Biology subjects

Ansari, M. A.

Publications and source records attributed to Ansari, M. A..

15 recordsLinked to original sources

bioRxiv2024

Lineage-aware evolutionary analysis of hepatitis C virus within-host dynamics

Analysis of viral genetic data has previously revealed distinct within-host population structures in both untreated and interferon-treated chronic hepatitis C virus (HCV) infections. While multiple subpopulations persisted during the infection, each subpopulation was observed only intermittently. However, it was unknown whether similar patterns were also present after Direct Acting Antiviral (DAA) treatment, where viral populations were often assumed to go through narrow bottlenecks. Here we tested for the maintenance of population structure after DAA treatment failure. We analysed whole-genome next-generation sequencing data generated from a randomised study using DAAs (the BOSON study). We focused on samples collected from patients (N=84) who did not achieve sustained virological response (i.e. treatment failure) and had sequenced virus from multiple timepoints. For each individual, we tracked concordance in nucleotide variant frequencies through time. Using a sliding window approach, we applied sequenced-based and tree-based clustering algorithms across the entire HCV genome. Finally, we reconstructed viral haplotypes and estimated lineage specific within-host divergence rates from the haplotype phylogenies. Distinct viral subpopulations were maintained among a high proportion of individuals post DAA treatment failure. Using maximum likelihood modelling and model comparison, we found an overdispersion of viral evolutionary rates among individuals, and significant differences in evolutionary rates between lineages within individuals. These results suggest the virus is compartmentalised within individuals, with the varying evolutionary rates due to different viral replication rates or different selection pressures. We propose lineage awareness in future analyses of HCV evolution and infections to avoid conflating patterns from distinct lineages, and to recognise the likely existence of unsampled subpopulations.

evolutionary biology↗

bioRxiv2024

Castanet: a pipeline for rapid analysis of targeted multi-pathogen genomic data

MotivationTarget enrichment strategies generate genomic data from multiple pathogens in a single process, greatly improving sensitivity over metagenomic sequencing and enabling cost-effective, high throughput surveillance and clinical applications. However, uptake by research and clinical laboratories is constrained by an absence of computational tools that are specifically designed for the analysis of multi-pathogen enrichment sequence data. Here we present the Castanet pipeline: an analysis pipeline for end-to-end processing and consensus sequence generation for use with multi-pathogen enrichment sequencing data. Castanet is designed to work with short-read data produced by existing targeted enrichment strategies, but can be readily deployed on any BAM file generated by another methodology. It is packaged with usability features, including graphical interface and installer script. ResultsIn addition to genome reconstruction, Castanet reports method-specific metrics that enable quantification of capture efficiency, estimation of pathogen load, differentiation of low-level positives from contamination, and assessment of sequencing quality. Castanet can be used as a traditional end-to-end pipeline for consensus generation, but its strength lies in the ability to process a flexible, pre-defined set of pathogens of interest directly from multi-pathogen enrichment experiments. In our tests, Castanet consensus sequences were accurate reconstructions of reference sequences, including in instances where multiple strains of the same pathogen were present. Castanet performs effectively on standard laptop computers and can process the entire output of a 96-sample enrichment sequencing run (50M reads) using a single batch process command, in < 2 h. Availability and ImplementationSource code freely available under GPL-3 license at https://github.com/MultipathogenGenomics/castanet, implemented in Python 3.10 and supported in Ubuntu Linux 22.04 and other Bash-like environments. The data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB77004.

bioinformatics↗

bioRxiv2023

Inference of host-pathogen interaction matrices from genome-wide polymorphism data

