Biology subjects

Nishimura, L.

Publications and source records attributed to Nishimura, L..

4 recordsLinked to original sources

bioRxiv2026

Quantitative host-response mapping enables phenotypic profiling of viruses detected by virome sequencing

Animal-derived viruses have repeatedly caused human outbreaks, motivating efforts to identify and characterize viruses circulating in animals. Virome sequencing has accelerated animal virus discovery, but phenotypic characterization remains difficult to scale. Although generated for viral detection, virome RNA-seq data also capture host transcriptional responses to infection. Here, we developed ViroChart, a comparative transcriptomic framework that quantifies activities across biological pathways and maps diverse mammalian host responses into a common, interpretable low-dimensional space. ViroChart enables clustering of infected samples by host response pattern and quantitative assessment of host response similarity between samples. In virome data sets from wild and farmed animals, some lung samples infected with poorly characterized betacoronaviruses showed host responses resembling those induced by viruses capable of causing fatal disease. ViroChart also revealed host-response heterogeneity in natural infections: among rats with zoonotic rodent-borne viruses, most showed weak responses, consistent with asymptomatic infection, but some showed pronounced inflammation characteristic of acute infection. ViroChart thus enables phenotypic characterization and prioritization of newly detected viruses by leveraging the same RNA-seq data from which their genomes were detected.

microbiology↗

bioRxiv2026

DIANA: Deep Learning Identification and Assessment of Ancient DNA

The field of ancient metagenomics provides insights into past microbiomes, but with a growing dataset size, methods that rely on reference databases have limited scope. Here, we introduce DIANA, a multi-task neural network that predicts key metadata categories from unitig abundances. Trained on 2,597 run accessions (1.72 Tbp of assembled unitig sequences), DIANA accurately identifies sample host (94.6%), community type (90.0%), and material (88.9%) on held-out test data and demonstrates robust generalisation on an independent validation set. A key innovation is DIANAs ability to perform semantic generalisation, correctly classifying samples with labels unseen during training -- such as novel subspecies -- to their appropriate parent categories. By leveraging both known and uncharacterized genomic sequences, DIANA provides a rapid, data-driven system for metadata validation and quality control, accelerating discovery in ancient metagenomics research.

bioinformatics↗

bioRxiv2026

Host innate immune response profiling reveals hidden viral infections across diverse animal species

Virus discovery using RNA-seq data from wildlife and livestock offers a powerful strategy for identifying unknown pathogens with pandemic potential. However, conventional approaches rely on homology-based searches that have limited sensitivity for highly divergent viruses, are computationally intensive at scale, and cannot distinguish true infections from contamination. Viral infection induces interferon-stimulated genes (ISGs), key components of the frontline antiviral defense, and their expression serves as a robust indicator of viral infection. Here, we developed a host-response-based virus discovery framework that rapidly quantifies ISG expression and predicts viral infection status. Applying this framework to [~]210,000 RNA-seq data sets from diverse mammalian and avian species, we identified hidden viral infections across diverse hosts, including those caused by highly divergent viruses missed by a conventional approach. Our framework complements existing virus discovery strategies by adding host innate immune response context and enabling computationally efficient prescreening for scalable viral surveillance. HighlightsO_LIHost response-based virus discovery in wildlife and livestock RNA-seq data C_LIO_LIQuantify interferon-stimulated gene (ISG) expression and predict viral infection C_LIO_LIAnalysis of [~]210,000 RNA-seq data sets reveals hidden viral infections C_LIO_LIDetects highly divergent viruses and scalable viral surveillance through rapid prescreening C_LI

microbiology↗

bioRxiv2020

De novo virus inference and host prediction from metagenome using CRISPR spacers

Viruses are the most numerous biological entity, existing in all environments and infecting all cellular organisms. Compared with cellular life, the evolution and origin of viruses are poorly understood; viruses are enormously diverse and most lack sequence similarity to cellular genes. To uncover viral sequences without relying on either reference viral sequences from databases or marker genes known to characterize specific viral taxa, we developed an analysis pipeline for virus inference based on clustered regularly interspaced short palindromic repeats (CRISPR). CRISPR is a prokaryotic nucleic acid restriction system that stores memory of previous exposure. Our protocol can infer viral sequences targeted by CRISPR and predict their hosts using unassembled short-read metagenomic sequencing data. Analysing human gut metagenomic data, we extracted 11,391 terminally redundant CRISPR-targeted sequences which are likely complete circular genomes of viruses or plasmids. The sequences include 257 complete crAssphage family genomes, 11 genomes larger than 200 kilobases, 766 genomes of Microviridae species, 114 genomes of Inoviridae species and many entirely novel genomes of unknown taxa. We predicted the host(s) of approximately 70% of discovered genomes by linking protospacers to taxonomically assigned CRISPR direct repeats. These results support that our protocol is efficient for de novo inference of viral genomes and host prediction. In addition, we investigated the origin of the diversity-generating retroelement (DGR) locus of the crAssphage family. Phylogenetic analysis and gene locus comparisons indicate that DGR is orthologous in human gut crAssphages and shares a common ancestor with baboon-derived crAssphage; however, the locus has likely been lost in multiple lineages recently.

genomics↗