摘要
Hepatitis B is a well-known risk factor for the development of fiver cancer and is closely associated with patient morbidity and mortality. Viral mutants and variants have the potential to evade immune response and prolong infection, and thus it is crucial to develop a methodology for the rapid identification of multi-strain hepatitis infections in patients. Here we describe a method based on selective region amplification of viral genome and deep sequencing, which may be used for rapid identification of multi-strain hepatitis B virus （HBV） infection in patients. The method works even with significantly low amounts of patients＇ serum samples, where the wet-lab procedures take about 1.5 days, followed by a quick bioinformatic analysis to reveal the final results. Our method can potentially be applied to the rapid and reliable identification of multi-strain HBV infection and help improve treatment regiments.
Hepatitis B is a well-known risk factor for the development of fiver cancer and is closely associated with patient morbidity and mortality. Viral mutants and variants have the potential to evade immune response and prolong infection, and thus it is crucial to develop a methodology for the rapid identification of multi-strain hepatitis infections in patients. Here we describe a method based on selective region amplification of viral genome and deep sequencing, which may be used for rapid identification of multi-strain hepatitis B virus （HBV） infection in patients. The method works even with significantly low amounts of patients＇ serum samples, where the wet-lab procedures take about 1.5 days, followed by a quick bioinformatic analysis to reveal the final results. Our method can potentially be applied to the rapid and reliable identification of multi-strain HBV infection and help improve treatment regiments.
基金
ACKNOWLEDGEMENTS This work was supported in part by funding from the Ministry of Science and Technology of China （973 grant No. 2015CB553402）, National Natural Science Foundation of China （grant No. 31470532）, the Tsinghua University-Peking University Center for Life Sciences （CLS）, the Tsinghua-Janssen Scholarship and research grant, and the Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases.
