Dynamic Energy Landscapes of Riboswitches Help Interpret Conformational Rearrangements and Function

Riboswitches are RNAs that modulate gene expression by ligand-induced conformational changes. However, the way in which sequence dictates alternative folding pathways of gene regulation remains unclear. In this study, we compute energy landscapes, which describe the accessible secondary structures for a range of sequence lengths, to analyze the transcriptional process as a given sequence elongates to full length. In line with experimental evidence, we find that most riboswitch landscapes can be characterized by three broad classes as a function of sequence length in terms of the distribution and barrier type of the conformational clusters: low-barrier landscape with an ensemble of different conformations in equilibrium before encountering a substrate; barrier-free landscape in which a direct, dominant “downhill” pathway to the minimum free energy structure is apparent; and a barrier-dominated landscape with two isolated conformational states, each associated with a different biological function. Sharing concepts with the “new view” of protein folding energy landscapes, we term the three sequence ranges above as the sensing, downhill folding, and functional windows, respectively. We find that these energy landscape patterns are conserved in various riboswitch classes, though the order of the windows may vary. In fact, the order of the three windows suggests either kinetic or thermodynamic control of ligand binding. These findings help understand riboswitch structure/function relationships and open new avenues to riboswitch design

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

Computational Investigation of the Switching Mechanism in Riboswitches

Gene Regulation is one of the most important mechanisms at the basis of the widespread diffusion and versatility of organism. It provides the cell with an effective control means over structure and functions, with a direct connection to cellular differentiation, morphogenesis, and adaptability. Until recently it has been common belief that only proteins were involved in gene regulation; later, regulatory functions have been discovered also for non-coding RNA; in particular, since the discovery of riboswitches such a simplistic perception has changed. Riboswitches are regulatory elements, usually found in 5' untranslated regions of bacterial mRNA, directly interacting with metabolites as a means of regulating expression of the coding region via a secondary structural switch. Modelling riboswitch structural rearrangement would be a greater advance, not only for understanding key processes of this RNA functional elements, but will also speed up riboswitches engineering leading to a huge improvement in biotechnological application of these genetic switches

Hierarchical dynamics of individual RNA helix base pair formation and disruption

This thesis explores the RNA folding problem using single-molecule field effect transistors (smFETs) to measure the lifetimes of individual RNA base-pairing rearrangements. In the course of this research, considerable computational, chemical, and engineering contributions were developed so that the single-molecule measurements could be conducted and quantified. These advancements have allowed, on the basis of the smFET data collected herein, the quantification of a kinetic model for RNA stem-loop structures which has been generalized to quantitatively explore the phenomenological observation that an RNA found in the bacillus subtilis strain acts as a metabolite-sensing switch, allowing RNA polymerase to transcribe the messenger RNA when the metabolite is present and preventing transcription when the metabolite is absent. Together, the data presented quantify a simple model for the base pairing rearrangements that underlie RNA folding

Conformational dynamics in microRNAs : the example of miR-34a targeting Sirt1 mRNA

In biology, regulatory mechanisms are essential to achieve complex tasks, as virtually every process can be positively or negatively modulated in its outcome, upon different cues. In humans, microRNAs (miRNAs) constitute a fundamental layer of post-transcriptional gene expression regulation. This class of molecules finely tune protein expression, by downregulating messenger RNAs (mRNA) levels and their translation. The mechanism by which miRNAs find and act upon their targets primarily relies on their nucleotide sequence, relative to the corresponding binding site on the mRNA. The development of an exhaustive miRNA–mRNA interactome is particularly attractive because of the profound implication for basic biology as well as for diagnostics and therapeutics in human health. However, computational prediction of target sites and associated downregulation levels, using the limited sequence determinants available, is still an outstanding challenge in the field. In this thesis, we bring forward the hypothesis that modeling of miRNA–mRNA pairs might benefit from considering the inherent structural flexibility of these complexes, at the molecular level. In the introductory chapter, we present the structural features of RNAs with a focus on their conformational dynamics and NMR spectroscopy as a tool to investigate these motions. The molecular details of miRNA biogenesis and function are later introduced to contextualize the results of Paper I. Finally, the challenges associated with RNA sample preparation are discussed in light of the work presented in Paper II. In Paper I, we show that a miRNA–mRNA pair involved in a cancer-regulating pathway exploits its flexibility to toggle between lower and higher target repression states. This study shows that suboptimal structures of a given miRNA–mRNA pair, that are overlooked by computational prediction and that often elude experimental detection, can be functionally relevant and are essential to draw a mechanistic picture of miRNA function. The methods used in Paper I for RNA sample preparation and molecular simulation are described in Paper II and II, respectively. While these methods were essential to achieve the results of Paper I, they also find widespread application in the RNA field

