Biology subjects

Matsumoto, H.

Publications and source records attributed to Matsumoto, H..

29 records · Page 2Linked to original sources

bioRxiv2022

PhyGraFT: a network-based method for phylogenetic trait analysis

With the determination of numerous viral and bacterial genome sequences, phylogeny-trait associations are now being studied. In these studies, phylogenetic trees were first reconstructed, and trait data were analyzed based on the reconstructed tree. However, in some cases, such as fast evolution sequences and gene-sharing network data, reconstructing the phylogenetic tree is challenging. In such cases, network-thinking, instead of tree-thinking, is gaining attention. Here, we propose a novel network-thinking approach, PhyGraFT, to analyze trait data from the network. We validated that PhyGraFT can find phylogenetic signals and associations of traits with the simulation dataset. We applied PhyGraFT for influenza type A and virome gene-sharing datasets. As a result, we identified several evolutionary structures and their associated traits. Our approach is expected to provide novel insights into network-thinking not only for typical phylogenetics but also for various biological data, such as antibody evolution.

bioinformatics↗

bioRxiv2022

MOCCS profile analysis clarifies the cell type dependency of transcription factor-binding sequences and cis-regulatory SNPs in humans

Transcription factors (TFs) show heterogeneous DNA-binding specificities in individual cells and whole organisms in natural conditions): de novo motif discovery usually provides multiple motifs even from a single ChIP-seq sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored. Here, we propose MOCCS profiles, the new representation of DNA-binding specificity of TFs, which describes a ChIP-seq sample as a profile of TF-binding specificity scores (MOCCS2scores) for every k-mer sequence. Using our k-mer-based motif discovery method MOCCS2, we systematically computed MOCCS profiles for >10,000 human TF ChIP-seq samples across diverse TFs and cell types. Comparison of MOCCS profiles revealed the global distributions of DNA-binding specificities, and found that one-third of the analyzed TFs showed differences in DNA-binding specificities across cell types. Moreover, we showed that the differences in MOCCS2scores ({Delta}MOCCS2scores) predicted the effect of variants on TF binding, validated by in vitro and in vivo assay datasets. We also demonstrate {Delta}MOCCS2scores can be used to interpret non-coding GWAS-SNPs as TF-affecting SNPs and provide their candidate responsible TFs and cell types. Our study provides the basis for investigating gene expression regulation and non-coding disease-associated variants in humans.

bioinformatics↗

bioRxiv2020

Novel metric for hyperbolic phylogenetic tree embeddings

Advances in experimental technologies such as DNA sequencing have opened up new avenues for the applications of phylogenetic methods to various fields beyond their traditional application in evolutionary investigations, extending to the fields of development, differentiation, cancer genomics, and immunogenomics. Thus, the importance of phylogenetic methods is increasingly being recognized, and the development of a novel phylogenetic approach can contribute to several areas of research. Recently, the use of hyperbolic geometry has attracted attention in artificial intelligence research. Hyperbolic space can better represent a hierarchical structure compared to Euclidean space, and can therefore be useful for describing and analyzing a phylogenetic tree. In this study, we developed a novel metric that considers the characteristics of a phylogenetic tree for representation in hyperbolic space. We compared the performance of the proposed hyperbolic embeddings, general hyperbolic embeddings, and Euclidean embeddings, and confirmed that our method could be used to more precisely reconstruct evolutionary distance. We also demonstrate that our approach is useful for predicting the nearest-neighbor node in a partial phylogenetic tree with missing nodes. This study highlights the utility of adopting a geometric approach for further advancing the applications of phylogenetic methods. The demo code is attached as a supplementary file in a compiled jupyter notebook. The code used for analyses is available on GitHub at https://github.com/hmatsu1226/HyPhyTree.

bioinformatics↗

bioRxiv2020

Distinct temporal difference error signals in dopamine axons in three regions of the striatum in a decision-making task

Different regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum, while mice performed in a perceptual and value-based decision task. Surprisingly, dopamine axon activity was similar across all three areas. At a glance, the activity multiplexed different variables such as stimulus-associated values, confidence and reward feedback at different phases of the task. Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes in the expected reward, i.e. the temporal difference error. A major difference between these areas was the overall activity level of reward responses: reward responses in dorsolateral striatum (DLS) were positively shifted, lacking inhibitory responses to negative prediction error. Tenets of habit and skill can be explained by this positively biased dopamine signal in DLS.

