NMR structure and Mg(2+) binding of an RNA segment that underlies the L7/L12 stalk in the E.coli 50S ribosomal subunit

Helix 42 of Domain II of Escherichia coli 23S ribosomal RNA underlies the L7/L12 stalk in the ribosome and may be significant in positioning this feature relative to the rest of the 50S ribosomal subunit. Unlike the Haloarcula marismortui and Deinococcus radiodurans examples, the lower portion of helix 42 in E.coli contains two consecutive G•A oppositions with both adenines on the same side of the stem. Herein, the structure of an analog of positions 1037–1043 and 1112–1118 in the helix 42 region is reported. NMR spectra and structure calculations support a cis Watson–Crick/Watson–Crick (cis W.C.) G•A conformation for the tandem (G•A)(2) in the analog and a minimally perturbed helical duplex stem. Mg(2+) titration studies imply that the cis W.C. geometry of the tandem (G•A)(2) probably allows O6 of G20 and N1 of A4 to coordinate with a Mg(2+) ion as indicated by the largest chemical shift changes associated with the imino group of G20 and the H8 of G20 and A4. A cross-strand bridging Mg(2+) coordination has also been found in a different sequence context in the crystal structure of H.marismortui 23S rRNA, and therefore it may be a rare but general motif in Mg(2+) coordination

Patterns and rates of exonic de novo mutations in autism spectrum disorders

Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified1,2. To identify further genetic risk factors, we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n= 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant and the overall rate of mutation is only modestly higher than the expected rate. In contrast, there is significantly enriched connectivity among the proteins encoded by genes harboring de novo missense or nonsense mutations, and excess connectivity to prior ASD genes of major effect, suggesting a subset of observed events are relevant to ASD risk. The small increase in rate of de novo events, when taken together with the connections among the proteins themselves and to ASD, are consistent with an important but limited role for de novo point mutations, similar to that documented for de novo copy number variants. Genetic models incorporating these data suggest that the majority of observed de novo events are unconnected to ASD, those that do confer risk are distributed across many genes and are incompletely penetrant (i.e., not necessarily causal). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5 to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favor of CHD8 and KATNAL2 as genuine autism risk factors

Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls

We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD. © 2013 Liu et al

The application of cluster analysis in the intercomparison of loop structures in RNA

We have developed a computational approach for the comparison and classification of RNA loop structures. Hairpin or interior loops identified in atomic resolution RNA structures were intercompared by conformational matching. The root-mean-square deviation (RMSD) values between all pairs of RNA fragments of interest, even if from different molecules, are calculated. Subsequently, cluster analysis is performed on the resulting matrix of RMSD distances using the unweighted pair group method with arithmetic mean (UPGMA). The cluster analysis objectively reveals groups of folds that resemble one another. To demonstrate the utility of the approach, a comprehensive analysis of all the terminal hairpin tetraloops that have been observed in 15 RNA structures that have been determined by X-ray crystallography was undertaken. The method found major clusters corresponding to the well-known GNRA and UNCG types. In addition, two tetraloops with the unusual primary sequence UMAC (M is A or C) were successfully assigned to the GNRA cluster. Larger loop structures were also examined and the clustering results confirmed the occurrence of variations of the GNRA and UNCG tetraloops in these loops and provided a systematic means for locating them. Nineteen examples of larger loops that closely resemble either the GNRA or UNCG tetraloop were found in the large ribosomal RNAs. When the clustering approach was extended to include all structures in the SCOR database, novel relationships were detected including one between the ANYA motif and a less common folding of the GAAA tetraloop sequence

Rare Complete Knockouts in Humans: Population Distribution and Significant Role in Autism Spectrum Disorders

To characterize the role of rare complete human knockouts in autism spectrum disorders (ASD), we identify genes with homozygous or compound heterozygous loss-of-function (LoF) variants (defined as nonsense and essential splice sites) from exome sequencing of 933 cases and 869 controls. We identify a two-fold increase in complete knockouts of autosomal genes with low rates of LoF variation (≤5% frequency) in cases and estimate a 3% contribution to ASD risk by these events, confirming this observation in an independent set of 563 probands and 4,605 controls. Outside the pseudo-autosomal regions on the X-chromosome, we similarly observe a significant 1.5-fold increase in rare hemizygous knockouts in males, contributing to another 2% of ASDs in males. Taken together these results provide compelling evidence that rare autosomal and X-chromosome complete gene knockouts are important inherited risk factors for ASD