Disease-Associated Mutations That Alter the RNA Structural Ensemble

Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the untranslated regions (UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, β-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD), and Hypertension), we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5′ UTRs of FTL and RB1) SNP–induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a “RiboSNitch,” that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble

Robustness and uncertainties in global multivariate wind-wave climate projections

Understanding climate-driven impacts on the multivariate global wind-wave climate is paramount to effective offshore/coastal climate adaptation planning. However, the use of single-method ensembles and variations arising from different methodologies has resulted in unquantified uncertainty amongst existing global wave climate projections. Here, assessing the first coherent, community-driven, multi-method ensemble of global wave climate projections, we demonstrate widespread ocean regions with robust changes in annual mean significant wave height and mean wave period of 5–15% and shifts in mean wave direction of 5–15°, under a high-emission scenario. Approximately 50% of the world’s coastline is at risk from wave climate change, with ~40% revealing robust changes in at least two variables. Furthermore, we find that uncertainty in current projections is dominated by climate model-driven uncertainty, and that single-method modelling studies are unable to capture up to ~50% of the total associated uncertainty

Loss of functional pRB is not a ubiquitous feature of B-cell malignancies

Human cancers frequently sustain genetic mutations that alter the function of their G1 cell cycle control check point. These include changes to the retinoblastoma gene and to the genes that regulate its phosphorylation, such as the cyclin-dependent kinase inhibitor p16(INK4a). Altered expression of retinoblastoma protein (pRb) is associated with non-Hodgkin's lymphoma, particularly centroblastic and Burkitt's lymphomas. pRb is expressed in normal B-cells and its regulatory phosphorylation pathway is activated in response to a variety of stimuli. Since human B-lymphoma-derived cell lines are often used as in vitro model systems to analyse the downstream effects of signal transduction, we examined the functional status of pRb in a panel of human B-cell lines. We identified eleven cell lines which express the hyperphosphorylated forms of pRb. Furthermore, we suggest that the pRb protein appears to be functional in these cell lines

Gaia data release 1, the photometric data

CONTEXT. This paper presents an overview of the photometric data that are part of the first Gaia data release. AIMS. The principles of the processing and the main characteristics of the Gaia photometric data are presented. METHODS. The calibration strategy is outlined briefly and the main properties of the resulting photometry are presented. RESULTS. Relations with other broadband photometric systems are provided. The overall precision for the Gaia photometry is shown to be at the milli-magnitude level and has a clear potential to improve further in future releases

Gaia Data Release 1: Open cluster astrometry: Performance, limitations, and future prospects

Context. The first Gaia Data Release contains the Tycho-Gaia Astrometric Solution (TGAS). This is a subset of about 2 million stars for which, besides the position and photometry, the proper motion and parallax are calculated using Hipparcos and Tycho-2 positions in 1991.25 as prior information. Aims. We investigate the scientific potential and limitations of the TGAS component by means of the astrometric data for open clusters. Methods. Mean cluster parallax and proper motion values are derived taking into account the error correlations within the astrometric solutions for individual stars, an estimate of the internal velocity dispersion in the cluster, and, where relevant, the effects of the depth of the cluster along the line of sight. Internal consistency of the TGAS data is assessed. Results. Values given for standard uncertainties are still inaccurate and may lead to unrealistic unit-weight standard deviations of least squares solutions for cluster parameters. Reconstructed mean cluster parallax and proper motion values are generally in very good agreement with earlier Hipparcos-based determination, although the Gaia mean parallax for the Pleiades is a significant exception. We have no current explanation for that discrepancy. Most clusters are observed to extend to nearly 15 pc from the cluster centre, and it will be up to future Gaia releases to establish whether those potential cluster-member stars are still dynamically bound to the clusters. Conclusions. The Gaia DR1 provides the means to examine open clusters far beyond their more easily visible cores, and can provide membership assessments based on proper motions and parallaxes. A combined HR diagram shows the same features as observed before using the Hipparcos data, with clearly increased luminosities for older A and F dwarfs

Gaia Data Release 1: Summary of the astrometric, photometric, and survey properties

At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. We summarize Gaia DR1 and provide illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Gaia DR1 consists of: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the Hipparcos and Tycho-2 catalogues and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set,consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ~3000 Cepheid and RR Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas/yr for the proper motions. A systematic component of ~0.3 mas should be added to the parallax uncertainties. For the subset of ~94000 Hipparcos stars in the primary data set, the proper motions are much more precise at about 0.06 mas/yr. For the secondary astrometric data set, the typical uncertainty of the positions is ~10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ~0.03 mag over the magnitude range 5 to 20.7. Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data