Polarization-dependence of palladium deposition on ferroelectric lithium niobate (0001) surfaces

We investigate the effect of ferroelectric polarization direction on the geometric properties of Pd deposited on the positive and negative surfaces of LiNbO$_3$ (0001). We predict preferred geometries and diffusion properties of small Pd clusters using density functional theory, and use these calculations as the basis for kinetic Monte Carlo simulations of Pd deposition on a larger scale. Our results show that on the positive surface, Pd atoms favor a clustered configuration, while on the negative surface, Pd atoms are adsorbed in a more dispersed pattern due to suppression of diffusion and agglomeration. This suggests that the effect of LiNbO$_3$ polarization direction on the catalytic activity of Pd [J. Phys. Chem. \textbf{88}, 1148 (1984)] is due, at least in part, to differences in adsorption geometry. Further investigations using these methods can aid the search for catalysts whose activities switch reversibly with the polarization of their ferroelectric substrates

High-resolution Xist binding maps reveal 2-step spreading during X-inactivation

The Xist long noncoding RNA (lncRNA) is essential for X-chromosome inactivation (XCI), the process by which mammals compensate for unequal numbers of sex chromosomes1-3. During XCI, Xist coats the future inactive X (Xi)4 and recruits Polycomb Repressive Complex 2 (PRC2) to the X-inactivation center (Xic)5. How Xist spreads silencing on a 150 Mb scale is unclear. Here we generate high-resolution maps of Xist binding on the X chromosome across a developmental time course using CHART-seq. In female cells undergoing XCI de novo, Xist follows a two-step mechanism, initially targeting gene-rich islands before spreading to intervening gene-poor domains. Xist is depleted from genes that escape XCI but may concentrate near escapee boundaries. Xist binding is linearly proportional to PRC2 density and H3 lysine 27 trimethylation (H3K27me3), suggesting co-migration of Xist and PRC2. Interestingly, when the Xi is acutely stripped off Xist in post-XCI cells, Xist recovers quickly within both gene-rich and -poor domains on a time-scale of hours instead of days, suggesting a previously primed Xi chromatin state. We conclude that Xist spreading takes distinct stage-specific forms: During initial establishment, Xist follows a two-step mechanism, but during maintenance, Xist spreads rapidly to both gene-rich and -poor regions

UV Irradiation Induces a Non-coding RNA that Functionally Opposes the Protein Encoded by the Same Gene

The transcription-related DNA damage response was analyzed on a genome-wide scale with great spatial and temporal resolution. Upon UV irradiation, a slowdown of transcript elongation and restriction of gene activity to the promoter-proximal ∼25 kb is observed. This is associated with a shift from expression of long mRNAs to shorter isoforms, incorporating alternative last exons (ALEs) that are more proximal to the transcription start site. Notably, this includes a shift from a protein-coding ASCC3 mRNA to a shorter ALE isoform of which the RNA, rather than an encoded protein, is critical for the eventual recovery of transcription. The non-coding ASCC3 isoform counteracts the function of the protein-coding isoform, indicating crosstalk between them. Thus, the ASCC3 gene expresses both coding and non-coding transcript isoforms with opposite effects on transcription recovery after UV-induced DNA damage

Development of Computational Tools to Analyze New Experimental Technologies for the Study of Noncoding RNA

The advent of high throughput DNA sequencing has vastly accelerated transcriptome-wide profiling of RNA, revealing thousands of new noncoding RNA genes in humans and across the phylogenetic tree. Many of these noncoding RNAs are similar in length and processing to messenger RNAs and are referred to as long noncoding RNAs (lncRNAs). Some lncRNAs had been identified decades earlier and have genetic and biochemical evidence for function, e.g. the Xist RNA, which is the master regulator of X-chromosome inactivation in female mammals. Meanwhile, the functions (or lack thereof) of many lncRNA genes are unclear, and the detailed mechanisms of lncRNAs with known functions are also often unknown. Beyond identification of new RNA genes, high throughput sequencing has also enabled the adaptation of biochemical methods that were traditionally read out for one target RNA at a time to a transcriptome-wide scale, while sometimes revealing new types of information or making possible the study of RNAs within complex or in vivo samples. This enables unprecedented characterization of the activities of both noncoding RNA genes and regulatory regions within messenger RNAs, providing potentially critical information. Each new assay brings specific analysis challenges, including data normalization, scale of interpretation, statistical overdispersion, and limited numbers of replicate experiments. In this thesis, I have developed and applied computational and statistical methods to aid the interpretation of new technologies for the study of noncoding RNA. In the first chapter, I review the state of the field for the study of lncRNAs and general analysis challenges presented in the interpretation of high throughput sequencing data. In the second and third chapters, I describe preliminary work in my PhD analyzing two technologies developed by collaborators: Capture Hybridization of RNA Targets (CHART) to reveal the spreading pattern of the Xist RNA across the X chromosome (ch. 2); and separation of labeled RNA populations using improved disulfide chemistry for the study of RNA dynamics (ch. 3). In the fourth chapter, I develop a new analysis method to model the statistical overdispersion of RNA chemical probing data and apply this model to investigate the contribution of variability in chemical probing data on resulting RNA secondary structure predictions. The methods described here may facilitate the use of the described technologies for integrative analysis to help distinguish candidate lncRNAs and specific regions within them for further study, as well as RNA regulatory regions in which mutations may cause disease

