Biology subjects

Isagawa, T.

Publications and source records attributed to Isagawa, T..

6 recordsLinked to original sources

bioRxiv2026

Evo 2 Predicts Cardiomyopathy-Associated Variants and Elucidates Their Underlying Mechanisms

BackgroundAlthough advances in next-generation sequencing have accelerated the identification of genetic variants in cardiomyopathy, interpreting variants of uncertain significance (VUS) remains a clinical challenge. Evo 2 is a high-resolution genomic artificial intelligence model capable of predicting pathogenicity across large sequence contexts and enabling mechanistic interpretation; however, its application in cardiovascular genetics is limited. Here, we evaluated the utility of Evo 2 for assessing the pathogenicity and underlying mechanisms of cardiomyopathy-associated variants. MethodsWe used Evo 2 to predict the pathogenicity of single-nucleotide variants in cardiomyopathy-related genes listed on ClinVar. We assessed the ability of the model to identify characteristic structural features in both coding and noncoding regions using internal representation such as embeddings, and to infer the molecular mechanisms of variants within these regions. ResultsEvo 2 demonstrated high predictive accuracy for pathogenicity, achieving an AUROC of 0.983 and an AUPRC of 0.915. Notably, sparse autoencoders (SAEs) from embeddings identified features corresponding to higher-order structural features, including coiled-coil and actin-binding domains characteristic of cardiomyopathy-related proteins, and accurately detected mutations known to disrupt these domains. The model recognized the binding motif of the cardiac-enriched transcription factor TBX5 with SAEs and accurately predicted a single-nucleotide polymorphism affecting TBX5 binding affinity after supervised fine-tuning. ConclusionsEvo 2 demonstrated strong performance for both predicting pathogenicity and extracting biological features of cardiomyopathy-associated variants. It may represent a powerful emerging tool for evaluating VUS in cardiovascular medicine.

genomics↗

bioRxiv2026

The REFLEX system enables in vivo identification of perivascular angiogenic macrophages in the heart

Direct identification of physically interacting cells in vivo remains challenging because conventional interactome analyses infer signaling partners from transcriptomes and cannot reveal which cells are in direct contact. In pressure-overload induced cardiac remodeling, VEGF-A plays a central role in the maintenance of vascular integrity and cardiac function. However, the cell type which produces VEGF-A and how the VEGF-A peptide is delivered to vascular endothelial cells remains unclear. Here, we developed a genetically encoded platform that combines REFLEX mice with HUNTERuni-seq, enabling unbiased detection and transcriptional profiling of the cells that physically interact with vascular endothelial cells. The REFLEX and HUNTERuni-seq approach identified subpopulations of Vegfa positive macrophages which we named perivascular angiogenic macrophages (PVAMs). Although the amount of VEGF-A in PVAMs is small, loss of VEGF-A in PVAMs impaired angiogenesis and systolic function during pressure overload. We additionally show that direct contact between PVAMs and endothelial cells is critical for the delivery of VEGF-A to endothelial cells. Conventional interactome analysis predicted that cardiomyocytes as dominant sources of VEGF-A in the heart. However, cardiomyocyte Vegfa deletion had no effect on capillary density nor systolic function in a model of heart failure. These results suggest that VEGF-A signaling does not rely on free diffusion through the interstitium and that cellular proximity and physical contact between PVAMs and endothelial cells are the key determinants of effective signal delivery. Together, these findings establish REFLEX and HUNTERuni-seq as a versatile platform for uncovering biologically critical cell-to-cell interactions and provide new insight into intercellular communication in pathological tissue contexts.

cell biology↗

bioRxiv2026

Cell-autonomous thermogenesis of macrophage alters its antibacterial function

Dynamic temperature gradients exist across the bodies of endothermic animals, from the core to peripheral organ, resulting in the physiological cold environment in superficial regions. Consequently, macrophages distributed throughout the body must be able to adapt not only to thermoneutral conditions but also to colder environments. In fact, it is known that environmental temperature influences macrophage immune responses. However, the thermo-responsive mechanisms of macrophage have been largely unexplored. Here we show that macrophage themselves maintains intracellular temperature under physiological cold condition by increasing proton leak index (defined as mitochondrial proton leak per spare respiratory capacity). We further identified a contribution of ADP/ATP carrier (AAC) to this increase in proton leak index. This cell-autonomous thermogenesis pathway, which does not depend on neural or hormonal inputs, highlights the potential for local and organ-specific temperature regulation. Moreover, cold stress reduced mitochondrial membrane potential, which in turn suppressed the expression of the antimicrobial peptide Resistin-like molecule alpha (RETNLA) and diminished antibacterial properties. Together, these findings suggest that macrophages generate heat whereas compromising antibacterial properties, thereby increasing susceptibility to bacterial infection in physiological cold environment. This adaptation mechanism may underscore the important role of temperature homeostasis in non-adipocyte cells.