Coevolution is defined as the evolutionary change in one antagonist (host) in response to changes in the other antagonist (pathogen). At the genetic level, these changes are determined by genotype x genotype (GxG) interactions. We build on a general theoretical model of a host-pathogen interaction to derive four indices to retrieve key features of GxG interactions. The four developed indices extract relevant information from polymorphism data of randomly sampled uninfected hosts as well as infected hosts and their respective pathogen strains. Using these indices as summary statistics in an Approximate Bayesian Computation method, we can show their power to discriminate between GxG interaction matrices. Second, we apply our ABC method to a SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains supplemented by polymorphism data of 503 individuals from the 1,000 genomes project. As our indices encompass and extend previous natural co-GWAs we recover many of the associations previously reported for this dataset and infer their underlying interaction matrix. We reveal a new candidate gene for resistance to HCV in the human genome, and two groups of significant GxG associations exhibiting gene-for-gene interactions. We suggest that the inferred types of GxG interactions result from the recent expansion, adaptation and low prevalence of the HCV virus population in Europe. Significance statementWhy are some host individuals susceptible/resistant to infection by certain pathogen genotypes and others not? Understanding the genetic characteristics of genes driving host-pathogen interactions is crucial to predict epidemics. We develop four indices based on a mathematical model and build a Bayesian statistical method computing these indices on full genome data of infected hosts and their infecting pathogen strains and data of non-infected hosts. We can pinpoint the genes underlying host-pathogen interactions and infer their characteristics. Applying our framework to data from European humans and the Hepatitis C virus, we discover a new potential resistance gene in humans and reveal how the virus has adapted in the last 150 years to match the genetic diversity of the European human population.

evolutionary biology↗

bioRxiv2023

Pan-genotypic probe-based enrichment to improve efficiency of Hepatitis B virus sequencing

Hepatitis B Virus (HBV) genome sequencing can be used to provide more complete genetic information at the population and individual level to shed light on the limitations of current interventions, and inform new strategies for elimination. HBV sequencing is challenging due to the partially dsDNA genome, high diversity, low viral loads and presence of large amounts of host genetic material in clinical samples. Here we describe the design and use of a pan-genotypic panel of 74 HBV specific capture-probes and nuclease treatment in improving sequencing efficiency. We processed 20 plasma samples (viral loads 1.98 to 4.07 log10, genotypes A-E) and three positive controls (human total brain RNA and bacteriophage lambda DNA) in triplicate to compare DNAse vs. RNAse vs. no nuclease treatment. We prepared libraries using the Takara Bio SMARTer Stranded Total RNA-Seq Kit v3, split the library in two, enriching half with the custom-designed probe panel and xGen Hybridization and Wash Kit (IDT), the other half was not enriched. Both libraries were sequenced on the NovaSeq6000 platform with 2x150nt paired-end reads. Capture resulted in a 47,970 fold increase in the number of reads mapped to the HBV genome in the "no nuclease" arm (243 HBV reads per million reads sequenced in the capture pool vs. 5x10-3 reads per million in the no-capture pool). Out of 20 samples, only 1 without capture generated HBV reads (viral load 3.89 log10 IU/ml) vs. 19 samples with capture. HBV sequence yield was increased in the capture arm and resulted in 2.30 log10 (95% confidence interval 1.99 - 2.48 log10) increase in HBV reads (per million reads sequenced) per log10 increase in viral load. The proportion of HBV reads increased a median of 12 fold with RNAse treatment. We developed a targeted pan-genotypic sequencing method using a custom panel of biotinylated oligos that increases the sequencing efficacy of HBV. This method will allow us to gain a better insight into HBV diversity.

genomics↗

bioRxiv2021

Mutation bias implicates RNA editing in a wide range of mammalian RNA viruses

The rapid evolution of RNA viruses has been long considered to result from a combination of high copying error frequencies during RNA replication, short generation times and the consequent extensive fixation of neutral or adaptive changes over short periods. While both the identities and sites of mutations are typically modelled as being random, recent investigations of sequence diversity of SARS coronavirus 2 (SARS-CoV-2) have identified a preponderance of C->U transitions, potentially driven by an APOBEC-like RNA editing process. The current study investigated whether this phenomenon could be observed in the more genetically diverse datasets of other RNA viruses. Using a 5% divergence filter to infer directionality, 18 from 32 datasets of aligned coding region sequences from a diverse range of mammalian RNA viruses (including Picornaviridae, Flaviviridae, Matonaviridae, Caliciviridae and Coronaviridae) showed a >2-fold base composition normalised excess of C->U transitions compared to U->C (range 2.1x-7.5x). C->U transitions showed a weak favoured 5 U upstream context consistent with previous analyses of APOBEC-mediated RNA targeting. Amongst several genomic compositional and structural parameters, the presence of genome scale RNA secondary structure (GORS) was associated with C->U/U->C transition asymmetries (p < 0.001), potentially reflecting the documented structure dependence of APOBEC-mediated RNA editing. Using the association index metric, C->U changes were specifically over-represented at phylogenetically uninformative sites, consistent with extensive homoplasy documented in SARS-CoV-2. Excess C->U substitutions accounted for 15-20% of standing sequence variability of HCV and other RNA viruses; RNA editing may therefore represent a potent driver of RNA virus sequence diversification and longer term evolution. Author SummaryThe rapid evolution of RNA viruses is thought to arise from high mutation frequencies during replication and the rapid accumulation of genetic changes over time in response to its changing environments. This study describes an additional potent factor that contributes to the evolution of RNA infecting mammals, the deliberate mutation of the viral genome by host antiviral pathways active within the cell when it becomes infected. This so called "genome editing" by one or more APOBEC enzymes leads to characteristic C->U mutations that damage the viruss ability to replicate. While this pathway is well characterised as an antiviral defence against HIV and other retroviruses, this study provides evidence for its activity against a wide range of human and veterinary viruses, including HCV and foot and mouth disease virus. APOBEC-driven mutations accounted for 15-20% of standing sequence variability of RNA virus groups, representing a potent driver of RNA virus sequence diversification.

