Dataset Information

Sequencing-Guided Design of Genetically Encoded Small RNAs Targeting CAG Repeats for Selective Inhibition of Mutant Huntingtin [II]

ABSTRACT: Huntington’s disease (HD) is an incurable neurodegenerative disorder caused by genetic expansion of a CAG repeat sequence in one allele of the huntingtin (HTT) gene. Reducing expression of the mutant HTT (mutHTT) protein has remained a clear therapeutic goal but reduction of wild-type HTT (wtHTT) is undesirable as it compromises gene function and potential therapeutic efficacy. One promising allele- selective approach involves targeting the CAG repeat expansion with steric binding small RNAs bearing central mismatches. However, successful genetic encoding requires consistent placement of mismatches to the target within the small RNA guide sequence, which involves 5' processing precision by cellular enzymes. Here, we used small RNA sequencing to monitor processing precision of a limited set of CAG repeat- targeted small RNAs expressed from multiple scaffold contexts. Small RNA sequencing identified expression constructs with high guide strand 5' processing precision that also conferred promising allele-selective inhibition of mutHTT. However, mRNA-seq revealed varying degrees of transcriptome-wide off-target effects, including certain CAG repeat-containing mRNAs. These results support the continued investigation and optimization of genetically encoded repeat-targeted small RNAs for allele-selective HD gene therapy and underscore the value of sequencing methods to balance specificity with allele selectivity during the design and selection process.

ORGANISM(S): Homo sapiens

PROVIDER: GSE264218 | GEO | 2024/05/01

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:In Huntington’s disease (HD), expanded HTT CAG repeat length correlates strongly with age at motor onset, indicating that it determines the rate of the disease process leading to diagnostic clinical manifestations. Similarly, in normal individuals, HTT CAG repeat length is correlated with biochemical differences that reveal it as a functional polymorphism. Here, we tested the hypothesis that gene expression signatures can capture continuous, length-dependent effects of the HTT CAG repeat. Using gene expression datasets for 107 HD and control lymphoblastoid cell lines, we constructed mathematical models in an iterative manner, based upon CAG correlated gene expression patterns in randomly chosen training samples, and tested their predictive power in test samples. Predicted CAG repeat lengths were significantly correlated with experimentally determined CAG repeat lengths, whereas models based upon randomly permuted CAGs were not at all predictive. Predictions from different batches of mRNA for the same cell lines were significantly correlated, implying that CAG length-correlated gene expression is reproducible. Notably, HTT expression was not itself correlated with HTT CAG repeat length. Taken together, these findings confirm the concept of a gene expression signature representing the continuous effect of HTT CAG length and not primarily dependent on the level of huntingtin expression. Such global and unbiased approaches, applied to additional cell types and tissues, may facilitate the discovery of therapies for HD by providing a comprehensive view of molecular changes triggered by HTT CAG repeat length for use in screening for and testing compounds that reverse effects of the HTT CAG expansion. To evaluate the continuous analytical approach as a strategy to discover the molecular consequences of the HTT CAG repeat, genome-wide gene expression datasets were generated from a panel of 107 human lymphoblastoid cell lines with HTT CAGs spanning the entire spectrum of allele sizes.

Dataset Information

Sequencing-Guided Design of Genetically Encoded Small RNAs Targeting CAG Repeats for Selective Inhibition of Mutant Huntingtin [II]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets