Biology subjects

Kobayashi, T. J.

Publications and source records attributed to Kobayashi, T. J..

16 recordsLinked to original sources

bioRxiv2026

Unsupervised identification of low-frequency antigen-specific TCRs using distance-based anomaly scoring

Identifying antigen-specific T cell receptors (TCRs) within the diverse human repertoire remains challenging due to their extremely low frequencies, often as rare as one per million cells. Here, we propose a novel unsupervised approach that detects low-frequency antigen-specific TCRs through distance-based anomaly detection in TCR sequence space. Our method is based on the observation that antigen-specific TCRs preferentially localize at the periphery of V gene clusters rather than cluster centers. Using TCRdist3 to quantify sequence distances, we identify query TCRs that are anomalous compared to reference repertoires within their V-J gene combinations. We validated this approach across three immunological contexts: COVID-19 infection, influenza vaccination, and yellow fever vaccination. For SARS-CoV-2-specific TCR detection in a COVID-19 patient, our method demonstrated 34.3% accuracy, significantly outperforming similarity-based (ALICE: 8.0%) and frequency-based methods (edgeR: 5.8%, the Pogorelyy method: 6.3%), and uniquely detected low-frequency antigen-specific TCRs at clone count one. The minimal overlap with conventional approaches ([≤]6.7%) indicates our method captures distinct TCR clones overlooked by existing analyses. This spatial distribution-based paradigm provides a complementary strategy for TCR specificity detection, particularly valuable for identifying rare antigen-specific clones essential for understanding immune responses.

bioinformatics↗

bioRxiv2026

Predicting mutation-rate variation across the genome using epigenetic data

Mutation rate variation is a fundamental driver of evolution, yet how it is locally patterned across genomes and structured by chromatin context remains unresolved. Here, we integrate genome-wide profiles of histone marks, DNA methylation and chromatin accessibility in Arabidopsis thaliana with de novo mutation data to model mutation probability at the level of coding sequence (CDS). Using non-negative matrix factorization, we identify 15 combinatorial epigenetic patterns whose graded mixtures stratify CDSs into six classes with distinct mutation probabilities. A generalized linear model based on pattern weights predicts local mutation probability and outperforms models based on sequence context, expression and classical genomic categories. These patterns capture context-dependent variation that is obscured by gene-level summaries and single-feature analyses. Cluster-level differences are partly retained in mutation-accumulation lines, indicating persistence into heritable mutational input. Under hypoxia, stress-responsive chromatin remodeling redistributes epigenetic contexts associated with higher predicted mutation probability toward hypoxia-responsive genes and DNA-repair pathways. Together, our results provide a CDS-resolved and interpretable framework linking combinatorial epigenomic context to mutational input, clarifying how dynamic chromatin states shape local mutation-rate heterogeneity.

bioinformatics↗

bioRxiv2025

TCR repertoire analysis quantifies effect of irradiation to homeostasis of iNKT cell development in the thymus of mice

Thymic T cell development shows homeostasis in which T cell production recovers after temporal impairment by various stressors. Invariant natural killer T (iNKT) cells in the thymus contribute to recovery of thymic development after irradiation owing to their greater tolerance to irradiation compared to conventional thymocytes. However, whether and how iNKT cell development recovers from irradiation remains unknown. Here we show that iNKT cells in the thymus exhibits much slower post-irradiation recovery than conventional thymocytes. We compared T cell receptor (TCR) alpha chain repertoire of the thymus in 42 days after irradiation and quantified the sustained decline in the number of iNKT cells against other thymocytes. We found that fluctuation of V and J genes usage does not correlate with the number iNKT cells suggesting that differentiation process of iNKT cells after TCR gene rearrangement is impaired by irradiation. Mathematical modelling of recovery dynamics of iNKT cells implied that lack of rapid proliferation immediately after irradiation unlike conventional thymocytes contributes to the prolonged reduction of iNKT cell population. These findings may suggest possibility on association between the prolonged reduction of iNKT cells after irradiation and autoimmune diseases caused by irradiation in bone marrow transplantation.

immunology↗

bioRxiv2025

Mixing features of transcription factors and genes enables accurate prediction of gene regulation relationships for unknown transcription factors

Identifying regulatory relationships between transcription factors (TFs) and genes is essential to understand diverse biological phenomena related to gene expression. Recently, deep learning-based models to predict TFs that bind to genes from nucleotide sequences of the target genes have been developed, yet these models are trained to predict known TFs only. In this study we developed a deep learning model, GReNIMJA (Gene Regulatory Network Inference by Mixing and Jointing features of Amino acid and nucleotide sequences), to predict gene regulation even by unknown TFs. Our model is designed to mix the features of the TF amino acid sequences and nucleotide sequences of the target genes using a 2D LSTM architecture and to perform binary classification with the aim of determining the presence or absence of a regulatory relationship. The accuracy of our model in predicting regulatory relationships was 84.4% for known TFs (higher than those of conventional models) and 68.5% for unknown TFs; the latter is an unsolved task for conventional deep learning-based models. We expect our model to advance identification of unknown gene regulatory networks and contribute to the understanding of diverse biological phenomena.

