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Characterization of influenza A(H1N1)pdm09 isolates of Peru using HRM, a post PCR molecular biology method



Priscila Lope1,3, Huaringa Maribel1, Mayta Egma3, Bailon Henri2, Padilla Carlos2



1Laboratorio de Referencia Nacional de Virus Respiratorio, Centro Nacional de Salud Publica, Instituto Nacional de Salud, Lima, Peru; 2Laboratorio de Referencia Nacional de Biotecnologia y Biologia Molecular. Centro Nacional de Salud Publica. Instituto Nacional de Salud. Lima. Peru; 3Laboratorio de virologia. Universidad Nacional Mayor de San Marcos. Lima. Peru;



Priscila Lope - E-mail: priscila.lope@unmsm.edu.pe; *Corresponding author


Article Type

Research Article



Received September 28, 2019; Revised October 6, 2019; Accepted October 7, 2019; Published October 10, 2019



Influenza caused by A(H1N1)pdm09 is a public health issue with severe conditions in vulnerable populations leading to death. Therefore, it is of interest to characterize and monitor influenza A(H1N1)pdm09 genotypes using High Resolution Melting (HRM), a post PCR molecular biology method. We used HRM analysis (using RotorGene Q thermocycler) to characterize A(H1N1)pdm09 genotypes from several places of Peru. RNA was purified from nasal and pharyngeal swab samples referred to LRNVR-INS, synthesized cDNA, and then the hemagglutinin gene and matrix fragment were amplified. Thus, 287 samples positive for influenza A(H1N1)pdm09 were identified
across Peru where places like Lima, Piura, and Arequipa documented highest number of cases. The HRM data was analyzed and results showed different profiles which were further grouped into four genotypes for the HA (A, B, C, D) and 3 for the M (a, b, c) genes. We also report ten genotypes (I-X) of virus using combined HA (hemagglutinin) and M gene profiles representing a national geography. The prevalent genotypes are I and II with a frequency of 35.89% (103) and 29.27% (84), respectively linking with severe acute respiratory infection.



High Resolution Melting, H1N1 Influenza Virus Subtype A, Genotype



Lope et al. Bioinformation 15(9): 640-645 (2019)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.