genomics↗

bioRxiv2020

Global prevalence and phylogeny of hepatitis B virus (HBV) drug and vaccine resistance mutations

IntroductionVaccination and anti-viral therapy with nucleos(t)ide analogues (NAs) are key approaches to reducing the morbidity, mortality and transmission of hepatitis B virus (HBV) infection. However, the efficacy of these interventions may be reduced by the emergence of drug resistance-associated mutations (RAMs) and/or vaccine escape mutations (VEMs). We have assimilated data on the global prevalence and distribution of HBV RAMs/VEMs from publicly available data and explored the evolution of these mutations. MethodsWe analysed sequences downloaded from the Hepatitis B Virus Database, and calculated prevalence of 41 RAMs and 38 VEMs catalogued from published studies. We generated maximum likelihood phylogenetic trees and used treeBreaker to investigate the distribution of selected mutations across tree branches. We performed phylogenetic molecular clock analyses using BEAST to estimate the age of mutations. ResultsRAM M204I/V had the highest prevalence, occurring in 3.8% (109/2838) of all HBV sequences in our dataset, and a significantly higher rate in genotype C sequence at 5.4% (60/1102, p=0.0007). VEMs had an overall prevalence of 1.3% (37/2837) and had the highest prevalence in genotype C and in Asia at 2.2% (24/1102; p=0.002) and 1.6% (34/2109; p=0.009) respectively. Phylogenetic analysis suggested that most RAM/VEMs arose independently, however RAMs including A194T, M204V and L180M formed clusters in genotype B. We show evidence that polymorphisms associated with drug and vaccine resistance may have been present in the mid 20th century suggesting that they can arise independently of treatment/ vaccine exposure. DiscussionHBV RAMs/VEMs have been found globally and across genotypes, with the highest prevalence observed in genotype C variants. Screening for the genotype and for resistant mutations may help to improve stratified patient treatment. As NAs and HBV vaccines are increasingly being deployed for HBV prevention and treatment, monitoring for resistance and advocating for better treatment regimens for HBV remains essential.

evolutionary biology↗

bioRxiv2020

Impact of virus subtype and host IFNL4 genotype on large-scale RNA structure formation in the genome of hepatitis C virus

Mechanisms underlying the ability of hepatitis C virus (HCV) to establish persistent infections and induce progressive liver disease remain poorly understood. HCV is one of several positive-stranded RNA viruses capable of establishing persistence in their immunocompetent vertebrate hosts, an attribute associated with formation of large scale RNA structure in their genomic RNA. We developed novel methods to analyse and visualise genome-scale ordered RNA structure (GORS) predicted from the increasingly large datasets of complete genome sequences of HCV. Structurally conserved RNA secondary structure in coding regions of HCV localised exclusively to polyprotein ends (core, NS5B). Coding regions elsewhere were also intensely structured based on elevated minimum folding energy difference (MFED) values, but the actual stem-loop elements involved in genome folding were structurally entirely distinct, even between subtypes 1a and 1b. Dynamic remodelling was further evident from comparison of HCV strains in different host genetic background. Significantly higher MFED values, greater suppression of UpA dinucleotide frequencies and restricted diversification were found in subjects with the TT genotype of the rs12979860 SNP in the IFNL4 gene compared to the CC (non-expressing) allele. These structural and compositional associations with expression of interferon-{lambda}4 were recapitulated on a larger scale by higher MFED values and greater UpA suppression of genotype 1 compared to genotype 3a, associated with previously reported HCV genotype-associated differences in hepatic interferon-stimulated gene induction. Associations between innate cellular responses with HCV structure and further evolutionary constraints represents an important new element in RNA virus evolution and the adaptive interplay between virus and host.

