Biology subjects

Yamaguchi, R.

Publications and source records attributed to Yamaguchi, R..

34 records · Page 2Linked to original sources

bioRxiv2023

Visuomotor activity associated with conditioned hand movements in premotor cortex of blindsight macaques

Some patients with damage to the primary visual cortex (V1) can respond to visual stimuli in their affected visual field, even though they report loss of visual awareness. This phenomenon is termed blindsight. To clarify the visuomotor transformation for hand movement control in the blindsight condition, we conducted multichannel electrocorticographical recordings from the ipsilesional frontal cortex including the frontal eye field, premotor, primary motor, and somatosensory areas during a delayed conditioned two-alternative forced choice manual response task with arbitrary assignment of push-pull manual responses in relation to the visual target cue locations before and after unilateral V1 lesioning in macaque monkeys. Before lesioning, the activity in the dorsal premotor cortex (PMd) showed short latency task-related {theta} ~ band responses that were significantly higher in the successful trials than in the error/miss trials at approximately 200 ms after target cue onset. At 2-3 months after lesioning, the monkeys regained a >80% success rate in response to appearance of the target cue in the lesion-affected visual field. At this stage, similar task-related {theta} ~ band target cue responses were observed in the PMd. Further, Granger causality from the PMd to the primary motor cortex was enhanced in the {theta} ~ , {gamma}, and high-{gamma} bands during the delay period of the task. These results suggested that the PMd plays a crucial role in mediating the visual signals for execution of hand movements in blindsight monkeys. Significant StatementBlindsight is a curious phenomenon in which the patients with damage to the primary visual cortex can still respond to visual stimulus in their blinded visual field despite loss of awareness. Previously we clarified the critical circuits for visually guided saccadic eye movements in blindsight macaques, however, those for the control of hand movements are still unclear. Here, we have recorded the multichannel electrocorticography in the frontal cortices during the conditioned manual response task and found that the dorsal premotor cortex exhibits task-related {theta} ~ band target cue responses at approximately 200 ms after the visual cue onset, similarly to the intact state. This is the first report of electrophysiological recordings in the frontal lobe of the blindsight subjects.

neuroscience↗

bioRxiv2022

Contact-number-driven virus evolution: a multi-level modeling framework for the evolution of acute or persistent RNA virus infection

Viruses evolve in infected host populations, and host population dynamics affect viral evolution. RNA viruses with a short duration of infection and a high peak viral load, such as and SARS-CoV-2, are maintained in human populations. By contrast, RNA viruses characterized by a long infection duration and a low peak viral load (e.g., borna disease virus) can be maintained in nonhuman populations, and why the persistent viruses evolved has been rarely explored. Here, using a multi-level modeling approach including both individual-level virus infection dynamics and population-scale transmission, we consider virus evolution based on the host environment, specifically, the effect of the contact history of infected hosts. We found that, with a highly dense contact history, viruses with a high virus production rate but low accuracy are likely to be optimal, resulting in a short infectious period with a high peak viral load. In contrast, with a low-density contact history, viral evolution is toward low virus production but high accuracy, resulting in long infection durations with low peak viral load. Our study sheds light on the origin of persistent viruses and why acute viral infections but not persistent virus infection tends to prevail in human society.

evolutionary biology↗

bioRxiv2022

The phoenix hypothesis of speciation

Genetic divergence among allopatric populations builds reproductive isolation over time and is thought to be the major mechanism underlying the formation of new species. This process is accelerated when populations face a changing environment, but abrupt change also places populations at risk of extinction. Here we use simulations of Fishers geometric model with explicit population dynamics to explore the genetic changes that occur in the face of extreme environmental changes to which populations must adapt or go extinct. We show that evolutionary rescue leads to the fixation of mutations whose effects are larger on average and that these mutations are more likely to lead to reproductive isolation, compared with populations not at risk of extinction. We refer to the formation of new species from the ashes of populations in decline as the phoenix hypothesis of speciation. The phoenix hypothesis predicts more substantial hybrid fitness breakdown among populations surviving a higher extinction risk. The hypothesis was supported when many loci underlie adaptation. When, however, there was only a small number of potential rescue mutations, we found that mutations fixed in different populations were more likely to be identical, with parallel changes reducing isolation. With a limited genomic potential for adaptation, we find support for a modified version of the phoenix hypothesis where reproductive isolation builds fastest in populations subject to an intermediate extinction risk. While processes driving extinction lead to the loss of lineages with deep evolutionary histories, they may also generate new taxa, albeit taxa with minimal genetic differences.

