From Brain to Blood: Uncovering Potential Therapeutical Targets and Biomarkers for Huntington's Disease Using an Integrative RNA-Seq Analytical Platform (BDASeq®)

Autor(es)

Dias Pinto, Joao Rafael ; Faustinoni Neto, Benedito ; Munhoz, Luciana ; Kerkis, Irina ; Araldi, Rodrigo Pinheiro

• Tipo de documento: Artigo Científico
• Fonte: CELLS; v. 14, n. 13, p. 33-pg., 2025-06-25.

Resumo

Background: Huntington's Disease (HD) remains without disease-modifying treatments, with existing therapies primarily targeting chorea symptoms and offering limited benefits. This study aims to identify druggable genes and potential biomarkers for HD, focusing on using RNA-Seq analysis to uncover molecular targets and improve clinical trial outcomes. Methods: We reanalyzed transcriptomic data from six independent studies comparing cortex samples of HD patients and healthy controls. The Propensity Score Matching (PSM) algorithm was applied to match cases and controls by age. Differential expression analysis (DEA) coupled with machine learning algorithms were coupled to identify differentially expressed genes (DEGs) and potential biomarkers in HD. Results: Our analysis identified 5834 DEGs, including 394 putative druggable genes involved in processes like neuroinflammation, metal ion dysregulation, and blood-brain barrier dysfunction. These genes' expression levels correlated with CAG repeat length, disease onset, and progression. We also identified FTH1 as a promising biomarker for HD, with its expression downregulated in the prefrontal cortex and upregulated in peripheral blood in a CAG repeat-dependent manner. Conclusions: This study highlights the potential of FTH1 as both a biomarker and a therapeutic target for HD. Advanced bioinformatics approaches like RNA-Seq and PSM are crucial for uncovering novel targets in HD, paving the way for better therapeutic interventions and improved clinical trial outcomes. Further validation of FTH1 ' s role is needed to confirm its utility in HD. (AU)

• Processo FAPESP: 24/07829-5 - Desenvolvimento de um modelo analítico-preditivo baseado na integração de inteligência artificial e dados transcriptômicos aplicado à identificação de alvos moleculares de interesse (bio)farmacêutico
• Beneficiário: Rodrigo Pinheiro Araldi
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisa Inovativa em Pequenas Empresas - PIPE
• Processo FAPESP: 24/17577-3 - Desenvolvimento de um modelo analítico-preditivo baseado na integração de inteligência artificial e dados transcriptômicos aplicado à identificação de alvos moleculares de interesse (bio)farmacêutico
• Beneficiário: Rodrigo Pinheiro Araldi
• Modalidade de apoio: Bolsas no Brasil - Pesquisa Inovativa em Pequenas Empresas - PIPE
• Processo FAPESP: 23/06116-2 - Desenvolvimento de um modelo analítico-preditivo baseado na integração de inteligência artificial e dados transcriptômicos aplicado à identificação de alvos moleculares de interesse (bio)farmacêutico
• Beneficiário: Rodrigo Pinheiro Araldi
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisa Inovativa em Pequenas Empresas - PIPE
• Processo FAPESP: 23/10353-0 - Desenvolvimento de um modelo analítico-preditivo baseado na integração de inteligência artificial e dados transcriptômicos aplicado à identificação de alvos moleculares de interesse (bio)farmacêutico
• Beneficiário: Rodrigo Pinheiro Araldi
• Modalidade de apoio: Bolsas no Brasil - Pesquisa Inovativa em Pequenas Empresas - PIPE