neuroscience↗

bioRxiv2020

Persistent thermal input controls steering behavior in Caenorhabditis elegans

Motile organisms actively detect environmental signals and migrate to a preferable environment. Especially, small-size animals convert subtle difference in sensory input into orientation behavioral output for directly steering toward a destination, but the neural mechanisms underlying steering behavior remain elusive. Here, we analyze a C. elegans thermotactic behavior in which a small number of neurons are shown to mediate steering toward a destination temperature. We construct a neuroanatomical model and use an evolutionary algorithm to find configurations of the model that reproduce empirical thermotactic behavior. We find that, in all the evolved models, steering rates are modulated by persistent thermal signals sensed through forward locomotion. Persistent temperature increment lessens steering rates resulting in straight movement of model worms, whereas temperature decrement enlarges steering rates resulting in curvy movement. This relationship between temperature change and steering rates reproduces the empirical thermotactic migration up thermal gradients and steering bias toward higher temperature. Further, spectrum decomposition of neural activities in model worms show that thermal signals are transmitted from a sensory neuron to motor neurons on the longer time scale than sinusoidal locomotion of C. elegans. Our results suggest that employments of persistent sensory signals enable small-size animals to steer toward a destination in natural environment with variable, noisy, and subtle cues. Author summaryA free-living nematode Caenorhabditis elegans memorizes an environmental temperature and steers toward the remembered temperature on a thermal gradient. How does the C. elegans brain, consisting of 302 neurons, achieve this thermotactic steering behavior? Here, we address this question through neuroanatomical modeling and simulation analyses. We find that persistent thermal input modulates steering rates of model worms; worms run straight when they move up to a destination temperature, whereas run crookedly when move away from the destination. As a result, worms steer toward the destination temperature as observed in experiments. Our analysis also shows that persistent thermal signals are transmitted from a thermosensory neuron to dorsal and ventral neck motor neurons, regulating the balance of dorsoventral muscle contractions of model worms and generating steering behavior. This study indicates that C. elegans can steer toward a destination temperature without processing acute thermal input that informs to which direction it should steer. Such indirect mechanism of steering behavior is potentially employed in other motile organisms.

neuroscience↗

bioRxiv2019

Repeated phenotypic selection for cuticular blackness of armyworm larvae decreased stress resistance

Armyworm Mythimna separata larvae show changes in cuticle darkening depending on population densities and are roughly categorized into two phenotypes, a pale brown solitary type and black-colored gregarious type. Although the color difference in both larval types is apparent, it remains ambiguous whether any change in physiological traits accompanies the cuticle darkening. To answer this query, we repeated genetic selection of the blackness phenotype over one hundred generations in our laboratory colony and produced a black-colored (BL) strain. Comparison between non-selected control (CTL) and BL strains revealed an increased fecundity and adult life span in the BL strain compared with the CTL strain. In contrast, BL strain larvae were found to be significantly more sensitive to heat stress than CTL strain larvae. Hemolymph reactive oxygen species (ROS) levels were higher in the BL strain than in the CTL strain irrespective of stress. Antioxidant activities of the hemolymph were not significantly different between the two strains under non-stress condition, but the activities increased to higher levels in the CTL strain than those in the BL strain after heat stress. Activities and gene expression levels of antioxidant enzymes such as catalase and superoxide dismutase (SOD) in the fat body were significantly higher in CTL strain larvae than in BL strain larvae after heat treatment. Analysis of heat stress tolerance of F1 hybrids of CTL and BL strain adults showed that phenotype of stress tolerance was inherited maternally. These results indicate a trade-off between reproductive activity and stress resistance during repeated genetic selection.\n\nSummary statementDiscrete cuticle color change from whitish to blackish, which was created by repeating the reciprocal crossing of selected dark-colored individuals, increased fecundity but lowered stress tolerance in the armyworm.

physiology↗

bioRxiv2019

A NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data

Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types, and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses. Accordingly, we have developed an approach to discover overlooked differentially expressed (DE) gene regions that complements annotation-based methods. We applied our algorithm to two datasets and discovered several intriguing DE transcripts, including a transcript related to the modulation of neural stem/progenitor cell differentiation.

bioinformatics↗

bioRxiv2018

Blocking the Necroptosis Pathway Decreases RPE and Photoreceptor Damage Induced by NaIO3