systems biology↗

bioRxiv2024

Stoichiometric Rays: A simple method to compute the controllable set of enzymatic reaction systems

Understanding deaths and life-death boundaries of cells is a fundamental challenge in biological sciences. In this study, we present a theoretical framework for investigating cell death. We conceptualize cell death as a controllability problem within dynamical systems, and compute the life-death boundary through the development of "stoichiometric rays". This method utilizes enzyme activity as control parameters, exploiting the inherent property of enzymes to enhance reaction rates without affecting thermodynamic potentials. This approach facilitates the efficient evaluation of the global controllability of models. We demonstrate the utility of our framework using its application to a toy metabolic model, where we delineate the life-death boundary. The formulation of cell death through mathematical principles provides a foundation for the theoretical study of cellular mortality. SIGNIFICANCE STATEMENTWhat is death? This fundamental question in biology lacks a clear theoretical framework despite numerous experimental studies. In this study, we present a new way to understand cell death by looking at how cells can or cannot control their states. We define a "dead state" as a state from which a cell cannot return to being alive. Our method, called "Stoichiometric Rays", helps determine if a cells state is dead based on enzymatic reactions. By using this method, we can quantify the life-death boundary in metabolic models. The present framework provides a theoretical basis and a tool for understanding cell death.

systems biology↗

bioRxiv2023

Mitochondrial protein C15ORF48 is a stress-independent inducer of autophagy that regulates oxidative stress and autoimmunity

Autophagy is primarily activated by cellular stress, such as starvation or mitochondrial damage. However, stress-independent autophagy is activated by unknown mechanisms in several cell types, such as thymic epithelial cells (TECs). Here we report that the mitochondrial protein, C15ORF48, is a critical inducer of stress-independent autophagy. Mechanistically, C15ORF48 reduces the mitochondrial membrane potential and lowers intracellular ATP levels, thereby activating AMP-activated protein kinase and its downstream Unc-51-like kinase 1. Interestingly, C15ORF48 induction of autophagy upregulates intracellular glutathione levels, promoting cell survival by reducing oxidative stress. Mice deficient in C15orf48 showed a reduction in stress-independent autophagy in TECs, but not in typical starvation-induced autophagy in skeletal muscles. Moreover, C15orf48-/- mice developed autoimmunity, which is consistent with the fact that the stress-independent autophagy in TECs is crucial for the thymic self-tolerance. These results suggest that C15ORF48 induces stress-independent autophagy, thereby regulating oxidative stress and self-tolerance.

cell biology↗

bioRxiv2021

MotifBoost: k-mer based data-efficient immune repertoire classification method

The repertoire of T cell receptors encodes various types of immunological information. Machine learning is indispensable for decoding such information from repertoire datasets measured by next-generation sequencing. In particular, the classification of repertoires is the most basic task, which is relevant for a variety of scientific and clinical problems. Supported by the recent appearance of large datasets, efficient but data-expensive methods have been proposed. However, it is unclear whether they can work efficiently when the available sample size is severely restricted as in practical situations. In this study, we demonstrate that the their performances are impaired catastrophically below critical sample sizes. To overcome this, we propose MotifBoost, which exploits the information of short motifs of TCRs. MotifBoost can perform the classification as efficiently as a deep learning method on large datasets while providing more stable and reliable results on small datasets. We also clarify that the robustness of MotifBoost can be attributed to the efficiency of motifs as representation features of repertoires. Finally, by comparing predictions of these methods, we show that the whole sequence identity and sequence motifs encode partially different information and that a combination of such complementary information is necessary for further development of repertoire analysis.

bioinformatics↗

bioRxiv2021

Deep learning-based algorithm for predicting the live birth potential of mouse embryos

In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, the rate of live birth following clinical ART remains low worldwide, suggesting that grading is inaccurate. One explanation is that grading is classically based on the characteristic shape of embryos at a limited number of developmental stages and does not consider the shape of embryos and intracellular structures, e.g., nuclei, at various stages important for normal embryogenesis. Therefore, here we developed a Normalized Multi-View Attention Network (NVAN) that directly predicts live birth potential from nuclear structural features in live-cell fluorescence images taken of mouse embryos across a wide range of stages. The classification accuracy of our method was 83.87%, which greatly exceeded that of existing machine-learning methods and that of visual inspection by embryo culture specialists. By visualizing the features that contributed most to the prediction of live birth potential, we found that the size and shape of the cell nucleus at the morula stage and at the time of cell division were important for live birth prediction. We anticipate that our method will help ART and developmental engineering as a new basic technology for IVF embryo selection.

