HIV-1-Associated Neurocognitive Disorders: Is HLA-C Binding Stability to β2-Microglobulin a Missing Piece of the Pathogenetic Puzzle?

AIDS dementia complex (ADC) and HIV-associated neurocognitive disorders (HAND) are complications of HIV-1 infection. Viral infections are risk factors for the development of neurodegenerative disorders. Aging is associated with low-grade inflammation in the brain, i.e., the inflammaging. The molecular mechanisms linking immunosenescence, inflammaging and the pathogenesis of neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease, are largely unknown. ADC and HAND share some pathological features with AD and may offer some hints on the relationship between viral infections, neuroinflammation, and neurodegeneration. β2-microglobulin (β2m) is an important pro-aging factor that interferes with neurogenesis and worsens cognitive functions. Several studies published in the 80–90s reported high levels of β2m in the cerebrospinal fluid of patients with ADC. High levels of β2m have also been detected in AD. Inflammatory diseases in elderly people are associated with polymorphisms of the MHC-I locus encoding HLA molecules that, by associating with β2m, contribute to cellular immunity. We recently reported that HLA-C, no longer associated with β2m, is incorporated into HIV-1 virions, determining an increase in viral infectivity. We also documented the presence of HLA-C variants more or less stably linked to β2m. These observations led us to hypothesize that some variants of HLA-C, in the presence of viral infections, could determine a greater release and accumulation of β2m, which in turn, may be involved in triggering and/or sustaining neuroinflammation. ADC is the most severe form of HAND. To explore the role of HLA-C in ADC pathogenesis, we analyzed the frequency of HLA-C variants with unstable binding to β2m in a group of patients with ADC. We found a higher frequency of unstable HLA-C alleles in ADC patients, and none of them was harboring stable HLA-C alleles in homozygosis. Our data suggest that the role of HLA-C variants in ADC/HAND pathogenesis deserves further studies. If confirmed in a larger number of samples, this finding may have practical implication for a personalized medicine approach and for developing new therapies to prevent HAND. The exploration of HLA-C variants as risk factors for AD and other neurodegenerative disorders may be a promising field of study

Comprendre les processus de formation d'aérosols organiques dans l'atmosphère à l'aide de marqueurs moléculaires : une approche combinée mesures-modèle

L’aérosol organique (AO) constitue une large fraction des particules de l’air ambiant qui ont des impacts majeurs sur la qualité de l’air et le climat. Ses sources et processus de formation, surtout pour l’AO secondaire (AOS), sont encore méconnus induisant sa sous-estimation par les modèles de qualité de l’air. Ce travail a pour objectif d’améliorer la modélisation de l’AO en implémentant des émissions et processus de formation de marqueurs moléculaires organiques dans le modèle de chimie-transport CHIMERE. Il est basé sur la comparaison entre des sorties de modèle et de mesures réalisées en région parisienne (site périurbain du SIRTA, 25 km SO de Paris) en 2015 et sur 10 sites urbains en hiver 2014-2015. 25 marqueurs d’AOS biogénique et anthropique ont été quantifiés en phase particulaire et gazeuse et la formation de 10 a été simulée. L’évolution des concentrations en lévoglucosan (marqueur de la combustion de biomasse) a aussi été modélisée. Les résultats ont montré que les émissions de sources ou précurseurs (manquantes ou sous-estimées), les concentrations en radicaux (NO, HO2 et RO2) et le défaut de voies de formation, sont des paramètres clés pour la simulation des marqueurs d’AOS. Une faible dépendance à la T°C du partage gaz-particule a été observée alors que le partage hydrophile non idéal, souvent négligé, semble essentiel. Le lévoglucosan est bien modélisé, même si des sous-estimations existent dans certaines régions et une importante fraction gazeuse théorétique a été mise en évidence. La comparaison mesures/modèle de marqueurs moléculaires est un outil puissant pour évaluer les émissions, les processus physico-chimiques et à terme, estimer les sources d’AO.Organic aerosols (OA) account for a large fraction of ambient air particulate matter and have strong impacts on air quality and climate. As their sources and atmospheric formation processes, notably for secondary OA (SOA), are still not fully understood, their concentrations are often underestimated by air quality models. This work aimed at improving OA modelling by implementing specific organic molecular marker emissions and formation processes into the chemistry-transport model CHIMERE. It was based on the comparison of model outputs with measurements from field studies performed in the Paris region (suburban site of SIRTA, 25 km SW of Paris) over 2015 and 10 French urban locations in winter 2014-2015. 25 biogenic and anthropogenic SOA markers have been quantified in both, particulate and gas phases and the formation pathways of 10 have been developed and simulated using CHIMERE. The evolution of levoglucosan concentrations (biomass burning marker) has been also modeled. The results obtained showed that sources and precursor emissions (missing or underestimated), radical concentrations (NO, HO2 and RO2) and the lack of formation pathways, are key parameters for the simulation of SOA markers. Gas/particle partitioning seemed poorly linked to the T°C while the inclusion of hydrophilic non-ideal partitioning, usually neglected, seemed essential. Levoglucosan was well simulated, even if some underestimations existed in some regions. A significant theoretical gaseous fraction was also highlighted. The model/measurements comparison of molecular markers is a powerful tool to evaluate precursor emissions, physicochemical processes and in the end, to estimate OA sources

Comprendre les processus de formation d'aérosols organiques dans l'atmosphère à l'aide de marqueurs moléculaires : une approche combinée mesures-modèle

Organic aerosols (OA) account for a large fraction of ambient air particulate matter and have strong impacts on air quality and climate. As their sources and atmospheric formation processes, notably for secondary OA (SOA), are still not fully understood, their concentrations are often underestimated by air quality models. This work aimed at improving OA modelling by implementing specific organic molecular marker emissions and formation processes into the chemistry-transport model CHIMERE. It was based on the comparison of model outputs with measurements from field studies performed in the Paris region (suburban site of SIRTA, 25 km SW of Paris) over 2015 and 10 French urban locations in winter 2014-2015. 25 biogenic and anthropogenic SOA markers have been quantified in both, particulate and gas phases and the formation pathways of 10 have been developed and simulated using CHIMERE. The evolution of levoglucosan concentrations (biomass burning marker) has been also modeled. The results obtained showed that sources and precursor emissions (missing or underestimated), radical concentrations (NO, HO2 and RO2) and the lack of formation pathways, are key parameters for the simulation of SOA markers. Gas/particle partitioning seemed poorly linked to the T°C while the inclusion of hydrophilic non-ideal partitioning, usually neglected, seemed essential. Levoglucosan was well simulated, even if some underestimations existed in some regions. A significant theoretical gaseous fraction was also highlighted. The model/measurements comparison of molecular markers is a powerful tool to evaluate precursor emissions, physicochemical processes and in the end, to estimate OA sources.L’aérosol organique (AO) constitue une large fraction des particules de l’air ambiant qui ont des impacts majeurs sur la qualité de l’air et le climat. Ses sources et processus de formation, surtout pour l’AO secondaire (AOS), sont encore méconnus induisant sa sous-estimation par les modèles de qualité de l’air. Ce travail a pour objectif d’améliorer la modélisation de l’AO en implémentant des émissions et processus de formation de marqueurs moléculaires organiques dans le modèle de chimie-transport CHIMERE. Il est basé sur la comparaison entre des sorties de modèle et de mesures réalisées en région parisienne (site périurbain du SIRTA, 25 km SO de Paris) en 2015 et sur 10 sites urbains en hiver 2014-2015. 25 marqueurs d’AOS biogénique et anthropique ont été quantifiés en phase particulaire et gazeuse et la formation de 10 a été simulée. L’évolution des concentrations en lévoglucosan (marqueur de la combustion de biomasse) a aussi été modélisée. Les résultats ont montré que les émissions de sources ou précurseurs (manquantes ou sous-estimées), les concentrations en radicaux (NO, HO2 et RO2) et le défaut de voies de formation, sont des paramètres clés pour la simulation des marqueurs d’AOS. Une faible dépendance à la T°C du partage gaz-particule a été observée alors que le partage hydrophile non idéal, souvent négligé, semble essentiel. Le lévoglucosan est bien modélisé, même si des sous-estimations existent dans certaines régions et une importante fraction gazeuse théorétique a été mise en évidence. La comparaison mesures/modèle de marqueurs moléculaires est un outil puissant pour évaluer les émissions, les processus physico-chimiques et à terme, estimer les sources d’AO

