Search bioRxiv⌕ Search

Biology subjects

Koya, J.

Publications and source records attributed to Koya, J..

2 recordsLinked to original sources

HTLV-1-Induced Neuroimmunome Correlates with Disease Progression and Severity

Human T-cell lymphotropic virus type 1 (HTLV-1) is a retrovirus that infects approximately 5-10 million people worldwide. While most individuals remain asymptomatic, a significant subset develops debilitating neuroinflammatory or malignant disorders, including adult T-cell leukemia/lymphoma (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). To unravel the systemic molecular mechanisms underlying HTLV-1 pathogenesis, we employed a multi-dimensional systems biology approach, integrating bulk transcriptomic data from total PBMCs (n = 200) with single-cell RNA sequencing (scRNA-seq) from 233,093 peripheral blood mononuclear cells (PBMCs). Our analysis revealed a consistent and clinically relevant neuroimmune signature within leukocytes, termed the neuroimmunome, comprising a set of differentially expressed genes shared across the nervous and immune systems. Through dimensionality reduction and machine learning techniques, such as PCA, gradient boosting, and MANOVA with bootstrapping, we identified potential biomarkers predictive of HTLV-1-driven leukemogenesis, which were subsequently validated across ATL, HAM/TSP, and asymptomatic cohorts via flow cytometry. Notably, expression levels of proteins such as ATF4 and SKIL were strongly correlated with proviral load, suggesting that sustained neuroimmune dysregulation may contribute to disease progression. These findings highlight a previously underappreciated neuroimmunological layer, redefining HTLV-1-associated disease as a condition deeply rooted in neuroimmune network disruption within leukocytes and offering potential novel targets.

molecular biology↗

Precise characterization of somatic structural variations and mobile element insertions from paired long-read sequencing data with nanomonsv

We present our novel software, nanomonsv, for detecting somatic structural variations (SVs) using tumor and matched control long-read sequencing data with a single-base resolution. The current version of nanomonsv includes two detection modules, Canonical SV module, and Single breakend SV module. Using paired long-read sequencing data from three cancer and their matched lymphoblastoid lines, we demonstrate that Canonical SV module can identify somatic SVs that can be captured by short-read technologies with higher precision and recall than existing methods. In addition, we have developed a workflow to classify mobile element insertions while elucidating their in-depth properties, such as 5 truncations, internal inversions, as well as source sites for 3 transductions. Furthermore, Single breakend SV module enables the detection of complex SVs that can only be identified by long-reads, such as SVs involving highly-repetitive centromeric sequences, and LINE1- and virus-mediated rearrangements. In summary, our approaches applied to cancer long-read sequencing data can reveal various features of somatic SVs and will lead to a better understanding of mutational processes and functional consequences of somatic SVs.

bioinformatics↗