Identification of Autism Spectrum Disorders associated Long Non-Coding RNAs shows connections to the synaptic transmission pathway.

Autism spectrum disorders (ASD) are a pervasive neurodevelopmental disorder with thousands of implicated genes that converge on specific brain tissues, developmental times and molecular pathways. Although, the exact causes of these dysfunctional pathways in neurodevelopment are still unknown, it is likely due to aberrant expression of crucial regulatory genes. Recently, there has been a surge of evidence for the emerging regulatory mechanisms of long non-coding RNAs (lncRNAs). To explore the underlying connections between ASD neurodevelopment and aberrantly expressed lncRNAs we analyzed RNA-seq data from the temporal cortex of ASD brains with matched controls to identify differentially expressed lncRNAs, the majority of which are functionally uncharacterized. Next, we extracted expression profiles of our candidate ASD associated lncRNAs and known autism risk genes from a comprehensive neurodevelopmental transcriptome dataset. We constructed a weighted gene co-expression network to functionally characterize the candidate lncRNAs in normal tissue, utilizing a guilt by association approach. We found a biologically significant module enriched for processes related to synaptic functioning such as ion channel activity, which also contained the majority of our candidate lncRNAs. In addition, the lncRNAs present in this module show peak neurodevelopmental expression at 10-12 months, a developmentally convergent period in ASD

The WRKY transcription factor superfamily: its origin in eukaryotes and expansion in plants

BACKGROUND: WRKY proteins are newly identified transcription factors involved in many plant processes including plant responses to biotic and abiotic stresses. To date, genes encoding WRKY proteins have been identified only from plants. Comprehensive search for WRKY genes in non-plant organisms and phylogenetic analysis would provide invaluable information about the origin and expansion of the WRKY family. RESULTS: We searched all publicly available sequence data for WRKY genes. A single copy of the WRKY gene encoding two WRKY domains was identified from Giardia lamblia, a primitive eukaryote, Dictyostelium discoideum, a slime mold closely related to the lineage of animals and fungi, and the green alga Chlamydomonas reinhardtii, an early branching of plants. This ancestral WRKY gene seems to have duplicated many times during the evolution of plants, resulting in a large family in evolutionarily advanced flowering plants. In rice, the WRKY gene family consists of over 100 members. Analyses suggest that the C-terminal domain of the two-WRKY-domain encoding gene appears to be the ancestor of the single-WRKY-domain encoding genes, and that the WRKY domains may be phylogenetically classified into five groups. We propose a model to explain the WRKY family's origin in eukaryotes and expansion in plants. CONCLUSIONS: WRKY genes seem to have originated in early eukaryotes and greatly expanded in plants. The elucidation of the evolution and duplicative expansion of the WRKY genes should provide valuable information on their functions

Predicting siRNA potency with random forests and support vector machines

Abstract Background Short interfering RNAs (siRNAs) can be used to knockdown gene expression in functional genomics. For a target gene of interest, many siRNA molecules may be designed, whereas their efficiency of expression inhibition often varies. Results To facilitate gene functional studies, we have developed a new machine learning method to predict siRNA potency based on random forests and support vector machines. Since there were many potential sequence features, random forests were used to select the most relevant features affecting gene expression inhibition. Support vector machine classifiers were then constructed using the selected sequence features for predicting siRNA potency. Interestingly, gene expression inhibition is significantly affected by nucleotide dimer and trimer compositions of siRNA sequence. Conclusions The findings in this study should help design potent siRNAs for functional genomics, and might also provide further insights into the molecular mechanism of RNA interference

BeetleBase: the model organism database for Tribolium castaneum

BeetleBase () is an integrated resource for the Tribolium research community. The red flour beetle (Tribolium castaneum) is an important model organism for genetics, developmental biology, toxicology and comparative genomics, the genome of which has recently been sequenced. BeetleBase is constructed to integrate the genomic sequence data with information about genes, mutants, genetic markers, expressed sequence tags and publications. BeetleBase uses the Chado data model and software components developed by the Generic Model Organism Database (GMOD) project. This strategy not only reduces the time required to develop the database query tools but also makes the data structure of BeetleBase compatible with that of other model organism databases. BeetleBase will be useful to the Tribolium research community for genome annotation as well as comparative genomics

Computational Analysis of Missense Mutations Causing Snyder-Robinson Syndrome

The Snyder-Robinson syndrome is caused by missense mutations in the spermine sythase gene that encodes a protein (SMS) of 529 amino acids. Here we investigate, in silico, the molecular effect of three missense mutations, c.267G\u3eA (p.G56S), c.496T\u3eG (p.V132G), and c.550T\u3eC (p.I150T) in SMS that were clinically identified to cause the disease. Single-point energy calculations, molecular dynamics simulations, and pKa calculations revealed the effects of these mutations on SMS\u27s stability, flexibility, and interactions. It was predicted that the catalytic residue, Asp276, should be protonated prior binding the substrates. The pKa calculations indicated the p.I150T mutation causes pKa changes with respect to the wild-type SMS, which involve titratable residues interacting with the S-methyl-5′-thioadenosine (MTA) substrate. The p.I150T missense mutation was also found to decrease the stability of the C-terminal domain and to induce structural changes in the vicinity of the MTA binding site. The other two missense mutations, p.G56S and p.V132G, are away from active site and do not perturb its wild-type properties, but affect the stability of both the monomers and the dimer. Specifically, the p.G56S mutation is predicted to greatly reduce the affinity of monomers to form a dimer, and therefore should have a dramatic effect on SMS function because dimerization is essential for SMS activity. Hum Mutat 31:1043–1049, 2010