Towards structural models for the Ebola UTR regions using experimental SHAPE probing data

National audienceNext-Generation Sequencing (NGS) technologies have opened new perspectives to refine the process of predicting the secondary structure(s) of structured non-coding RNAs. Herein, we describe an integrated modeling strategy, based on the SHAPE chemistry, to infer structural insight from deep se-quencing data. Our approach is based on a pseudo-energy minimization, incorporating additional information from evolutionary data (compensatory mutations) and SHAPE experiments (reactivity scores) within an iterative procedure. Preliminary results reveal conserved and stable structures within UTRs of the Ebola Genome, that are both thermodynamically-stable and highly supported by SHAPE accessibility analysis

New evidence of a mitochondrial genetic background paradox: Impact of the J haplogroup on the A3243G mutation

International audienceBackground: The A3243G mutation in the tRNALeu gene (UUR), is one of the most common pathogenic mitochondrial DNA (mtDNA) mutations in France, and is associated with highly variable and heterogeneous disease phenotypes. To define the relationships between the A3243G mutation and mtDNA backgrounds, we determined the haplogroup affiliation of 142 unrelated French patients – diagnosed as carriers of the A3243G mutation – by control-region sequencing and RFLP survey of their mtDNAs. Results: The analysis revealed 111 different haplotypes encompassing all European haplogroups, indicating that the 3243 site might be a mutational hot spot. However, contrary to previous findings, we observed a statistically significant underepresentation of the A3243G mutation on haplogroup J in patients (p = 0.01, OR = 0.26, C.I. 95%: 0.08–0.83), suggesting that might be due to a strong negative selection at the embryo or germ line stages. Conclusion: Thus, our study supports the existence of mutational hotspot on mtDNA and a "haplogroup J paradox," a haplogroup that may increase the expression of mtDNA pathogenic mutations, but also be beneficial in certain environmental contexts

Développement d'une stratégie innovante de modélisation de la structure des ARN : étude des mécanismes moléculaires des IRES de type III

