Non-Coding RNA Prediction and Verification in Saccharomyces cerevisiae

Non-coding RNA (ncRNA) play an important and varied role in cellular function. A significant amount of research has been devoted to computational prediction of these genes from genomic sequence, but the ability to do so has remained elusive due to a lack of apparent genomic features. In this work, thermodynamic stability of ncRNA structural elements, as summarized in a Z-score, is used to predict ncRNA in the yeast Saccharomyces cerevisiae. This analysis was coupled with comparative genomics to search for ncRNA genes on chromosome six of S. cerevisiae and S. bayanus. Sets of positive and negative control genes were evaluated to determine the efficacy of thermodynamic stability for discriminating ncRNA from background sequence. The effect of window sizes and step sizes on the sensitivity of ncRNA identification was also explored. Non-coding RNA gene candidates, common to both S. cerevisiae and S. bayanus, were verified using northern blot analysis, rapid amplification of cDNA ends (RACE), and publicly available cDNA library data. Four ncRNA transcripts are well supported by experimental data (RUF10, RUF11, RUF12, RUF13), while one additional putative ncRNA transcript is well supported but the data are not entirely conclusive. Six candidates appear to be structural elements in 5′ or 3′ untranslated regions of annotated protein-coding genes. This work shows that thermodynamic stability, coupled with comparative genomics, can be used to predict ncRNA with significant structural elements

Genetics ignite focus on microglial inflammation in Alzheimer’s disease

In the past five years, a series of large-scale genetic studies have revealed novel risk factors for Alzheimer’s disease (AD). Analyses of these risk factors have focused attention upon the role of immune processes in AD, specifically microglial function. In this review, we discuss interpretation of genetic studies.  We then focus upon six genes implicated by AD genetics that impact microglial function: TREM2, CD33, CR1, ABCA7, SHIP1, and APOE. We review the literature regarding the biological functions of these six proteins and their putative role in AD pathogenesis. We then present a model for how these factors may interact to modulate microglial function in AD