Cryptosporidium infections in asymptomatic calves up to 4 months in Poland: a cross-sectional population study

Abstract Cattle cryptosporidiosis is noted worldwide with varied frequency of infection prevalence depending on geographical, environmental and husbandry factors. In this study, the prevalence of Cryptosporidium infections in cattle was determined on the basis of molecular results obtained by testing 1601 faecal samples collected from calves up to 4 months of age housed in all Polish provinces from 2014 to 2018. Detection and identification of Cryptosporidium species was performed at the 18 small subunit ribosomal RNA (18S rRNA) locus by conducting PCR–RFLP analysis of the amplified DNA fragments. The prevalence of Cryptosporidium infections in the cattle population was 45.3% (CI 95%: 42.8–47.7; 725/1601). The infected animals were housed on 233/267 (87.3%) of monitored farms with regional prevalence ranging from 27.8 to 62%. The restriction pattern of 18S rRNA amplicons for positive samples was characteristic of C. parvum, C. bovis, C. ryanae, C. andersoni, and unexpectedly also of C. baileyi and C. suis. Infections of C. bovis and C. ryanae prevailed in the studied cattle population relegating C. parvum to third in prevalence. Likewise, mixed infections caused by C. bovis and C. ryanae as well as C. parvum and C. bovis were observed. A relationship between the infecting parasite species and animal breed was found. For instance, C. parvum prevailed in Black and White lowland breed, C. ryanae in Limousine cattle and C. andersoni in dairy animals of mixed dairy breeds. Furthermore, differences in prevalence of particular parasite species between cattle breeds were also shown

Wild Boar as a Sylvatic Reservoir of Hepatitis E Virus in Poland: A Cross-Sectional Population Study

The most important wildlife species in the epidemiology of hepatitis E virus (HEV) infections are wild boars, which are also the main reservoir of the virus in a sylvatic environment. The aim of the study was a serological and molecular assessment of the prevalence of HEV infections in wild boars in Poland. In total, 470 pairs of samples (wild boar blood and livers) and 433 samples of faeces were tested. An ELISA (ID.vet, France) was used for serological analysis. For the detection of HEV RNA, real-time (RT)-qPCR was employed. The presence of specific anti-HEV IgG antibodies was found in 232 (49.4%; 95%CI: 44.7–54%) sera, with regional differences observed in the seroprevalence of infections. HEV RNA was detected in 57 (12.1%, 95%CI: 9.3–15.4%) livers and in 27 (6.2%, 95%CI: 4.1–8.9%) faecal samples, with the viral load ranging from 1.4 to 1.7 × 1011 G.C./g and 38 to 9.3 × 107 G.C./mL, respectively. A correlation between serological and molecular results of testing of wild boars infected with HEV was shown. HEV infections in wild boars appeared to be common in Poland

Identification of pig-specific Cryptosporidium species in mixed infections using Illumina sequencing technology

Nowadays molecular methods are widely used in epidemiological studies of Cryptosporidium infections in humans and animals. However to gain better understanding of parasite species or genotypes, especially when mixed infections are noticed, highly sensitive tools with adequate resolution power need to be employed. In this article, we report an application of the next generation sequencing method (NGS) for detection and characterisation of Cryptosporidium species concurrently present in pig faeces. A mixture of Cryptosporidium DNA obtained from two faecal samples was amplified at the 18 SSU rRNA gene locus and the resulting amplicons were subsequently used for MiSeq sequencing. Although initial molecular analyses indicated the possible presence of another Cryptosporidium species other than Cryptosporidium scrofarum and Cryptosporidium suis, deep sequencing only confirmed the presence of pig-specific Cryptosporidium

Detection of myxoma virus in the classical form of myxomatosis using an AGID assay: statistical assessment of the assay’s diagnostic performance

The aim of the study was to estimate the diagnostic sensitivity (DSe) and specificity (DSp) of an agar gel immunodiffusion (AGID) assay for detection of myxoma virus (MYXV) in the classical form of myxomatosis and to compare its diagnostic performance to that of molecular methods (IAC-PCR, OIE PCR, and OIE real-time PCR)

Quantitative farm-to-fork risk assessment model for norovirus and hepatitis A virus in European leafy green vegetable and berry fruit supply chains

Fresh produce that is contaminated with viruses may lead to infection and viral gastroenteritis or hepatitis when consumed raw. It is thus important to reduce virus numbers on these foods. Prevention of virus contamination in fresh produce production and processing may be more effective than treatment, as sufficient virus removal or inactivation by post-harvest treatment requires high doses that may adversely affect food quality. To date knowledge of the contribution of various potential contamination routes is lacking. A risk assessment model was developed for human norovirus, hepatitis A virus and human adenovirus in raspberry and salad vegetable supply chains to quantify contributions of potential contamination sources to the contamination of produce at retail. These models were used to estimate public health risks. Model parameterization was based on monitoring data from European supply chains and literature data. No human pathogenic viruses were found in the soft fruit supply chains; human adenovirus (hAdV) was detected, which was additionally monitored as an indicator of fecal pollution to assess the contribution of potential contamination points. Estimated risks per serving of lettuce based on the models were 3×10(-4) (6×10(-6)-5×10(-3)) for NoV infection and 3×10(-8) (7×10(-10)-3×10(-6)) for hepatitis A jaundice. The contribution to virus contamination of hand-contact was larger as compared with the contribution of irrigation, the conveyor belt or the water used for produce rinsing. In conclusion, viral contamination in the lettuce and soft fruit supply chains occurred and estimated health risks were generally low. Nevertheless, the 97.5% upper limit for the estimated NoV contamination of lettuce suggested that infection risks up to 50% per serving might occur. Our study suggests that attention to full compliance for hand hygiene will improve fresh produce safety related to virus risks most as compared to the other examined sources, given the monitoring results. This effect will be further aided by compliance with other hygiene and water quality regulations in production and processing facilities

INSaFLU-TELEVIR: an open web-based bioinformatics suite for viral metagenomic detection and routine genomic surveillance

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Implementation of clinical metagenomics and pathogen genomic surveillance can be particularly challenging due to the lack of bioinformatics tools and/or expertise. In order to face this challenge, we have previously developed INSaFLU, a free web-based bioinformatics platform for virus next-generation sequencing data analysis. Here, we considerably expanded its genomic surveillance component and developed a new module (TELEVIR) for metagenomic virus identification. Results: The routine genomic surveillance component was strengthened with new workflows and functionalities, including (i) a reference-based genome assembly pipeline for Oxford Nanopore technologies (ONT) data; (ii) automated SARS-CoV-2 lineage classification; (iii) Nextclade analysis; (iv) Nextstrain phylogeographic and temporal analysis (SARS-CoV-2, human and avian influenza, monkeypox, respiratory syncytial virus (RSV A/B), as well as a "generic" build for other viruses); and (v) algn2pheno for screening mutations of interest. Both INSaFLU pipelines for reference-based consensus generation (Illumina and ONT) were benchmarked against commonly used command line bioinformatics workflows for SARS-CoV-2, and an INSaFLU snakemake version was released. In parallel, a new module (TELEVIR) for virus detection was developed, after extensive benchmarking of state-of-the-art metagenomics software and following up-to-date recommendations and practices in the field. TELEVIR allows running complex workflows, covering several combinations of steps (e.g., with/without viral enrichment or host depletion), classification software (e.g., Kaiju, Kraken2, Centrifuge, FastViromeExplorer), and databases (RefSeq viral genome, Virosaurus, etc.), while culminating in user- and diagnosis-oriented reports. Finally, to potentiate real-time virus detection during ONT runs, we developed findONTime, a tool aimed at reducing costs and the time between sample reception and diagnosis. Conclusions: The accessibility, versatility, and functionality of INSaFLU-TELEVIR are expected to supply public and animal health laboratories and researchers with a user-oriented and pan-viral bioinformatics framework that promotes a strengthened and timely viral metagenomic detection and routine genomics surveillance. INSaFLU-TELEVIR is compatible with Illumina, Ion Torrent, and ONT data and is freely available at https://insaflu.insa.pt/ (online tool) and https://github.com/INSaFLU (code).This study was partially supported by the TELEVIR project, the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. The improvement of the computational capacity of the online tool and its integration in INSA genomic surveillance workflows was also co-funded by the European Union through the Health Emergency Preparedness and Response (HERA) grant “Grant/2021/PHF/23776″ and the project “Sustainable use and integration of enhanced infrastructure into routine genome-based surveillance and outbreak investigation activities in Portugal” (https://www.insa.min-saude.pt/category/projectos/geneo/) on behalf of EU4H programme (EU4H-2022-DGA-MS-IBA-1). The development of the findONTime tool and a few platform updates performed in 2023 were also co-financed through the DURABLE project. The DURABLE project has been co-funded by the European Union, under the EU4Health Programme (EU4H), Project no. 101102733. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. IZSLER participation was partially funded by the Italian national Research program no. B93C22001210001: CCM-SURVEID—Studio pilota per la sorveglianza di potenziali minacce da malattie infettive emergenti (EIDs) di origine virale mediante una piattaforma diagnostica basata sul sequenziamento metagenomico di nuova generazione (mNGS). CISA-INIA-CSIC participation was partially funded by MCIN/AEI/10.13039/501100011033 and by the EU “NextGenerationEU”/PRTR” through the Spanish project no. PLEC2021-007968: Development of New Technologies to Track Emerging Infectious Threats in Wildlife and the Environment (NEXTHREAT). Rafael Mamede was supported by the Fundação para a Ciência e Tecnologia (FCT) (grant 2020.08493.BD).info:eu-repo/semantics/publishedVersio