Conformational changes in the adenine riboswitch

Riboswitches are cis-acting genetic control elements that have been found in the un- traslated region of some mRNAs in bacteria and plants. Riboswitches are known to regulate the genetic expression by means of conformational changes triggered by highly specific interactions of the aptamer with the sensed metabolite. The non-coding sequence in the mRNA of add gene from V. vulnificus contains an adenine responsive riboswitch. Classical molecular dynamics simulations of its aptamer have been performed, both in presence and absence of its physiological ligand starting from the experimental crystal structure. We first use steered MD to induce the opening of the P1 stem and investigate its stability. Our results show that the ligand directly stabilizes the P1 stem by means of stacking interactions quantitatively consistent with thermodynamic data. Then, using both umbrella sampling and a combination of metadynamics and hamiltonian replica exchange, we show that the formation of L2-L3 kissing complex cooperates with ligand binding and we quantify the ligand-induced stabilization. In this context also the influ- ence given by either the monovalent cations or divalent cations was evaluated. Confor- mational changes at pairings detailed level are characterized using a recently introduced technique that is able to distinguish and classify each interaction (i.e. Watson-Crick base pair, non-canonical bp, stacking). Results are compatible with known experimental measurements and shed a new light on the ligand-dependent folding mechanism of the adenine riboswitch

Synthetic Regulation of Eukaryotic Gene Expression by Noncoding RNA

Synthetic biological systems promise to combine the spectacular diversity of biological functionality with engineering principles to design new life to address many pressing needs. As these engineered systems advance in sophistication, there is ever-greater need for customizable, situation-specific expression of desired genes. However, existing gene control platforms are generally not modular, or do not display performance requirements required for robust phenotypic responses to input signals. This work expands the capabilities of eukaryotic gene control in two important directions. For development of greater modularity, we extend the use of synthetic self-cleaving ribozyme switches to detect changes in input protein levels and convey that information into programmed gene expression in eukaryotic cells. We demonstrate both up- and down-regulation of levels of an output transgene by more than 4-fold in response to rising input protein levels, with maximal output gene expression approaching the highest levels observed in yeast. In vitro experiments demonstrate protein-dependent ribozyme activity modulation. We further demonstrate the platform in mammalian cells. Our switch devices do not depend on special input protein activity, and can be tailored to respond to any input protein to which a suitable RNA aptamer can be developed. This platform can potentially be employed to regulate the expression of any transgene or any endogenous gene by 3’ UTR replacement, allowing for more complex cell state-specific reprogramming. We also address an important concern with ribozyme switches, and riboswitch performance in general, their dynamic range. While riboswitches have generally allowed for versatile and modular regulation, so far their dynamic ranges of output gene modulation have been modest, generally at most 10-fold. We address this shortcoming by developing a modular genetic amplifier for near-digital control of eukaryotic gene expression. We combine ribozyme switch-mediated regulation of a synthetic TF with TF-mediated regulation of an output gene. The amplifier platform allows for as much as 20-fold regulation of output gene expression in response to input signal, with maximal expression approaching the highest levels observed in yeast, yet being tunable to intermediate and lower expression levels. EC50 values are more than 4 times lower than in previously best-performing non-amplifier ribozyme switches. The system design retains the modular-input architecture of the ribozyme switch platform, and the near-digital dynamic ranges of TF-based gene control. Together, these developments suggest great potential for the wide applicability of these platforms for better-performing eukaryotic gene regulation, and more sophisticated, customizable reprogramming of cellular activity.</p

Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data

Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid (RNA) structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2’ Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by “gel gazing.” SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human “gazing” by identifying features of the SHAPE profile that human experts agree “looks” like a riboSNitch. Additionally, when an RNA molecule folds, it does not always adopt a single, well-defined conformation. The folding energy landscape of the RNA is highly dependent on sequence and the molecular environment. Endogenous molecules, especially in the cellular context, will in some cases completely alter the energy landscape and therefore the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for larger RNAs including most messenger RNAs (mRNAs). We propose here a robust approach for visualizing the conformational ensemble of RNAs particularly well suited for in vitro vs. in vivo comparisons.Doctor of Philosoph

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure