Computational prediction of the PolyQ and CAG repeat spinocerebellar ataxia network based on sequence identity to untranslated regions

Abstract

Computational prediction of biological networks would be a tremendous asset to systems biology and personalized medicine. In this paper, we use a moving window bioinformatic screen to identify transcripts with partial identity to the 5′ and 3′UTRs of the polyQ spinocerebellar ataxia (SCA) genes ATXN1, ATXN2, ATXN3, ATXN7, TBP and CACNA1A and the CAG repeat expansion gene PPP2R2B. We find that the bioinformatic screen enriches for transcripts that encode proteins that interact and that have functions relevant to polyQ SCA. Transcription control and RNA binding are the primary functional groups represented in the proteins from the combined screens. The insulin growth factor pathway, the WNT pathway, long term potentiation, melanogenesis and ATM mediated DNA repair pathways were identified as important pathways. UGUUU repeats were identified as an abundant motif in the SCA network and PAXIP1, CELF2, CREBBP, EBF1, PLEKHG4, SRSF4, C5orf42, NFIA, STK24, and YWHAG were identified as statistically significant proteins in the polyQ and PPP2R2B network.