A transcriptional program underlying the circannual rhythms of gonadal development in medaka.

To cope with seasonal environmental changes, organisms have evolved approximately 1-y endogenous circannual clocks. These circannual clocks regulate various physiological properties and behaviors such as reproduction, hibernation, migration, and molting, thus providing organisms with adaptive advantages. Although several hypotheses have been proposed, the genes that regulate circannual rhythms and the underlying mechanisms controlling long-term circannual clocks remain unknown in any organism. Here, we show a transcriptional program underlying the circannual clock in medaka fish (Oryzias latipes). We monitored the seasonal reproductive rhythms of medaka kept under natural outdoor conditions for 2 y. Linear regression analysis suggested that seasonal changes in reproductive activity were predominantly determined by an endogenous program. Medaka hypothalamic and pituitary transcriptomes were obtained monthly over 2 y and daily on all equinoxes and solstices. Analysis identified 3,341 seasonally oscillating genes and 1,381 daily oscillating genes. We then examined the existence of circannual rhythms in medaka via maintaining them under constant photoperiodic conditions. Medaka exhibited approximately 6-mo free-running circannual rhythms under constant conditions, and monthly transcriptomes under constant conditions identified 518 circannual genes. Gene ontology analysis of circannual genes highlighted the enrichment of genes related to cell proliferation and differentiation. Altogether, our findings support the "histogenesis hypothesis" that postulates the involvement of tissue remodeling in circannual time-keeping.

Somite boundary determination in normal and clock-less vertebrate embryos.

Vertebrate segments called somites are generated by periodic segmentation of the presomitic mesoderm (PSM). In the most accepted theoretical model for somite segmentation, the clock and wavefront (CW) model, a clock that ticks to determine particular timings and a wavefront that moves posteriorly are presented in the PSM, and somite positions are determined when the clock meets the posteriorly moving wavefront somewhere in the PSM. Over the last two decades, it has been revealed that the molecular mechanism of the clock and wavefront in vertebrates is based on clock genes including Hes family transcription factors and Notch effectors that oscillate within the PSM to determine particular timings and fibroblast growth factor (FGF) gradients, acting as the posteriorly moving wavefront to determine the position of somite segmentation. A clock-less condition in the CW model was predicted to form no somites; however, irregularly sized somites were still formed in mice and zebrafish, suggesting that this was one of the limitations of the CW model. Recently, we performed interdisciplinary research of experimental and theoretical biological studies and revealed the mechanisms of somite boundary determination in normal and clock-less conditions by characterization of the FGF/extracellular signal-regulated kinase (ERK) activity dynamics. Since features of the molecular clock have already been described in-depth in several reviews, we summarized recent findings regarding the role of FGF/ERK signaling in somite boundary formation and described our current understanding of how FGF/ERK signaling contributes to somitogenesis in normal and clock-less conditions in this review.

Noise-resistant developmental reproducibility in vertebrate somite formation.

The reproducibility of embryonic development is remarkable, although molecular processes are intrinsically stochastic at the single-cell level. How the multicellular system resists the inevitable noise to acquire developmental reproducibility constitutes a fundamental question in developmental biology. Toward this end, we focused on vertebrate somitogenesis as a representative system, because somites are repeatedly reproduced within a single embryo whereas such reproducibility is lost in segmentation clock gene-deficient embryos. However, the effect of noise on developmental reproducibility has not been fully investigated, because of the technical difficulty in manipulating the noise intensity in experiments. In this study, we developed a computational model of ERK-mediated somitogenesis, in which bistable ERK activity is regulated by an FGF gradient, cell-cell communication, and the segmentation clock, subject to the intrinsic noise. The model simulation generated our previous in vivo observation that the ERK activity was distributed in a step-like gradient in the presomitic mesoderm, and its boundary was posteriorly shifted by the clock in a stepwise manner, leading to regular somite formation. Here, we showed that this somite regularity was robustly maintained against the noise. Removing the clock from the model predicted that the stepwise shift of the ERK activity occurs at irregular timing with irregular distance owing to the noise, resulting in somite size variation. This model prediction was recently confirmed by live imaging of ERK activity in zebrafish embryos. Through theoretical analysis, we presented a mechanism by which the clock reduces the inherent somite irregularity observed in clock-deficient embryos. Therefore, this study indicates a novel role of the segmentation clock in noise-resistant developmental reproducibility.

Identification of animal behavioral strategies by inverse reinforcement learning.

Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making and is fundamental to dissect information processing done by the nervous system. However, methods for quantifying such behavioral strategies have not been fully established. In this study, we developed an inverse reinforcement-learning (IRL) framework to identify an animal's behavioral strategy from behavioral time-series data. We applied this framework to C. elegans thermotactic behavior; after cultivation at a constant temperature with or without food, fed worms prefer, while starved worms avoid the cultivation temperature on a thermal gradient. Our IRL approach revealed that the fed worms used both the absolute temperature and its temporal derivative and that their behavior involved two strategies: directed migration (DM) and isothermal migration (IM). With DM, worms efficiently reached specific temperatures, which explains their thermotactic behavior when fed. With IM, worms moved along a constant temperature, which reflects isothermal tracking, well-observed in previous studies. In contrast to fed animals, starved worms escaped the cultivation temperature using only the absolute, but not the temporal derivative of temperature. We also investigated the neural basis underlying these strategies, by applying our method to thermosensory neuron-deficient worms. Thus, our IRL-based approach is useful in identifying animal strategies from behavioral time-series data and could be applied to a wide range of behavioral studies, including decision-making, in other organisms.

Revisiting chemoaffinity theory: Chemotactic implementation of topographic axonal projection.

Neural circuits are wired by chemotactic migration of growth cones guided by extracellular guidance cue gradients. How growth cone chemotaxis builds the macroscopic structure of the neural circuit is a fundamental question in neuroscience. I addressed this issue in the case of the ordered axonal projections called topographic maps in the retinotectal system. In the retina and tectum, the erythropoietin-producing hepatocellular (Eph) receptors and their ligands, the ephrins, are expressed in gradients. According to Sperry's chemoaffinity theory, gradients in both the source and target areas enable projecting axons to recognize their proper terminals, but how axons chemotactically decode their destinations is largely unknown. To identify the chemotactic mechanism of topographic mapping, I developed a mathematical model of intracellular signaling in the growth cone that focuses on the growth cone's unique chemotactic property of being attracted or repelled by the same guidance cues in different biological situations. The model presented mechanism by which the retinal growth cone reaches the correct terminal zone in the tectum through alternating chemotactic response between attraction and repulsion around a preferred concentration. The model also provided a unified understanding of the contrasting relationships between receptor expression levels and preferred ligand concentrations in EphA/ephrinA- and EphB/ephrinB-encoded topographic mappings. Thus, this study redefines the chemoaffinity theory in chemotactic terms.

Reconstruction of Spatial Thermal Gradient Encoded in Thermosensory Neuron AFD in Caenorhabditis elegans.

During navigation, animals process temporal sequences of sensory inputs to evaluate the surrounding environment. Thermotaxis of Caenorhabditis elegans is a favorable sensory behavior to elucidate how navigating animals process sensory signals from the environment. Sensation and storage of temperature information by a bilaterally symmetric pair of thermosensory neurons, AFD, is essential for the animals to migrate toward the memorized temperature on a thermal gradient. However, the encoding mechanisms of the spatial environment with the temporal AFD activity during navigation remain to be elucidated. Here, we show how the AFD neuron encodes sequences of sensory inputs to perceive spatial thermal environment. We used simultaneous calcium imaging and tracking system for a freely moving animal and characterized the response property of AFD to the thermal stimulus during thermotaxis. We show that AFD neurons respond to shallow temperature increases with intermittent calcium pulses and detect temperature differences with a critical time window of 20 s, which is similar to the timescale of behavioral elements of C. elegans, such as turning. Convolution of a thermal stimulus and the identified response property successfully reconstructs AFD activity. Conversely, deconvolution of the identified response kernel and AFD activity reconstructs the shallow thermal gradient with migration trajectory, indicating that AFD activity and the migration trajectory are sufficient as the encoded signals for thermal environment. Our study demonstrates bidirectional transformation between environmental thermal information and encoded neural activity. SIGNIFICANCE STATEMENT: Deciphering how information is encoded in the nervous system is an important challenge for understanding the principles of information processing in neural circuits. During navigation behavior, animals transform spatial information to temporal patterns of neural activity. To elucidate how a sensory system achieves this transformation, we focused on a thermosensory neuron in Caenorhabditis elegans called AFD, which plays a major role in a sensory behavior. Using tracking and calcium imaging system for freely moving animals, we identified the response property of the AFD. The identified response property enabled us to reconstruct both neural activity from a temperature stimulus and a spatial thermal environment from neural activity. These results shed light on how a sensory system encodes the environment.

