Dataset Information

Identification of species and sources of Cryptosporidium oocysts in storm waters with a small-subunit rRNA-based diagnostic and genotyping tool.

ABSTRACT: The identification of Cryptosporidium oocysts in environmental samples is largely made by the use of an immunofluorescent assay. In this study, we have used a small-subunit rRNA-based PCR-restriction fragment length polymorphism technique to identify species and sources of Cryptosporidium oocysts present in 29 storm water samples collected from a stream in New York. A total of 12 genotypes were found in 27 positive samples; for 4 the species and probable origins were identified by sequence analysis, whereas the rest represent new genotypes from wildlife. Thus, this technique provides an alternative method for the detection and differentiation of Cryptosporidium parasites in environmental samples.

SUBMITTER: Xiao L

PROVIDER: S-EPMC92489 | biostudies-literature | 2000 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of species and sources of Cryptosporidium oocysts in storm waters with a small-subunit rRNA-based diagnostic and genotyping tool.

Xiao L L Alderisio K K Limor J J Royer M M Lal A A AA

Applied and environmental microbiology 20001201 12

The identification of Cryptosporidium oocysts in environmental samples is largely made by the use of an immunofluorescent assay. In this study, we have used a small-subunit rRNA-based PCR-restriction fragment length polymorphism technique to identify species and sources of Cryptosporidium oocysts present in 29 storm water samples collected from a stream in New York. A total of 12 genotypes were found in 27 positive samples; for 4 the species and probable origins were identified by sequence analy ...[more]

PMID: 11097935

Similar Datasets

Project description:BackgroudCryptosporidium species are zoonotic protozoan parasites responsible for gastroenteritis in various animals and humans. The diagnosis of Cryptosporidium presents many challenges. This research attempted to match the diagnostic efficiency of the modified Ziehl-Neelsen technique (mZN), immunochromatographic assays (IC), and enzyme-linked immunosorbent assay (ELISA) for the detection of Cryptosporidium in faecal samples of cattle in Kuwait. In addition, polymerase chain reaction (PCR) was utilised to determine the predominant species infecting cattle in Kuwait and correlating the detected species with the results of different diagnostic tests used, the presence or absence of clinical signs, and the age group of the infected cattle.ResultsOf 400 analysed faecal samples, Cryptosporidium positive samples were 23%, 15.25%, and 14% using IC, ELISA, and mZN. IC had the highest sensitivity (74.07%), and mZN had the highest specificity (98.29%) using a composite reference standard (CRS) as a gold standard. The rapid IC test results in high false-positive results of cryptosporidiosis, whereas using mZN alone is insufficient to declare a negative faecal sample. Only 74.5% (35/47) of Cryptosporidium-positive samples by the three assays could be amplified by PCR. This study was the first to genotype Cryptosporidium in Kuwait. Cryptosporidium parvum (n = 26) was the dominant species detected from cattle samples, followed by C. andersoni (n = 6), C. bovis (n = 2), and C. raynae (n = 1). The findings showed a statistically relevant relationship between diarrhoea and the detection of Cryptosporidium spp. in faecal samples of cattle (p-value = 0.0003). Pre-weaned calves were the most vulnerable age group to Cryptosporidium spp. infection (p-value = 0.0007).ConclusionFor screening of Cryptosporidium infection in faecal samples, antigen detection or PCR methods combined with one of the microscopy techniques should be used. Cryptosporidium parvum was the prepoderant Cryptosporidium spp. recovered from cattle samples in Kuwait followed by C. andersoni. Cryptosporidium parvum is a significant risk factor for diarrhoea in pre-weaned calves. However, further study is needed as many other causes of diarrhoea in calves must be ruled out before a diagnosis of Cryptosporidium diarrhoea can be made.

Project description:The structure of rhizobial communities nodulating Acacia in southeastern Australia from south Queensland to Tasmania was investigated by a molecular approach. A total of 118 isolates from nodule samples from 13 different Acacia species collected at 44 sites were characterized by small-subunit (SSU) ribosomal DNA (rDNA) PCR-restriction fragment length polymorphism analysis. Nine rhizobial genomospecies were identified, and these taxa corresponded to previously described genomospecies (B. Lafay and J. J. Burdon, Appl. Environ. Microbiol. 64:3989-3997, 1998). Eight of these genomospecies belonged to the Bradyrhizobium lineage and accounted for 96.6% of the isolates. The remaining genomospecies corresponded to Rhizobium tropici. For analysis of geographic patterns, results were grouped into five latitudinal regions regardless of host origin. In each region, as observed previously for rhizobial isolates taken from non-Acacia legumes (Lafay and Burdon, Appl. Environ. Microbiol. 64:3989-3997, 1998), rhizobial communities were dominated by one or two genomospecies, the identities of which varied from place to place. Despite this similarity in patterns, the most abundant genomospecies for Acacia isolates differed from the genomospecies found in the non-Acacia-derived rhizobial collection, suggesting that there is a difference in nodulation patterns of the Mimosoideae and the Papilionoideae. Only two genomospecies were both widespread and relatively abundant across the range of sites sampled. Genomospecies A was found in all regions except the most northern sites located in Queensland, whereas genomospecies B was not detected in Tasmania. This suggests that genomospecies A might be restricted to the more temperate regions of Australia, whereas in contrast, genomospecies B occurs in different climatic and edaphic conditions across the whole continent. The latter hypothesis is supported by the presence of genomospecies B in southwestern Australia, based on partial SSU rDNA sequence data (N. D. S. Marsudi, A. R. Glenn, and M. J. Dilworth, Soil Biol. Biochem. 31:1229-1238, 1998).

Dataset Information

Identification of species and sources of Cryptosporidium oocysts in storm waters with a small-subunit rRNA-based diagnostic and genotyping tool.

Publications

Identification of species and sources of Cryptosporidium oocysts in storm waters with a small-subunit rRNA-based diagnostic and genotyping tool.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets