PhD Scientific Days 2026

Poster Session 3.U - Molecular Medicine

High-Throughput Profiling of Nucleotide Repeat Expansion Disease Models

Name of the presenter

Szepesi-Nagy, Istvan

Institute/workplace of the presenter

HUN-REN RCNS Institute of Molecular Life Sciences

Authors

István Szepesi-Nagy¹, Dr. Marcell Cserhalmi²
1: HUN-REN RCNS Institute of Molecular Life Sciences
2: HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences

Text of the abstract

Introduction
Nucleotide repeat expansion disorders (NREDs), including Huntington’s disease, spinocerebellar ataxias, and ALS/FTD, are inherited neurodegenerative diseases characterized by progressive neuronal dysfunction, synaptic failure, and selective vulnerability of specific brain regions. Despite the availability of numerous disease-focused databases, no dedicated resource currently exists for large-scale proteogenomic profiling of NREDs.
Aims
To establish a scalable and reproducible framework for high-throughput proteomic analysis of NRED models and generate a comprehensive resource of disease-associated protein alterations linked to neural vulnerability and neurodegeneration.
Method
Proteomics datasets derived from brain and neural tissues were integrated using complementary meta-analytical strategies. Raw mass spectrometry (MS) datasets were reprocessed to generate normalized differential expression profiles, while consistently altered proteins across studies were identified using predefined selection criteria. To enable large-scale and reproducible analysis, the FragPipe proteomics suite was deployed within a high-performance computing (HPC) environment. In parallel, we developed FragFlow, a Nextflow-based workflow that automates MS data processing and incorporates downstream statistical analysis through the FragPipe-Analyst platform.
Results
The developed workflow enabled efficient and reproducible processing of large-scale proteomics datasets across multiple NRED models. Integration of reanalyzed datasets identified consistent protein alterations associated with neuronal degeneration, synaptic dysfunction, and selective neural vulnerability. FragFlow improved scalability and automation of proteomics analysis within HPC environments, supporting systematic cross-study comparisons and large-scale meta-analyses.
Conclusion
This study establishes a high-throughput proteogenomic framework for NRED research that combines automated MS data processing with integrative meta-analysis. The resulting workflow and curated protein profiles provide a valuable resource for investigating molecular mechanisms underlying neurodegeneration and for identifying potential therapeutic targets in repeat expansion disorders.
Funding
I.S.N. is supported by the 2025-2.1.2-EKÖP-KDP-2025-00007 University Research Scholarship Programme of the Ministry for Culture and Innovation.