RNA is a macromolecule composed of nitrogenous bases that can match each other and adopt various structures (secondary, tertiary). This secondary structure consists in the combination of different patterns such as helix, loops that are interconnected by junctions. The characterization of this structure can be carried out using biophysical techniques (crystallography, NMR and Cryo-EM) but also by biochemical techniques called "RNA structure probing". Biological macromolecules (RNAses) or small chemical molecules can probe the structure of RNA by identifying nucleotides in single strand conformation. At the same time, softwares based on thermodynamic calculations, make it possible to predict secondary structures (RNAStructure, RNAFold...). The proposed structure corresponds to the structure with the lowest calculated free energy. Some of these programs can use experimental probing data as constraints to improve the prediction of the secondary structure. However these predictions remain inaccurate for molecules longer than 100 nucleotides. In particular because the thermodynamic model is incomplete but also because an RNA adopts several conformations. Based on this observation and in collaboration with Dr. Ponty, the first part of my thesis was devoted to the development of new strategies for modeling the secondary structure of RNAs. This method is based on multi-probing experiments on an original benchmark RNA : the ribozyme of Didymium iridis. According to the model obtained by X-ray diffraction, this RNA possesses numerous non-canonical base pair (base pair rarely predicted by the prediction software), several pseudoknots and the capacity to form two conformers. A first strategy to develop a model from multiple experimental data was developed and evaluated. Then, a mutant bank was created and probed in order to improve the algorithms the prediction of the structures and especially to detect the alternative conformations. The second part of my thesis is dedicated to the study of the molecular mechanisms of IRES type III. IRES (Internal Ribosome Entry Site) are structures present in the 5' untranslated region (5'UTR) or in the intergenic regions of certain cellular or viral RNAs. These structures allow the recruitment of the translational machinery directly or near the initiator codon. IRES are classified into four categories based on their structure and the cellular factors required to recruit the translational machinery. Type III IRES, epitomized by the Hepatitis C Virus (HCV) IRES, has stable structures in the 5'UTR region organized into three domains and recruits a small number of initiation factors (eIFs). Other viruses such as Classical Swine Fever Virus (CSFV), Seneca Valley Virus (SVV) or border virus (BDV) have similar IRES with the exception of an additional subdomain called IIId2. The role of this sub-domain is not clear, in collaboration with several teams we studied its impact on the translation and replication of these viruses.L'ARN est une macromolécule composée de bases azotées pouvant s'apparier entre elles et adopter diverses structures (secondaire, tertiaire). Cette structure secondaire est constituée de différents motifs comme des hélices, des boucles reliés entre-elles par des jonctions. La caractérisation de cette structure peut être réalisée au moyen de techniques biophysiques (cristallographie, RMN et Cryo-EM) mais également aux moyens de techniques biochimiques dites de « probing ». Des macromolécules biologiques (RNAses) ou de petites molécules chimiques permettent de sonder la structure de l'ARN en identifiant les nucléotides en simples brins. Parallèlement, il existe des logiciels qui, basés sur des calculs thermodynamiques, permettent de prédire les structures secondaires (RNAStructure, RNAFold...). La structure proposée correspond à la structure ayant l'énergie libre calculée la plus faible. Certains de ces logiciels peuvent utiliser les données expérimentales de probing comme contraintes afin d'améliorer la prédiction de la structure secondaire. Cependant ces prédictions restent inexactes pour des molécules de plus de 100 nucléotides, ceci notamment parce que le modèle thermodynamique est incomplet mais aussi parce qu'un ARN adopte plusieurs conformations. Partant de ce constat et en collaboration avec le Dr Ponty, la première partie de ma thèse a été consacrée au développement de nouvelles stratégies de modélisation de la structure secondaire des ARN. Cette méthode est basée sur des expériences de multi-probing sur un ARN de référence original : le ribozyme de Didymium iridis. D'après le modèle obtenu par diffraction aux rayons X, cet ARN possède de nombreux appariements non canoniques (appariements rarement prédit par les logiciels de prédiction), plusieurs pseudonoeuds et existe sous la forme de deux conformères. Une première stratégie qui permet d'élaborer un modèle à partir de données expérimentales multiples été développée et évaluée. Ensuite, une banque de mutants a été créée et sondée afin d'améliorer les algorithmes la prédiction des structures et surtout pour détecter les conformations alternatives. La seconde partie de ma thèse est dédiée à l'étude des mécanismes moléculaires des IRES de type III. Les IRES (Site d'Entrée Interne des Ribosomes) sont des structures présentes dans la région 5' non traduite (5'UTR) ou dans les régions intergéniques de certains ARN cellulaires ou viraux. Ces structures permettent le recrutement de la machinerie traductionnelle directement ou à proximité du codon initiateur. Les IRES sont classés en quatre catégories en fonction de leur structure et des facteurs cellulaires nécessaires au recrutement de la machinerie traductionnelle. Les IRES de type III dont le chef de file est l'IRES du virus de l'hépatite C (HCV) possèdent des structures stables dans la région 5'UTR organisées en trois domaines et recrute un faible nombre de facteurs de l'initiation (eIFs). D'autres virus comme ceux de de la peste classique du porc (CSFV), Seneca Valley (SVV) ou le virus des frontières (BDV) possèdent des IRES semblables à l'exception d'un sous-domaine supplémentaire appelé IIId2. Le rôle de ce sous domaine n'est pas clair, en collaboration avec plusieurs équipes nous avons étudié son impact sur la traduction et la réplication de ces virus

Development of an innovative strategy for modeling RNA structure : study of the molecular mechanisms of type III IRES