Modélisation de la formation des aérosols organiques secondaires issus des feux de végétation dans la région euro-méditerranéenne

To improve the modeling of secondary organic aerosol (SOA) formation of vegetation fires in chemistry transport models, a new chemical mechanism is developed. It represents the oxidation of major VOCs (those with high SOA yields and high emission factors). The Polyphemus air quality model is evaluated in the Euro-Mediterranean region during the summer of 2007. A sensitivity study on the relative influence of VOCs and semi-volatile organic compounds (SVOCs) on SOA formation is performed.Pour améliorer la modélisation de la formation des aérosols organiques secondaires (AOS) des feux de végétation dans les modèles de chimie transport, un nouveau mécanisme chimique est développé. Il représente l'oxydation des principaux COVs (ceux ayant des rendements en AOS élevés et des facteurs d'émissions élevés). Le modèle de qualité de l'air Polyphemus est évalué sur la région Euro-Méditerranéenne pendant l'été 2007. Une étude de sensibilité sur l'influence relative des COVs et des composés organiques semi volatils (COVs) sur la formation d'AOS est effectuée

One Year Comparison of SOA Markers Modelling and Measurements : Seasonality and Gas/Particle Partitioning

Secondary Organic Aerosols (SOA) account for a significant part of particulate matter (PM). Simulating their formation and fate remains challenging, since to date, air quality models usually underestimated them. SOA formation processes include multistep heterogeneous mechanisms, and their parametrizations involve several variables such as kinetic data and physical properties not well known. A better assessment of the SOA composition notably requires the understanding of the thermodynamic equilibrium of SOA compounds between the phases involved. This is usually done using either, theoretical, or experimental data. However, the validation of the modelling parametrizations developed must be done by comparison between measurement and modeling data. In this work, various key molecules, well known as SOA markers, have been modeled, using CHIMERE air quality model, through detailed formation pathways including the calculation of their gas/particle partitioning. Modelled concentration values have been compared to measurements of biogenic, (e.g. pinonic acid, pinic acid and MBTCA: α pinene oxidation by products), and anthropogenic (e.g. DHOPA: toluene SOA marker) SOA markers, performed on both, gaseous and particulate phases. Mechanisms have been developed based on data obtained from the Master Chemical Mechanism (NCAS, Universities of Leeds and York) and the scientific literature. The gas/particle partitioning has been calculated by using saturated vapor pressure and Henry’s constant values through the thermodynamic model SOAP. The gas phase chemistry has been simulated using MELCHIOR2. Biogenic emissions have been computed with the MEGAN 2.1 algorithm. The comparison has been done with SOA marker measurements performed for a one year period (2015) at the SIRTA sampling site (25 km SW from Paris city center). Filters and PUF (polyurethane foam) samples were collected every third day (24 h sampling) and then analysed by GC MS after derivatization using native standards. The capacity of the model to reproduce seasonal variations of concentrations and the gas/particle partitioning (influenced by several parameters such as humidity and temperature) was evaluated

Benefits of cross modelling and field measurement approaches on the evaluation of SOA distribution: a case study in Grenoble, France

Organic Aerosols represent a major fraction of particulate matter in ambient air, that influence significantly the climate and air quality. Their concentration and composition show a large seasonal and regional variability. Primary emission sources have been widely studied, and are now well known and apportioned. VOCs (Volatile Organic Compounds) can undergo photo-oxidation reacting with light and oxidants like OH, NO3, O3, producing less volatile compounds that, through coagulation or nucleation, can form Secondary Organic Aerosols (SOA), which account to a significant part of total OA. To date, air quality models does not succeed to well simulate the SOA fraction in the PM concentration forecasts. The aim of this work is to evaluate the benefit of the combination of field measurement and modelling on the evaluation of the SOA distribution. Final goal is to improve models for the prediction of SOA formation and contribution in the ambient air. Aerosol filter samples have been collected at the urban station of “Les Frenes” in Grenoble (France) in 2013 every third day for one year and already included a large aerosol chemical characterization (Tomaz et al. 2016). The samples were extracted by QuEChERS (Quick Easy Cheap Effective Rugged and Safe) (Albinet, Tomaz, and Lestremau 2013) and and analyzed by GCMS after derivatization with MSTFA+1%TMCS. Quantification of SOA markers (e.g. SOA-Biogenic: pinic acid, pinonic acid, 2-methylerythritol, β- caryophyllinic acid, MBTCA, SOA-Anthropogenic: DHOPA, DHOBA, SOA-PAH: hydroxypyrene, 4 – nitro – 1 - naphtol, 1 acenaphtenol, SOA-Biomass Burning: methyl-nitrocathecols) was done using native standards. The estimation of the SOA (or SOC) contribution from individual precursor was performed using the SOA tracers method proposed by (Kleindienst et al. 2007). This approach uses ratios obtained by chamber studies between markers produced and the amount of precursors introduced. The chemistry-transport model CHIMERE (Menut et al. 2013) was used for SOA distribution modeling, taking both anthropogenic and biogenic markers into account. For selected SOA marker, the atmospheric formation pathway was sought in the literature and inserted in the model. Kinetic data were taken from The Master Chemical Mechanism database (National Centre for Atmospheric Science, Universities of Leeds and York). The simulation was performed over Europe and at regional scale (Figure 1). The novelty of in this work relies in the synergy between the analysis of field data and the improvement of the model. The results from one-year campaign measurement was compared for the first time to the output of modeling simulation on a regional scale. This kind of approach is required in order to get an overview of the SOA distribution at a local scale, since the actual concentrations are often underestimated. This is the first step towards a better understanding of the processes occurring in the atmosphere in order to improve atmospheric chemistry models and efficiency of air quality control policies

Analysis and determination of secondary organic aerosol (SOA) tracers (markers) in particulate matter standard reference material (SRM 1649b, urban dust)

International audienc

Modelling of SOA markers: simulation through detailed mechanisms and validation by comparison with measurements. A new approach to understand SOA formation.

Secondary organic aerosol (SOA) is formed via the oxidation of both anthropogenic and biogenic gas-phase organic compounds and is a large and often dominant fraction of total OA (Kroll and Seinfeld, 2008). To assess the sources of secondary organic carbon, SOA “tracer” method related to specific precursors has been developed (Kleindienst et al., 2007). Due to the importance of SOA contribution on the total PM mass, it is necessary to develop atmospheric chemistry models that properly describe the formation of the SOA markers in the atmosphere in order to improve the understanding of SOA formation and to enhance air quality forecasts. In this context, the modelling approach developed has to be compared with data obtained through field measurements. The aim of this work is to implement SOA tracer mechanisms inside the air quality model CHIMERE and to compare the model with field measurements results. This comparison gives an insight on the ability of the model to form SOA from specific precursors and on several processes (e.g. emissions, gas/particle partitioning). Measurements of SOA markers were performed at SIRTA station, representing the suburban background air quality conditions of the Paris region (about 25 km SW from Paris city center). PM10 samples were collected every third day all over the year 2015. SOA markers have been quantified using native standard compounds by LC/M-MS and/or GC/MS after derivatization with MSTFA+1%TMCS. SOA marker concentrations (Figure 1) were compared with the results of the model. The mechanisms describing the formation of the markers were introduced into the chemistry-transport model CHIMERE (Menut et al. 2013). Mechanisms for the formation of SOA markers were taken from the Master Chemical Mechanism (MCM, NCAS, Universities of Leeds and York) otherwise, data were sought in the scientific literature. The gas phase mechanism simulation was performed using MELCHIOR2, the partitioning between particulate phase and gaseous phase was calculated using the thermodynamic model SOAP (Couvidat and Sartelet, 2015). Biogenic emissions were computed with MEGAN 2.1 algorithm (Guenther et al., 2012). Simulated markers included both biogenic, (e.g; pinonic acid, pinic acid and MBTCA from α-pinene oxidation), and anthropogenic (e.g. DHOPA and nitrophenols from toluene oxidation) precursors