evolutionary biology↗

bioRxiv2021

Identification of Bacteriophages Using Deep Representation Model with Pre-training

MotivationBacteriophages/Phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models. ResultsWe propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions. AvailabilityThe codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT. Contactyaozhong@ims.u-tokyo.ac.jp and imoto@hgc.jp Supplementary informationSupplementary data are available at BioRxiv online.

bioinformatics↗

bioRxiv2021

On the application of BERT models for nanopore methylation detection

MotivationDNA methylation is a common epigenetic modification, which is widely associated with various biological processes, such as gene expression, aging, and disease. Nanopore sequencing provides a promising methylation detection approach through monitoring abnormal signal shifts for detecting modified bases in target motif regions. Recently, model-based approaches, especially those with deep learning models, have achieved significant performance improvements on nanopore methylation detection. In this work, we explore using bidirectional encoder representations from transformers (BERT) for doing the task, which can provide non-recurrent neural structures for fast parallel computation. ResultsWe find original BERT architecture does not work as well as the bidirectional recurrent neural network (biRNN) on the nanopore methylation prediction task. Through further analysis, we observe recurrent patterns of positional-signal-shift in the context window surrounding target 5-methylcytosine (5mC) and N6-methyladenine (6mA) motifs. We propose a refined BERT with relative position representation and center hidden units concatenation, which takes account of task-specific characters into modeling. We perform systematic evaluations in-sample and cross-sample. The experiment results show that the refined BERT model can achieve competitive or even better results than the state-of-the-art biRNN model, while the model inference speed is about 6x faster. Besides, on the cross-sample evaluation of datasets from the different research groups, BERT models demonstrate a good generalization performance. AvailabilityThe source code and data are available at https://github.com/yaozhong/methBERT Contactyaozhong@ims.u-tokyo.ac.jp

bioinformatics↗

bioRxiv2021

Extended applicability of causal inference to compositional data by reciprocal logarithmic ratio transformation

Understanding state transition caused by several intrinsic and extrinsic factors such as environmental change and increased stress of human activities has been a significant focus in ecological studies. Analysis of time-series data is indispensable to identify a causal relationship between the possible factors and community change. Among several time-series analysis, a nonlinear time series analysis method called empirical dynamic modeling (EDM) has been recently applied to infer causality of community change that arose from intraspecies interactions. EDM allows model-free analysis to estimate the degree of action strength of intraspecific and interspecific interactions at the population level. Convergent cross-mapping (CCM) is an empirical dynamic analysis method used to suggest the existence of causality by reconstructing the state space of a dynamical system from a time series of observations without assuming any explicit mathematical equations. Although CCM allows for inferring directional interpretation of causal relationships from multivariate time series data, one of the major challenges is its non-applicability to compositional data, a common representation of next generation sequencing data such as microbiome. This study aimed to explore a practical approach applied explicitly to compositional data analysis. More specifically, we propose a heuristic but useful transformation that enables CCM to be applied to compositional data. The proposed transformation has demonstrated its applicability to compositional data equivalent to the conventional CCM to untransformed data. Application of the proposed transformation to sequence-based microbial community profiling data provides reasonable implication to the possible causal relationship during state transition. Author summarySeveral types of ecological studies such as trophic changes in lakes, marine plankton communities, forest ecosystems, terrestrial ecosystems, and interactions between plants and soils have employed time-series analyses for identifying the factors that might cause state changes. With the rise of next-generation sequencing, our understanding of these ecosystems is expanding to analyze sequence data. A nonlinear time series analysis method, termed empirical dynamic modeling, has been recently applied for analyzing time-series data. Among different empirical dynamic modeling methods, convergent cross-mapping (CCM) is frequently used to infer a causal relationship. Although CCM enables the directional interpretation of causal relationships, it cannot be applied to compositional data analysis. This study proposes a novel type of transformation, Reciprocal Logarithmic Ratio (RLR) transformation, that enables CCM to be applied to compositional data. With RLR-transformation, CCM results for compositional data are comparable to those for absolute data, and it is confirmed that the transformation is applicable to sequence data as well. The RLR-transformation is expected to provide a better understanding of ecological interactions by estimating causal relationships in compositional data.

