A Schistosoma mansoni tri- and tetramer microsatellite catalog for genetic population diversity and differentiation |
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| Affiliation: | 1. Center for Global Health and Diseases, Case Western Reserve University, Biomedical Research Building, 2109 Adelbert Rd., Cleveland, OH 44106, USA;2. University of New Mexico, Department of Anthropology, Albuquerque, 1 University of New Mexico, NM 87131, USA;3. Bahiana School of Medicine and Public Health, Av. Silveira Martins, n° 3386, Salvador, Bahia 41150-100, Brazil;4. Gonçalo Moniz Research Centre, Oswaldo Cruz Foundation, Rua Waldemar Falcão, 121 Brotas, Salvador, Bahia 40296-710, Brazil;5. School of Medicine, Federal University of Bahia, Salvador, Bahia, Brazil;6. Yale School of Public Health, Yale University, New Haven, CT, USA;7. Department of Tropical Medicine, Tulane School of Public Health and Tropical Medicine, Tidewater Building, 1440 Canal Street, New Orleans, LA 70112, USA;1. Instituto de Patobiología Veterinaria (INTA-CONICET), CICVyA, INTA-Castelar, 1686 Hurlingham, Argentina;2. CONICET, Ciudad de Buenos Aires, Argentina;1. Department of Epidemiology and Statistics, School of Public Health, Soochow University, Suzhou, China;2. Key Laboratory of National Health and Family Planning Commission on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, China;3. Centre for Emerging, Endemic and Exotic Diseases (CEEED), Department of Pathology and Population Sciences, Royal Veterinary College, University of London, London, United Kingdom;1. School of Veterinary Medicine & Science, University of Nottingham, Sutton Boningto, LE12 5RD, UK;2. Department of Infection Biology, Institute of Infection & Global Health, University of Liverpool, L3 5RF, UK;3. Division of Microbiology and Parasitology, School of Medical Sciences, University of Phayao, Thailand;1. CSIRO Agriculture and Food, Livestock & Aquaculture, Queensland Biosciences Precinct, 306 Carmody Road, Brisbane, QLD 4067, Australia;2. Institute for Glycomics, Griffith University, Gold Coast Campus, Gold Coast, QLD 4222, Australia;3. CSIRO Agriculture and Food, Livestock & Aquaculture, Castray Esplanade, Battery Point, TAS 7004, Australia;1. Mitrani Department of Desert Ecology, Swiss Institute of Dryland Environmental and Energy Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Midreshet Ben-Gurion, Israel;2. Agricultural Research Council-Onderstepoort Veterinary Institute, Onderstepoort, South Africa;3. Department of Conservation Ecology and Entomology, Stellenbosch University, Matieland, South Africa |
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| Abstract: | All Schistosoma mansoni tri- and tetranucleotide repeat microsatellites published as of December 2018 were identified. All 52 were evaluated for autosomal location, strength of amplification, scorability and behavior as single-copy loci by polyacrylamide and capillary gel electrophoresis. Of these, 27 were unique, autosomal, polymorphic, easily scored and single copy as assessed on pooled adult worm DNA from two different continental origins and adult worm clones. These microsatellites were distributed across all seven autosomal chromosomes. On laboratory strains their heterozygosity ranged from 0.22 to 0.77. Individual markers had 5–13 alleles, allelic richness of 2–10 and an effective allele number of 1.3–8.14. Those infected by Schistosoma mansoni carry many genetically distinct, sexually reproducing parasites, therefore, for an individual infection the complete allele frequency profile of their progeny consists of a pool of DNA from multiple diploid eggs. Using a set of 25 microsatellites, we calculated allele frequency profiles of eggs in fecal samples from people in two Brazilian communities separated by 6 km: Jenipapo (n = 80) and Volta do Rio (n = 38). There were no a priori characteristics that could predict the performance of markers in natural infections based on their performance with laboratory strains. Increasing marker number did not change accuracy for differentiation and diversity but did improve precision. Our data suggest that using a random set of 10–20 microsatellites appears to result in values that exhibit low standard deviations for diversity and differentiation indices. All identified microsatellites as well as PCR conditions, allele size, primer sequences and references for all tri- and tetramer microsatellites markers presented in this work are available at: https://sites.google.com/case.edu/cwru-and-fiocruz-wdrc/home. |
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| Keywords: | Population genetics Differentiation Brazil Kenya Pooled samples |
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