Nonrandom contribution of left and right testes to germline transmission from mouse spermatogonial stem cells.

Vast amounts of sperm are produced from spermatogonial stem cells (SSCs), which continuously undergo self-renewal. We examined the possible effect of laterality in male germline transmission efficiency of SSCs using a spermatogonial transplantation technique. We transplanted the same number of wild-type and Egfp transgenic SSCs in the same or different testes of individual recipient mice and compared the fertility of each type of recipient by natural mating. Transgenic mice were born within 3 months after transplantation regardless of the transplantation pattern. However, transgenic offspring were born at a significantly increased frequency when wild-type and transgenic SSCs were transplanted separately. In addition, this type of recipient sired significantly more litters that consisted exclusively of transgenic mice, which suggested that left and right testes have different time windows for fertilization. Thus, laterality plays an important role in germline transmission patterns from SSCs.

Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model.

Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction.

Fluctuation of Rac1 activity is associated with the phenotypic and transcriptional heterogeneity of glioma cells.

Phenotypic heterogeneity of cancer cells is caused not only by genetic and epigenetic alterations but also by stochastic variation of intracellular signaling molecules. Using cells that stably express Förster resonance energy transfer (FRET) biosensors, we show here a correlation between a temporal fluctuation in the activity of Rac1 and the invasive properties of C6 glioma cells. By using long-term time-lapse imaging, we found that Rac1 activity in C6 glioma cells fluctuated over a timescale that was substantially longer than that of the replication cycle. Because the relative level of Rac1 activity in each cell was unaffected by a suspension-adhesion procedure, we were able to sort C6 glioma cells according to the levels of Rac1 activity, yielding Rac1(high) and Rac1(low) cells. The Rac1(high) cells invaded more efficiently than did Rac1(low) cells in a Matrigel invasion assay. We assessed the transcriptional profiles of Rac1(high) and Rac1(low) cells and performed gene ontology analysis. Among the 14 genes that were most associated with the term 'membrane' (membrane-related genes) in Rac1(high) cells, we identified four genes that were associated with glioma invasion and Rac1 activity by using siRNA knockdown experiments. Among the transcription factors upregulated in Rac1(high) cells, Egr2 was found to positively regulate expression of the four membrane-related invasion-associated genes. The identified signaling network might cause the fluctuations in Rac1 activity and the heterogeneity in the invasive capacity of glioma cells.

Adaptive discrimination between harmful and harmless antigens in the immune system by predictive coding.

The immune system discriminates between harmful and harmless antigens based on past experiences; however, the underlying mechanism is largely unknown. From the viewpoint of machine learning, the learning system predicts the observation and updates the prediction based on prediction error, a process known as "predictive coding." Here, we modeled the population dynamics of T cells by adopting the concept of predictive coding; conventional and regulatory T cells predict the antigen concentration and excessive immune response, respectively. Their prediction error signals, possibly via cytokines, induce their differentiation to memory T cells. Through numerical simulations, we found that the immune system identifies antigen risks depending on the concentration and input rapidness of the antigen. Further, our model reproduced history-dependent discrimination, as in allergy onset and subsequent therapy. Taken together, this study provided a novel framework to improve our understanding of how the immune system adaptively learns the risks of diverse antigens.

Few-shot prediction of amyloid ß accumulation from mainly unpaired data on biomarker candidates.

The pair-wise observation of the input and target values obtained from the same sample is mandatory in any prediction problem. In the biomarker discovery of Alzheimer's disease (AD), however, obtaining such paired data is laborious and often avoided. Accumulation of amyloid-beta (Aß) in the brain precedes neurodegeneration in AD, and the quantitative accumulation level may reflect disease progression in the very early phase. Nevertheless, the direct observation of Aß is rarely paired with the observation of other biomarker candidates. To this end, we established a method that quantitatively predicts Aß accumulation from biomarker candidates by integrating the mostly unpaired observations via a few-shot learning approach. When applied to 5xFAD mouse behavioral data, the proposed method predicted the accumulation level that conformed to the observed amount of Aß in the samples with paired data. The results suggest that the proposed model can contribute to discovering Aß predictability-based biomarkers.

Decoding reward-curiosity conflict in decision-making from irrational behaviors.

