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.

The stochasticity of chromosome organization was investigated by fluorescently labeling genetic loci in live Escherichia coli cells. In spite of the common assumption that the chromosome is well modeled by an unstructured polymer, measurements of the locus distributions reveal that the E. coli chromosome is precisely organized into a nucleoid filament with a linear order. Loci in the body of the nucleoid show a precision of positioning within the cell of better than 10% of the cell length. The precision of interlocus distance of genomically-proximate loci was better than 4% of the cell length. The measured dependence of the precision of interlocus distance on genomic distance singles out intranucleoid interactions as the mechanism responsible for chromosome organization. From the magnitude of the variance, we infer the existence of an as-yet uncharacterized higher-order DNA organization in bacteria. We demonstrate that both the stochastic and average structure of the nucleoid is captured by a fluctuating elastic filament model. chromosome segregation | chromosome structure | nucleoid structure | polymer physics P rokaryotic chromosomes are organized into a compact DNA-protein complex called the nucleoid (1, 2). The physical structure of chromosomes has functional consequences, for example it affects gene regulation from the simplest prokaryotes (1) to multicellular organisms (3). Nucleoid organization and condensation also appear to play a central role in chromosome segregation: Mutants with defective chromosome segregation are typically accompanied by abnormal nucleoid organization or condensation (2). Although a significant number of such genes have been identified by genetic screens, the mechanism by which these molecular players effect the cellular-scale nucleoid structure is not yet understood (2). Similarly, the mechanism by which prokaryotic chromosomes are segregated is still hotly debated (2, 4, 5). The apparent dispensability of a mitotic-spindle-like mechanism in chromosome segregation in Escherichia coli (2, 4) has led to speculation that nucleoid organization and segregation may be the result of several redundant mechanisms, including polymer physics-embodied by the combined effects of entropy, confinement, and excluded volume-rather than resulting from the action of dedicated cellular machinery alone (2, 6, 7). This paper complements earlier work by focusing on the measurement and theoretical interpretation of two classes of statistical measures of chromosome organization: (i) the distributions of the positions of individual loci within the cell; and (ii) the distributions of displacements between pairs of genetic loci. We argue that the measurement and analysis of these distributions sheds light on the mechanisms of chromosomal positioning that have not been revealed in earlier measurements. The cellular-scale structure of the circular Caulobacter crescentus chromosome has already been shown to be linearly organized between replication cycles, with the origin of replication at one pole and the ter...

Genome-wide association studies have identified hundreds of genetic associations for complex psychiatric disorders and cognitive traits. However, interpretation of most of these findings is complicated by the presence of many significant and highly correlated genetic variants located in noncoding regions. Here, we address this issue by creating a high-resolution map of the three-dimensional (3D) genome organization by applying Hi-C to adult and fetal brain cortex with concomitant RNA-seq, open chromatin (ATAC-seq), and ChIP-seq data (H3K27ac, H3K4me3, and CTCF). Extensive analyses established the quality, information content, and salience of these new Hi-C data. We used these data to connect 938 significant genetic loci for schizophrenia, intelligence, ADHD, alcohol dependence, Page 2 Alzheimer's disease, anorexia nervosa, autism spectrum disorder, bipolar disorder, major depression, and educational attainment to 8,595 genes (with 42.1% of these genes implicated more than once). We show that assigning genes to traits based on proximity provides a limited view of the complexity of GWAS findings and that gene set analyses based on functional genomic data provide an expanded view of the biological processes involved in the etiology of schizophrenia and other complex brain traits.URLs 1000 Genomes Selection Browser,

Structure mapping experiments (using probes such as dimethyl sulfate [DMS], kethoxal, and T1 and V1 RNases) are used to determine the secondary structures of RNA molecules. The process is iterative, combining the results of several probes with constrained minimum free-energy calculations to produce a model of the structure. We aim to evaluate whether particular probes provide more structural information, and specifically, how noise in the data affects the predictions. Our approach involves generating ''decoy'' RNA structures (using the sFold Boltzmann sampling procedure) and evaluating whether we are able to identify the correct structure from this ensemble of structures. We show that with perfect information, we are always able to identify the optimal structure for five RNAs of known structure. We then collected orthogonal structure mapping data (DMS and RNase T1 digest) under several solution conditions using our high-throughput capillary automated footprinting analysis (CAFA) technique on two group I introns of known structure. Analysis of these data reveals the error rates in the data under optimal (low salt) and suboptimal solution conditions (high MgCl 2 ). We show that despite these errors, our computational approach is less sensitive to experimental noise than traditional constraint-based structure prediction algorithms. Finally, we propose a novel approach for visualizing the interaction of chemical and enzymatic mapping data with RNA structure. We project the data onto the first two dimensions of a multidimensional scaling of the sFold-generated decoy structures. We are able to directly visualize the structural information content of structure mapping data and reconcile multiple data sets.

The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into RiboNucleic Acid (RNA) can potentially affect cellular regulation (and cause disease) by altering the structure of the transcript. We performed a large-scale meta-analysis of Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data, which probes the structure of RNA. We found that several single point mutations exist that significantly disrupt RNA secondary structure in the five transcripts we analyzed. Thus, every RNA that is transcribed has the potential to be a “RiboSNitch;” where a SNP causes a large conformational change that alters regulatory function. Predicting the SNPs that will have the largest effect on RNA structure remains a contemporary computational challenge. We therefore benchmarked the most popular RNA structure prediction algorithms for their ability to identify mutations that maximally affect structure. We also evaluated metrics for rank ordering the extent of the structural change. Although no single algorithm/metric combination dramatically outperformed the others, small differences in AUC (Area Under the Curve) values reveal that certain approaches do provide better agreement with experiment. The experimental data we analyzed nonetheless show that multiple single point mutations exist in all RNA transcripts that significantly disrupt structure in agreement with the predictions.