Transmission genetics of drug-resistant hepatitis C virus
Abstract
Antiviral development is plagued by drug resistance and genetic barriers to resistance are needed. For HIV and hepatitis C virus (HCV), combination therapy has proved life-saving. The targets of direct-acting antivirals for HCV infection are NS3/4A protease, NS5A phosphoprotein and NS5B polymerase. Differential visualization of drug-resistant and -susceptible RNA genomes within cells revealed that resistant variants of NS3/4A protease and NS5A phosphoprotein are cis-dominant, ensuring their direct selection from complex environments. Confocal microscopy revealed that RNA replication complexes are genome-specific, rationalizing the non-interaction of wild-type and variant products. No HCV antivirals yet display the dominance of drug susceptibility shown for capsid proteins of other viruses. However, effective inhibitors of HCV polymerase exact such high fitness costs for drug resistance that stable genome selection is not observed. Barriers to drug resistance vary with target biochemistry and detailed analysis of these barriers should lead to the use of fewer drugs.
Article and author information
Author details
Funding
National Institutes of Health (U19-AI09662)
- Karla Kirkegaard
Canadian Institutes of Health Research (NCRTP-HepC Postdoctoral Fellowship)
- Nicholas van Buuren
American Liver Foundation (Postdoctoral Fellowship)
- Nicholas van Buuren
National Institutes of Health (NIH Director's Pioneer Award)
- Karla Kirkegaard
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- David M Knipe, Harvard Medical School, United States
Version history
- Received: October 6, 2017
- Accepted: March 22, 2018
- Accepted Manuscript published: March 28, 2018 (version 1)
- Version of Record published: April 25, 2018 (version 2)
- Version of Record updated: November 29, 2018 (version 3)
Copyright
© 2018, van Buuren et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,968
- views
-
- 288
- downloads
-
- 6
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cancer Biology
- Genetics and Genomics
Relapse of acute myeloid leukemia (AML) is highly aggressive and often treatment refractory. We analyzed previously published AML relapse cohorts and found that 40% of relapses occur without changes in driver mutations, suggesting that non-genetic mechanisms drive relapse in a large proportion of cases. We therefore characterized epigenetic patterns of AML relapse using 26 matched diagnosis-relapse samples with ATAC-seq. This analysis identified a relapse-specific chromatin accessibility signature for mutationally stable AML, suggesting that AML undergoes epigenetic evolution at relapse independent of mutational changes. Analysis of leukemia stem cell (LSC) chromatin changes at relapse indicated that this leukemic compartment underwent significantly less epigenetic evolution than non-LSCs, while epigenetic changes in non-LSCs reflected overall evolution of the bulk leukemia. Finally, we used single-cell ATAC-seq paired with mitochondrial sequencing (mtscATAC) to map clones from diagnosis into relapse along with their epigenetic features. We found that distinct mitochondrially-defined clones exhibit more similar chromatin accessibility at relapse relative to diagnosis, demonstrating convergent epigenetic evolution in relapsed AML. These results demonstrate that epigenetic evolution is a feature of relapsed AML and that convergent epigenetic evolution can occur following treatment with induction chemotherapy.
-
- Computational and Systems Biology
- Genetics and Genomics
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.