Epidemiologia molecular do vírus da Hepatite C: análise comparativa de diferentes regiões subgenômicas aplicadas a estudos de associação genética
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2016-01-18
Autores
Rossi, Livia Maria Gonçalves Rossi [UNESP]
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Editor
Universidade Estadual Paulista (Unesp)
Resumo
O vírus da Hepatite C (HCV) afeta cerca de 3% da população mundial. A cada ano, 3-4 milhões de novos casos são diagnosticados. A identificação de redes transmissão é complexa devido ao longo período de incubação, à falta de sintomas na fase aguda da doença e à heterogeneidade do HCV, que dificulta o estabelecimento de vínculos entre casos relacionados. Uma ampla caracterização das populações intra-hospedeiros pode ser realizada de forma eficiente através do sequenciamento de nova geração (NGS). Com base neste contexto, o sequenciamento de múltiplas regiões subgenômicas é uma solução às limitações impostas pela rápida evolução molecular do HCV. Variantes virais das regiões HVR1 e NS5A de 16 pacientes cronicamente infectados com o HCV, genótipos 1a e 1b, foram sequenciadas com a técnica de NGS. Os pacientes 1-7 compartilhavam fatores de risco, pertencendo ao mesmo grupo de usuários de drogas injetáveis, porém o parentesco genético desses casos não pode ser estabelecido com base apenas no sequenciamento da HVR1 (distância nucleotídica mínima entre 16-23). A amplificação de um fragmento maior (~450 pb), correspondente a um segmento da região NS5A, aprimorou a relação epidemiológica entre os pacientes 1-5, onde as distancias genéticas mínimas foram consideravelmente menores (9-13). Os pacientes 6 e 7 não compartilharam sequências com os outros cinco pacientes dessa rede, apresentando populações virais mais homogêneas. Adicionalmente, Median Joining Networks foram construídas para melhor analisar a variabilidade genética intra-hospedeiro. Em geral, observou-se que as sequências derivadas da NS5A formaram comunidades mais homogêneas e menos divergentes geneticamente. Assim, a tecnologia NGS e o sequenciamento das regiões subgenômicas HVR1 e NS5A podem ajudar a restaurar elos perdidos quando somente a região HVR1 é analisada, aprimorando portanto, a resolução de estudos de associação genética entre populações de HCV.
The hepatitis C virus (HCV) affects approximately 3% of the world's population. Each year 3-4 million new cases are diagnosed. The identification of transmission networks is complicated due to the characteristic long incubation period, the lack of symptoms during the acute phase of the disease and the heterogeneity of HCV, making it challenging to link related cases to a common source of infection. Extensive characterization of intra-host populations can be reliably archived using next generation sequencing (NGS) approaches. Sequencing of multiple and longer subgenomic regions has been proposed as an alternative to overcome the limitations imposed by the rapid molecular evolution of the HCV HVR1. Thus, the NS5A and HVR1 regions of 16 chronically infected individuals, genotypes 1a and 1b, were sequenced using a NGS platform. Patients 1-7 shared risk factors and belonged to the same injection drug users network. However, genetic relatedness could not be established based on the HVR1 sequences (minimal nucleotide distance ranging from 16-23). Amplification and sequencing of a larger PCR fragment (~450 bp) targeting the NS5A region reestablished lost epidemiological links between patients 1-5. The minimum genetic distances in those patients were considerable smaller than the HVR1 counterparts (9-13). Patients 6 and 7 displayed a rather homogeneous viral population and were clearly not sharing any sequences with all other five patients in this network. Additionally, Median Joining Networks analysis was carried out to further analyze the intrahost genetic variability of all seven patients. Overall, NS5A sequences were significantly less diverse than their HVR1 equivalents. Thus, NGS technology and use of both HVR1 and NS5A sequences might help restored otherwise lost links when the HVR1 region alone is analyzed, improving the resolution of HCV genetic relatedness studies.
The hepatitis C virus (HCV) affects approximately 3% of the world's population. Each year 3-4 million new cases are diagnosed. The identification of transmission networks is complicated due to the characteristic long incubation period, the lack of symptoms during the acute phase of the disease and the heterogeneity of HCV, making it challenging to link related cases to a common source of infection. Extensive characterization of intra-host populations can be reliably archived using next generation sequencing (NGS) approaches. Sequencing of multiple and longer subgenomic regions has been proposed as an alternative to overcome the limitations imposed by the rapid molecular evolution of the HCV HVR1. Thus, the NS5A and HVR1 regions of 16 chronically infected individuals, genotypes 1a and 1b, were sequenced using a NGS platform. Patients 1-7 shared risk factors and belonged to the same injection drug users network. However, genetic relatedness could not be established based on the HVR1 sequences (minimal nucleotide distance ranging from 16-23). Amplification and sequencing of a larger PCR fragment (~450 bp) targeting the NS5A region reestablished lost epidemiological links between patients 1-5. The minimum genetic distances in those patients were considerable smaller than the HVR1 counterparts (9-13). Patients 6 and 7 displayed a rather homogeneous viral population and were clearly not sharing any sequences with all other five patients in this network. Additionally, Median Joining Networks analysis was carried out to further analyze the intrahost genetic variability of all seven patients. Overall, NS5A sequences were significantly less diverse than their HVR1 equivalents. Thus, NGS technology and use of both HVR1 and NS5A sequences might help restored otherwise lost links when the HVR1 region alone is analyzed, improving the resolution of HCV genetic relatedness studies.
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Palavras-chave
Hepatite C, HCV, Sequenciamento de nova geração, Redes de transmissão, NGS, Transmission networks, Next-generation sequencing, Epidemiologia molecular, Molecular epidemiology