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Kishimoto, A.

Publications and source records attributed to Kishimoto, A..

2 recordsLinked to original sources

Nationwide multi-omics profiling of Japanese jack mackerel reveals geographic gut microbiome structuring despite host panmixia

Host genetic markers often fail to resolve regional origins in highly connected or panmictic marine species. The Japanese jack mackerel, Trachurus japonicus, is a commercially important fishery species around Japan that shows little or no detectable population structure. Here, we used nationwide multi-omics profiling to compare host genomic variation and gut microbiome composition in wild T. japonicus collected from coastal regions across Japan. We generated MIG-seq data for 43 individuals and 16S rRNA gene profiles for 24 individuals; after quality filtering, 19 individuals remained for matched host-microbiome comparison. Genome-wide host SNP analyses showed weak or absent geographic population structure, consistent with previous evidence of panmixia in Japanese waters. In contrast, gut microbiome composition showed geographic structuring based on Bray-Curtis dissimilarity and PERMANOVA, and this pattern was not explained by proximity to river mouths or host-related variables. Locality- or individual-associated bacterial lineages contributed to the observed differences in the microbiome, while chloroplast-associated and Cyanobacteria-assigned ASVs suggested recent dietary or environmental input. These results indicate that gut microbiome can show regional biological variation not apparent from host genetic markers alone. Our study provides a proof-of-concept example of integrating host genomics and gut microbiome profiling to evaluate regional characteristics and origins in highly connected marine animals.

microbiology↗

Identification of bacterial drug-resistant cells by the convolutional neural network in transmission electron microscope images

The emergence of bacteria that are resistant to antibiotics is common in areas where antibiotics are used widely. The current standard procedure for detecting bacterial drug resistance is based on bacterial growth under antibiotic treatments. Here we describe the morphological changes in enoxacin-resistant Escherichia coli cells and the computational method used to identify these resistant cells in transmission electron microscopy (TEM) images without using antibiotics. Our approach was to create patches from TEM images of enoxacin-sensitive and enoxacin-resistant E. coli strains, use a convolutional neural network for patch classification, and identify the strains on the basis of the classification results. The proposed method was highly accurate in classifying cells, achieving an accuracy rate of 0.94. Using a gradient-weighted class activation mapping to visualize the region of interest, enoxacin-resistant and enoxacin-sensitive cells were characterized by comparing differences in the envelope. Moreover, Pearsons correlation coefficients suggested that four genes, including lpp, the gene encoding the major outer membrane lipoprotein, were strongly associated with the image features of enoxacin-resistant cells.

systems biology↗