PurposeSodium iodate (NaIO3) has been extensively used as a retinotoxin to induce RPE cell damage and degeneration of photoreceptors in vitro and in vivo. RIP-Kinase dependent programmed necrosis is an important redundant cell death pathway involved in photoreceptor cell death. We wanted to determine whether these pathways are actively involved in RPE and photoreceptor cell death after NaIO3 insult.\n\nMethodsARPE-19 cells were exposed to different concentrations of NaIO3 in the presence or absence of various concentrations of a RIPK inhibitor (Nec-1) or a pan-caspase inhibitor (Z-VAD), individually or combined. Cell death was determined at different time points by MTT (Sigma-Aldrich), LDH (Promega) and TUNEL (Millipore) assay. C57BL/6 and RIP3-/- mice were treated with a peritoneal injection of NaIO3 and eyes were enucleated at day 3 or 7. TUNEL staining was used to evaluate photoreceptor cell death. Photoreceptor cell loss was evaluated by measuring the thickness of outer nuclear layer (ONL). Microglia in the ONL were quantified in a retinal whole mount with Iba-1 antibody. RPE degeneration was also assessed in a RPE whole mount, with ZO-1 antibody.\n\nResultsNaIO3 resulted in significant cell death of ARPE-19 cells. Treatment with Nec-1 resulted in better protection than treatment with Z-VAD (P<0.01). A synergistic protective effect was observed when co-treating the cells with Nec-1 and Z-VAD. Nec-1 treatment also decreased the ARPE-19 mitochondrial damage caused by NaIO3. In vivo administration of NaIO3 resulted in significant RPE and photoreceptor destruction with substantial inflammatory cell infiltration. RIP3 knockout animals displayed considerably less RPE and photoreceptor cell loss, as well as drastically less inflammation.\n\nConclusionsProgrammed necrosis is an important cell death pathway mediating NaIO3 RPE and photoreceptor cell toxicity. Blocking the necroptosis pathway may serve as a novel therapeutic strategy for various RPE degenerative diseases.

biochemistry↗

bioRxiv2017

An efficient gene regulatory network inference algorithm for early Drosophila melanogaster embryogenesis

The spatial patterns of gene expression in early Drosophila melanogaster embryogenesis have been studied experimentally and theoretically to reveal the molecular basis of morphogenesis. In particular, the gene regulatory network (GRN) of gap genes has been investigated through mathematical modeling and simulation. Although these simulation-based approaches are useful for describing complex dynamics and have revealed several important regulations in spatial patterning, they are computationally intensive because they optimize GRN with iterative simulation. Recently, the advance of experimental technologies is enabling the acquisition of comprehensive spatial expression data, and an efficient algorithm will be necessary to analyze such large-scale data. In this research, we developed an efficient algorithm to infer the GRN based on a linear reaction-diffusion model. First, we qualitatively analyzed the GRNs of gap genes and pair-rule genes based on our algorithm and showed that two mutual repressions are fundamental regulations. Then, we inferred the GRN from gap gene data, and identified asymmetric regulations in addition to the two mutual repressions. We analyzed the effect of these asymmetric regulations on spatial patterns, and showed that they have the potential to adjust peak position. Our algorithm runs in sub-second time, which is significantly smaller than the runtime of simulation-based approaches (between 8 and 160 h, for exmaple). Neverthe-less, our inferred GRN was highly correlated with the simulation-based GRNs. We also analyzed the gap gene network of Clogmia albipunctata and showed that different mutual repression regulations might be important in comparison with those of Drosophila melanogaster. As our algorithm can infer GRNs efficiently and can be applied to several different network analysis, it will be a valuable approach for analyzing large-scale data.

bioinformatics↗

bioRxiv2017

Correction of autoimmune IL2RA mutations in primary human T cells using non-viral genome targeting

Human T cells are central to physiological immune homeostasis, which protects us from pathogens without collateral autoimmune inflammation. They are also the main effectors in most current cancer immunotherapy strategies1. Several decades of work have aimed to genetically reprogram T cells for therapeutic purposes2-5, but as human T cells are resistant to most standard methods of large DNA insertion these approaches have relied on recombinant viral vectors, which do not target transgenes to specific genomic sites6, 7. In addition, the need for viral vectors has slowed down research and clinical use as their manufacturing and testing is lengthy and expensive. Genome editing brought the promise of specific and efficient insertion of large transgenes into target cells through homology-directed repair (HDR), but to date in human T cells this still requires viral transduction8, 9. Here, we developed a non-viral, CRISPR-Cas9 genome targeting system that permits the rapid and efficient insertion of individual or multiplexed large (>1 kilobase) DNA sequences at specific sites in the genomes of primary human T cells while preserving cell viability and function. We successfully tested the potential therapeutic use of this approach in two settings. First, we corrected a pathogenic IL2RA mutation in primary T cells from multiple family members with monogenic autoimmune disease and demonstrated enhanced signalling function. Second, we replaced the endogenous T cell receptor (TCR) locus with a new TCR redirecting T cells to a cancer antigen. The resulting TCR-engineered T cells specifically recognized the tumour antigen, with concomitant cytokine release and tumour cell killing. Taken together, these studies provide preclinical evidence that non-viral genome targeting will enable rapid and flexible experimental manipulation and therapeutic engineering of primary human immune cells.

genetics↗

bioRxiv2016

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.\n\nIn this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.\n\nThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.

bioinformatics↗