Human Ezrin: Prediction of the 3D structure and interactions with mTOR

The protein Ezrin, is a member of the ERM family (Ezrin, Radixin and Moesin) that links the F-actin to the plasma membrane. The protein is made of three domains namely the FERM domain, a central α-helical domain and the CERMAD domain. The residues in Ezrin such as Ser66, Tyr145, Tyr353 and Tyr477 regulate the function of the protein through phosphorylation. The protein is found in two distinct conformations of active and dormant (inactive) state. The initial step during the conformation change is the breakage of intramolecular interaction in dormant Ezrin by phosphorylation of residue Thr567. The dormant structure of human Ezrin was predicted computationally since only partial active form structure was available. The validation analysis showed that 99.7% residues were positioned in favored, allowed and generously allowed regions of the Ramachandran plot. The Z-score of Ezrin was −7.36, G-factor was 0.1, and the QMEAN score of the model was 0.61 indicating a good model for human Ezrin. The comparison of the conformations of the activated and dormant Ezrin showed a major shift in the F2 lobe (residues 142-149 and 161-177) while changes in the conformation induced mobility shifts in lobe F3 (residues 261 to 267). The 3D positions of the phosphorylation sites Tyr145, Tyr353, Tyr477, Tyr482 and Thr567 were also located. Using targeted molecular dynamic simulation, the molecular movements during conformational change from active to dormant were visualized. The dormant Ezrin auto-inhibits itself by a head-to-tail interaction of the N-terminal and C-terminal residues. The trajectory shows the breakage of the interactions and mobility of the CERMAD domain away from the FERM domain. Protein docking and clustering analysis were used to predict the residues involved in the interaction between dormant Ezrin and mTOR. Residues Tyr477 and Tyr482 were found to be involved in interaction with mTOR.

Disruption of amylase genes by RNA interference affects reproduction in the Pacific oyster Crassostrea gigas

Feeding strategies and digestive capacities can have important implications for variation in energetic pathways associated with ecological and economically important traits, such as growth or reproduction in bivalve species. Here, we investigated the role of amylase in the digestive processes of Crassostrea gigas, using in vivo RNA interference. This approach also allowed us to investigate the relationship between energy intake by feeding and gametogenesis in oysters. Double-stranded (ds)RNA designed to target the two α-amylase genes A and B was injected in vivo into the visceral mass of oysters at two doses. These treatments caused significant reductions in mean mRNA levels of the amylase genes: −50.7% and −59% mRNA A, and −71.9% and −70.6% mRNA B in 15 and 75 µg dsRNA-injected oysters, respectively, relative to controls. Interestingly, reproductive knock-down phenotypes were observed for both sexes at 48 days post-injection, with a significant reduction of the gonad area (−22.5% relative to controls) and germ cell under-proliferation revealed by histology. In response to the higher dose of dsRNA, we also observed reductions in amylase activity (−53%) and absorption efficiency (−5%). Based on these data, dynamic energy budget modeling showed that the limitation of energy intake by feeding that was induced by injection of amylase dsRNA was insufficient to affect gonadic development at the level observed in the present study. This finding suggests that other driving mechanisms, such as endogenous hormonal modulation, might significantly change energy allocation to reproduction, and increase the maintenance rate in oysters in response to dsRNA injection.

First complete genome sequence of a capsicum chlorosis tospovirus isolate from Australia with an unusually large S RNA intergenic region

The first complete genome sequence of capsicum chlorosis virus (CaCV) from Australia was determined using a combination of Illumina HiSeq RNA and Sanger sequencing technologies. Australian CaCV had a tripartite genome structure like other CaCV isolates. The large (L) RNA was 8913 nucleotides (nt) in length and contained a single open reading frame (ORF) of 8634 nt encoding a predicted RNA-dependent RNA polymerase (RdRp) in the viral-complementary (vc) sense. The medium (M) and small (S) RNA segments were 4846 and 3944 nt in length, respectively, each containing two non-overlapping ORFs in ambisense orientation, separated by intergenic regions (IGR). The M segment contained ORFs encoding the predicted non-structural movement protein (NSm; 927 nt) and precursor of glycoproteins (GP; 3366 nt) in the viral sense (v) and vc strand, respectively, separated by a 449-nt IGR. The S segment coded for the predicted nucleocapsid (N) protein (828 nt) and non-structural suppressor of silencing protein (NSs; 1320 nt) in the vc and v strand, respectively. The S RNA contained an IGR of 1663 nt, being the largest IGR of all CaCV isolates sequenced so far. Comparison of the Australian CaCV genome with complete CaCV genome sequences from other geographic regions showed highest sequence identity with a Taiwanese isolate. Genome sequence comparisons and phylogeny of all available CaCV isolates provided evidence for at least two highly diverged groups of CaCV isolates that may warrant re-classification of AIT-Thailand and CP-China isolates as unique tospoviruses, separate from CaCV.

