Regulation of Nav1.7 and Nav1.8 voltage-gated sodium channels by Nedd4-2 and β-subunits and its implication in neuropathic pain

In mammals, the presence of excitable cells in muscles, heart and nervous system is crucial and allows fast conduction of numerous biological information over long distances through the generation of action potentials (AP). Voltage-gated sodium channels (Navs) are key players in the generation and propagation of AP as they are responsible for the rising phase of the AP. Navs are heteromeric proteins composed of a large pore-forming a-subunit (Nav) and smaller ß-auxiliary subunits. There are ten genes encoding for Navl.l to Nav1.9 and NaX channels, each possessing its own specific biophysical properties. The excitable cells express differential combinations of Navs isoforms, generating a distinct electrophysiological signature. Noteworthy, only when anchored at the membrane are Navs functional and are participating in sodium conductance. In addition to the intrinsic properties of Navs, numerous regulatory proteins influence the sodium current. Some proteins will enhance stabilization of membrane Navs while others will favour internalization. Maintaining equilibrium between the two is of crucial importance for controlling cellular excitability. The E3 ubiquitin ligase Nedd4-2 is a well-characterized enzyme that negatively regulates the turnover of many membrane proteins including Navs. On the other hand, ß-subunits are known since long to stabilize Navs membrane anchoring. Peripheral neuropathic pain is a disabling condition resulting from nerve injury. It is characterized by the dysregulation of Navs expressed in dorsal root ganglion (DRG) sensory neurons as highlighted in different animal models of neuropathic pain. Among Navs, Nav1.7 and Nav1.8 are abundantly and specifically expressed in DRG sensory neurons and have been recurrently incriminated in nociception and neuropathic pain development. Using the spared nerve injury (SNI) experimental model of neuropathic pain in mice, I observed a specific reduction of Nedd4-2 in DRG sensory neurons. This decrease subsequently led to an upregulation of Nav1.7 and Nav1.8 protein and current, in the axon and the DRG neurons, respectively, and was sufficient to generate neuropathic pain-associated hyperexcitability. Knocking out Nedd4-2 specifically in nociceptive neurons led to the same increase of Nav1.7 and Nav1.8 concomitantly with an increased thermal sensitivity in mice. Conversely, rescuing Nedd4-2 downregulation using viral vector transfer attenuated neuropathic pain mechanical hypersensitivity. This study demonstrates the significant role of Nedd4-2 in regulating cellular excitability in vivo and its involvement in neuropathic pain development. The role of ß-subunits in neuropathic pain was already demonstrated in our research group. Because of their stabilization role, the increase of ßl, ß2 and ß3 subunits in DRGs after SNI led to increased Navs anchored at the membrane. Here, I report a novel mechanism of regulation of a-subunits by ß- subunits in vitro; ßl and ß3-subunits modulate the glycosylation pattern of Nav1.7, which might account for stabilization of its membrane expression. This opens new perspectives for investigation Navs state of glycosylation in ß-subunits dependent diseases, such as in neuropathic pain. - Chez les mammifères, la présence de cellules excitables dans les muscles, le coeur et le système nerveux est cruciale; elle permet la conduction rapide de nombreuses informations sur de longues distances grâce à la génération de potentiels d'action (PA). Les canaux sodiques voltage-dépendants (Navs) sont des participants importants dans la génération et la propagation des PA car ils sont responsables de la phase initiale de dépolarisation du PA. Les Navs sont des protéines hétéromériques composées d'une grande sous-unité a (formant le pore du canal) et de petites sous-unités ß accompagnatrices. Il existe dix gènes qui codent pour les canaux sodiques, du Nav 1.1 au Nav 1.9 ainsi que NaX, chacun possédant des propriétés biophysiques spécifiques. Les cellules excitables expriment différentes combinaisons des différents isoformes de Navs, qui engendrent une signature électrophysiologique distincte. Les Navs ne sont fonctionnels et ne participent à la conductibilité du Na+, que s'ils sont ancrés à la membrane plasmique. En plus des propriétés intrinsèques des Navs, de nombreuses protéines régulatrices influencent également le courant sodique. Certaines protéines vont favoriser l'ancrage et la stabilisation des Navs exprimés à la membrane, alors que d'autres vont plutôt favoriser leur internalisation. Maintenir l'équilibre des deux processus est crucial pour contrôler l'excitabilité cellulaire. Dans ce contexte, Nedd4-2, de la famille des E3 ubiquitin ligase, est une enzyme bien caractérisée qui régule l'internalisation de nombreuses protéines, notamment celle des Navs. Inversement, les sous-unités ß sont connues depuis longtemps pour stabiliser l'ancrage des Navs à la membrane. La douleur neuropathique périphérique est une condition débilitante résultant d'une atteinte à un nerf. Elle est caractérisée par la dérégulation des Navs exprimés dans les neurones sensoriels du ganglion spinal (DRG). Ceci a été démontré à de multiples occasions dans divers modèles animaux de douleur neuropathique. Parmi les Navs, Nav1.7 et Nav1.8 sont abondamment et spécifiquement exprimés dans les neurones sensoriels des DRG et ont été impliqués de façon récurrente dans le développement de la douleur neuropathique. En utilisant le modèle animal de douleur neuropathique d'épargne du nerf sural (spared nerve injury, SNI) chez la souris, j'ai observé une réduction spécifique des Nedd4-2 dans les neurones sensoriels du DRG. Cette diminution avait pour conséquence l'augmentation de l'expression des protéines et des courants de Nav 1.7 et Nav 1.8, respectivement dans l'axone et les neurones du DRG, et était donc suffisante pour créer l'hyperexcitabilité associée à la douleur neuropathique. L'invalidation pour le gène codant pour Nedd4-2 dans une lignée de souris génétiquement modifiées a conduit à de similaires augmentations de Nav1.7 et Nav1.8, parallèlement à une augmentation à la sensibilité thermique. A l'opposé, rétablir une expression normale de Nedd4-2 en utilisant un vecteur viral a eu pour effet de contrecarrer le développement de l'hypersensibilité mécanique lié à ce modèle de douleur neuropathique. Cette étude démontre le rôle important de Nedd4-2 dans la régulation de l'excitabilité cellulaire in vivo et son implication dans le développement des douleurs neuropathiques. Le rôle des sous-unités ß dans les douleurs neuropathiques a déjà été démontré dans notre groupe de recherche. A cause de leur rôle stabilisateur, l'augmentation des sous-unités ßl, ß2 et ß3 dans les DRG après SNI, conduit à une augmentation des Navs ancrés à la membrane. Dans mon travail de thèse, j'ai observé un nouveau mécanisme de régulation des sous-unités a par les sous-unités ß in vitro. Les sous-unités ßl et ß3 régulent l'état de glycosylation du canal Nav1.7, et stabilisent son expression membranaire. Ceci ouvre de nouvelles perspectives dans l'investigation de l'état de glycosylation des Navs dans des maladies impliquant les sous-unités ß, notamment les douleurs neuropathiques.

Generation of a tightly regulated all-cis beta cell-specific tetracycline-inducible vector.

Ability to induce protein expression at will in a cell is a powerful strategy used by scientists to better understand the function of a protein of interest. Various inducible systems have been designed in eukaryotic cells to achieve this goal. Most of them rely on two distinct vectors, one encoding a protein that can regulate transcription by binding a compound X, and one hosting the cDNA encoding the protein of interest placed downstream of promoter sequences that can bind the protein regulated by compound X (e.g., tetracycline, ecdysone). The commercially available systems are not designed to allow cell- or tissue-specific regulated expression. Additionally, although these systems can be used to generate stable clones that can be induced to express a given protein, extensive screening is often required to eliminate the clones that display poor induction or high basal levels. In the present report, we aimed to design a pancreatic beta cell-specific tetracycline-inducible system. Since the classical two-vector based tetracycline-inducible system proved to be unsatisfactory in our hands, a single vector was eventually designed that allowed tight beta cell-specific tetracycline induction in unselected cell populations.

Rapid cohort generation and analysis of disease spectrum of large animal model of cone dystrophy.

Large animal models are an important resource for the understanding of human disease and for evaluating the applicability of new therapies to human patients. For many diseases, such as cone dystrophy, research effort is hampered by the lack of such models. Lentiviral transgenesis is a methodology broadly applicable to animals from many different species. When conjugated to the expression of a dominant mutant protein, this technology offers an attractive approach to generate new large animal models in a heterogeneous background. We adopted this strategy to mimic the phenotype diversity encounter in humans and generate a cohort of pigs for cone dystrophy by expressing a dominant mutant allele of the guanylate cyclase 2D (GUCY2D) gene. Sixty percent of the piglets were transgenic, with mutant GUCY2D mRNA detected in the retina of all animals tested. Functional impairment of vision was observed among the transgenic pigs at 3 months of age, with a follow-up at 1 year indicating a subsequent slower progression of phenotype. Abnormal retina morphology, notably among the cone photoreceptor cell population, was observed exclusively amongst the transgenic animals. Of particular note, these transgenic animals were characterized by a range in the severity of the phenotype, reflecting the human clinical situation. We demonstrate that a transgenic approach using lentiviral vectors offers a powerful tool for large animal model development. Not only is the efficiency of transgenesis higher than conventional transgenic methodology but this technique also produces a heterogeneous cohort of transgenic animals that mimics the genetic variation encountered in human patients.

Density-based hierarchical clustering of pyro-sequences on a large scale--the case of fungal ITS1.

