Listeria monocytogenes adjusts its membrane fluidity, ATPase activity and atpE transcription levels in response to cold and acid stress

Listeria monocytogenes is a foodborne pathogen that causes listeriosis, a disease associated with a high mortality rate. The organism responds to a variety of stresses by activating stress-response pathways and adjusting its membrane fluidity by altering its fatty acid composition. We hypothesize that the F0F1 ATPase plays a central role in L. monocytogenes' response to multiple stresses. Wild-type and mutant cells were used to investigate the response to cold, acid, and nisin. The branched-chain fatty acid deficient, cold-sensitive mutant strain, cld1, showed significantly higher membrane rigidity (r = 0.175) compared to its wild-type 10403s (r = 0.121) when grown at 30°C, but not at 15°C (r = 0.124 and 0.116, respectively). Strain cld1 adjusted its membrane fluidity to cold stress. The F0F1 ATPase activity in strain cld1 was 3-fold higher than that of wild-type strain 10403s (0.0267 vs 0.0097 μmole Pi/min.mg protein, respectively) when cells were grown at 30°C. Supplementing cld1's growth medium with the precursor to branched fatty acid, 2-methylbutyrate, restored its fluidity but not the F0F1 ATPase activity to wild-type levels. The acid tolerance response examined differences in initial acid-sensitivity and development of tolerance to lactic acid. Strain cld1 had decreased viability when directly exposed to pH 3.5 (2.64 log CFU/ml), but gained increased viability at pH 3.5 after exposure to pH 5.5 (8.62 log CFU/ml) compared to the wild-type. Finally, a genetic approach examined the F0F1 ATPase c-subunit (atpE) expression in strains 10403s and cld1 using real-time PCR. Strain cld1 showed a 10-fold lower atpE mRNA transcript compared to 10403s. When examining the generated data, we observed that strain cld1 has higher F0F1 ATPase activity, higher initial acid sensitivity, increased protection due to the acid tolerance response, and lower c-subunit mRNA compared to strain 10403s in addition to a rigid membrane. These results support the hypothesis that adjustments took place at the level of the F0F1 ATPase. The various data were pooled in a model in which the mutant strain cld1 has a smaller c-subunit carousel compared to its wild-type 10403s, and highlights the important role of the ATPase in microbial stress response.Ph.D.Includes bibliographical references (p. 116-130)by Mohamed Z. Badaoui Najja

Changes in Listeria monocytogenes Membrane Fluidity in Response to Temperature Stress▿

Listeria monocytogenes is a food-borne pathogen that has been implicated in many outbreaks associated with ready-to-eat products. Listeria adjusts to various stresses by adjusting its membrane fluidity, increasing the uptake of osmoprotectants and cryoprotectants, and activating the σB stress factor. The present work examines the regulation of membrane fluidity through direct measurement based on fluorescent anisotropy. The membrane fluidities of L. monocytogenes Scott A, NR30, wt10403S, and cld1 cells cultured at 15 and 30°C were measured at 15 and 30°C. The membrane of the cold-sensitive mutant (cld1) was more rigid than the membranes of the other strains when grown at 30°C, but when grown at 15°C, it was able to adjust its membrane to approach the rigidity of the other strains. The difference in rigidities, as determined at 15 and 30°C, was greater in liposomes than in whole cells. The rates of fluidity adjustment and times required for whole cells to adjust to a different temperature were similar among strains but different from those of liposomes. This suggests that the cells had a mechanism for homeoviscous adaptation that was absent in liposomes

Road Selection Using Multicriteria Fusion for the Road-Matching Problem

Abstract—This paper presents a road selection strategy for novel road-matching methods that are designed to support realtime navigational features within Advanced Driving-Assistance Systems (ADAS). Selecting the most likely segment(s) is a crucial issue for the road-matching problem. The selection strategy merges several criteria using Belief theory. Particular attention is given to the development of belief functions from measurements and estimations of relative distances, headings, and velocities. Experimental results using data from antilock brake system sensors, the differential Global Positioning System receiver, and the accurate digital roadmap illustrate the performances of this approach, particularly in ambiguous situations

Localisation dynamique d'un véhicule sur une carte routière numérique pour l'assistance à la conduite

Dans cette thèse de doctorat, l'intérêt de la cartographie routière numérique pour la - conception de nouvelles applications d'aide à la conduite automobile ou l'amélioration d'applications existantes est présenté et discuté. Il ressort que la localisation dynamique d'un véhicule sur une carte routière numérique précise est un point clé. Ce problème nécessite l'utilisation d'un système de positionnement par satellites (GPS), d'une base de données géographiques (la carte routière numérique) et d'un Système d'Information Géographique (SIG) pour accéder à ces données. Pour suppléer le GPS en cas de mauvaise visibilité satellitaire, on propose d'utiliser des capteurs proprioceptifs, en l'occurrence les capteurs odométriques du système de freinage d'urgence de l'automobile. Pour localiser dynamiquement le véhicule, nous proposons d'utiliser des méthodes dynamiques d'observation d'état de façon à fusionner les mesures du GPS, les mesures des capteurs proprioceptifs et les données de la carte. Nous nous sommes intéressés plus particulièrement à la conception de méthodes de recherche de la route sur laquelle le véhicule roule, avec une quantification du degré de certitude de cette étape de sélection, en fonction de la qualité des informations manipulées et de la configuration géométrique du réseau routier autour du véhicule. Nous présentons deux grandes classes de méthodes de sélection de routes: une approche probabiliste gaussienne basée sur la distance de Mahalanobis et une approche crédibiliste s'inscrivant dans le cadre de la fusion de données multi-sources. Des résultats expérimentaux illustrent les performances des approches développées et on présente comment une telle solution peut être implémentée dans un système informatique temps-réel centralisé ou distribué.COMPIEGNE-BU (601592101) / SudocSudocFranceF

A Road-Matching Method for Precise Vehicle Localization using Belief Theory and Kalman Filtering

International audienceThis paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle's pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS

Road Selection using Multi-Criteria Fusion for the Roadmap-Matching Problem

International audienceThis paper presents a road selection strategy for novel road-matching methods that are designed to support real-time navigational features within Advanced Driving-Assistance Systems (ADAS). Selecting the most likely segment(s) is a crucial issue for the road-matching problem. The selection strategy merges several criteria using Belief theory. Particular attention is given to the development of belief functions from measurements and estimations of relative distances, headings, and velocities. Experimental results using data from antilock brake system sensors, the differential Global Positioning System receiver, and the accurate digital roadmap illustrate the performances of this approach, particularly in ambiguous situations

Improved Outdoor Localization Based on Weighted Kullback-Leibler Divergence for Measurements Diagnosis

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A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter

International audienceThis paper addresses an important issue for intelligent transportation system, namely the ability of vehicles to safely and reliably localize themselves within an a priori known road map network. For this purpose, we propose an approach based on hybrid dynamic bayesian networks enabling to implement in a unified framework two of the most successful families of probabilistic model commonly used for localization: linear Kalman filters and Hidden Markov Models. The combination of these two models enables to manage and manipulate multi-hypotheses and multi-modality of observations characterizing Map Matching problems and it improves integrity approach. Another contribution of the paper is a chained-form state space representation of vehicle evolution which permits to deal with non-linearity of the used odometry model. Experimental results, using data from encoders’ sensors, a DGPS receiver and an accurate digital roadmap, illustrate the performance of this approach, especially in ambiguous situations