Humans and animals are not always rational. They not only rationally exploit rewards but also explore an environment owing to their curiosity. However, the mechanism of such curiosity-driven irrational behavior is largely unknown. Here, we developed a decision-making model for a two-choice task based on the free energy principle, which is a theory integrating recognition and action selection. The model describes irrational behaviors depending on the curiosity level. We also proposed a machine learning method to decode temporal curiosity from behavioral data. By applying it to rat behavioral data, we found that the rat had negative curiosity, reflecting conservative selection sticking to more certain options and that the level of curiosity was upregulated by the expected future information obtained from an uncertain environment. Our decoding approach can be a fundamental tool for identifying the neural basis for reward-curiosity conflicts. Furthermore, it could be effective in diagnosing mental disorders.

Optimal COVID-19 testing strategy on limited resources.

The last three years have been spent combating COVID-19, and governments have been seeking optimal solutions to minimize the negative impacts on societies. Although two types of testing have been performed for this-follow-up testing for those who had close contact with infected individuals and mass-testing of those with symptoms-the allocation of resources has been controversial. Mathematical models such as the susceptible, infectious, exposed, recovered, and dead (SEIRD) model have been developed to predict the spread of infection. However, these models do not consider the effects of testing characteristics and resource limitations. To determine the optimal testing strategy, we developed a testing-SEIRD model that depends on testing characteristics and limited resources. In this model, people who test positive are admitted to the hospital based on capacity and medical resources. Using this model, we examined the infection spread depending on the ratio of follow-up and mass-testing. The simulations demonstrated that the infection dynamics exhibit an all-or-none response as infection expands or extinguishes. Optimal and worst follow-up and mass-testing combinations were determined depending on the total resources and cost ratio of the two types of testing. Furthermore, we demonstrated that the cumulative deaths varied significantly by hundreds to thousands of times depending on the testing strategy, which is encouraging for policymakers. Therefore, our model might provide guidelines for testing strategies in the cases of recently emerging infectious diseases.

Intercellular exchange of Wnt ligands reduces cell population heterogeneity during embryogenesis.

Wnt signaling is required to maintain bipotent progenitors for neural and paraxial mesoderm cells, the neuromesodermal progenitor (NMP) cells that reside in the epiblast and tailbud. Since epiblast/tailbud cells receive Wnt ligands produced by one another, this exchange may average out the heterogeneity of Wnt signaling levels among these cells. Here, we examined this possibility by replacing endogenous Wnt3a with a receptor-fused form that activates signaling in producing cells, but not in neighboring cells. Mutant mouse embryos show a unique phenotype in which maintenance of many NMP cells is impaired, although some cells persist for long periods. The epiblast cell population of these embryos increases heterogeneity in Wnt signaling levels as embryogenesis progresses and are sensitive to retinoic acid, an endogenous antagonist of NMP maintenance. Thus, mutual intercellular exchange of Wnt ligands in the epiblast cell population reduces heterogeneity and achieves robustness to environmental stress.

Single-cell transcriptome atlas of Drosophila gastrula 2.0.

During development, positional information directs cells to specific fates, leading them to differentiate with their own transcriptomes and express specific behaviors and functions. However, the mechanisms underlying these processes in a genome-wide view remain ambiguous, partly because the single-cell transcriptomic data of early developing embryos containing accurate spatial and lineage information are still lacking. Here, we report a single-cell transcriptome atlas of Drosophila gastrulae, divided into 77 transcriptomically distinct clusters. We find that the expression profiles of plasma-membrane-related genes, but not those of transcription-factor genes, represent each germ layer, supporting the nonequivalent contribution of each transcription-factor mRNA level to effector gene expression profiles at the transcriptome level. We also reconstruct the spatial expression patterns of all genes at the single-cell stripe level as the smallest unit. This atlas is an important resource for the genome-wide understanding of the mechanisms by which genes cooperatively orchestrate Drosophila gastrulation.

Dopamine error signal to actively cope with lack of expected reward.

To obtain more of a particular uncertain reward, animals must learn to actively overcome the lack of reward and adjust behavior to obtain it again. The neural mechanisms underlying such coping with reward omission remain unclear. Here, we developed a task in rats to monitor active behavioral switch toward the next reward after no reward. We found that some dopamine neurons in the ventral tegmental area exhibited increased responses to unexpected reward omission and decreased responses to unexpected reward, following the opposite responses of the well-known dopamine neurons that signal reward prediction error (RPE). The dopamine increase reflected in the nucleus accumbens correlated with behavioral adjustment to actively overcome unexpected no reward. We propose that these responses signal error to actively cope with lack of expected reward. The dopamine error signal thus cooperates with the RPE signal, enabling adaptive and robust pursuit of uncertain reward to ultimately obtain more reward.

