Literature DB >> 29574921

Naturally occurring antiviral drug resistance in HIV patients who are mono-infected or co-infected with HBV or HCV in China.

Haohui Deng1,2, Xizi Deng2, Yu Liu1, Ying Xu1, Yun Lan2, Ming Gao2, Min Xu2, Hongbo Gao2, Xiexing Wu2, Baolin Liao2, Weilie Chen2, Miaoxian Zhao1, Fengyu Hu2, Zhanhui Wang1.   

Abstract

Drug resistance mutations (DRMs) may reduce the efficacy of antiviral therapy. However, the studies focused on naturally occurring, pre-existing DRMs among co-infected patients in China are limited. To investigate DRMs prevalence in treatment-naïve human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) mono- and co-infected patients in China, a total of 570 patients were recruited for this study. DRMs sequences were amplified and successfully sequenced in 481 of these patients, who were grouped into three cohorts: (i) The HBV cohort included 100 HIV/HBV co-infected and 110 HBV mono-infected patients who were sequenced for HBV; (ii) The HCV cohort included 91 patients who were HIV/HCV co-infected and 72 who were HCV mono-infected for HCV sequencing; and (iii) The HIV cohort included 39 HIV mono-infected, 22 HIV/HCV, and 47 HIV/HBV co-infected patients for HIV sequencing. Next-generation sequencing and Sanger sequencing were used in this study. The results showed that in the HCV cohort, HCV genotypes 6a (P < 0.001) and 3b (P = 0.004) were more prevalent in HIV/HCV co-infected patients, however, the prevalence of HBV and HIV genotypes were similar within the HBV and HIV cohorts. HBV DRMs prevalence was significantly higher in HIV/HBV co-infected than HBV mono-infected patients (8.0% vs 0.9%, P = 0.015), whereas HCV and HIV DRMs did not differ within the HCV and HIV cohort (P > 0.05). This study revealed that HBV DRMs were more prevalent in HIV/HBV co-infected patients in China, while DRMs in HCV and HIV patients did not differ. Further dynamic surveillance of DRMs may be needed.
© 2018 Wiley Periodicals, Inc.

Entities:  

Keywords:  drug resistance mutations; hepatitis B virus; hepatitis C virus; human immunodeficiency virus; next-generation sequencing

Mesh:

Year:  2018        PMID: 29574921     DOI: 10.1002/jmv.25078

Source DB:  PubMed          Journal:  J Med Virol        ISSN: 0146-6615            Impact factor:   2.327


  3 in total

1.  Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

Authors:  Margaret C Steiner; Keylie M Gibson; Keith A Crandall
Journal:  Viruses       Date:  2020-05-19       Impact factor: 5.048

2.  Prevalence of resistance-associated substitutions to direct-acting antiviral agents in hemodialysis and renal transplant patients infected with hepatitis C virus.

Authors:  Rita Chelly Felix Tavares; Ana Cristina de Castro Amaral Feldner; João Renato Rebello Pinho; Fernanda de Mello Malta; Roberto José Carvalho-Filho; Rúbia Anita Ferraz Santana; Vanessa Fusco Duarte de Castro; Gregório Tadeu Fernando Dastoli; Juliana Custódio Lima; Maria Lucia Cardoso Gomes Ferraz
Journal:  Infect Drug Resist       Date:  2018-10-25       Impact factor: 4.003

3.  A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China.

Authors:  Yi Yin; Mingyue Xue; Lingen Shi; Tao Qiu; Derun Xia; Gengfeng Fu; Zhihang Peng
Journal:  Biomed Res Int       Date:  2021-03-29       Impact factor: 3.411

  3 in total

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