Showing papers by "Dennis P. Wall published in 2008"

Heterogeneous dysregulation of microRNAs across the autism spectrum

Abstract: microRNAs (miRNAs) are ~21 nt transcripts capable of regulating the expression of many mRNAs and are abundant in the brain. miRNAs have a role in several complex diseases including cancer as well as some neurological diseases such as Tourette’s syndrome and Fragile x syndrome. As a genetically complex disease, dysregulation of miRNA expression might be a feature of autism spectrum disorders (ASDs). Using multiplex quantitative polymerase chain reaction (PCR), we compared the expression of 466 human miRNAs from postmortem cerebellar cortex tissue of individuals with ASD (n = 13) and a control set of non-autistic cerebellar samples (n = 13). While most miRNAs levels showed little variation across all samples suggesting that autism does not induce global dysfunction of miRNA expression, some miRNAs among the autistic samples were expressed at significantly different levels compared to the mean control value. Twenty-eight miRNAs were expressed at significantly different levels compared to the non-autism control set in at least one of the autism samples. To validate the finding, we reversed the analysis and compared each non-autism control to a single mean value for each miRNA across all autism cases. In this analysis, the number of dysregulated miRNAs fell from 28 to 9 miRNAs. Among the predicted targets of dysregulated miRNAs are genes that are known genetic causes of autism such Neurexin and SHANK3. This study finds that altered miRNA expression levels are observed in postmortem cerebellar cortex from autism patients, a finding which suggests that dysregulation of miRNAs may contribute to autism spectrum phenotype.

Testing the accuracy of eukaryotic phylogenetic profiles for prediction of biological function.

Abstract: A phylogenetic profile captures the pattern of gene gain and loss throughout evolutionary time. Proteins that interact directly or indirectly within the cell to perform a biological function will often co-evolve, and this co-evolution should be well reflected within their phylogenetic profiles. Thus similar phylogenetic profiles are commonly used for grouping proteins into functional groups. However, it remains unclear how the size and content of the phylogenetic profile impacts the ability to predict function, particularly in Eukaryotes. Here we developed a straightforward approach to address this question by constructing a complete set of phylogenetic profiles for 31 fully sequenced Eukaryotes. Using Gene Ontology as our gold standard, we compared the accuracy of functional predictions made by a comprehensive array of permutations on the complete set of genomes. Our permutations showed that phylogenetic profiles containing between 25 and 31 Eukaryotic genomes performed equally well and significantly better than all other permuted genome sets, with one exception: we uncovered a core of group of 18 genomes that achieved statistically identical accuracy. This core group contained genomes from each branch of the eukaryotic phylogeny, but also contained several groups of closely related organisms, suggesting that a balance between phylogenetic breadth and depth may improve our ability to use Eukaryotic specific phylogenetic profiles for functional annotations.