Biology subjects

Kuwahara, H.

Publications and source records attributed to Kuwahara, H..

6 recordsLinked to original sources

bioRxiv2023

Emergence of putative energy parasites within Clostridia revealed by genome analysis of a novel endosymbiotic clade

The Clostridia is a dominant bacterial class in the guts of various animals and are considered to nutritionally contribute to the animal host. Here, we discovered clostridial endosymbionts of cellulolytic protists in termite guts, which have never been reported with evidence. We obtained (near-)complete genome sequences of three endosymbiotic Clostridia, each associated with a different parabasalid protist species with various infection rates: Trichonympha agilis, Pseudotrichonympha grassii, and Devescovina sp. All these protists are previously known to harbor permanently-associated, mutualistic Endomicrobia or Bacteroidales that supplement nitrogenous compounds. The genomes of the endosymbiotic Clostridia were small in size (1.0-1.3 Mbp) and exhibited signatures of an obligately-intracellular parasite, such as an extremely limited capability to synthesize amino acids, cofactors, and nucleotides and a disrupted glycolytic pathway with no known net ATP-generating system. Instead, the genomes encoded ATP/ADP translocase and, interestingly, regulatory proteins that are unique to eukaryotes in general and are possibly used to interfere with host cellular processes. These three genomes formed a clade with metagenome-assembled genomes (MAGs) derived from the guts of other animals, including human and ruminants, and the MAGs shared the characteristics of parasites. Gene flux analysis suggested that the acquisition of the ATP/ADP translocase gene in a common ancestor was probably key to the emergence of this parasitic clade. Taken together, we provide novel insights into the multilayered symbiotic system in the termite gut by adding the presence of parasitism and present an example of the emergence of putative energy parasites from a dominant gut bacterial clade.

microbiology↗

bioRxiv2021

Deep learning identifies and quantifies recombination hotspot determinants

Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we develop a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes, and species. In addition to being able to identify hotspot regions and the well-known determinants accurately, RHSNet is sensitive to the difference between different PRDM9 alleles and different sexes, and can generalize to PRDM9-lacking species. The cross-sex, cross-population, and cross-species studies suggest that the proposed method has the potential to identify and quantify the evolutionary determinant motifs. TeaserRHSNet can accurately identify and quantify recombination hotspot determinants across different studies, sexes, populations, and species.

bioinformatics↗

bioRxiv2019

Analysis of the effects of related fingerprints on molecular similarity using an eigenvalue entropy approach

Two-dimensional (2D) chemical fingerprints are widely used as numerical features for the quantification of structural similarity of chemical compounds, which is an important step in similarity-based virtual screening (VS). Here, using an eigenvalue-based entropy approach, we sought to identify 2D fingerprints with little to no contribution to shaping the eigenvalue distribution of the feature matrix as related fingerprints and examined the degree to which these related 2D fingerprints influence molecular similarity scores via Tanimoto coefficient. We found that there are many related fingerprints in publicly available fingerprint schemes and that their presence in the feature set tends to decrease the similarity scores. Our results have implication in the optimal selection of 2D fingerprints and the identification of potential hits for compounds with target biological activity in VS.

bioinformatics↗

bioRxiv2019

An RNA-seq quantification method for analysis of transcriptional aberrations

Transcriptome level analysis has been shown to have the great potential for clinical utility. Here, we introduce omega, a between-sample RNA-seq quantification to estimate the abundance level of functional mRNAs which is suitable for analysis of transcriptional aberrations and molecular diagnostics of a range of genetic diseases. By using five diagnosed cases of Mendelian diseases as a case study, we show evidence that omega can improve the signal to detect genes with deleterious transcriptional aberrations and drastically reduce the disease-gene search space.

bioinformatics↗

bioRxiv2019

Stable maintenance of a hidden switch as a way to increase the gene expression stability

In response to severe stress, wild-type organisms can release alternative phenotypes that are hidden under normal conditions and are associated with underlying genetic variations. A number of such stress-induced phenotypic switchings have been reported to be based on reactivation of hidden thresholds; under the normal condition, a high barrier separating alternative phenotypes ensures the expression of single discrete phenotype, but a severe perturbation can lower the barrier to a level at which to expose cryptic alternatives. While the importance of such threshold-based switches as the mechanism to generate adaptive novelties under variable environments has been appreciated, it still remains elusive how naturally selected organisms can maintain the phenotypic switching capability when such switching has been disused for a long period of time. Here, through the use of computer simulation, we analyzed adaptive evolution of gene circuits under stabilizing selection. We found that different strategies evolved to acquire reduced levels of gene expression noise around the optimum expression level. To incrementally improve the gene expression stability from a founding population with bistable individuals, the evolution consistently took the direction to raise the height of the potential barrier of bistable systems. Our results demonstrate that hidden phenotypic switches can be stably maintained during environmental stasis, facilitating the release of potentially adaptive phenotypic alternatives in the event of substantial perturbations.

evolutionary biology↗

bioRxiv2019

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

MotivationProper prioritization of candidate genes is essential to the genome-based diagnostics of a range of genetic diseases. However, it is a highly challenging task involving limited and noisy knowledge of genes, diseases and their associations. While a number of computational methods have been developed for the disease gene prioritization task, their performance is largely limited by manually crafted features, network topology, or pre-defined rules of data fusion. ResultsHere, we propose a novel graph convolutional network-based disease gene prioritization method, PGCN, through the systematic embedding of the heterogeneous network made by genes and diseases, as well as their individual features. The embedding learning model and the association prediction model are trained together in an end-to-end manner. We compared PGCN with five state-of-the-art methods on the Online Mendelian Inheritance in Man (OMIM) dataset for tasks to recover missing associations and discover associations between novel genes and diseases. Results show significant improvements of PGCN over the existing methods. We further demonstrate that our embedding has biological meaning and can capture functional groups of genes. AvailabilityThe main program and the data are available at https://github.com/lykaust15/Disease_gene_prioritization_GCN.

bioinformatics↗