A systematic review of Hepatitis B virus (HBV) prevalence and genotypes in Kenya: Data to inform clinical care and health policy

The aim of this systematic review and meta-analysis is to evaluate available prevalence and viral sequencing data representing chronic hepatitis B (CHB) infection in Kenya. More than 20% of the global disease burden from CHB is in Africa, however there is minimal high quality seroprevalence data from individual countries and little viral sequencing data available to represent the continent. We undertook a systematic review of the prevalence and genetic data available for hepatitis B virus (HBV) in Kenya using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2020 checklist. We identified 23 studies reporting HBV prevalence and 8 studies that included HBV genetic data published in English between January 2000 and December 2021. We assessed study quality using the Joanna Briggs Institute critical appraisal checklist. Due to study heterogeneity, we divided the studies to represent low, moderate, high and very high-risk for HBV infection, identifying 8, 7, 5 and 3 studies in these groups, respectively. We calculated pooled HBV prevalence within each group and evaluated available sequencing data. Pooled HBV prevalence was 3.4% (95% CI 2.7–4.2%), 6.1% (95% CI 5.1–7.4%), 6.2% (95% CI 4.64–8.2) and 29.2% (95% CI 12.2–55.1), respectively. Study quality was overall low; only three studies detailed sample size calculation and 17/23 studies were cross sectional. Eight studies included genetic information on HBV, with two undertaking whole genome sequencing. Genotype A accounted for 92% of infections. Other genotypes included genotype D (6%), D/E recombinants (1%) or mixed populations (1%). Drug resistance mutations were reported by two studies. There is an urgent need for more high quality seroprevalence and genetic data to represent HBV in Kenya to underpin improved HBV screening, treatment and prevention in order to support progress towards elimination targets

An enrichment protocol and analysis pipeline for long read sequencing of the hepatitis B virus transcriptome

Hepatitis B virus (HBV) is one of the smallest human DNA viruses and its 3.2 Kb genome encodes multiple overlapping open reading frames, making its viral transcriptome challenging to dissect. Previous studies have combined quantitative PCR and Next Generation Sequencing to identify viral transcripts and splice junctions, however the fragmentation and selective amplification used in short read sequencing precludes the resolution of full length RNAs. Our study coupled an oligonucleotide enrichment protocol with state-of-the-art long read sequencing (PacBio) to identify the repertoire of HBV RNAs. This methodology provides sequencing libraries where up to 25 % of reads are of viral origin and enable the identification of canonical (unspliced), non-canonical (spliced) and chimeric viral-human transcripts. Sequencing RNA isolated from de novo HBV infected cells or those transfected with 1.3 × overlength HBV genomes allowed us to assess the viral transcriptome and to annotate 5' truncations and polyadenylation profiles. The two HBV model systems showed an excellent agreement in the pattern of major viral RNAs, however differences were noted in the abundance of spliced transcripts. Viral-host chimeric transcripts were identified and more commonly found in the transfected cells. Enrichment capture and PacBio sequencing allows the assignment of canonical and non-canonical HBV RNAs using an open-source analysis pipeline that enables the accurate mapping of the HBV transcriptome

Interpreting viral deep sequencing data with GLUE

Using deep sequencing technologies such as Illumina’s platform, it is possible to obtain reads from the viral RNA population revealing the viral genome diversity within a single host. A range of software tools and pipelines can transform raw deep sequencing reads into Sequence Alignment Mapping (SAM) files. We propose that interpretation tools should process these SAM files, directly translating individual reads to amino acids in order to extract statistics of interest such as the proportion of different amino acid residues at specific sites. This preserves per-read linkage between nucleotide variants at different positions within a codon location. The samReporter is a subsystem of the GLUE software toolkit which follows this direct read translation approach in its processing of SAM files. We test samReporter on a deep sequencing dataset obtained from a cohort of 241 UK HCV patients for whom prior treatment with direct-acting antivirals has failed; deep sequencing and resistance testing have been suggested to be of clinical use in this context. We compared the polymorphism interpretation results of the samReporter against an approach that does not preserve per-read linkage. We found that the samReporter was able to properly interpret the sequence data at resistance-associated locations in nine patients where the alternative approach was equivocal. In three cases, the samReporter confirmed that resistance or an atypical substitution was present at NS5A position 30. In three further cases, it confirmed that the sofosbuvir-resistant NS5B substitution S282T was absent. This suggests the direct read translation approach implemented is of value for interpreting viral deep sequencing data

Genome-to-genome analysis highlights the effect of the human innate and adaptive immune systems on the hepatitis C virus

