Detection of hepatitis B virus genotypes and mutants: current status

Characterization results of the hepatitis B virus (HBV) genome before, during and after antiviral treatment have changed in the last year for a number of reasons. First of all, with the introduction of more nucleoside and nucleotide antiviral therapies, it has become clear that variants or mutants do emerge in time. Viral genomic changes in the HBV polymerase gene can result in a direct antiviral effect, but compensatory mutations can also be identified during prolonged treatment periods. Furthermore, there is an increasing number of reports suggesting that HBV genotypes can be related to, for example, disease progression or the effect of antiviral treatment itself (alpha-interferon or lamivudine). Combined with HBV DNA viral load monitoring, an increase in viral load or a limited reduction during treatment is indicative of genomic changes related to resistance. However, these genomic changes can also be present in the absence of an increase in HBV DNA. Methodologies for the detection of these variants, as well as the determination of genotypes, are rather straightforward. Sequence analysis is time-consuming and expensive, but provides the most information, particularly if not all information on mutations related to antiviral resistance is known. However, the sensitivity of direct sequencing for the presence of minor variant populations is poor, and no mixtures of variant populations are, in general, detected. The ability to detect minor populations and, if possible, even quantify them, gives more insightful information on the dynamic evolution of the virus itself. Antiviral treatment can result in the appearance of more than one population of variants, which can be present for a prolonged period of time, and even remain undetected with current technologies. However, screening specifically for these variant populations before starting treatment for so-called untreated patients (who have received no antiviral treatment for, e.g., 6 months) has already shown that the effects of treatment can be biased. Furthermore, the detection of more dynamic viral populations - including both wild-type and resistant variants - during, but also after therapy, does provide helpful information in the analysis of virological data. Technologies enabling the detection and quantification of these variant populations are presented and discussed.