L'ARN est une macromolécule composée de bases azotées pouvant s'apparier entre elles et adopter diverses structures (secondaire, tertiaire). Cette structure secondaire est constituée de différents motifs comme des hélices, des boucles reliés entre-elles par des jonctions. La caractérisation de cette structure peut être réalisée au moyen de techniques biophysiques (cristallographie, RMN et Cryo-EM) mais également aux moyens de techniques biochimiques dites de « probing ». Des macromolécules biologiques (RNAses) ou de petites molécules chimiques permettent de sonder la structure de l'ARN en identifiant les nucléotides en simples brins. Parallèlement, il existe des logiciels qui, basés sur des calculs thermodynamiques, permettent de prédire les structures secondaires (RNAStructure, RNAFold...). La structure proposée correspond à la structure ayant l'énergie libre calculée la plus faible. Certains de ces logiciels peuvent utiliser les données expérimentales de probing comme contraintes afin d'améliorer la prédiction de la structure secondaire. Cependant ces prédictions restent inexactes pour des molécules de plus de 100 nucléotides, ceci notamment parce que le modèle thermodynamique est incomplet mais aussi parce qu'un ARN adopte plusieurs conformations. Partant de ce constat et en collaboration avec le Dr Ponty, la première partie de ma thèse a été consacrée au développement de nouvelles stratégies de modélisation de la structure secondaire des ARN. Cette méthode est basée sur des expériences de multi-probing sur un ARN de référence original : le ribozyme de Didymium iridis. D'après le modèle obtenu par diffraction aux rayons X, cet ARN possède de nombreux appariements non canoniques (appariements rarement prédit par les logiciels de prédiction), plusieurs pseudonoeuds et existe sous la forme de deux conformères. Une première stratégie qui permet d'élaborer un modèle à partir de données expérimentales multiples été développée et évaluée. Ensuite, une banque de mutants a été créée et sondée afin d'améliorer les algorithmes la prédiction des structures et surtout pour détecter les conformations alternatives. La seconde partie de ma thèse est dédiée à l'étude des mécanismes moléculaires des IRES de type III. Les IRES (Site d'Entrée Interne des Ribosomes) sont des structures présentes dans la région 5' non traduite (5'UTR) ou dans les régions intergéniques de certains ARN cellulaires ou viraux. Ces structures permettent le recrutement de la machinerie traductionnelle directement ou à proximité du codon initiateur. Les IRES sont classés en quatre catégories en fonction de leur structure et des facteurs cellulaires nécessaires au recrutement de la machinerie traductionnelle. Les IRES de type III dont le chef de file est l'IRES du virus de l'hépatite C (HCV) possèdent des structures stables dans la région 5'UTR organisées en trois domaines et recrute un faible nombre de facteurs de l'initiation (eIFs). D'autres virus comme ceux de de la peste classique du porc (CSFV), Seneca Valley (SVV) ou le virus des frontières (BDV) possèdent des IRES semblables à l'exception d'un sous-domaine supplémentaire appelé IIId2. Le rôle de ce sous domaine n'est pas clair, en collaboration avec plusieurs équipes nous avons étudié son impact sur la traduction et la réplication de ces virus.RNA is a macromolecule composed of nitrogenous bases that can match each other and adopt various structures (secondary, tertiary). This secondary structure consists in the combination of different patterns such as helix, loops that are interconnected by junctions. The characterization of this structure can be carried out using biophysical techniques (crystallography, NMR and Cryo-EM) but also by biochemical techniques called "RNA structure probing". Biological macromolecules (RNAses) or small chemical molecules can probe the structure of RNA by identifying nucleotides in single strand conformation. At the same time, softwares based on thermodynamic calculations, make it possible to predict secondary structures (RNAStructure, RNAFold...). The proposed structure corresponds to the structure with the lowest calculated free energy. Some of these programs can use experimental probing data as constraints to improve the prediction of the secondary structure. However these predictions remain inaccurate for molecules longer than 100 nucleotides. In particular because the thermodynamic model is incomplete but also because an RNA adopts several conformations. Based on this observation and in collaboration with Dr. Ponty, the first part of my thesis was devoted to the development of new strategies for modeling the secondary structure of RNAs. This method is based on multi-probing experiments on an original benchmark RNA : the ribozyme of Didymium iridis. According to the model obtained by X-ray diffraction, this RNA possesses numerous non-canonical base pair (base pair rarely predicted by the prediction software), several pseudoknots and the capacity to form two conformers. A first strategy to develop a model from multiple experimental data was developed and evaluated. Then, a mutant bank was created and probed in order to improve the algorithms the prediction of the structures and especially to detect the alternative conformations. The second part of my thesis is dedicated to the study of the molecular mechanisms of IRES type III. IRES (Internal Ribosome Entry Site) are structures present in the 5' untranslated region (5'UTR) or in the intergenic regions of certain cellular or viral RNAs. These structures allow the recruitment of the translational machinery directly or near the initiator codon. IRES are classified into four categories based on their structure and the cellular factors required to recruit the translational machinery. Type III IRES, epitomized by the Hepatitis C Virus (HCV) IRES, has stable structures in the 5'UTR region organized into three domains and recruits a small number of initiation factors (eIFs). Other viruses such as Classical Swine Fever Virus (CSFV), Seneca Valley Virus (SVV) or border virus (BDV) have similar IRES with the exception of an additional subdomain called IIId2. The role of this sub-domain is not clear, in collaboration with several teams we studied its impact on the translation and replication of these viruses