STRUCTURAL ANALYSIS OF RIBOSOME BINDING ELEMENTS OF THE 3´ UTR IN TWO POSITIVE-STRAND PLANT RNA VIRUSES

Turnip crinkle virus (TCV) and Pea enation mosaic virus (PEMV) are two positive (+)-strand RNA viruses that are used to investigate the regulation of translation and replication due to their small size and simple genomes. Both viruses contain cap-independent translation elements (CITEs) within their 3´ untranslated regions (UTRs) that fold into tRNA-shaped structures (TSS) according to nuclear magnetic resonance and small angle x-ray scattering analysis (TCV) and computational prediction (PEMV). Specifically, the TCV TSS can directly associate with ribosomes and participates in RNA-dependent RNA polymerase (RdRp) binding. The PEMV kissing-loop TSS (kl-TSS) can simultaneously bind to ribosomes and associate with the 5´ UTR of the viral genome. Mutational analysis and chemical structure probing methods provide great insight into the function and secondary structure of the two 3´ CITEs. However, lack of 3-D structural information has limited our understanding of their functional dynamics. Here, I report the folding dynamics for the TCV TSS using optical tweezers (OT), a single molecule technique. My study of the unfolding/folding pathways for the TCV TSS has provided an unexpected unfolding pathway, confirmed the presence of Ψ3 and hairpin elements, and suggested an interconnection between the hairpins and pseudoknots. In addition, this study has demonstrated the importance of the adjacent upstream adenylate-rich sequence for the formation of H4a/Ψ3 along with the contribution of magnesium to the stability of the TCV TSS. In my second project, I report on the structural analysis of the PEMV kl-TSS using NMR and SAXS. This study has re-confirmed the base-pair pattern for the PEMV kl-TSS and the proposed interaction of the PEMV kl-TSS with its interacting partner, hairpin 5H2. The molecular envelope of the kl-TSS built from SAXS analysis suggests the kl-TSS has two functional conformations, one of which has a different shape from the previously predicted tRNA-shaped form. Along with applying biophysical methods to study the structural folding dynamics of RNAs, I have also developed a technique that improves the production of large quantities of recombinant RNAs in vivo for NMR study. In this project, I report using the wild-type and mutant E.coli strains to produce cost-effective, site-specific labeled, recombinant RNAs. This technique was validated with four representative RNAs of different sizes and complexity to produce milligram amounts of RNAs. The benefit of using site-specific labeled RNAs made from E.coli was demonstrated with several NMR techniques.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Rola białka Hfq w oddziaływaniu niekodujących RNA bakterii z sekwencją kodującą mRNA

Wydział Biologii: Instytut Biologii Molekularnej i Biotechnologii

Shape and chemically anisotropic colloidal particles: A theoretical study of their structure, phase behavior, and slow dynamics