Stem cell homeostasis regulated by hierarchy and neutral competition.

Tissue stem cells maintain themselves through self-renewal while constantly supplying differentiating cells. Two distinct models have been proposed as mechanisms of stem cell homeostasis. According to the classical model, there is hierarchy among stem cells, and master stem cells produce stem cells by asymmetric division; whereas, according to the recent model, stem cells are equipotent and neutrally compete. However, the mechanism remains controversial in several tissues and species. Here, we developed a mathematical model linking the two models, named the hierarchical neutral competition (hNC) model. Our theoretical analysis showed that the combination of the hierarchy and neutral competition exhibited bursts in clonal expansion, which was consistent with experimental data of rhesus macaque hematopoiesis. Furthermore, the scaling law in clone size distribution, considered a unique characteristic of the recent model, was satisfied even in the hNC model. Based on the findings above, we proposed the criterion for distinguishing the three models based on experiments.

Regulation of male germline transmission patterns by the Trp53-Cdkn1a pathway.

A small number of offspring are born from the numerous sperm generated from spermatogonial stem cells (SSCs). However, little is known regarding the rules and molecular mechanisms that govern germline transmission patterns. Here we report that the Trp53 tumor suppressor gene limits germline genetic diversity via Cdkn1a. Trp53-deficient SSCs outcompeted wild-type (WT) SSCs and produced significantly more progeny after co-transplantation into infertile mice. Lentivirus-mediated transgenerational lineage analysis showed that offspring bearing the same virus integration were repeatedly born in a non-random pattern from WT SSCs. However, SSCs lacking Trp53 or Cdkn1a sired transgenic offspring in random patterns with increased genetic diversity. Apoptosis of KIT+ differentiating germ cells was reduced in Trp53- or Cdkn1a-deficient mice. Reduced CDKN1A expression in Trp53-deficient spermatogonia suggested that Cdkn1a limits genetic diversity by supporting apoptosis of syncytial spermatogonial clones. Therefore, the TRP53-CDKN1A pathway regulates tumorigenesis and the germline transmission pattern.

Hierarchical modeling of mechano-chemical dynamics of epithelial sheets across cells and tissue.

Collective cell migration is a fundamental process in embryonic development and tissue homeostasis. This is a macroscopic population-level phenomenon that emerges across hierarchy from microscopic cell-cell interactions; however, the underlying mechanism remains unclear. Here, we addressed this issue by focusing on epithelial collective cell migration, driven by the mechanical force regulated by chemical signals of traveling ERK activation waves, observed in wound healing. We propose a hierarchical mathematical framework for understanding how cells are orchestrated through mechanochemical cell-cell interaction. In this framework, we mathematically transformed a particle-based model at the cellular level into a continuum model at the tissue level. The continuum model described relationships between cell migration and mechanochemical variables, namely, ERK activity gradients, cell density, and velocity field, which could be compared with live-cell imaging data. Through numerical simulations, the continuum model recapitulated the ERK wave-induced collective cell migration in wound healing. We also numerically confirmed a consistency between these two models. Thus, our hierarchical approach offers a new theoretical platform to reveal a causality between macroscopic tissue-level and microscopic cellular-level phenomena. Furthermore, our model is also capable of deriving a theoretical insight on both of mechanical and chemical signals, in the causality of tissue and cellular dynamics.

Model-based prediction of spatial gene expression via generative linear mapping.

Decoding spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) data has become a fundamental technique for understanding multicellular systems; however, existing computational methods lack both accuracy and biological interpretability due to their model-free frameworks. Here, we introduce Perler, a model-based method to integrate scRNA-seq data with reference in situ hybridization (ISH) data. To calibrate differences between these datasets, we develop a biologically interpretable model that uses generative linear mapping based on a Gaussian mixture model using the Expectation-Maximization algorithm. Perler accurately predicts the spatial gene expression of Drosophila embryos, zebrafish embryos, mammalian liver, and mouse visual cortex from scRNA-seq data. Furthermore, the reconstructed transcriptomes do not over-fit the ISH data and preserved the timing information of the scRNA-seq data. These results demonstrate the generalizability of Perler for dataset integration, thereby providing a biologically interpretable framework for accurate reconstruction of spatial transcriptomes in any multicellular system.