Maturation cervicale par la dinoprostone (comparaison rétrospective de deux méthodes)

ROUEN-BU Médecine-Pharmacie (765402102) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Computational methods for comparing and integrating multiple probing assays to predict RNA secondary structure

International audience1- Introduction:RNA structure is a key to understand retroviruses’s mechanisms e.g. HIV. Many prediction approaches suggesting accurate structures are available but they could be further improved by both taking advantage of Next generation sequencing technology and new experimental techniques (Enzymatic and SHAPE).2 - Experimental probing dataIn this poster, we present an integrative approach based on using many experimental data, resulting from sequencing, to direct predictions with the aim to find an accurate structure lying in the intersection of different sources of experiments. From one side, to reveal single nucleotide, reactivity profiles resulting from a SHAPE technology were used as “soft constraints”, meaning that the reactivity values were translated into pseudo-energies as described (Lorenz et al, 2016). From the other side, RNAses cleavage was used with two enzymes V and T targeting respectively paired and unpaired nucleotides. Reactivity scores resulting from those two experiments are used as hard constraints, forcing positions that exceed a specific threshold to be paired(case of Enzymatic-V) or unpaired(case of Enzymatic-T).3-stochastic sampling:At the thermodynamic equilibrium, a given RNA can have many alternative structures, where each structure could be characterized by a probability within the space of all the possible conformations (Boltzmann ensemble). This probability is related to the energy of the structure, the highest the energy needed to break pairs present in the structure the highest is its probability in the ensemble.We admit that the optimal structure(s) should be energetically stable and supported by several experimental data. For this reason, we coupled a stochastic sampling from the Boltzmann ensembles associated with the experimentally derived constraints, with a clustering across experimental conditions, to generate a structural models that are well-supported by available data.4-The work-flow description: 1. Experimental data from different conditions(SHAPE,Enzymatic-T, Enzymatic-V) were analysed to extract reactivity profiles that will serve as constraints. 2. We sampled 2000 structures per condition: We perform a Boltzmann sampling (Ding et al, 2005) to generate a predefined number of stable structures, compatible with the constraints derived for each condition. We used the stochastic sampling mode of RNAsubopt (-p option) to generate energetically stable structures that are either fully compliant with constraints derived from enzymatic data (hard constraints(Mathews et al,2004)), or constitute reasonable trade-offs between thermodynamic stability and compatibility with SHAPE data (soft constraints, using thepseudo-potentials of Deigan et al. (Deigan et al, 2009), see (Lorenz et al,2016) for details). 3. We merge the structures while keeping labels to retain the origin of each structure. 4. In order to detect structures with affinity to each other, The merged sets of models were clustered, using the base-pair distance as a measure of dissimilarity, the distance between two structures corresponds to the number of base pairs needed to break and to build in order to go from a structure to an other. A clustering algorithms (affinity propagation (Wang et al, 2007) implemented in the scikit-learn Python package (Pedregosa et al, 2011) is used to agglomerate and identify recurrent structures. One of the advantages of affinity propagation resides in its low computational requirements. 