5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Molecular identification of four Sarcocystis species in cattle from Lithuania, including S. hominis, and development of a rapid molecular detection method

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Six Sarcocystis species are known to use cattle ( Bos taurus) as the intermediate host, two of which, S. hominis and S. heydorni, are zoonotic. There is a need for a method that will enable rapid identification of the Sarcocystis species in cattle.

          Methods

          The diaphragm muscles of 102 cattle from Lithuania were examined for the presence of Sarcocystis spp., using two different methods for species identification. Individual sarcocysts were isolated from squash preparations of the diaphragm muscle under the light microscope, followed by genetic characterisation of excised cysts using sequence analysis of the 18S rRNA ( 18S rRNA) and cytochrome c oxidase subunit I ( cox1) genes. The same cattle muscle samples were digested and species-specific PCR analyses targeting cox1 were developed to identify the Sarcocystis isolates to the species level.

          Results

          Under the light microscope, sarcocysts were detected in 87.3% of animals, and Sarcocystis infection was verified in all digested samples. Three species, namely S. cruzi ( n = 20), S. bovifelis ( n = 23) and S. hirsuta ( n = 6), were identified by DNA sequence analysis of isolated sarcocysts. Based on sequence analysis of cox1, the level of genetic variability depended on Sarcocystis species and geographical location. Four Sarcocystis species, S. cruzi (96.1%), S. bovifelis (71.6%), S. hirsuta (30.4%) and S. hominis (13.7%), were confirmed in the digested samples. In individual samples, the most common finding was two species of Sarcocystis (44.1%), followed by three species (26.5%), a single species (24.5%) and four species (4.9%).

          Conclusions

          Although examination of tissue preparations under the light microscrope did not detect any sarcocysts belonging to S. hominis, this species was identified in the digested samples subjected to a cox1-specific PCR analysis. These results demonstrate the need for effective molecular diagnosis techniques to detect Sarcocystis spp., which may be present at a lower prevalence and not detectable among the limited number of sarcocysts identified individually under the light microscope.

          Related collections

          Most cited references49

          • Record: found
          • Abstract: found
          • Article: not found

          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows.

              We present here a new version of the Arlequin program available under three different forms: a Windows graphical version (Winarl35), a console version of Arlequin (arlecore), and a specific console version to compute summary statistics (arlsumstat). The command-line versions run under both Linux and Windows. The main innovations of the new version include enhanced outputs in XML format, the possibility to embed graphics displaying computation results directly into output files, and the implementation of a new method to detect loci under selection from genome scans. Command-line versions are designed to handle large series of files, and arlsumstat can be used to generate summary statistics from simulated data sets within an Approximate Bayesian Computation framework. © 2010 Blackwell Publishing Ltd.
                Bookmark

                Author and article information

                Contributors
                prakaspetras@gmail.com
                genetike@gmail.com
                vytautasjanusk@gmail.com
                francesco.chiesa@unito.it
                agne.terese@gmail.com
                egle.rudaityte@gmail.com
                serviene@gmail.com
                saulius.petkevicius@lsmuni.lt
                dalius.butkauskas@gamtc.lt
                Journal
                Parasit Vectors
                Parasit Vectors
                Parasites & Vectors
                BioMed Central (London )
                1756-3305
                7 December 2020
                7 December 2020
                2020
                : 13
                : 610
                Affiliations
                [1 ]GRID grid.435238.b, ISNI 0000 0004 0522 3211, Nature Research Centre, ; Vilnius, Lithuania
                [2 ]GRID grid.45083.3a, ISNI 0000 0004 0432 6841, Lithuanian University of Health Science, ; Kaunas, Lithuania
                [3 ]GRID grid.7605.4, ISNI 0000 0001 2336 6580, Department of Veterinary Science, , University of Turin, ; Turin, Italy
                Article
                4473
                10.1186/s13071-020-04473-9
                7720396
                33287879
                bcb71584-324c-4d1a-ab79-13ea050bf0e3
                © The Author(s) 2020

                Open AccessThis 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.

                History
                : 11 August 2020
                : 5 November 2020
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Parasitology
                cattle,sarcocystis hominis,trypsin digestion,molecular identification,cox1,18s rrna gene
                Parasitology
                cattle, sarcocystis hominis, trypsin digestion, molecular identification, cox1, 18s rrna gene

                Comments

                Comment on this article