achieved as many genes exhibit similar dynamic expression responses, irrespective of inputs including unforeseen inputs. The similarity of the responses is statistically confirmed by applying dynamic time warping and dynamic mode decomposition methods. As complex entangled networks are commonly observed in the data in gene regulatory networks whereas global gene expression responses are measured in transcriptome analysis in microbial experiments, the present results give an answer how cells make adaptive responses and also provide a novel design principle for cellular networks. Author summaryRecent experimental advances have demonstrated that cells often have appropriate, robust responses to environmental changes, including those that have not previously been experienced. It is known that accurate and rapid responses can be achieved by simple unidirectional networks that connect straightforwardly input and outputs. However, such responses were not robust to perturbations. Here we have made numerical evolution of gene regulatory networks with mutual activation and inhibitions, and uncovered that complex entangled networks including many feedforward and feedback paths can make robust input-output responses, when each gene expression is not accurate. Remarkably, they make appropriate responses even to unforeseen environmental changes, as are supported by global, correlated responses across genes that are similar for all input signals. The results explain why cells adopt complex gene regulatory networks and exhibit global expression changes, even though they may not be advantageous in terms of their energy cost or response speed. The present results are consistent with the recent experiments on microbial gene expression changes and network analyses. This investigation provides insights into how cells survive fluctuating and unforeseen unpredictable environmental changes, and gives a universal conceptual framework to go beyond the standard picture based on a combination of network motifs.

biophysics↗

bioRxiv2020

Dynamical Systems Approach to Evolution-Development Congruence: Revisiting Haeckel's Recapitulation Theory

It is acknowledged that embryonic development has tendency to proceed from common toward specific. Ernst Haeckel raised the question of why that tendency prevailed through evolution, and the question remains unsolved. Here, we revisit Haeckels recapitulation theory, i.e., the parallelism between evolution and development through numerical evolution and dynamical systems theory. By using intracellular gene-expression dynamics with cell-to-cell interaction over spatially aligned cells to represent the developmental process, gene regulation networks (GRN) that govern these dynamics evolve under the selection pressure to achieve a prescribed spatial gene expression pattern. For most numerical evolutionary experiments, the evolutionary pattern changes over generations, as well as the developmental pattern changes governed by the evolved GRN exhibit remarkable similarity. Both pattern changes consisted of several epochs where stripes are formed in a short time, whereas for other temporal regimes, pattern hardly changes. In evolution, these quasi-stationary generations are needed to achieve relevant mutations, whereas in development, they are due to some gene expressions that vary slowly and control the pattern change. These successive epochal changes in development and evolution are represented as common bifurcations in dynamical systems theory, regulating working network structure from feedforward subnetwork to those containing feedback loops. The congruence is the correspondence between successive acquisition of subnetworks through evolution and changes in working subnetworks in development. Consistency of the theory with the segmentation gene-expression dynamics is discussed. Novel outlook on recapitulation and heterochrony are provided, testable experimentally by the transcriptome and network analysis.

evolutionary biology↗

bioRxiv2020

Microbial Potlatch: Cell-level adaptation of leakiness of metabolites leads to resilient symbiosis among diverse cells

Microbial communities display extreme diversity, facilitated by the secretion of chemicals that can create new niches. However, it is unclear why cells often secrete even essential metabolites after evolution. By noting that cells can enhance their own growth rate by leakage of essential metabolites, we show that such leaker cells can benefit from coexistence with cells that consume the leaked chemicals in the environment. This leads to an unusual form of mutualism between "leaker" and "consumer" cells, resulting in frequency-dependent coexistence of multiple microbial species, as supported by extensive simulations. Remarkably, such symbiotic relationships generally evolve when each species adapts its leakiness to optimize its own growth rate under crowded conditions and nutrient limitations, leading to ecosystems with diverse species exchanging many metabolites with each other. In addition, such ecosystems are resilient against structural and environmental perturbations. Thus, we present a new basis for diverse, complex microbial ecosystems.