5. The next step consists on identifying clusters that are homogeneous, stable and well supported by experimental evidences, leading to the identification of the following objective criteria:-Present conditions that informs as about the diversity of the cluster: Our primary target are clusters compatible with multiple experimental conditions. However, the larger sampled sets required for reproducibility tend to populate each cluster with structures fromall conditions. We thus associated with each cluster the number of represented conditions, defined as the number of conditions for whichthe accumulated Boltzmann probability in the cluster exceeds a predefined threshold.-Boltzmann weight that is a measure of stability: Structures that are found in a given cluster may be unstable, and should be treated asoutliers. For this reason, we computed the cumulated normalized Boltzmann probabilities within the cluster, to favor stable clustersconsisting of stable structures;- Average Cluster Distance to count for coherence: We observed a general tendency of clustering algorithms to create heterogeneousclusters when faced with noisy data. We thus associated with each cluster the mean distance between pairs of structures, estimated asthe average distance to the MEA (Lu et al, 2009) for the sake of efficiency, in order to neglect clusters that were too diverse.6. The next steps consist on choosing cluster(s) with high coherence, diversity and stability. for this purpose we restricted our analysis to clusters that were found on the 3D Pareto Frontier (Mattson Messac, 2005) with respect to the three mentioned above criteria .7. After detecting the optimal Pareto cluster(s), we need to identify representative structure for each cluster. We chose the maximum expectedaccuracy (MEA) structure (Lu et al, 2009) as the representative structure for each cluster, which is defined as the secondary structure whose structural elements have highest accumulated Boltzmann probability within the cluster.5 Results:This resulted in 2 structures which we narrowed down to a single candidate using compatibility with the 1M7 SHAPE data as a final discriminatory criterion

IPANEMAP: Integrative Probing Analysis of Nucleic Acids Empowered by Multiple Accessibility Profiles

International audienceThe manual production of reliable RNA structure models from chemical probing experiments benefits from the integration of information derived from multiple protocols and reagents. However, the interpretation of multiple probing profiles remains a complex task, hindering the quality and reproducibility of modeling efforts. We introduce IPANEMAP, the first automated method for the modeling of RNA structure from multiple probing reactivity profiles. Input profiles can result from experiments based on diverse protocols, reagents, or collection of variants, and are jointly analyzed to predict the dominant conformations of an RNA. IPANEMAP combines sampling, clustering, and multi-optimization, to produce secondary structure models that are both stable and well-supported by experimental evidences. The analysis of multiple reactivity profiles, both publicly available and produced in our study, demonstrates the good performances of IPANEMAP, even in a mono probing setting. It confirms the potential of integrating multiple sources of probing data, informing the design of informative probing assays. Availability: IPANEMAP is freely downloadable at https://github.com/afafbioinfo/IPANEMAP Contact: [email protected] Supplementary information available at NAR online

The interplay between molecular flexibility and RNA chemical probing reactivities analyzed at the nucleotide level via an extensive molecular dynamics study

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RNA secondary structure modelling following the IPANEMAP workflow

