In this study, for the detection of distinct genotype of E. histolytica of human, a nested Real-Time PCR amplification of SREHP gene using SYBR Green I and melting curve analysis was done. A total of 60 specimens (stool and liver aspirate specimens), which were found Entamoeba histolytica positive by E. histolytica specific ELISA and ssrRNA gene PCR, were selected and the experiment was conducted during the period of July 2003 to June 2004. After melting curve analysis of amplified PCR products from these isolates of stool and liver aspirate specimens, 5 genotypes were found belonging to the melting temperatures 84°C, 83°C, 82°C, 81°C and 79°C. All these 5 genotypes were present in intestinal amoebiasis patients and when the genotypes from intestinal amoebiasis patients were compared with the genotypes of amoebic liver amoebiasis patients, the genotype 84°C melting temperature was found to be absent in amoebic liver amoebiasis patients. For both the cases of intestinal and amoebic liver amoebiasis patients the genotype belonging to 83°C melting temperature was more prevalent than the other genotypes which suggest that this genotype is more responsible for the development of amoebiasis. In comparison to conventional PCR method where we found 23 different banding patterns, the Real-Time PCR and melting curve analysis method was found to be more reliable for the detection of distinct genotypes of E. histolytica because with this method we found only 5 genotypes. In conclusion, this Real-Time PCR using SYBR Green I and melting curve analysis for the genotyping of E. histolytica, excludes the need of post PCR manipulations and would be helpful for the rapid detection and screening of E. histolytica genotypes among endemic population and also for the epidemiological study. Key words: Entamoeba histolytica, genotype, diarrhoea, Real-Time PCR, melting curve analysis doi:10.3329/bjvm.v4i1.1526 Bangl. J. Vet. Med. (2006). 4 (1): 53-60
Rapid diagnosis of human brucellosis by SYBR Green I-based real-time PCR assay and melting curve analysis in serum samples