MOTIVATION: Analysis of millions of pyro-sequences is currently playing a crucial role in the advance of environmental microbiology. Taxonomy-independent, i.e. unsupervised, clustering of these sequences is essential for the definition of Operational Taxonomic Units. For this application, reproducibility and robustness should be the most sought after qualities, but have thus far largely been overlooked. RESULTS: More than 1 million hyper-variable internal transcribed spacer 1 (ITS1) sequences of fungal origin have been analyzed. The ITS1 sequences were first properly extracted from 454 reads using generalized profiles. Then, otupipe, cd-hit-454, ESPRIT-Tree and DBC454, a new algorithm presented here, were used to analyze the sequences. A numerical assay was developed to measure the reproducibility and robustness of these algorithms. DBC454 was the most robust, closely followed by ESPRIT-Tree. DBC454 features density-based hierarchical clustering, which complements the other methods by providing insights into the structure of the data. AVAILABILITY: An executable is freely available for non-commercial users at ftp://ftp.vital-it.ch/tools/dbc454. It is designed to run under MPI on a cluster of 64-bit Linux machines running Red Hat 4.x, or on a multi-core OSX system. CONTACT: dbc454@vital-it.ch or nicolas.guex@isb-sib.ch.

Comparative performance of supertree algorithms in large data sets using the soapberry family (Sapindaceae) as a case study.

For the last 2 decades, supertree reconstruction has been an active field of research and has seen the development of a large number of major algorithms. Because of the growing popularity of the supertree methods, it has become necessary to evaluate the performance of these algorithms to determine which are the best options (especially with regard to the supermatrix approach that is widely used). In this study, seven of the most commonly used supertree methods are investigated by using a large empirical data set (in terms of number of taxa and molecular markers) from the worldwide flowering plant family Sapindaceae. Supertree methods were evaluated using several criteria: similarity of the supertrees with the input trees, similarity between the supertrees and the total evidence tree, level of resolution of the supertree and computational time required by the algorithm. Additional analyses were also conducted on a reduced data set to test if the performance levels were affected by the heuristic searches rather than the algorithms themselves. Based on our results, two main groups of supertree methods were identified: on one hand, the matrix representation with parsimony (MRP), MinFlip, and MinCut methods performed well according to our criteria, whereas the average consensus, split fit, and most similar supertree methods showed a poorer performance or at least did not behave the same way as the total evidence tree. Results for the super distance matrix, that is, the most recent approach tested here, were promising with at least one derived method performing as well as MRP, MinFlip, and MinCut. The output of each method was only slightly improved when applied to the reduced data set, suggesting a correct behavior of the heuristic searches and a relatively low sensitivity of the algorithms to data set sizes and missing data. Results also showed that the MRP analyses could reach a high level of quality even when using a simple heuristic search strategy, with the exception of MRP with Purvis coding scheme and reversible parsimony. The future of supertrees lies in the implementation of a standardized heuristic search for all methods and the increase in computing power to handle large data sets. The latter would prove to be particularly useful for promising approaches such as the maximum quartet fit method that yet requires substantial computing power.

Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort.

BACKGROUND: Genotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets. RESULTS: Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs. CONCLUSION: Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

Mynodbcsv: Lightweight Zero-Config Database Solution for Handling Very Large CSV Files.

Volumes of data used in science and industry are growing rapidly. When researchers face the challenge of analyzing them, their format is often the first obstacle. Lack of standardized ways of exploring different data layouts requires an effort each time to solve the problem from scratch. Possibility to access data in a rich, uniform manner, e.g. using Structured Query Language (SQL) would offer expressiveness and user-friendliness. Comma-separated values (CSV) are one of the most common data storage formats. Despite its simplicity, with growing file size handling it becomes non-trivial. Importing CSVs into existing databases is time-consuming and troublesome, or even impossible if its horizontal dimension reaches thousands of columns. Most databases are optimized for handling large number of rows rather than columns, therefore, performance for datasets with non-typical layouts is often unacceptable. Other challenges include schema creation, updates and repeated data imports. To address the above-mentioned problems, I present a system for accessing very large CSV-based datasets by means of SQL. It's characterized by: "no copy" approach - data stay mostly in the CSV files; "zero configuration" - no need to specify database schema; written in C++, with boost [1], SQLite [2] and Qt [3], doesn't require installation and has very small size; query rewriting, dynamic creation of indices for appropriate columns and static data retrieval directly from CSV files ensure efficient plan execution; effortless support for millions of columns; due to per-value typing, using mixed text/numbers data is easy; very simple network protocol provides efficient interface for MATLAB and reduces implementation time for other languages. The software is available as freeware along with educational videos on its website [4]. It doesn't need any prerequisites to run, as all of the libraries are included in the distribution package. I test it against existing database solutions using a battery of benchmarks and discuss the results.