International audienceRNA secondary structure modelling has been a challenge since the early days of molecular biology. Although algorithms for RNA structure modelling are more and more efficient and accurate, they significantly benefit from the integration of experimental structure probing data. RNA structure probing consists in submitting an RNA to enzymes or small molecules that specifically react with individual nucleotides according to their pairing status. Most enzymes used are single strand specific RNAses (RNAses T1, U2, nuclease S1 …) with the notable exception of the double strand specific RNAse V1. Although they are low molecular weight proteins, they are too bulky to access some nucleotides of a folded RNA. Small molecules can essentially reach any nucleotide and most of them are also single-strand specific although psoralen has recently been successfully used a double strand probe (Lu et al., 2016). For the longest time, RNA probing experiments remained tedious and rather qualitative than quantitative. RNA structure probing recently reached the medium, and then high, throughput. Pioneered and mostly developed within the Weeks lab, the SHAPE technology uses small molecules that react with flexible ribose, thus essentially reporting single-stranded nucleotides with some subtleties (Frezza et al., 2019; Steen et al., 2012). A medium throughput version of the SHAPE protocol was first developed based on capillary electrophoresis, later to be transformed into a high throughput method using next generation sequencing. The same workflows can be applied to more traditional probes such as DiMethyl Sulfate (DMS) and N-Cyclohexyl-N′-(2-morpholinoethyl)carbodiimide metho-p-toluenesulfonate (CMCT) that reveal unpaired A,C and G,U respectively. It appeared that different probes provide complementary information that further improves RNA structure prediction. We therefore developed IPANEMAP, an experimental and computational workflow that models RNA secondary structure from different sets of RNA structure probing performed with different probes, and/or in different conditions and/or on mutants (Saaidi et al. Submitted). This workflow relies on medium or high throughput structure probing, and combines statistical sampling, clustering (Ding and Lawrence, 2003) and pseudo-potentials (Deigan et al, 2009). The method was shown to produce more accurate and stable predictions than other workflows developed to date, even when a single reactivity profile is available, while the availability of multiple reactivities was shown to increase robustness and, to a lesser extent, accuracy of the modeling (Saaidi et al. Submitted). Below, we detail a whole IPANEMAP workflow, starting with experimental probing with DMS and/or CMCT and/or SHAPE reagent. Such probing can be carried out in various relevant conditions (varying température, Mg2+ concentration, introducing point mutations in the RNA to be modeled etc) (Saaidi et al. Submitted). Two versions of the experimental procedure (medium throughput and high throughput) are proposed, DMS and CMCT probing were adapted from Ehresmann et al. and Brunel et al. while the SHAPE probing is described in K. Weeks team publications (Karabiber et al., 2013; Low and Weeks, 2010a; Mortimer and Weeks, 2007; Smola et al., 2015a; Wilkinson et al., 2006, 2008). We then detail instructions for executing the IPANEMAP algorithm to obtain the RNA secondary structure model

Mesoporous Pt-TiO2 thin films: Photocatalytic efficiency under UV and visible light

International audienceThe preparation, characterization, and photoactivity of mesoporous Pt-TiO2 thin films were investigated. Titania based thin films were prepared by evaporation induced self assembly (EISA) process, Pt being introduced by a non-photochemical process via two different ways. Whatever the synthesis method and the Pt content (between 0.5% and 3%), the prepared films were thin (around 200nm) and mesoporous with an average pore size of 5nm. XPS surface analysis showed that Pt was present in different oxidation states: mainly Pt0 but also Pt2+ and Pt4+. The presence of Pt0 nanoparticles was also clearly evidenced by Transmission Electronic Microscopy. The photocatalytic efficiency of the mesoporous titania films was determined by two different tests under both UV and visible light: the reaction rates and photonic efficiencies were determined for the mineralization of stearic acid (SA) at the gas-solid interface and for the solid-liquid photobleaching of Methylene Blue (MB). In all the cases, Pt-doped titania films were less active than pure TiO2 films under UV light and slightly more active under visible light. Preliminary results on the HO production on the surface of the films were obtained from the reaction of a specific probe (terephtalic acid, TA) in basic aqueous solutions. These results were discussed relative to literature data