ecology↗

bioRxiv2020

Autotoxin-mediated voluntary triage in starved yeast community

When organisms face crises, such as starvation, every individual should adapt to environmental changes (1, 2), or the community alters their behaviour (3-5). Because a stressful environment reduces the carrying capacity (6), the population size of unicellular organisms shrinks in such conditions (7, 8). However, the uniform stress response of the cell community may lead to overall extinction or severely damage their entire fitness. How microbial communities accommodate this dilemma remains poorly understood. Here, we demonstrate an elaborate strategy of the yeast community against glucose starvation, named the voluntary triage. During starvation, yeast cells release some autotoxins, such as leucic acid and L-2keto-3methylvalerate, which can even kill the cells producing them. Although it may look like mass suicide at first glance, cells use epigenetic "tags" to adapt to the autotoxin inheritably. If non-tagged latecomers, regardless of whether they are closely related, try to invade the habitat, autotoxins kill them and inhibit their growth, but the tagged cells can selectively survive. Phylogenetically distant fission and budding yeast (9) share this strategy using the same autotoxins, which implies that the universal system of voluntary triage may be relevant to the major evolutional transition from unicellular to multicellular organisms (10).

microbiology↗

bioRxiv2020

Functional Sensitivity and Mutational Robustness of Proteins

Sensitivity and robustness appear to be contrasting concepts. However, natural proteins are robust enough to tolerate random mutations, meanwhile be susceptible enough to sense environmental signals, exhibiting both high functional sensitivity (i.e., plasticity) and mutational robustness. Uncovering how these two aspects are compatible is a fundamental question in the protein dynamics and genotype-phenotype relation. In this work, a general framework is established to analyze the dynamics of protein systems under both external and internal perturbations. We introduce fluctuation entropy for the functional sensitivity and the spectrum entropy for the mutational robustness. The compatibility of sensitivity and robustness is analyzed by the optimization of two entropies, which leads to the power-law vibration spectrum of proteins. These power-law behaviors are confirmed extensively by protein data, as a hallmark of criticality. Moreover, the dependence of functional sensitivity and mutational robustness on the protein size suggests a general evolutionary constraint for proteins with different chain lengths. This framework can also establish a general link of the criticality with robustness-plasticity compatibility, both of which are ubiquitous features in biological systems.

biophysics↗

bioRxiv2019

Molecular Diversity and Network Complexity in Growing Protocells

A great variety of molecular components is encapsulated in cells. Each of these components is replicated for cell reproduction. To address an essential role of the huge diversity of cellular components, we study a model of protocells that convert resources into catalysts with the aid of a catalytic reaction network. As the resources are limited, it is shown that diversity in intracellular components is increased to allow the use of diverse resources for cellular growth. Scaling relation is demonstrated between resource abundances and molecular diversity. We then study how the molecule species diversify and complex catalytic reaction networks develop through the evolutionary course. It is shown that molecule species first appear, at some generations, as parasitic ones that do not contribute to replication of other molecules. Later, the species turn to be host species that support the replication of other species. With this successive increase of host species, a complex joint network evolves. The present study sheds new light on the origin of molecular diversity and complex reaction network at the primitive stage of a cell.

biophysics↗

bioRxiv2019

The origin of the central dogma through conflicting multi-level selection

The central dogma of molecular biology rests on two kinds of asymmetry between genomes and enzymes: informatic asymmetry, where information flows from genomes to enzymes but not from enzymes to genomes; and catalytic asymmetry, where enzymes provide chemical catalysis but genomes do not. How did these asymmetries originate? Here we show that these asymmetries can spontaneously arise from conflict between selection at the molecular level and selection at the cellular level. We developed a model consisting of a population of protocells, each containing a population of replicating catalytic molecules. The molecules are assumed to face a trade-off between serving as catalysts and serving as templates. This trade-off causes conflicting multilevel selection: serving as catalysts is favoured by selection between protocells, whereas serving as templates is favoured by selection between molecules within protocells. This conflict induces informatic and catalytic symmetry breaking, whereby the molecules differentiate into genomes and enzymes, establishing the central dogma. We show mathematically that the symmetry breaking is caused by a positive feedback between Fishers reproductive values and the relative impact of selection at different levels. This feedback induces a division of labour between genomes and enzymes, provided variation at the molecular level is sufficiently large relative to variation at the cellular level, a condition that is expected to hinder the evolution of altruism. Taken together, our results suggest that the central dogma is a logical consequence of conflicting multilevel selection.

