Franck Picard

Bio: Franck Picard is an academic researcher from University of Lyon. The author has contributed to research in topics: Cluster analysis & Poisson distribution. The author has an hindex of 24, co-authored 73 publications receiving 3096 citations. Previous affiliations of Franck Picard include university of lille & Institut national de la recherche agronomique.

The auxin signalling network translates dynamic input into robust patterning at the shoot apex

Abstract: The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.

A mixture model for random graphs

Abstract: The Erdos---Renyi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.

A statistical approach for array CGH data analysis.

Abstract: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome.

Melanocortin 4 Receptor Mutations in a Large Cohort of Severely Obese Adults: Prevalence, Functional Classification, Genotype-Phenotype Relationship, and Lack of Association with Binge Eating

Abstract: Context: Heterozygous mutations in the melanocortin-4 receptor (MC4R) gene are the most common monogenic form of severe obesity in children. There are conflicting reports regarding the prevalence, nature, and pathogenic effects of MC4R mutations in adults with severe late-onset obesity. Objective: Our objective was to determine the prevalence of MC4R mutations in a cohort of severely obese adults and to determine the clinical phenotype and the phenotype-genotype relationship within adult MC4R mutation carriers. Design and Setting: We conducted an observational study at a referral center. Patients or Other Participants: Participants included 769 adult patients with body mass index of at least 35 kg/m2 and 444 nonobese control individuals. Interventions: There were no interventions. Main Outcome Measures: We assessed the prevalence of pathogenic MC4R mutations, functional characteristics of the detected mutations, phenotype, and phenotype-genotype relationship within mutation carriers. Results: The global p...

Constitutive activity of the melanocortin-4 receptor is maintained by its N-terminal domain and plays a role in energy homeostasis in humans.

Abstract: The melanocortin-4 receptor (MC4R), a centrally expressed G protein–coupled receptor (GPCR), is essential for the maintenance of long-term energy balance in humans. Mutations in MC4R are the most common genetic cause of obesity. Since activation of this receptor leads to a decrease in food intake, MC4R is also a major therapeutic target for the treatment of obesity. Control of MC4R activity in vivo is modulated by the opposing effects of the anorexigenic agonist α–melanocyte-stimulating hormone (α-MSH) and the orexigenic antagonist agouti-related protein (AGRP). In addition, experiments in vitro have demonstrated that the human MC4R has an intrinsic constitutive activity on which AGRP also acts as an inverse agonist. The physiological role of this constitutive activity in the control of energy balance as well as the domain of the protein implicated in its maintenance are unknown. By systematically studying functional defects in naturally occurring MC4R mutations from obese patients, we defined a cluster of N-terminal mutations that selectively impair the constitutive activity of the receptor. Further characterization of this domain demonstrated that it functions as a tethered intramolecular ligand that maintains the constitutive activity of MC4R and may provide novel avenues for the design of drugs targeting this receptor. Our results also suggest that the tonic satiety signal provided by the constitutive activity of MC4R may be required for maintaining long-term energy homeostasis in humans.

Optimal Detection of Changepoints With a Linear Computational Cost

Abstract: In this article, we consider the problem of detecting multiple changepoints in large datasets. Our focus is on applications where the number of changepoints will increase as we collect more data: for example, in genetics as we analyze larger regions of the genome, or in finance as we observe time series over longer periods. We consider the common approach of detecting changepoints through minimizing a cost function over possible numbers and locations of changepoints. This includes several established procedures for detecting changing points, such as penalized likelihood and minimum description length. We introduce a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost, which, under mild conditions, is linear in the number of observations. This compares favorably with existing methods for the same problem whose computational cost can be quadratic or even cubic. In simulation studies, we show that our new method can...

The evolution of gene expression levels in mammalian organs

Abstract: Changes in gene expression are thought to underlie many of the phenotypic differences between species. However, large-scale analyses of gene expression evolution were until recently prevented by technological limitations. Here we report the sequencing of polyadenylated RNA from six organs across ten species that represent all major mammalian lineages (placentals, marsupials and monotremes) and birds (the evolutionary outgroup), with the goal of understanding the dynamics of mammalian transcriptome evolution. We show that the rate of gene expression evolution varies among organs, lineages and chromosomes, owing to differences in selective pressures: transcriptome change was slow in nervous tissues and rapid in testes, slower in rodents than in apes and monotremes, and rapid for the X chromosome right after its formation. Although gene expression evolution in mammals was strongly shaped by purifying selection, we identify numerous potentially selectively driven expression switches, which occurred at different rates across lineages and tissues and which probably contributed to the specific organ biology of various mammals.

changepoint: An R Package for Changepoint Analysis

Abstract: One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been developed to provide users with a choice of multiple changepoint search methods to use in conjunction with a given changepoint method and in particular provides an implementation of the recently proposed PELT algorithm. This article describes the search methods which are implemented in the package as well as some of the available test statistics whilst highlighting their application with simulated and practical examples. Particular emphasis is placed on the PELT algorithm and how results differ from the binary segmentation approach.