International Meeting for Autism Research (London, May 15-17, 2008): MicroRNA expression profiling in autism: noncoding RNAs and autism susceptibility gene identification

MicroRNA expression profiling in autism: noncoding RNAs and autism susceptibility gene identification

Friday, May 16, 2008
Champagne Terrace/Bordeaux (Novotel London West)
10:30 AM
Z. Talebizadeh , Genetics, Children's Mercy Hospital and University of Missouri-Kansas City, Kansas City, MO
M. F. Theodoro , Genetics, Children's Mercy Hospital and University of Missouri-Kansas City, Kansas City, MO
M. G. Butler , Genetics, Children's Mercy Hospital and University of Missouri-Kansas City, Kansas City, MO
Background: An estimated 98% of the transcriptional output in humans and other mammals are consisted of noncoding RNAs (ncRNAs) that do not code for protein but have other functions in cells. Two main groups of ncRNAs are microRNAs and snoRNAs. MicroRNAs are small RNA molecules containing approximately 22 nucleotides that regulate the expression of genes by binding to the 3’-untranslated regions of specific mRNA directing translational repression or transcript degradation. Despite growing evidence for regulatory influence of ncRNAs in gene expression, particularly in brain function, this group of regulatory factors has not been evaluated in autism spectrum disorders.

Objectives: To understand the role of microRNAs in the etiology of autism.

Methods: We evaluated global expression profiling of 470 mature human microRNAs in lymphoblastoid cell lines from 6 subjects with autism compared with 6 matched controls using microarray technology and quantitative RT-PCR. Samples were ascertained from the Autism Genetics Resource Exchange (AGRE).

Results: Differential expression (either higher or lower) for 9 of the 470 microRNAs (i.e., miR-132, miR-23a, miR-23b, miR-146a, miR-146b, miR-663, miR-363, miR-92, and miR-320) was observed in our autism samples compared with controls. Potential target genes for these microRNAs were identified using publically available programs: PicTar, TargetScan, and miRanda. There were several genes of neurological interest, particularly for autism, among the predicted targets for these 9 microRNAs. Our results suggest that autism candidate genes are overrepresented as targets amongst the differentially expressed microRNAs compared to a randomly selected set of microRNAs.

Conclusions: Overall, our study suggests that evaluation of microRNA expression may have potential in identifying pathways implicated in autism. To detect the impact of specific microRNA misregulation on the expression level of their target genes, a more detailed experimental design is needed to correlate microRNAs and mRNAs expression levels in an expanded number of subjects.