Modelling aerosol molecular markers in a 3D air quality model: Focus on anthropogenic organic markers

International audienceWe developed and implemented in the 3D air quality model CHIMERE the formation of several key anthropogenic aerosol markers including one primary anthropogenic marker (levoglucosan) and 4 secondary anthropogenic markers (nitrophenols, nitroguaiacols, methylnitrocatechols and phthalic acid). Modelled concentrations have been compared to measurements performed at 12 locations in France for levoglucosan in winter 2014–15, and at a sub-urban station in the Paris region over the whole year 2015 for secondary molecular markers. While a good estimation of levoglucosan concentrations by the model has been obtained for a few sites, a strong underestimation was simulated for most of the stations especially for western locations due to a probable underestimation of residential wood burning emissions. The simulated ratio between wood burning organic matter and particulate phase levoglucosan is constant only at high OM values (>10 μg m−3) indicating that using marker contribution ratio may be valid only under certain conditions. Concentrations of secondary markers were well reproduced by the model for nitrophenols and nitroguaiacols but were underestimated for methylnitrocatechols and phthalic acid highlighting missing formation pathways and/or precursor emissions. By comparing modelled to measured Gas/Particle Partitioning (GPP) of markers, the simulated partitioning of Semi-Volatile Organic Compounds (SVOCs) was evaluated. Except for nitroguaiacols and nitrophenols when ideality was assumed, the GPP for all the markers was underestimated and mainly driven by the hydrophilic partitioning. SVOCs GPP, and more generally of all SVOC contributing to the formation of SOA, could therefore be significantly underestimated by air quality models, especially when only the partitioning on the organic phase is considered. Our results show that marker modelling can give insights on some processes (such as precursor emissions or missing mechanisms) involved in SOA formation and could prove especially useful to evaluate the GPP in 3D air quality models

XRF analysis searching for fingerprint elemental profile in south-eastern Sicily tomatoes

Abstract The implementation of analytical techniques able to certify food quality and origin in a fast and non-destructive way is becoming a widespread need in the agri-food sector. Among the physical non-destructive techniques, X-ray fluorescence (XRF) spectrometry is often used to analyze the elemental composition of biological samples. In this study, X-ray fluorescence (XRF) elemental profiles were measured on tomato samples belonging to different geographical areas in Sicily (Italy). The purpose of this investigation was aiming to establish a protocol for in-situ measurement and analysis able to provide quality assessment and traceability of PGI agri-food products, specifically sustaining health safety and self qualifying bio-chemical signature. In detail, sampling was performed in one of the most tomato productive area of south-eastern Sicily (Pachino district), characterised by a relative higher amount of Organic Carbon and Cation Exchange Capacity, and compared with samples from other growing areas of Sicily, falling in Ragusa province and Mt. Etna region. Experimental data were analyzed in the framework of multivariate analysis by using principal component analysis and further validated by discriminant analysis. The results show the presence of specific elemental signatures associated to several characterizing elements. This methodology establishes the possibility to disentangle a clear fingerprint pattern associated to the geographical origin of an agri-food product