developmental biology↗

bioRxiv2021

Acceleration of Evolutionary Processes by Learning and Extended Fisher's Fundamental Theorem

Natural selection is general and powerful concept not only to explain evolutionary processes of biological organisms but also to design engineering systems such as genetic algorithms and particle filters. There is a surge of interest, both from biology and engineering, in considering natural selection of intellectual agents that can learn individually. Learning by individual agents of better behaviors for survival may accelerate the evolutionary processes by natural selection. We have accumulating pieces of evidence that organisms can transmit its information to the next generation via epigenetic states or memes. Also, such idea is important for engineering applications to improve the genetic algorithms and the particle filter. To accelerate the evolutionary process, an agent should change their strategy so that the population fitness increases the most. Equivalently, an agent should update the strategy towards a gradient (derivative) of the population fitness with respect to the strategy. However, it has not yet been clarified whether and how an agent can estimate the gradient and accelerate the evolutionary process. We also lack methodology to quantify the acceleration to understand and predict the impact of learning. In this paper, we address these problems. We show that an learning agent can accelerate the evolutionary process by proposing ancestral learning, which uses the information transmitted from the ancestor (ancestral information) via epigenetic states or memes. Numerical experiments show that ancestral learning actually accelerates the evolutionary process. We next show that the ancestral information is sufficient to estimate the gradient. In particular, learning can accelerate the evolutionary process without communications between agents. Finally, to quantify the acceleration, we extend the Fishers fundamental theorem (FF-thm) for natural selection to ancestral learning. The conventional FF-thm relates the speed of evolution by natural selection to the variety of the individual fitness in the population. Our extended FF-thm relates the acceleration of the evolutionary process to the variety of individual fitness of the agent. By the theorem, we can quantitatively understand when and why learning is beneficial.

evolutionary biology↗

bioRxiv2021

Representation and inference of size control laws by neural network aided point processes

The regulation and coordination of cell growth and division is a long-standing problem in cell physiology. Recent single-cell measurements using microfluidic devices provide quantitative time-series data of various physiological parameters of cells. To clarify the regulatory laws and associated relevant parameters such as cell size, mathematical models have been constructed based on physical insights over the phenomena and tested by their capabilities to reproduce the measured data. However, such a conventional model construction by abduction faces a constant risk that we may overlook important parameters and factors especially when complicated time series data is concerned. In addition, comparing a model and data for validation is not trivial when we work on noisy multi-dimensional data. Using cell size control as an example, we demonstrate that this problem can be addressed by employing a neural network (NN) method, originally developed for history-dependent temporal point processes. The NN can effectively segregate history-dependent deterministic factors and unexplainable noise from a given data by flexibly representing functional forms of the deterministic relation and noise distribution. With this method, we represent and infer birth and division cell size distributions of bacteria and fission yeast. The known size control mechanisms such as adder model are revealed as the conditional dependence of the size distributions on history and their Markovian properties are shown sufficient. In addition, the inferred NN model provides a better data representation for the abductive model searching than descriptive statistics. Thus, the NN method can work as a powerful tool to process the noisy data for uncovering hidden dynamic laws.

systems biology↗

bioRxiv2020

Understanding Adaptive Immune System as Reinforcement Learning

The adaptive immune system of vertebrates can detect, respond to, and memorize diverse pathogens from past experience. While the selection of T helper (Th) clones is the simple and established mechanism to recognize and memorize new pathogens, the question that still remains unexplored is how the Th cells can acquire better ways to bias the responses of immune cells for eliminating pathogens more efficiently by translating the recognized antigen information into regulatory signals. In this work, we address this problem by associating the adaptive immune network organized by the Th cells with reinforcement learning (RL). By employing recent advancements of network-based RL, we show that the Th immune network can acquire the association between antigen patterns of and the effective responses to pathogens. Moreover, the clonal selection as well as other inter-cellular interactions are derived as a learning rule of this network. We also demonstrate that the stationary clone-size distribution after learning shares characteristic features with those observed experimentally. Our theoretical framework may contribute to revising and renewing our understanding of adaptive immunity as a learning system.

systems biology↗

bioRxiv2018

Deciphering Latent Growth-States from Cellular Lineage Trees

Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial persistence. To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its inheritance. Although recent advancements in microfluidic technology offer us useful lineage data, they are insufficient to directly identify the phenotypes of the cells. An alternative approach is to infer the phenotype from the lineage data by latent-variable estimation. To this end, however, we must resolve the bias problem in the inference from lineage called survivorship bias. In this work, we clarify how the survivor bias distorts statistical estimations. We then propose a latent-variable estimation algorithm without the survivorship bias from lineage trees based on an expectation-maximization (EM) algorithm, which we call Lineage EM algorithm (LEM). LEM provides a statistical method to identify the traits of the cells applicable to various kinds of lineage data.

evolutionary biology↗

bioRxiv2018

Quantitative analysis reveals reciprocal regulations underlying recovery dynamics of thymocytes and thymic environment

Thymic crosstalk, a set of reciprocal regulations between thymocytes and thymic environment, is relevant for orchestrating the appropriate development of thymocytes as well as the recovery of the thymus from various exogenous insults. Nevertheless, the dynamic and regulatory aspects of the thymic crosstalk have not yet been clarified. In this work, we inferred the interactions shaping the thymic crosstalk and its resultant dynamics between the thymocytes and the thymic epithelial cells (TECs) by quantitative analysis and modelling of the recovery dynamics induced by irradiation. The analysis identified regulatory interactions consistent with the known molecular evidence and revealed their dynamic roles in the recovery process. Moreover, the analysis also predicted, and a subsequent experiment verified a new regulation of CD4+CD8+ double positive (DP) thymocytes, which temporarily increases their proliferation rate upon the decrease in their population size. Our model established the pivotal step towards a dynamic understanding of the thymic crosstalk as a regulatory network system.

immunology↗

bioRxiv2018

Transfer RNA Genes Affect Chromosome Structure and Function via Local Effects

The genome is packaged and organized in an ordered, non-random manner and specific chromatin segments contact nuclear substructures to mediate this organization. While transfer RNA genes (tDNAs) are essential for the generation of tRNAs, these loci are also binding sites for transcription factors and architectural proteins and are thought to play an important role in the organization of the genome. In this study, we investigate the role of tDNAs in genomic organization and chromosome function by editing a chromosome so that it lacks any tDNAs. Surprisingly our analyses of this tDNA-less chromosome show that loss of tDNAs does not grossly affect chromosome folding or chromosome tethering. However, loss of tDNAs affects local nucleosome positioning and the binding of SMC proteins at these loci. The absence of tDNAs also leads to changes in centromere clustering and a reduction in the frequency of long range HML-HMR heterochromatin clustering. We propose that the tDNAs primarily affect local chromatin structure that result in effects on long-range chromosome architecture.

molecular biology↗

bioRxiv2018

Development of Convolutional Neural Network Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Mouse Development

In embryology, analytical approaches such as segmentation are applied to acquiring quantitative criteria from time-series three-dimensional microscopic images. When used to segment cells or intracellular organelles, several current machine learning techniques outperform traditional image processing algorithms. However segmentation algorithms still have unsolved problems, especially in bioimage processing. The most critical issue is that the existing algorithms can perform only semantic segmentation, which distin-guishes whether a pixel is within an object (for example, nucleus) or not. In this study, we implemented a novel segmentation algorithm, based on deep learning, which segments each nucleus and adds different labels to the detected objects. This segmentation algorithm is called instance segmentation. Our instance segmentation algorithm, implemented as a neural network, which we named QCA Net, substantially out-performed 3D U-Net, which is the best semantic segmentation algorithm that uses deep learning. Using QCA Net, we quantified the nuclear number, volume, surface area, and center of gravity coordinates during the development of mouse embryos. In particular, QCA Net distinguished nuclei of embryonic cells from those of polar bodies formed in meiosis. We consider that QCA Net can greatly contribute to bioimage segmentation in embryology by generating quantitative criteria from segmented images.

systems biology↗

bioRxiv2017

Quantification of inter-sample differences in T cell receptor sequences

Inter-sample comparisons of the T cell receptor (TCR) repertoire are crucial for gaining a better understanding into the immunological states determined by different collections of T cells from different donor sites, cell types, and genetic and pathological backgrounds. As a theoretical approach for the quantitative comparison, previous studies utilized the Poisson abundance models and the conventional methods in ecology, which focus on the abundance distribution of observed TCR sequences. However, these methods ignore the details of the measured sequences and are consequently unable to identify sub-repertoires that might have the contributions to the observed inter-sample differences. In this paper, we propose a new comparative approach based on TCR sequence information, which can estimate the low-dimensional structure by projecting the pairwise sequence dissimilarities in high-dimensional sequence space. The inter-sample differences are then quantified according to information-theoretic measures among the distributions of data estimated in the embedded space. Using an actual dataset of TCR sequences in transgenic mice that have strong restrictions on somatic recombination, we demonstrate that our proposed method can accurately identify the inter-sample hierarchical structure, which is consistent with that estimated by previous methods based on abundance or count information. Moreover, we identified the key sequences that contribute to the pairwise sample differences. Such identification of the sequences contributing to variation in immune cell repertoires may provide substantial insight for the development of new immunotherapies and vaccines.

immunology↗