Biology subjects

Nagao, C.

Publications and source records attributed to Nagao, C..

3 recordsLinked to original sources

bioRxiv2025

Development of an RNA Aptamer as a Therapeutic Agent for Synucleinopathies

The aggregation of -synuclein (Syn), a 140-mer protein, has been implicated in the pathogenesis of Parkinsons disease, multiple system atrophy, and dementia with Lewy bodies. KTKEGV repeats (KR) of Syn are key mediators of prion-like propagation and neurodegeneration. Despite the availability of symptomatic treatments, no current therapy effectively delays disease progression. Here we report a 77-nucleotide (nt) RNA aptamer (1R6) with potent affinity and selectivity for Syn1-95 (KD = 18 nM) through in vitro selection. 1R6 significantly inhibited Syn oligomerization and {beta}-sheet-rich fibril assembly and promoted disaggregation of preformed fibrils. Additionally, 1R6 suppressed Syn seeding, as determined by FRET-based cellular biosensor cell assay. Cellular studies revealed that 1R6 cotransfection completely prevented Syn-induced cytotoxicity. To assess the protective effects of 1R6 in vivo, we used a Drosophila melanogaster model expressing human Syn in neurons. Flies fed with 1R6 showed improved locomotor defects, reduced photoreceptor degeneration, and decreased Syn levels in the head. Structural characterization through 1H-15N heteronuclear multiple quantum correlation nuclear magnetic resonance experiments demonstrated that 1R6 targets KR motifs, a finding further supported by in silico simulations. Our findings indicate that RNA aptamers, such as 1R6, may represent promising therapeutic candidates for synucleinopathies, thus opening new avenues in the treatment of these diseases.

neuroscience↗

bioRxiv2025

Proteomic and in silico dissection of MetaAggregates in amyotrophic lateral sclerosis brains

RNA-binding proteins (RBPs), key translation regulators, are thought to be involved in the pathogenesis of amyotrophic lateral sclerosis (ALS). The pathological entities associated with ALS are known as "MetaAggregates": heterogeneous coaggregates composed of amyloids, RBPs, and RNA G-quadruplexes (rG4s). In this study, to explore the molecular constituents of ALS-associated MetaAggregates, we developed a proteomic approach using a psoralen-conjugated RBP and crosslinked it with a biotinylated rG4 to enable the isolation of MetaAggregates from ALS brain extracts. Single-cell RNA-seq using in vitro ALS models identified ELAVL4 as a cytoplasmic RBP and revealed the enrichment of an IGFBP2-derived rG4 structure in ALS-specific neurons. Mass spectrometry and amyloidogenicity-based principal component analysis revealed 79 candidate proteins with roles in RNA processing, metabolism, trafficking, and stress responses. Docking simulations highlighted a subset of proteins with potential pro-aggregation characteristics, diverse cytosolic associations and functional links to RNA processing relevant to ALS. Through proteomic and in silico dissection of ALS-associated MetaAggregates, the findings of this study establish a conceptual framework for the exploration of unrecognized amyloidogenic drivers of neurodegeneration.

neuroscience↗

bioRxiv2023

An attention network for predicting T cell receptor-peptide binding can associate attention with interpretable protein structural properties

Understanding how a T cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR-peptide interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of the Transformer, a source-target-attention neural network, to predict TCR-peptide binding solely from the amino acid sequences of the TCRs complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of TCR-peptide binding, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large and small attention groups, we identified statistically significant properties associated with the largely attended residues, such as hydrogen bonds within the CDR3. The dataset that we have created and our models ability to provide an interpretable prediction of TCR-peptide binding should increase our knowledge of molecular recognition and pave the way to designing new therapeutics.

bioinformatics↗