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Kawano-Sugaya, T.

Publications and source records attributed to Kawano-Sugaya, T..

4 recordsLinked to original sources

Chromosome organization of Entamoeba histolytica and Entamoeba dispar

Entamoeba histolytica is a clinically important pathogenic eukaryote and the causative agent of amoebic dysentery. Entamoeba dispar, a nonpathogenic commensal species that resides in the human colon, is the closest sibling species, and serves as an appropriate comparator for genome-wide analysis. Although the genome of E. histolytica is approximately 26.9 Mb, and the largest known genome within the genus, that of E. invadens, is approximately 40.9 Mb, obtaining high-quality assemblies in this genus has remained challenging due to extensive repetitive regions, tRNA gene arrays, and aneuploidy. Here, we used PacBio HiFi sequencing to assemble the genomes of the pathogenic E. histolytica and the nonpathogenic E. dispar. We reconstructed all 36 chromosomes of E. histolytica and 35 chromosomes of E. dispar, assembling each as a single continuous DNA sequence (contig). The two species exhibited high genome-wide nucleotide similarity and conserved synteny at the amino acid level. At one end of each chromosome, we identified tRNA arrays, whereas the opposite end lacked such arrays, resulting in an asymmetric chromosomal architecture. Analysis of unique-read depth revealed widespread aneuploidy in both species: E. histolytica is predominantly tetraploid, whereas E. dispar is diploid, a conclusion further supported by SNP allele-frequency distributions. These assemblies provide a robust foundation for comparative genomics in Entamoeba and offer detailed insights into chromosome-end structure and ploidy.

genomics↗

Draft genome of the marine Entamoeba species reveals reduction in the gene family repertoire associated with pathogenicity and lateral gene transfer for adaptation to the marine environment

Entamoeba is the amoebozoan parasite commonly found in the intestines of animals. E. marina is the first exception isolated from marine sediments, possibly adapting from animal intestines to the sea. However, the evolutionary process of E. marina remains uncertain due to the lack of a genome sequence. Here, we present the de novo genome and transcriptome of E. marina using Oxford Nanopore MinION and Illumina HiSeq/MiSeq. The genome of E. marina is approximately 37.5 Mbp in length and consisted of 202 contigs, which is the second longest followed by E. invadens. E. marina showed significant reduction in the major virulence-associated gene families, including cysteine proteases, lysosomal enzyme transporters, and surface galactose/N-acetylglucosamine-specific lectins, suggesting diversification, more specifically reduction of pathogenicity-related genes. Genome and RNA-seq analyses also indicated genes either conserved throughout eukaryotes or laterally transferred from prokaryotes, and potentially responsible for salt tolerance. Our study provides insights into the mechanism underlying the lifestyle changes in the evolution of parasitic eukaryotes.

genomics↗

Single Amplified Genome Catalog Reveals the Dynamics of Mobilome and Resistome in the Human Microbiome

The increase in metagenome-assembled genomes (MAGs) has significantly advanced our understanding of the functional characterization and taxonomic assignment within the human microbiome. However, MAGs, as population consensus genomes, often mask heterogeneity among species and strains, thereby obfuscating the precise relationships between microbial hosts and mobile genetic elements (MGEs). In contrast, single amplified genomes (SAGs) derived via single-cell genome sequencing can capture individual genomic content, including MGEs. We present the bbsag20 dataset, which encompasses 17,202 human-associated prokaryotic SAGs and 869 MAGs, spanning 647 gut and 312 oral bacterial species. The SAGs revealed diverse bacterial lineages and MGEs with a broad host range that were absent in the MAGs and traced the translocation of oral bacteria to the gut. Importantly, our SAGs linked individual mobilomes to resistomes and meticulously charted a dynamic network of antibiotic resistance genes (ARGs) on MGEs, pinpointing potential ARG reservoirs in the microbial community.

microbiology↗

Haplotype Explorer: an infection cluster visualization tool for spatiotemporal dissection of the COVID-19 pandemic

The worldwide eruption of COVID-19 that began in Wuhan, China in late 2019 reached 10 million cases by late June 2020. In order to understand the epidemiological landscape of the COVID-19 pandemic, many studies have attempted to elucidate phylogenetic relationships between collected viral genome sequences using haplotype networks. However, currently available applications for network visualization are not suited to understand the COVID-19 epidemic spatiotemporally, due to functional limitations That motivated us to develop Haplotype Explorer, an intuitive tool for visualizing and exploring haplotype networks. Haplotype Explorer enables people to dissect epidemiological consequences via interactive node filters to provide spatiotemporal perspectives on multimodal spectra of infectious diseases, including introduction, outbreak, expansion, and containment, for given regions and time spans. Here, we demonstrate the effectiveness of Haplotype Explorer by showing an example of its visualization and features. The demo using SARS-CoV-2 genome sequences is available at https://github.com/TKSjp/HaplotypeExplorer SummaryA lot of software for network visualization are available, but existing software have not been optimized to infection cluster visualization against the current worldwide invasion of COVID-19 started since 2019. To reach the spatiotemporal understanding of its epidemics, we developed Haplotype Explorer. It is superior to other applications in the point of generating HTML distribution files with metadata searches which interactively reflects GISAID IDs, locations, and collection dates. Here, we introduce the features and products of Haplotype Explorer, demonstrating the time-dependent snapshots of haplotype networks inferred from total of 4,282 SARS-CoV-2 genomes.

genomics↗