SERS-based viral load quantification of hepatitis B virus from PCR products

Abstract

Hepatitis B is a contagious liver disorder caused by hepatitis B virus and if not treated at an early stage, it becomes chronic and results in liver cirrhosis and hepatocellular carcinoma which can even lead to death. In present study, surface-enhanced Raman spectroscopy (SERS) is employed for the analysis of polymerase chain reaction (PCR) products of DNA extracted from hepatitis B virus (HBV) infected patients in comparison with healthy individuals. SERS spectral features are identified which are solely present in the HBV positive samples and consistently increase in intensities with increase in viral load which can be considered as a SERS spectral marker for HBV infection. For sake of understanding, these various levels of viral loads in this study are classified as low (1-1000 IU), medium (1000-10,000 IU), high (above 10,000 IU) and negative control (>1). In order to explore the efficiency of SERS for discrimination of SERS spectral datasets of different samples of varying viral loads and healthy individuals, principal component analysis (PCA) is applied. PCA is used for comparison of these classes including low, medium and high levels of viral loads with each other and with healthy class. Moreover, partial least square discriminant analysis and partial least square regression analysis are employed for the classification of different levels of viral loads in the HBV positive samples and prediction of viral loads in the unknown samples, respectively. PLS-DA is applied for validity of classification and its sensitivity and specificity was found to be 89% and 98% respectively. PLSR model was constructed for prediction of viral loads on the bases of SERS spectral markers of HBV infection with goodness value of 0.9031 and value of root means square error (RMSE) 0.2923. PLSR model also proved to be valid for prediction of blind sample.

Keywords: Hepatitis B, Viral DNA; Partial least square regression analysis; Principal component analysis; SERS; Silver nanoparticles; Viral load.