Dataset Information

Prediction of the Spatial Origin of Puumala Virus Infections Using L Segment Sequences Derived from a Generic Screening PCR.

ABSTRACT: To screen diagnostic specimens for the presence of hantavirus genomes or to identify new hantaviruses in nature, the pan-hanta L-PCR assay, a broadly reactive nested reverse transcription polymerase chain reaction (RT-PCR) assay targeting the L segment, is highly preferred over other assays because of its universality and high sensitivity. In contrast, the geographic allocation of Puumala virus strains to defined outbreak regions in Germany was previously done based on S segment sequences. We show that the routinely generated partial L segment sequences resulting from the pan-hanta L-PCR assay provide sufficient phylogenetic signal to inform the molecular epidemiology of the Puumala virus. Consequently, an additional S segment analysis seems no longer necessary for the identification of the spatial origin of a virus strain.

SUBMITTER: Weiss S

PROVIDER: S-EPMC6724045 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Prediction of the Spatial Origin of Puumala Virus Infections Using L Segment Sequences Derived from a Generic Screening PCR.

Weiss Sabrina S Klempa Boris B Tenner Beate B Kruger Detlev H DH Hofmann Jörg J

Viruses 20190730 8

To screen diagnostic specimens for the presence of hantavirus genomes or to identify new hantaviruses in nature, the pan-hanta L-PCR assay, a broadly reactive nested reverse transcription polymerase chain reaction (RT-PCR) assay targeting the L segment, is highly preferred over other assays because of its universality and high sensitivity. In contrast, the geographic allocation of Puumala virus strains to defined outbreak regions in Germany was previously done based on S segment sequences. We sh ...[more]

PMID: 31366168

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Project description:BackgroundTo predict the risk of infectious diseases originating in wildlife, it is important to identify habitats that allow the co-occurrence of pathogens and their hosts. Puumala hantavirus (PUUV) is a directly-transmitted RNA virus that causes hemorrhagic fever in humans, and is carried and transmitted by the bank vole (Myodes glareolus). In northern Sweden, bank voles undergo 3-4 year population cycles, during which their spatial distribution varies greatly.MethodsWe used boosted regression trees; a technique inspired by machine learning, on a 10 - year time-series (fall 2003-2013) to develop a spatial predictive model assessing seasonal PUUV hazard using micro-habitat variables in a landscape heavily modified by forestry. We validated the models in an independent study area approx. 200 km away by predicting seasonal presence of infected bank voles in a five-year-period (2007-2010 and 2015).ResultsThe distribution of PUUV-infected voles varied seasonally and inter-annually. In spring, micro-habitat variables related to cover and food availability in forests predicted both bank vole and infected bank vole presence. In fall, the presence of PUUV-infected voles was generally restricted to spruce forests where cover was abundant, despite the broad landscape distribution of bank voles in general. We hypothesize that the discrepancy in distribution between infected and uninfected hosts in fall, was related to higher survival of PUUV and/or PUUV-infected voles in the environment, especially where cover is plentiful.ConclusionsMoist and mesic old spruce forests, with abundant cover such as large holes and bilberry shrubs, also providing food, were most likely to harbor infected bank voles. The models developed using long-term and spatially extensive data can be extrapolated to other areas in northern Fennoscandia. To predict the hazard of directly transmitted zoonoses in areas with unknown risk status, models based on micro-habitat variables and developed through machine learning techniques in well-studied systems, could be used.

Project description:BackgroundThe accurate quantification of antigens at low concentrations over a wide dynamic range is needed for identifying biomarkers associated with disease and detecting protein interactions in high-throughput microarrays used in proteomics. Here we report the development of an ultrasensitive quantitative assay format called immunoliposome polymerase chain reaction (ILPCR) that fulfills these requirements. This method uses a liposome, with reporter DNA encapsulated inside and biotin-labeled polyethylene glycol (PEG) phospholipid conjugates incorporated into the outer surface of the liposome, as a detection reagent. The antigenic target is immobilized in the well of a microplate by a capture antibody and the liposome detection reagent is then coupled to a biotin-labeled second antibody through a NeutrAvidin bridge. The liposome is ruptured to release the reporter DNA, which serves as a surrogate to quantify the protein target using real-time PCR.ResultsA liposome detection reagent was prepared, which consisted of a population of liposomes ~120 nm in diameter with each liposome possessing ~800 accessible biotin receptors and ~220 encapsulated reporters. This liposome detection reagent was used in an assay to quantify the concentration of carcinoembryonic antigen (CEA) in human serum. This ILPCR assay exhibited a linear dose-response curve from 10-10 M to 10-16 M CEA. Within this range the assay coefficient of variance was <6?% for repeatability and <2?% for reproducibility. The assay detection limit was 13 fg/mL, which is 1,500-times more sensitive than current clinical assays for CEA. An ILPCR assay to quantify HIV-1 p24 core protein in buffer was also developed.ConclusionsThe ILPCR assay has several advantages over other immuno-PCR methods. The reporter DNA and biotin-labeled PEG phospholipids spontaneously incorporate into the liposomes as they form, simplifying preparation of the detection reagent. Encapsulation of the reporter inside the liposomes allows nonspecific DNA in the assay medium to be degraded with DNase I prior to quantification of the encapsulated reporter by PCR, which reduces false-positive results and improves quantitative accuracy. The ability to encapsulate multiple reporters per liposome also helps overcome the effect of polymerase inhibitors present in biological specimens. Finally, the biotin-labeled liposome detection reagent can be coupled through a NeutrAvidin bridge to a multitude of biotin-labeled probes, making ILPCR a highly generic assay system.

Dataset Information

Prediction of the Spatial Origin of Puumala Virus Infections Using L Segment Sequences Derived from a Generic Screening PCR.

Publications

Prediction of the Spatial Origin of Puumala Virus Infections Using L Segment Sequences Derived from a Generic Screening PCR.

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