Metabolic Adaptation of Ralstonia solanacearum during Plant Infection: A Methionine Biosynthesis Case Study

MetE and MetH are two distinct enzymes that catalyze a similar biochemical reaction during the last step of methionine biosynthesis, MetH being a cobalamin-dependent enzyme whereas MetE activity is cobalamin-independent. In this work, we show that the last step of methionine synthesis in the plant pathogen Ralstonia solanacearum is under the transcriptional control of the master pathogenicity regulator HrpG. This control is exerted essentially on metE expression through the intermediate regulator MetR. Expression of metE is strongly and specifically induced in the presence of plant cells in a hrpG- and metR-dependent manner. metE and metR mutants are not auxotrophic for methionine and not affected for growth inside the plant but produce significantly reduced disease symptoms on tomato whereas disruption of metH has no impact on pathogenicity. The finding that the pathogen preferentially induces metE expression rather than metH in the presence of plant cells is indicative of a probable metabolic adaptation to physiological host conditions since this induction of metE occurs in an environment in which cobalamin, the required co-factor for MetH, is absent. It also shows that MetE and MetH are not functionally redundant and are deployed during specific stages of the bacteria lifecycle, the expression of metE and metH being controlled by multiple and distinct signals

Approximation of rejective sampling inclusion probabilities and application to high order correlations

This paper is devoted to rejective sampling. We provide an expansion of joint inclusion probabilities of any order in terms of the inclusion probabilities of order one, extending previous results by Hájek (1964) and Hájek (1981) and making the remainder term more precise. Following Hájek (1981), the proof is based on Edgeworth expansions. The main result is applied to derive bounds on higher order correlations, which are needed for the consistency and asymptotic normality of several complex estimators.Delft Institute of Applied MathematicsElectrical Engineering, Mathematics and Computer Scienc

ARTERIAL HEALING IN THE DOG AFTER INTRALUMINAL DELIVERY OF PULSED ND-YAG LASER ENERGY

Let (Xi)i≥1 be a stationary mean-zero Gaussian process with covariances $\rho(k)=\mathbb {E}(X_{1}X_{k+1})$ satisfying ρ(0) = 1 and ρ(k) = k−DL(k), where D is in (0, 1), and L is slowly varying at infinity. Consider the U-process {Un(r), r ∈ I} defined as Un(r) = 1/n(n−1) ∑1≤i≠j≤n1{G(Xi, Xj)≤r}, where I is an interval included in ℝ, and G is a symmetric function. In this paper, we provide central and noncentral limit theorems for Un. They are used to derive, in the long-range dependence setting, new properties of many well-known estimators such as the Hodges–Lehmann estimator, which is a well-known robust location estimator, the Wilcoxon-signed rank statistic, the sample correlation integral and an associated robust scale estimator. These robust estimators are shown to have the same asymptotic distribution as the classical location and scale estimators. The limiting distributions are expressed through multiple Wiener–Itô integrals.Supported in part by NSF Grants DMS-07-06786 and DMS-10-07616 at Boston University. (DMS-07-06786 - NSF at Boston University; DMS-10-07616 - NSF at Boston University

Robust estimation of the scale and of the autocovariance function of gaussian short and long-range dependent processes

Abstract. A desirable property of an autocovariance estimator is to be robust to the presence of additive outliers. It is well-known that the sample autocovariance, being based on moments, does not have this property. Hence, the use of an autocovariance estimator which is robust to additive outliers can be very useful for time-series modeling. In this paper, the asymptotic properties of the robust scale and autocovariance estimators proposed by Rousseeuw and Croux (1993) and Ma and Genton (2000) are established for Gaussian processes, with either short-range or long-range dependence. It is shown in the short-range dependence setting that this robust estimator is asymptotically normal at the rate √ n, where n is the number of observations. An explicit expression of the asymptotic variance is also given and compared to the asymptotic variance of the classical autocovariance estimator. In the long-range dependence setting, the limiting distribution displays the same behavior than that of the classical autocovariance estimator, with a Gaussian limit and rate √ n when the Hurst parameter H is less 3/4 and with a non-Gaussian limit (belonging to the second Wiener chaos) with rate depending on the Hurst parameter when H ∈ (3/4, 1). Some Monte-Carlo experiments are presented to illustrate our claims and the Nile River data is analyzed as an application. The theoretical results and the empirical evidence strongly suggest using the robust estimators as an alternative to estimate the dependence structure of Gaussian processes. 1

La creation de plantes resistantes

National audienc

The Composite Genome Of The Legume Symbiont Sinorhizobium Meliloti

The scarcity of usable nitrogen frequently limits plant growth. A tight metabolic association with rhizobial bacteria allows legumes to obtain nitrogen compounds by bacterial reduction of dinitrogen (N2) to ammonium (NH4+). We present here the annotated DNA sequence of the alpha-proteobacterium Sinorhizobium meliloti, the symbiont of alfalfa. The tripartite 6.7-megabase (Mb) genome comprises a 3.65-Mb chromosome, and 1.35-Mb pSymA and 1.68-Mb pSymB megaplasmids. Genome sequence analysis indicates that all three elements contribute, in varying degrees, to symbiosis and reveals how this genome may have emerged during evolution. The genome sequence will be useful in understanding the dynamics of interkingdom associations and of life in soil environments