Three new Scandinavian species of Culicoides (Culicoides): C. boyi sp. nov., C. selandicus sp. nov. and C. kalix sp. nov. (Diptera: Ceratopogonidae)

In the context of a major monitoring program of Culicoides in Denmark and Sweden due to the appearance of bluetongue disease in 2007–2008, a large number of specimens were collected by light traps and sorted morphologically, with COI barcodes generated for selected specimens. Three species are described as new to science based on both morphological and molecular data: Culicoides (Culicoides) boyi sp. nov. (Denmark: Jutland), C. (C.) selandicus sp. nov. (Denmark: Zealand) and C. (C.) kalix sp. nov. (Sweden: Norrbotten). All are diagnosed morphologically as well as by molecular barcoding. A key to slide-mounted females of all Scandinavian species of Culicoides (Culicoides) is presented

Identity and diversity of blood meal hosts of biting midges (Diptera: Ceratopogonidae: Culicoides Latreille) in Denmark

<p>Abstract</p> <p>Background</p> <p>Host preference studies in haematophagous insects e.g. <it>Culicoides</it> biting midges are pivotal to assess transmission routes of vector-borne diseases and critical for the development of veterinary contingency plans to identify which species should be included due to their risk potential. Species of <it>Culicoides</it> have been found in almost all parts of the world and known to live in a variety of habitats. Several parasites and viruses are transmitted by <it>Culicoides</it> biting midges including Bluetongue virus and Schmallenberg virus. The aim of the present study was to determine the identity and diversity of blood meals taken from vertebrate hosts in wild-caught <it>Culicoides</it> biting midges near livestock farms.</p> <p>Methods</p> <p>Biting midges were collected at weekly intervals for 20 weeks from May to October 2009 using light traps at four collection sites on the island Sealand, Denmark. Blood-fed female biting midges were sorted and head and wings were removed for morphological species identification. The thoraxes and abdomens including the blood meals of the individual females were subsequently subjected to DNA isolation. The molecular marker cytochrome oxidase I (COI barcode) was applied to identify the species of the collected biting midges (GenBank accessions JQ683259-JQ683374). The blood meals were first screened with a species-specific cytochrome b primer pair for cow and if negative with a universal cytochrome b primer pair followed by sequencing to identify mammal or avian blood meal hosts.</p> <p>Results</p> <p>Twenty-four species of biting midges were identified from the four study sites. A total of 111,356 <it>Culicoides</it> biting midges were collected, of which 2,164 were blood-fed. Specimens of twenty species were identified with blood in their abdomens. Blood meal sources were successfully identified by DNA sequencing from 242 (76%) out of 320 <it>Culicoides</it> specimens. Eight species of mammals and seven species of birds were identified as blood meal hosts. The most common host species was the cow, which constituted 77% of the identified blood meals. The second most numerous host species was the common wood pigeon, which constituted 6% of the identified blood meals.</p> <p>Conclusions</p> <p>Our results suggest that some <it>Culicoides</it> species are opportunistic and readily feed on a variety of mammals and birds, while others seems to be strictly mammalophilic or ornithophilic. Based on their number, dispersal potential and blood feeding behaviour, we conclude that <it>Culicoides</it> biting midges are potential vectors for many pathogens not yet introduced to Denmark.</p

Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning

Background: Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for farmers in Europe. Vector abundance is a key factor in determining the risk of vector-borne disease spread and it is, therefore, important to predict the abundance of Culicoides species involved in the transmission of these pathogens. The objectives of this study were to model and map the monthly abundances of Culicoides in Europe. Methods: We obtained entomological data from 904 farms in nine European countries (Spain, France, Germany, Switzerland, Austria, Poland, Denmark, Sweden and Norway) from 2007 to 2013. Using environmental and climatic predictors from satellite imagery and the machine learning technique Random Forests, we predicted the monthly average abundance at a 1 km2 resolution. We used independent test sets for validation and to assess model performance. Results: The predictive power of the resulting models varied according to month and the Culicoides species/ensembles predicted. Model performance was lower for winter months. Performance was higher for the Obsoletus ensemble, followed by the Pulicaris ensemble, while the model for Culicoides imicola showed a poor performance. Distribution and abundance patterns corresponded well with the known distributions in Europe. The Random Forests model approach was able to distinguish differences in abundance between countries but was not able to predict vector abundance at individual farm level. Conclusions: The models and maps presented here represent an initial attempt to capture large scale geographical and temporal variations in Culicoides abundance. The models are a first step towards producing abundance inputs for R0 modelling of Culicoides-borne infections at a continental scale

Multicentre comparison of a diagnostic assay: Aquaporin-4 antibodies in neuromyelitis optica

Objective Antibodies to cell surface central nervous system proteins help to diagnose conditions which often respond to immunotherapies. The assessment of antibody assays needs to reflect their clinical utility. We report the results of a multicentre study of aquaporin (AQP) 4 antibody (AQP4-Ab) assays in neuromyelitis optica spectrum disorders (NMOSD). Methods Coded samples from patients with neuromyelitis optica (NMO) or NMOSD (101) and controls (92) were tested at 15 European diagnostic centres using 21 assays including live (n=3) or fixed cell-based assays (n=10), flow cytometry (n=4), immunohistochemistry (n=3) and ELISA (n=1). Results Results of tests on 92 controls identified 12assays as highly specific (0-1 false-positive results). 32 samples from 50 (64%) NMO sera and 34 from 51 (67%) NMOSD sera were positive on at least two of the 12 highly specific assays, leaving 35 patients with seronegative NMO/spectrum disorder (SD). On the basis of a combination of clinical phenotype and the highly specific assays, 66 AQP4-Ab seropositive samples were used to establish the sensitivities (51.5-100%) of all 21 assays. The specificities (85.8-100%) were based on 92 control samples and 35 seronegative NMO/SD patient samples. Conclusions The cell-based assays were most sensitive and specific overall, but immunohistochemistry or flow cytometry could be equally accurate in specialist centres. Since patients with AQP4-Ab negative NMO/SD require different management, the use of both appropriate control samples and defined seronegative NMOSD samples is essential to evaluate these assays in a clinically meaningful way. The process described here can be applied to the evaluation of other antibody assays in the newly evolving field of autoimmune neurology

Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance

Background: Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe. Methods : We modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present. Results: The distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups. Conclusions: The duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain