Direct sequencing and reverse hybridization are currently the main methods for detecting drug-resistance mutations of hepatitis B virus (HBV). However, these methods do not enable haplotype analysis so they cannot be used to determine whether the mutations are co-located on the same viral genome. This limits the accurate identification of viral mutants that are resistant to drugs with a high genetic barrier. In our current study, ultra-deep pyrosequencing (UDPS) was used to detect HBV drug-resistance mutations in 25 entecavir-treated and five treatment-naive patients. Of the 25 entecavir-treated patients, 18 had experienced virological breakthrough and two exhibited reduced susceptibility to entecavir. The results obtained by UDPS were compared with those of direct sequencing, and the haplotypes of the drug-resistant HBV mutants were analysed. The average number of reads per patient covering the region in which drug-resistance mutations are located was 1735 (range 451-4526). UDPS detected additional drug-resistance mutations not detected by direct sequencing in 19 patients (mutation frequency range 1.1-23.8%). Entecavir-resistance mutations were found to be co-located on the same viral genome in all 20 patients displaying virological breakthrough or reduced susceptibility to entecavir. In conclusion, UDPS was not only sensitive and accurate in identifying drug-resistance mutations of HBV but also enabled haplotype analysis of the mutants. This method may offer significant advantages in explaining and predicting the responses of patients with HBV to antiviral therapy.
New human leucocyte antigen (HLA) alleles are assigned largely based on their sequence homologies due to lack of information on the serological reactivities. Artificial neural networks (ANNs) have been tested as a possible tool for helping to predict the serology of new alleles in the absence of serological information. However, an ANN analysis per se imparts no information regarding which residues are important in determining serological specificity. To address this issue, we extracted ANN weights of HLA residues. The ANN was trained using 139 HLA-A, 302 HLA-B and 136 HLA-DRB1 alleles, for which serological specificities were assigned in the 2004 Nomenclature Report. When the trained ANN was evaluated using alleles that were contained in the HLA Dictionary 2008 but had not been employed in the training set, the accuracy was 91% (29/32) for HLA-A, 91% (40/44) for HLA-B and 90% (9/10) for HLA-DR. Finally, ANN residue weights were extracted by summing the weights of directly connected ANN nodes. When we assessed the significance of the ANN residue weights by comparing our data with the results of epitope studies conducted by El-Awar and colleagues, we found that the ANN weights tended to be high at the epitopes described by El-Awar et al. Furthermore, the ANN weights extracted in this work could be used to explain ambiguous characteristics of serological specificities to some extent. Our data are thus considered to support the results of the epitope studies conducted by El-Awar and advance our understanding of the ambiguous serological specificities of some alleles.
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