ecology↗

bioRxiv2020

Parental population range expansion before secondary contact promotes heterosis

Population genomic analysis of hybrid zones is instrumental to our understanding of the evolution of reproductive isolation. Many temperate hybrid zones are formed by the secondary contact between two parental populations that have undergone post-glacial range expansion. Here we show that explicitly accounting for historical parental isolation followed by range expansion prior to secondary contact is fundamental for explaining genetic and fitness patterns in these hybrid zones. Specifically, ancestral population expansion can result in allele surfing where neutral or slightly deleterious mutations drift to high frequency at the expansion front. If these surfed deleterious alleles are recessive, they can contribute to substantial heterosis in hybrids produced at secondary contact, counteracting negative effects of Bateson-Dobzhansky-Muller incompatibilities (BDMIs) hence weakening reproductive isolation. When BDMIs are linked to such recessive deleterious alleles the fitness benefit of introgression at these loci can facilitate introgression at the BDMIs. The extent to which this occurs depends on the strength of selection against the linked deleterious alleles and the distribution of recombination across the chromosome. Finally, surfing of neutral loci can alter the expected pattern of population ancestry, thus accounting for historical population expansion is necessary to develop accurate null genomic models of secondary-contact hybrid zones.

evolutionary biology↗

bioRxiv2019

A multifunctional R package for identification of tumor-specific neoantigens

It is known that some mutated peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T-cells on the surface of a tumor cell. These peptides are termed neoantigen and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions, and structural variants. Beyond the existing applications, this package is capable of attaching and reflecting several additional information, e.g., wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information, and combinations of mutations to filter out infeasible peptides as neoantigen. AvailabilityThe R package is available at http://github/hase62/Neoantimon.

bioinformatics↗

bioRxiv2019

Subcellular localization of glypican-5 associates with dynamic cell motility and cell communication of the human mesenchymal stem cell line U3DT

Glypican-5 (GPC5) is a heparan sulfate proteoglycan (HSPG) localized to the plasma membrane. We previously reported that in the human mesenchymal stem cell line UE6E7T3, GPC5 is overexpressed in association with transformation and promotes cell proliferation by acting as a co-receptor for Sonic hedgehog signaling. In this study, we found using an immunofluorescence microscopy that in transformed cells (U3DT), GPC5 localized not only at primary cilia on the cell surface, but also at the leading edge of migrating cells, at the intercellular bridge and blebs during cytokinesis, and in extracellular vesicles. In each subcellular region, GPC5 colocalized with the small GTPase Rab11. These observations suggest that the colocalization of GPC5 with Rab11 is crucial for its function, and that its activity in each subcellular compartment promotes proliferation of U3DT cells. Our findings indicate that GPC5 plays active and essential roles in regulation of U3DT cell proliferation, and provides several insights into the functions of GPC5 that could be elucidated by future studies.

cell biology↗

bioRxiv2019

Nanopore base-calling from a perspective of instance segmentation

BackgroundNanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real time. Through detecting the change of ion currency signals during a DNA/RNA fragments pass through a nanopore, genotypes are determined. Currently, the accuracy of nanopore base-calling has a higher error rate than short-read base-calling. Through utilizing deep neural networks, the-state-of-the art nanopore base-callers achieve base-calling accuracy in a range from 85% to 95%.\n\nResultIn this work, we proposed a novel base-calling approach from a perspective of instance segmentation. Different from the previous sequence labeling approaches, we formulated the base-calling problem as a multi-label segmentation task. Meanwhile, we proposed a refined U-net model which we call UR-net that can model sequential dependencies for a one-dimensional segmentation task. The experiment results show that the proposed base-caller URnano achieves competitive results compared to recently proposed CTC-featured base-caller Chiron, on the same amount of training and test data for in-domain evaluation. Our results show that formulating the base-calling problem as a one-dimensional segmentation task is a promising approach.\n\nAvailabilityThe source code and data are available at https://github.com/yaozhong/URnano\n\nContactyaozhong@ims.u-tokyo.ac.jp\n\nSupplementary informationSupplementary data are available at attachment online.

bioinformatics↗

bioRxiv2019

Insights from Fisher’s geometric model on the likelihood of speciation under different histories of environmental change

The formation of new species via the accumulation of incompatible genetic changes is thought to result either from ecologically-based divergent natural selection or the order by which mutations happen to arise, leading to different evolutionary trajectories even under similar selection pressures. There is growing evidence in support of both ecological speciation and mutation-order speciation, but how different environmental scenarios affect the rate of species formation remains underexplored. We use a simple model of optimizing selection on multiple traits ("Fishers geometric model") to determine the conditions that generate genetic incompatibilities in a changing environment. We find that incompatibilities are likely to accumulate in isolated populations adapting to different environments, consistent with ecological speciation. Incompatibilities also arise when isolated populations face a similar novel environment; these cases of mutation-order speciation are particularly likely when the environment changes rapidly and favors the accumulation of large-effect mutations. In addition, we find that homoploid hybrid speciation is likely to occur either when new environments arise in between the parental environments or when parental populations have accumulated large-effect mutations following a period of rapid adaptation. Our results indicate that periods of rapid environmental change are particularly conducive to speciation, especially mutation-order or hybrid speciation.

evolutionary biology↗

bioRxiv2019

Intra-bin structural variant detection for whole genome sequencing data using U-net

MotivationFor short-read sequencing, read-depth based structural variant (SV) callers are difficult to find single-nucleotide-resolution breakpoints due to the bin-size limitation. ResultsIn this paper, we present RDBKE to enhance the breakpoint resolution of read-depth SV callers using deep segmentation model UNet. We show that UNet can be trained with a small amount of data and applied for breakpoint enhancement both in-sample and cross-sample. On both simulation and real data, RDBKE significantly increases the number of SVs with more precise breakpoints. Availabilitysource code of RDBKE is available at https://github.com/yaozhong/deepIntraSV Contactyaozhong@ims.u-tokyo.ac.jp

bioinformatics↗

bioRxiv2018

ALPHLARD: a Bayesian method for analyzing HLA genes from whole genome sequence data

Although human leukocyte antigen (HLA) genotyping based on amplicon, whole exome sequence (WES), and RNA sequence data has been achieved in recent years, accurate genotyping from whole genome sequence (WGS) data remains a challenge due to the low depth. Furthermore, there is no method to identify the sequences of unknown HLA types not registered in HLA databases. We developed a Bayesian model, called ALPHLARD, that collects reads potentially generated from HLA genes and accurately determines a pair of HLA types for each of HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1 genes at 6-digit resolution. Furthermore, ALPHLARD can detect rare germline variants not stored in HLA databases and call somatic mutations from paired normal and tumor sequence data. We illustrate the capability of ALPHLARD using 253 WES data and 25 WGS data from Illumina platforms. By comparing the results of HLA genotyping from SBT and amplicon sequencing methods, ALPHLARD achieved 98.8% for WES data and 98.5% for WGS data at 4-digit resolution. We also detected three somatic point mutations and one case of loss of heterozygosity in the HLA genes from the WGS data. ALPHLARD showed good performance for HLA genotyping even from low-coverage data. It also has a potential to detect rare germline variants and somatic mutations in HLA genes. It would help to fill in the current gaps in HLA reference databases and unveil the immunological significance of somatic mutations identified in HLA genes.