physiology↗

bioRxiv2025

PAH-former: Transfer Learning for Efficient Discovery of Pulmonary Arterial Hypertension-Associated Genes

Single-cell RNA sequencing (scRNA-seq) of patient samples holds promise for understanding disease mechanisms, but faces the challenge of excessive cost and effort in acquisition, processing, and data analysis, making it essential to leverage existing data. Pulmonary artery hypertension (PAH) is a refractory disease characterized by pulmonary vascular remodeling, and access to patient specimens is limited due to difficulties in tissue collection. In this study, we employed transfer learning with Geneformer, a deep learning algorithm pre-trained with scRNA-seq datasets and fine-tuned it with public PAH lung tissue data to identify the disease-relevant genes. The resulting algorithm, which we named PAH- former, demonstrated that its prediction accuracy varied significantly depending on the dataset used for fine-tuning. PAH-former enabled us to perform in silico perturbation analysis and identified PAH related genes. Loss-of-function PAH related genes in human pulmonary artery endothelial cells increased the expression of SOX18, a signature gene of PAH. This integration of artificial intelligence and biological experiments can significantly advance our understanding of molecular mechanisms of PAH.

bioinformatics↗

bioRxiv2025

A bright synthetic near-infrared luciferin enhances the capabilities of deep-tissue bioluminescence imaging using firefly luciferases

Synthetic bioluminescence reactions exhibiting near-infrared (NIR)-shifted spectra have been explored to improve deep-tissue imaging through the design of firefly luciferin analogues. Although the NIR bioluminescence reactions improve the tissue penetration of bioluminescence signals from deep tissues, their photon output is markedly lower compared to the natural reaction with D-luciferin and firefly luciferase (Fluc), often by an order of magnitude or more. Consequently, in most instances, the sensitivity of NIR bioluminescence imaging (NIR-BLI) has not yet substantially surpassed that of BLI with the natural firefly reaction. Here, we present a synthetic firefly luciferin, named AkaSuke, that generates intense NIR bioluminescence ({lambda}max = 680 nm) in reaction with Fluc, greatly improving the detection sensitivity beyond that of the D-luciferin/Fluc reaction for targeting deep tissue. AkaSuke enables sensitive visualizations of ectopic hematogenesis through entire tissues of mice over time following transplantation of bone marrow stem cells labeled with Fluc. We additionally identify a Japanese firefly luciferase, DkumLuc1, that displays higher catalytic activities for bioluminescence emission of AkaSuke compared to typical Fluc, resulting in detection sensitivity comparable to that of AkaLumine/Akaluc reaction, one of the most sensitive bioluminescence systems for deep tissue imaging. We further propose the potential of the AkaSuke/DkumLuc1 reaction as an orthogonal pair with the AkaLumine/Akaluc for sensitive dual-target tracking in mice. Overall results suggest that AkaSuke enhances the capabilities of deep-tissue bioluminescence imaging using Fluc and its variant, and could serve as an emerging benchmark for the molecular design of NIR luciferin analogues.

cancer biology↗

bioRxiv2022

PNPO-PLP Axis Senses Prolonged Hypoxia by Regulating Lysosomal Activity

Oxygen is critical for all metazoan organisms on the earth and impacts various biological processes in physiological and pathological conditions. While oxygen sensing systems inducing acute hypoxic response, including HIF pathway, have been identified, those operating in prolonged hypoxia remain to be elucidated. Here, we show that pyridoxine 5-phosphate oxidase (PNPO) that catalyzes bioactivation of vitamin B6 serves as an oxygen sensor and regulates lysosomal activity in macrophages. Decline of PNPO activity under prolonged hypoxia reduced an active form of vitamin B6, pyridoxal 5-phosphate (PLP), and inhibited lysosomal activity, leading to the augmentation of inflammatory response of macrophages. The PNPO-PLP axis creates a distinct layer of oxygen sensing, which gradually turns down and up the PLP-dependent metabolism according to prolonged changes in oxygen availability.

biochemistry↗