evolutionary biology↗

bioRxiv2018

Anti-CD137 monoclonal antibody enhances trastuzumab-induced, natural killer cell-mediated cytotoxicity against pancreatic cancer cell lines with low human epidermal growth factor-like receptor 2 expression

BackgroundBecause human epidermal growth factor-like receptor (HER) 2 is expressed on the surface of human pancreatic carcinoma cells to varying degrees, trastuzumab, an anti-HER2 monoclonal antibody (mAb), is expected to exert antibody-dependent, natural killer (NK) cell-mediated cytotoxicity (ADCC) against the cells. However, some reports found that the effect of trastuzumab against human pancreatic carcinoma cells was limited because most express only limited HER2.\n\nMethodsWe examined whether anti-CD137 stimulating mAb could enhance trastuzumab-mediated ADCC against Panc-1, a human pancreatic cancer cell line with low HER2 expression, in vitro.\n\nResultsSupplementation of anti-CD137 mAb could improve trastuzumab-mediated ADCC against Panc-1 which was insufficient without this stimulating antibody. The ADCC differed in individual cells, and this was related to the expression of CD137 on the surface of NK cells after trastuzumab stimulation in association with the Fc{gamma}-RIIIA polymorphism. NK cells with Fc{gamma}-RIIIA-VV/VF showed high levels of ADCC against Panc-1, but those with Fc{gamma}-RIIIA-FF did not show optimal ADCC. In addition, trastuzumab-mediated ADCC against the human pancreatic cancer cell line Capan-1 with high HER2 expression was generally high and not affected by the Fc{gamma}-RIIIA polymorphism.\n\nConclusionsThese results demonstrated that in Fc{gamma}-RIIIA-VV/VF-carrying hosts, trastuzumab plus CD137 mAb could induce effective ADCC against HER2-low-expressing pancreatic cancer cells. This also indicates the therapeutic potential for unresectable human pancreatic cancer, in which HER-2 expression is generally low.

immunology↗

bioRxiv2017

Theory For Transitions Between Exponential And Stationary Phases: Universal Laws For Lag Time

The quantitative characterization of bacterial growth has attracted substantial research attention since Monods pioneering study. Theoretical and experimental work have uncovered several laws for describing the exponential growth phase, in which the number of cells grows exponentially. However, microorganism growth also exhibits lag, stationary, and death phases under starvation conditions, in which cell growth is highly suppressed, for which quantitative laws or theories are markedly underdeveloped. In fact, the models commonly adopted for the exponential phase that consist of autocatalytic chemical components, including ribosomes, can only show exponential growth or decay in a population, and thus phases that halt growth are not realized. Here, we propose a simple, coarse-grained cell model that includes an extra class of macromolecular components in addition to the autocatalytic active components that facilitate cellular growth. These extra components form a complex with the active components to inhibit the catalytic process. Depending on the nutrient condition, the model exhibits the typical transitions among the lag, exponential, stationary, and death phases. Furthermore, the lag time needed for growth recovery after starvation follows the square root of the starvation time and is inversely related to the maximal growth rate. This is in agreement with experimental observations, in which the length of time of cell starvation is memorized in the slow accumulation of molecules. Moreover, the lag time distributed among cells is skewed with a long time tail. If the starvation time is longer, an exponential tail appears, which is also consistent with experimental data. Our theory further predicts a strong dependence of lag time on the speed of substrate depletion, which can be tested experimentally. The present model and theoretical analysis provide universal growth laws beyond the exponential phase, offering insight into how cells halt growth without entering the death phase.

systems 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↗