bioinformatics↗

bioRxiv2018

Immuno-genomic PanCancer Landscape Reveals Diverse Immune Escape Mechanisms and Immuno-Editing Histories

Immune reactions in the tumor micro-environment are one of the cancer hallmarks and emerging immune therapies have been proven effective in many types of cancer. To investigate cancer genome-immune interactions and the role of immuno-editing or immune escape mechanisms in cancer development, we analyzed 2,834 whole genomes and RNA-seq datasets across 31 distinct tumor types from the PanCancer Analysis of Whole Genomes (PCAWG) project with respect to key immuno-genomic aspects. We show that selective copy number changes in immune-related genes could contribute to immune escape. Furthermore, we developed an index of the immuno-editing history of each tumor sample based on the information of mutations in exonic regions and pseudogenes. Our immuno-genomic analyses of pan-cancer analyses have the potential to identify a subset of tumors with immunogenicity and diverse background or intrinsic pathways associated with their immune status and immuno-editing history.

genomics↗

bioRxiv2017

Interactions between immunotoxicants and parasite stress: implications for host health

Many organisms face a wide variety of biotic and abiotic stressors which reduce individual survival, interacting synergistically to further reduce fitness. Here we studied the effects of two such synergistically interacting stressors; immunotoxicant exposure and parasite infection. We model the dynamics of a within-host infection and the associated immune response of an individual. We consider both the indirect sub-lethal effects on immunosuppression and the direct effects on health and mortality of individuals exposed to toxicants. We demonstrate that sub-lethal exposure to toxicants can promote infection through the suppression of the immune system. This happens through the depletion of the immune response which causes rapid proliferation in parasite load. In addition, high toxicant exposure can alter cellular regulation and cause the breakdown of normal healthy tissue, from which we infer higher mortality risk of the host. We classify this breakdown into three phases of increasing toxicant stress, and demonstrate the range of conditions under which toxicant exposure causes failure at the within-host level. These phases are determined by the relationship between the immunity status, overall cellular health and the level of toxicant exposure. We discuss the implications of our model in the context of individual honey bee health. Our model provides an assessment of how pesticide stress and infection interact to cause the synergistic breakdown of the within-host dynamics of individual honey bees.

epidemiology↗

bioRxiv2017

Capturing the Difference in Humoral Immunity between Normal and Tumor Environments from RNA Sequences of B-Cell Receptors Using Supervised Machine Learning

The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for tumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such as the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments. However, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more difficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore the possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using supervised machine learning methods applied to RNA sequences of BCRs/Igs.\n\nRNA sequences of BCRs/Igs were obtained from matched normal and tumor specimens from 90 gastric cancer patients. BCR/Ig-features obtained in Rep-Seq were used to classify individual BCR/Ig sequences into normal or tumor classes. Different machine learning models using various features were constructed as well as gradient boosting machine (GBM) classifier combining these models. The results demonstrated that BCR/Ig sequences between normal and tumor microenvironments exhibit their differences. Next, by using a GBM trained to classify individual BCR/Ig sequences, we tried to classify sets of BCR/Ig sequences into normal or tumor classes. As a result, an area under the curve (AUC) value of 0.826 was achieved, suggesting that BCR/Ig repertoires have distinct sequence-level features in normal and tumor tissues.\n\nTo the best of our knowledge, this is the first study to show that BCR/Ig sequences derived from tumor and normal tissues have globally distinct patterns, and that these tissues can be effectively differentiated using BCR/Ig repertoires.

bioinformatics↗