Outcomes of hepatitis C virus (HCV) infection and treatment depend on viral and host genetic factors. Here we use human genome-wide genotyping arrays and new whole-genome HCV viral sequencing technologies to perform a systematic genome-to-genome study of 542 individuals who were chronically infected with HCV, predominantly genotype 3. We show that both alleles of genes encoding human leukocyte antigen molecules and genes encoding components of the interferon lambda innate immune system drive viral polymorphism. Additionally, we show that IFNL4 genotypes determine HCV viral load through a mechanism dependent on a specific amino acid residue in the HCV NS5A protein. These findings highlight the interplay between the innate immune system and the viral genome in HCV control

Interferon lambda 4 impacts the genetic diversity of hepatitis C virus

Hepatitis C virus (HCV) is a highly variable pathogen that frequently establishes chronic infection. This genetic variability is affected by the adaptive immune response but the contribution of other host factors is unclear. Here, we examined the role played by interferon lambda-4 (IFN-λ4) on HCV diversity; IFN-λ4 plays a crucial role in spontaneous clearance or establishment of chronicity following acute infection. We performed viral genome-wide association studies using human and viral data from 485 patients of white ancestry infected with HCV genotype 3a. We demonstrate that combinations of host genetic variants, which determine IFN-λ4 protein production and activity, influence amino acid variation across the viral polyprotein - not restricted to specific viral proteins or HLA restricted epitopes - and modulate viral load. We also observed an association with viral di-nucleotide proportions. These results support a direct role for IFN-λ4 in exerting selective pressure across the viral genome, possibly by a novel mechanism

SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway

Vaccines based on the spike protein of SARS-CoV-2 are a cornerstone of the public health response to COVID-19. The emergence of hypermutated, increasingly transmissible variants of concern (VOCs) threaten this strategy. Omicron (B.1.1.529), the fifth VOC to be described, harbours multiple amino acid mutations in spike, half of which lie within the receptor-binding domain. Here we demonstrate substantial evasion of neutralization by Omicron BA.1 and BA.2 variants in vitro using sera from individuals vaccinated with ChAdOx1, BNT162b2 and mRNA-1273. These data were mirrored by a substantial reduction in real-world vaccine effectiveness that was partially restored by booster vaccination. The Omicron variants BA.1 and BA.2 did not induce cell syncytia in vitro and favoured a TMPRSS2-independent endosomal entry pathway, these phenotypes mapping to distinct regions of the spike protein. Impaired cell fusion was determined by the receptor-binding domain, while endosomal entry mapped to the S2 domain. Such marked changes in antigenicity and replicative biology may underlie the rapid global spread and altered pathogenicity of the Omicron variant

Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection

Background & aimsHepatocellular carcinoma (HCC) prediction models can inform clinical decisions about HCC screening provided their predictions are robust. We conducted an external validation of 6 HCC prediction models for UK patients with cirrhosis and a HCV virological cure.MethodsPatients with cirrhosis and cured HCV were identified from the Scotland HCV clinical database (N = 2,139) and the STratified medicine to Optimise Treatment of Hepatitis C Virus (STOP-HCV) study (N = 606). We calculated patient values for 4 competing non-genetic HCC prediction models, plus 2 genetic models (for the STOP-HCV cohort only). Follow-up began at the date of sustained virological response (SVR) achievement. HCC diagnoses were identified through linkage to nation-wide cancer, hospitalisation, and mortality registries. We compared discrimination and calibration measures between prediction models.ResultsMean follow-up was 3.4-3.9 years, with 118 (Scotland) and 40 (STOP-HCV) incident HCCs observed. The age-male sex-ALBI-platelet count score (aMAP) model showed the best discrimination; for example, the Concordance index (C-index) in the Scottish cohort was 0.77 (95% CI 0.73-0.81). However, for all models, discrimination varied by cohort (being better for the Scottish cohort) and by age (being better for younger patients). In addition, genetic models performed better in patients with HCV genotype 3. The observed 3-year HCC risk was 3.3% (95% CI 2.6-4.2) and 5.1% (3.5-7.0%) in the Scottish and STOP-HCV cohorts, respectively. These were most closely matched by aMAP, in which the mean predicted 3-year risk was 3.6% and 5.0% in the Scottish and STOP-HCV cohorts, respectively.ConclusionsaMAP was the best-performing model in terms of both discrimination and calibration and, therefore, should be used as a benchmark for rival models to surpass. This study underlines the opportunity for 'real-world' risk stratification in patients with cirrhosis and cured HCV. However, auxiliary research is needed to help translate an HCC risk prediction into an HCC-screening decision.Lay summaryPatients with cirrhosis and cured HCV are at high risk of developing liver cancer, although the risk varies substantially from one patient to the next. Risk calculator tools can alert clinicians to patients at high risk and thereby influence decision-making. In this study, we tested the performance of 6 risk calculators in more than 2,500 patients with cirrhosis and cured HCV. We show that some risk calculators are considerably better than others. Overall, we found that the 'aMAP' calculator worked the best, but more work is needed to convert predictions into clinical decisions