Probing Xist RNA Structure in Cells Using Targeted Structure-Seq

<div><p>The long non-coding RNA (lncRNA) Xist is a master regulator of X-chromosome inactivation in mammalian cells. Models for how Xist and other lncRNAs function depend on thermodynamically stable secondary and higher-order structures that RNAs can form in the context of a cell. Probing accessible RNA bases can provide data to build models of RNA conformation that provide insight into RNA function, molecular evolution, and modularity. To study the structure of Xist in cells, we built upon recent advances in RNA secondary structure mapping and modeling to develop Targeted Structure-Seq, which combines chemical probing of RNA structure in cells with target-specific massively parallel sequencing. By enriching for signals from the RNA of interest, Targeted Structure-Seq achieves high coverage of the target RNA with relatively few sequencing reads, thus providing a targeted and scalable approach to analyze RNA conformation in cells. We use this approach to probe the full-length Xist lncRNA to develop new models for functional elements within Xist, including the repeat A element in the 5’-end of Xist. This analysis also identified new structural elements in Xist that are evolutionarily conserved, including a new element proximal to the C repeats that is important for Xist function.</p></div

DMS reactivity accurately identifies accessible nucleotides in 18S rRNA.

<p><b>A.</b> DMS reactivity profile (red) for A and C bases in the 18S rRNA. Data is shown for treatment with three different DMS conditions (low: 0.4% DMS v/v, 8 min; intermediate: 0.75% v/v DMS, 8 min; high: 2% DMS for 4 min. To emphasize the lower values, some positions (low, n = 17; intermediate, n = 19; high, n = 19) have reactivity above the maximum value shown (<i>P</i>_DMS(<i>i</i>) > 0.1). <b>B.</b> The location of strongly and moderately DMS-reactive nucleotides mapped onto a model of part of the murine 18S rRNA [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005668#pgen.1005668.ref043" target="_blank">43</a>]. The only conflict between the mapping data and the model in this region is indicated with an asterisk (415 nt). The data for the remainder of the structure are shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005668#pgen.1005668.s008" target="_blank">S3 Fig</a>.</p

Models for RNA conformation in the region containing the Xist C repeats.

<p><b>A.</b> A model for the region of Xist (3101–5090 nt) that overlaps the C repeats. The 14 tandem C repeats alternate in color between green and purple (to facilitate visualization). <b>B.</b> Detailed image depicting the repeated motif found in the C repeats (specifically for 4006-4125 nt) using notation as indicated in Figs <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005668#pgen.1005668.g004" target="_blank">4</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005668#pgen.1005668.g005" target="_blank">5</a>. <b>C.</b> Similar detailed image depicting a model of the non-repetitive region 3’ to the last C repeat which shows high evolutionary conservation. The target sequence of LNA-4978, which is predicted to disrupt the structure, and knocks Xist off the chromatin, is shown in yellow. For alignment, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005668#pgen.1005668.s041" target="_blank">S36 Fig</a>.</p

Examining the conformations of elements within Xist RNA using a mixture of computational approaches and Targeted Structure-Seq.

<p><b>A.</b> Xist RNA (purple) contains several repetitive elements (indicated as thick regions, labeled A-F), and a conserved stem-loop structure in exon 4 (labeled as e4SL). The predicted <i>z</i>-score for 150 nt windows calculated every 10 nt (negative and positive values in gray and blue, respectively). Boxes indicate overlapping windows with <i>z</i>-score <1<i>σ</i> Xist average, classified as low <i>z</i>-score regions. <b>B.</b> An overview of Targeted Structure-Seq. A target RNA (purple) can be methylated (red circles) with dimethylsulfate (DMS, shown with red methyl group) in cells. After RNA isolation, the sites of methylation are determined using reverse transcription with primers specific for the RNA of interest (purple) but not other RNAs (gray). Sequencing and analysis of these data can be used to determine the DMS reactivity at each base, and can aid in modeling of the cellular conformation of the RNA. <b>C.</b> To determine which termination events were caused by DMS (red) as opposed to spontaneous termination (blue), the termination and read-through events are counted. Using an untreated control to estimate the rate of spontaneous termination, the data is normalized to determine the DMS reactivity at each base (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005668#sec015" target="_blank">Materials and Methods</a> for details). These data can be used for free energy minimization to model the RNA conformation, which can be further validated by examining the evolutionary conservation of base pairing.</p