Transcending the prediction paradigm: novel applications of SHAPE to RNA function and evolution

Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) provides information on RNA structure at single-nucleotide resolution. It is most often used in conjunction with RNA secondary structure prediction algorithms as a probabilistic or thermodynamic restraint. With the recent advent of ultra-high-throughput approaches for collecting SHAPE data, the applications of this technology are extending beyond structure prediction. In this review, we discuss recent applications of SHAPE data in the transcriptomic context and how this new experimental paradigm is changing our understanding of these experiments and RNA folding in general. SHAPE experiments probe both the secondary and tertiary structure of an RNA, suggesting that model-free approaches for within and comparative RNA structure analysis can provide significant structural insight without the need for a full structural model. New methods incorporating SHAPE at different nucleotide resolutions are required to parse these transcriptomic data sets to transcend secondary structure modeling with global structural metrics. These 'multiscale' approaches provide deeper insights into RNA global structure, evolution, and function in the cell. For further resources related to this article, please visit the WIREs website

Structure-Function Relationships of Long Non-Coding RNAs in Living Cells

From the beginning of the era of molecular biology in the 1960s until the 1980s, RNA was widely regarded as a passive cellular messenger. However, the importance of RNA has been steadily emerging over the last 30 years and we now know that it is often a critical and central component of genetic regulation. Recently, long non-coding RNAs (lncRNA) have become the focus of intense research because of their roles in development and disease. For most functional RNAs, complex structural characteristics underlie the biological function of the molecule. However, the difficulty of de novo RNA structure prediction and the relatively low abundance of lncRNA transcripts have been roadblocks to experimental structure probing. As a result, very little is known about the structural features of lncRNAs. In this work, I present experimental and analytical methods that enable chemical structure probing of rare RNA transcripts and identification of stable RNA-protein interaction sites. First, I show that polymerase chain reactions can be used as an enrichment strategy that faithfully maintains structure-probing data. I then outline an analytical framework that enables statistically rigorous detection of RNA-protein interactions in living cells. Finally, I apply these new methodologies to the Xist lncRNA and present a data-driven secondary structure model that highlights the extensive structures present throughout the transcript. I then identify nearly 200 specific sites where Xist is strongly impacted by the cellular environment and use them to identify several new protein interaction domains within Xist. Together, this work provides new experimental and analytical tools, as well as many new insights on the relationship between lncRNA structure and function, that will enable rapid study of lncRNA structures in the future.Doctor of Philosoph

Selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) for direct, versatile and accurate RNA structure analysis

SHAPE chemistries exploit small electrophilic reagents that react with the 2′-hydroxyl group to interrogate RNA structure at single-nucleotide resolution. Mutational profiling (MaP) identifies modified residues based on the ability of reverse transcriptase to misread a SHAPE-modified nucleotide and then counting the resulting mutations by massively parallel sequencing. The SHAPE-MaP approach measures the structure of large and transcriptome-wide systems as accurately as for simple model RNAs. This protocol describes the experimental steps, implemented over three days, required to perform SHAPE probing and construct multiplexed SHAPE-MaP libraries suitable for deep sequencing. These steps include RNA folding and SHAPE structure probing, mutational profiling by reverse transcription, library construction, and sequencing. Automated processing of MaP sequencing data is accomplished using two software packages. ShapeMapper converts raw sequencing files into mutational profiles, creates SHAPE reactivity plots, and provides useful troubleshooting information, often within an hour. SuperFold uses these data to model RNA secondary structures, identify regions with well-defined structures, and visualize probable and alternative helices, often in under a day. We illustrate these algorithms with the E. coli thiamine pyrophosphate riboswitch, E. coli 16S rRNA, and HIV-1 genomic RNAs. SHAPE-MaP can be used to make nucleotide-resolution biophysical measurements of individual RNA motifs, rare components of complex RNA ensembles, and entire transcriptomes. The straightforward MaP strategy greatly expands the number, length, and complexity of analyzable RNA structures

PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions

From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems

Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system.Doctor of Philosoph

Translational Repression of Bacteriophage T4 DNA Polymerase Biosynthesis

The research described in this dissertation elucidated the mechanism by which bacteriophage T4 DNA polymerase regulates its own biosynthesis. Utilizing both in vivo and in vitro studies, I have shown that autogenous repression occurs at the level of translation. While T4 mutants defective in the structural gene for DNA polymerase (gene 43) overproduce the protein product (gp43) in vivo, they do not overproduce the corresponding mRNA. In vitro, purified DNA polymerase specifically inhibited the translation of its own transcripts. Further, it was demonstrated that gp43 binds its own mRNA at a site overlapping the ribosome initiation domain. Thus, T4 DNA polymerase is a specific translational repressor that presumably inhibits initiation of translation. The mRNA binding site (translational operator) for DNA polymerase includes 38-40 nucleotides upstream of the initiator AUG. The 5\u27 half of this translational operator contains a putative five base-pair stem and 8-base loop, whose existence is inferred from RNase digestion experiments and computer-assisted analysis of RNA folding. To ascertain the important RNA sequence and structural determinants for DNA polymerase binding, I carried out a mutational analysis of the translational operator via the in vitro construction of several operator variants. Operator mutants were subsequently assayed for the effect of each mutation on: 1) gp43/mRNA binding, in vitro 2) the in vivo levels of gp43 biosynthesis from plasmid encoded constructs and 3) in vivo level of gp43 synthesis in phage infections (carried out after introducing mutant operators into the phage genome by virus-plasmid recombination). Mutations that either disrupted the stem or altered particular loop residues, led to diminished binding of purified T4 DNA polymerase in vitro and to derepression of protein synthesis in vivo. Compensatory mutations that restored the stern pairing, with a sequence other than wild-type, restored in vitro binding but still exhibited a mutant phenotype in vivo. Results from loop substitutions suggest that the spatial arrangement of specific loop residues is a major criterion for specific binding of DNA polymerase to its mRNA operator. These studies demonstrate the effectiveness of genetic approaches in dissecting the rules that govern RNA-protein interactions

GENOME-SCALE STRUCTUROMICS FOR THE DISCOVERY OF FUNCTIONAL TRANSCRIPT DOMAINS

Ph.DDOCTOR OF PHILOSOPH

Microbial Analysis of Orange Complex Organisms of the Whole Saliva in Patients with Gingivitis and Gingival Recession using Next Generation Sequencing

BACKGROUND: Gingivitis is a reversible non-specific inflammatory reaction of the marginal gingiva but always precedes periodontitis. Gingival recession may occur as loss of attachment and inflammatory exacerbation due to accumulation of local factors, which results in the apical migration of gingival margin. It may be a phenotypic form of periodontitis. Individual susceptibility may be important for transition from gingivitis to periodontitis and has been examined using various risk markers such as genetic, microbial and immunological. The orange complex bacteria were thought to be the bridging species that represent a change between gingivitis and periodontitis, may be used as putative risk markers. AIM OF THE STUDY: This study aims to analyse and compare the prevalence of salivary orange complex bacterial species in periodontal health, gingivitis and gingival recession. OBJECTIVES: 1. To evaluate the Orange complex species in the saliva in Gingival health, Gingivitis and Gingival Recession patients using Next Generation Sequencing technique with Illumina sequencing method. 2. To compare the frequency distribution of salivary Orange complex bacteria in healthy, Gingivitis and Gingival Recession individuals. MATERIALS AND METHODS: In this study, Subjects were Periodontally evaluated and allocated into three groups as healthy controls (ten subjects), Gingivitis patients (ten subjects) and Gingival recession (ten patients). Orange complex microbiome was evaluated from Gingival health, Gingivitis and Gingival recession individuals using NGS technology with Illumina sequencing. Amplicons from V3-V4 hypervariable regions of 16S rRNA gene were sequenced. The frequency of distribution of Orange complex bacteria in Health, Gingivitis and Gingival recession were measured with Chi-square test. RESULTS: There was a statistically significant increase in the distribution of 5 organisms (Prevotella nigrescens P=0.008, S. constellatus P= 0.001, C. rectus P=0.014, P. intermedia P=0.015, C. gracilis P=0.001) in gingivitis and gingival recession group when compared to health. There was no statistically significant difference in distribution of Orange complex organisms between the gingivitis & gingival recession group. CONCLUSION: Members of the orange complex seem to be suitable candidates for use as microbial risk factors in gingivitis and gingival recession