A detailed non-equilibrium state diagram of shape-anisotropic particle fluids is constructed. The effects of particle shape are explored using Naive Mode Coupling Theory (NMCT), and a single particle Non-linear Langevin Equation (NLE) theory. The dynamical behavior of non-ergodic fluids are discussed. We employ a rotationally frozen approach to NMCT in order to determine a transition to center of mass (translational) localization. Both ideal and kinetic glass transitions are found to be highly shape dependent, and uniformly increase with particle dimensionality. The glass transition volume fraction of quasi 1- and 2- dimensional particles fall monotonically with the number of sites (aspect ratio), while 3-dimensional particles display a non-monotonic dependence of glassy vitrification on the number of sites. Introducing interparticle attractions results in a far more complex state diagram. The ideal non-ergodic boundary shows a glass-fluid-gel re-entrance previously predicted for spherical particle fluids. The non-ergodic region of the state diagram presents qualitatively different dynamics in different regimes. They are qualified by the different behaviors of the NLE dynamic free energy. The caging dominated, repulsive glass regime is characterized by long localization lengths and barrier locations, dictated by repulsive hard core interactions, while the bonding dominated gel region has short localization lengths (commensurate with the attraction range), and barrier locations. There exists a small region of the state diagram which is qualified by both glassy and gel localization lengths in the dynamic free energy. A much larger (high volume fraction, and high attraction strength) region of phase space is characterized by short gel-like localization lengths, and long barrier locations. The region is called the attractive glass and represents a 2-step relaxation process whereby a particle first breaks attractive physical bonds, and then escapes its topological cage. The dynamic fragility of fluids are highly particle shape dependent. It increases with particle dimensionality and falls with aspect ratio for quasi 1- and 2- dimentional particles. An ultralocal limit analysis of the NLE theory predicts universalities in the behavior of relaxation times, and elastic moduli. The equlibrium phase diagram of chemically anisotropic Janus spheres and Janus rods are calculated employing a mean field Random Phase Approximation. The calculations for Janus rods are corroborated by the full liquid state Reference Interaction Site Model theory. The Janus particles consist of attractive and repulsive regions. Both rods and spheres display rich phase behavior. The phase diagrams of these systems display fluid, macrophase separated, attraction driven microphase separated, repulsion driven microphase separated and crystalline regimes. Macrophase separation is predicted in highly attractive low volume fraction systems. Attraction driven microphase separation is charaterized by long length scale divergences, where the ordering length scale determines the microphase ordered structures. The ordering length scale of repulsion driven microphase separation is determined by the repulsive range. At the high volume fractions, particles forgo the enthalpic considerations of attractions and repulsions to satisfy hard core constraints and maximize vibrational entropy. This results in site length scale ordering in rods, and the sphere length scale ordering in Janus spheres, i.e., crystallization. A change in the Janus balance of both rods and spheres results in quantitative changes in spinodal temperatures and the position of phase boundaries. However, a change in the block sequence of Janus rods causes qualitative changes in the type of microphase ordered state, and induces prominent features (such as the Lifshitz point) in the phase diagrams of these systems. A detailed study of the number of nearest neighbors in Janus rod systems reflect a deep connection between this local measure of structure, and the structure factor which represents the most global measure of order.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Quantification of the effects of architectural traits on dry mass production and light interception of tomato canopy under different temperature regimes using a dynamic functional-structural plant model

There is increasing interest in evaluating the environmental effects on crop architectural traits and yield improvement. However, crop models describing the dynamic changes in canopy structure with environmental conditions and the complex interactions between canopy structure, light interception, and dry mass production are only gradually emerging. Using tomato (Solanum lycopersicum L.) as a model crop, a dynamic functional-structural plant model (FSPM) was constructed, parameterized, and evaluated to analyse the effects of temperature on architectural traits, which strongly influence canopy light interception and shoot dry mass. The FSPM predicted the organ growth, organ size, and shoot dry mass over time with high accuracy (>85%). Analyses of this FSPM showed that, in comparison with the reference canopy, shoot dry mass may be affected by leaf angle by as much as 20%, leaf curvature by up to 7%, the leaf length: width ratio by up to 5%, internode length by up to 9%, and curvature ratios and leaf arrangement by up to 6%. Tomato canopies at low temperature had higher canopy density and were more clumped due to higher leaf area and shorter internodes. Interestingly, dry mass production and light interception of the clumped canopy were more sensitive to changes in architectural traits. The complex interactions between architectural traits, canopy light interception, dry mass production, and environmental conditions can be studied by the dynamic FSPM, which may serve as a tool for designing a canopy structure which is 'ideal' in a given environment.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

The conformational landscape of RNA translational regulators and their potential as drug discovery targets

RNA is an underutilized target for drug discovery. Once thought to be a passive carrier of genetic information, RNA is now known to play a critical role in essentially all aspects of biology including signaling, gene regulation, catalysis, and retroviral infection. It is now well-established that RNA does not exist as a single static structure, but instead populates an ensemble of energetic minima along a free-energy landscape. Knowledge of this structural landscape has become an important goal for understanding its diverse biological functions. In this case, NMR spectroscopy has emerged as an important player in the characterization of RNA structural ensembles, with solution-state techniques accounting for almost half of deposited RNA structures in the PDB, yet the rate of RNA structure publication has been stagnant over the past decade. Several bottlenecks limit the pace of RNA structure determination by NMR: the high cost of isotopic labeling, tedious and ambiguous resonance assignment methods, and a limited database of RNA optimized pulse programs. We have addressed some of these challenges to NMR characterization of RNA structure with applications to various RNA-drug targets. These approaches will increasingly become integral to designing new therapeutics targeting RNA.

