Huntington disease (HD) is a dominant neurodegenerative disorder caused by a CAG repeat expansion in the Huntingtin gene (HTT). Heterozygous polymorphisms in cis with the mutation allow for allele-specific suppression of the pathogenic HTT transcript as a therapeutic strategy. To prioritize target selection, precise heterozygosity estimates are needed across diverse HD patient populations. Here we present the first comprehensive investigation of all common target alleles across the HTT gene, using 738 reference haplotypes from the 1000 Genomes Project and 2364 haplotypes from HD patients and relatives in Canada, Sweden, France, and Italy. The most common HD haplotypes (A1, A2, and A3a) define mutually exclusive sets of polymorphisms for allele-specific therapy in the greatest number of patients. Across all four populations, a maximum of 80% are treatable using these three target haplotypes. We identify a novel deletion found exclusively on the A1 haplotype, enabling potent and selective silencing of mutant HTT in approximately 40% of the patients. Antisense oligonucleotides complementary to the deletion reduce mutant A1 HTT mRNA by 78% in patient cells while sparing wild-type HTT expression. By suppressing specific haplotypes on which expanded CAG occurs, we demonstrate a rational approach to the development of allele-specific therapy for a monogenic disorder.
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http://dx.doi.org/10.1038/mt.2015.128 | DOI Listing |
Molecules
January 2025
Chair and Department of Biochemistry and Pharmacogenomics, Medical University of Warsaw, 1 Banacha Str., 02-097 Warsaw, Poland.
Vitamin B (thiamine) plays an important role in human metabolism. It is essential for the proper growth and development of the body and has a positive effect on the functioning of the digestive, cardiovascular, and nervous systems. Additionally, it stimulates the brain and improves the psycho-emotional state.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
Chair and Department of General Biology and Parasitology, Medical University of Warsaw, Chałubińskiego 5, 02-004 Warsaw, Poland.
Diabetes mellitus (DM) and neurodegenerative diseases/disturbances are worldwide health problems. The most common chronic conditions diagnosed in persons 60 years and older are type 2 diabetes mellitus (T2DM) and cognitive impairment. It was found that diabetes mellitus is a major risk for cognitive decline, dementia, Parkinson's disease (PD), Alzheimer's disease (AD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS) and other neurodegenerative disorders.
View Article and Find Full Text PDFGenes (Basel)
December 2024
Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy.
Background/objectives: Artificial intelligence and large language models like ChatGPT and Google's Gemini are promising tools with remarkable potential to assist healthcare professionals. This study explores ChatGPT and Gemini's potential utility in assisting clinicians during the first evaluation of patients with suspected neurogenetic disorders.
Methods: By analyzing the model's performance in identifying relevant clinical features, suggesting differential diagnoses, and providing insights into possible genetic testing, this research seeks to determine whether these AI tools could serve as a valuable adjunct in neurogenetic assessments.
Antioxidants (Basel)
January 2025
Laboratory of Molecular, Cellular and Genomic Biomedicine, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain.
Lipid metabolism plays a critical role in maintaining cellular integrity, especially within the nervous system, where lipids support neuronal structure, function, and synaptic plasticity. However, this essential metabolic pathway is highly susceptible to oxidative stress, which can lead to lipid peroxidation, a damaging process induced by reactive oxygen species. Lipid peroxidation generates by-products that disrupt many cellular functions, with a strong impact on proteostasis.
View Article and Find Full Text PDFSci Rep
January 2025
Cognition and Brain Plasticity Unit, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics.
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