Biology subjects

Shuto, H.

Publications and source records attributed to Shuto, H..

3 recordsLinked to original sources

bioRxiv2026

Inflammatory signaling drives SQSTM1-dependent suppression of interferon-γ-induced MHC class I

An immune-inflamed tumor microenvironment features interferon-{gamma} (IFN-{gamma}) and inflammatory cytokines, yet how cancer cells integrate these opposing signals to regulate immunogenicity remains poorly understood. Here, we investigated the effect of interleukin-1{beta} (IL-1{beta}) on IFN-{gamma}-induced major histocompatibility complex class I (MHC-I) expression in non-small cell lung cancer (NSCLC) cells. IL-1{beta} suppressed IFN-{gamma}-induced surface MHC-I expression despite preserved or enhanced expression of antigen presentation-related genes, indicating post-transcriptional regulation. This effect emerged 12-24 h after stimulation and accompanied accelerated MHC-I turnover without altering total protein abundance. RNA sequencing identified selective induction of autophagy-related genes under combined IFN-{gamma} and IL-1{beta} stimulation. Immunofluorescence and biochemical analyses demonstrated MHC-I redistribution to autophagosome-lysosome compartments. Mechanistically, the selective autophagy receptor SQSTM1 associated with MHC-I and was required for its surface downregulation, whereas NBR1 depletion and autophagy or lysosomal inhibition failed to restore MHC-I expression, indicating a nonredundant role for SQSTM1. These findings identify post-translational MHC-I trafficking as an underappreciated regulatory layer linking inflammatory signaling to the canonical IFN-{gamma}-MHC-I axis and reveal an SQSTM1-dependent mechanism controlling MHC-I surface availability in immune-inflamed tumors.

immunology↗

bioRxiv2026

MUSE enables cross-species multi-omics integration that incorporates transcriptional regulatory modules

Recent advances in evolutionary biology and biomedical research have promoted comparative analyses of cellular states and developmental processes across species, leading to the development of numerous cross-species alignment methods based on scRNA-seq data. However, alignments relying solely on RNA expression are strongly driven by lineage signals and cell typespecific transcriptional programs. As a result, they are limited in their ability to identify conserved regulatory modules across species and to compare regulatory logic beyond developmental lineages, thereby constraining biological interpretability. Meanwhile, recent technological developments have enabled the acquisition of multi-omics data, including chromatin accessibility, making it increasingly feasible to analyze and interpret conservation at the level of regulatory modules across species. Nevertheless, computational methods that can integratively handle such heterogeneous omics data and enable cross-species comparative analysis in a unified framework remain insufficiently established. To address this challenge, we propose Multi-omics Unified embedding across Species (MUSE), a novel framework for integrating multi-omics data across species. MUSE constructs a graph that captures relationships among features both within and across species, and learns a shared latent space based on this graph structure. By leveraging this integrated graph-based representation, MUSE enables cross-species alignment that preserves species-specific characteristics while reflecting similarities not only at the level of gene expression and chromatin states but also at the level of regulatory modules.

bioinformatics↗

bioRxiv2025

Hidden Markov Models Reveal Behavioral State Dynamics in Depth-Related Locomotion in Mice

Understanding how mice process and respond to visual depth cues is crucial for studying visual perception, yet traditional behavioral analyses often miss key aspects of this process, such as the dynamic transitions between behavioral states, the influence of environmental context, and the integration of multiple spatial cues that shape depth-related behaviors. Here we demonstrate that mouse responses to visual depth cues are more sophisticated than previously recognized, involving both direct avoidance behaviors and complex modulations of exploratory patterns. By combining a modified circular apparatus with Hidden Markov Model analysis, we reveal that mice transition between three distinct behavioral states--resting, exploring, and navigating--in response to visual depth cues. Using this framework, we uncover several fundamental aspects of mouse visual processing: depth perception has an optimal range of spatial frequencies, with strongest responses to patterns between 6-8 cm; visual processing integrates multiple spatial cues rather than triggering simple avoidance; and initial strong cliff-avoidance responses evolve into more nuanced behavioral adaptations over time. Comparisons between wild-type C57BL/6J mice (Mus musculus), retinal degeneration models (rd1-2J, C57BL/6J background, Mus musculus), and control conditions confirm that these behavioral patterns specifically reflect visual processing rather than general exploratory behavior. These findings reveal that mouse depth perception involves sophisticated neural processing that modulates overall exploratory behavior rather than simply triggering avoidance responses. Our approach establishes a new framework for analyzing complex behavioral sequences in neuroscience research, demonstrating how refined behavioral analysis can reveal previously undetectable aspects of sensory processing.

animal behavior and cognition↗