Alternate Conformations Regulate Ribosomal Recoding in a Positive-sense RNA Virus

Positive-sense RNA viruses are important animal, plant, insect and bacteria pathogens and constitute the largest group of RNA viruses. Due to the relatively small size of their genomes, these viruses have evolved a variety of non-canonical translation mechanisms to optimize coding capacity expanding their proteome diversity. One such strategy is codon redefinition or recoding. First described in viruses, recoding is a programmed translation event in which codon alterations are context dependent. Recoding takes place in a subset of messenger RNA (mRNAs) with some products reflecting new, and some reflecting standard, meanings. The ratio between the two is both critical and highly regulated. While a variety of recoding mechanisms have been documented, (ribosome shunting, stop-carry on, termination-reinitiation, and translational bypassing), the two most extensively employed by RNA viruses are Programmed Ribosomal Frameshifting (PRF) and Programmed Ribosomal Readthrough (PRT). While both PRT and PRF subvert normal decoding for expression of C-terminal extension products, the former involves an alteration of reading frame, and the latter requires decoding of a non-sense codon. Both processes occur at a low but defined frequency, and both require Recoding Stimulatory Elements (RSE) for regulation and optimum functionality. These stimulatory signals can be embedded in the RNA in the form of sequence or secondary structure, or trans-acting factors outside the mRNA such as proteins or micro RNAs (miRNA). Despite 40+ years of study, the precise mechanisms by which viral RSE mediate ribosome recoding for the synthesis of their proteins, or how the ratio of these products is maintained, is poorly defined. This study reveals that in addition to a long distance RNA:RNA interaction, three alternate conformations and a phylogenetically conserved pseudoknot regulate PRT in the carmovirus Turnip crinkle virus (TCV).

Bacterial community structure of the marine diatom Haslea ostrearia

The marine diatom Haslea ostrearia produces a water-soluble blue-pigment named marennine of economic interest (e.g. in aquaculture for the greening of oysters). Up to date the studies devoted to ecological conditions under which this microalga develops never took into account the bacterial-H. ostrearia relationships. In this study the bacterial community was analysed by PCR-TTGE before and after H. ostrearia isolation cells recovered from 4 localities, to distinguish the relative part of the biotope and the biocenose and eventually to describe the temporal dynamic of the structure of the bacterial community. The bacterial structure of the phycosphere differed strongly from that of the bulk sediment. The similarity between bacteria recovered from the biofilm and the suspended bacteria did not exceed 10% (vs. > 90% amongst biofilms). The differences in genetic fingerprints, more especially high between two H. ostrearia isolates showed also the highest differences in the bacterial structure as the result of specific metabolomics profiles. The non-targeted metabolomic investigation showed that these profiles were more distinct in case of bacteria-alga associations than for the H. ostrearia monoculture. At the scale of a culture cycle in laboratory conditions, the bacterial community was specific to the growth stage. When H. ostrearia was subcultured for 9 months, a shift in the bacterial structure was shown from 3-months subculturing and the bacterial structure stabilized afterwards (70-86% similarities). A first insight of the relationships between H. ostrearia and its surrounding bacteria was shown for a better understanding of the ecological feature of this diatom.

Preliminary metabolomic approach on the bacterial structure community of Haslea ostrearia

The marine diatom Haslea ostrearia [1] produces a water-soluble blue-pigment named marennine [2] of economic interest. But the lack of knowledge of the ecological conditions, under which this microalga develops in its natural ecosystem, more especially bacteria H. ostrearia interactions, prevents any optimization of its culture in well-controlled conditions. The structure of the bacterial community was analyzed by PCR-TTGE before and after the isolation of H. ostrearia cells recovered from 4 localities, to distinguish the relative part of the biotope and the biocenose and eventually to describe the temporal dynamic of the structure of the bacterial community at two time-scales. The differences in genetic fingerprints, more especially high between two H. ostrearia isolates (HO-R and HO-BM) showed also the highest differences in the bacterial structure [3] as the result of specific metabolomics profiles. The non-targeted metabolomic investigation showed that these profiles were more distinct in case of bacteria-alga associations than for the H. ostrearia monoculture Here we present a Q-TOF LC/MS metabolomic fingerprinting approach [3]: - to investigate differential metabolites of axenic versus non axenic H. ostrearia cultures. - to focus on the specific metabolites of a bacterial surrounding associated with the activation or inhibition of the microalga growing. The Agilent suite of data processing software makes feature finding, statistical analysis, and identification easier. This enables rapid transformation of complex raw data into biologically relevant metabolite information.

A Fuzzy Decision Strategy for Topic Identification and Dynamic Selection of Language Models

International audience