genomics↗

bioRxiv2020

SARS-CoV-2 genomics beyond the consensus sequence: evidence for circulating mixed viral populations

Extensive global sampling and whole genome sequencing of the pandemic virus SARS-CoV-2 have enabled researchers to characterise its spread, and to identify mutations that may increase transmission or enable the virus to escape therapies or vaccines. Two important components of viral spread are how frequently variants arise within individuals, and how likely they are to be transmitted. Here, we characterise the within-host diversity of SARS-CoV-2, and the extent to which genetic diversity is transmitted, by quantifying variant frequencies in 1390 clinical samples from the UK, many from individuals in known epidemiological clusters. We show that SARS-CoV-2 infections are characterised by low levels of within-host diversity across the entire viral genome, with evidence of strong evolutionary constraint in Spike, a key target of vaccines and antibody-based therapies. Although within-host variants can be observed in multiple individuals in the same phylogenetic or epidemiological cluster, highly infectious individuals with high viral load carry only a limited repertoire of viral diversity. Most viral variants are either lost, or occasionally fixed, at the point of transmission, consistent with a narrow transmission bottleneck. These results suggest potential vaccine-escape mutations are likely to be rare in infectious individuals. Nonetheless, we identified Spike variants present in multiple individuals that may affect receptor binding or neutralisation by antibodies. Since the fitness advantage of escape mutations in highly-vaccinated populations is likely to be substantial, resulting in rapid spread if and when they do emerge, these findings underline the need for continued vigilance and monitoring.

genomics↗

bioRxiv2020

Viral genome wide association study identifies novel hepatitis C virus polymorphisms associated with sofosbuvir treatment failure

Chronic hepatitis C virus (HCV) infection is a major cause of chronic liver disease, cirrhosis and hepatocellular carcinoma worldwide. With the recent development of direct acting antivirals (DAA), treatment of chronically infected patients has become highly effective although a subset of patients do not respond to therapy. Sofosbuvir is a common component of current de novo or salvage combination therapies. We used pre-treatment whole genome sequencing of HCV from 507 patients infected with HCV subtype 3a and treated with sofosbuvir containing regimens to detect viral polymorphisms associated with response to treatment. We found that three common polymorphisms present in HCV NS2 and NS3 proteins (not direct targets of sofosbuvir) were associated with reduced treatment response. These polymorphisms were enriched in post-treatment HCV sequences of patients unresponsive to treatment; they were also associated with lower reductions in viral load in the first week of therapy. The finding of polymorphisms in NS2 and NS3 proteins associated with poor treatment outcomes emphasises the value of more systematic genome-wide analyses of HCV in uncovering indirect but clinically relevant mechanisms of antiviral resistance.

microbiology↗

bioRxiv2019

Analysis of genomic-length HBV sequences to determine genotype and subgenotype reference sequences

Hepatitis B virus (HBV) is a diverse, partially double-stranded DNA virus, with 9 genotypes (A-I), and a putative 10th genotype (J), thus far characterised. Given the broadening interest in HBV sequencing, there is an increasing requirement for a consistent, unified approach to HBV genotype and subgenotype classification. We set out to generate an updated resource of reference sequences using the diversity of all genomic-length HBV sequences available in public databases. We collated and aligned genomic-length HBV sequences from public databases and used maximum-likelihood phylogenetic analysis to identify genotype clusters. Within each genotype, we examined the phylogenetic support for currently defined subgenotypes, as well as identifying well-supported clades and deriving reference sequences for them. An alignment of these reference sequences and maximum-likelihood phylogenetic trees of the sequences are provided to simplify classification. Based on the phylogenies generated, we present a comprehensive set of HBV reference sequences at the genotype and subgenotype level.

microbiology↗

bioRxiv2019

Targeted metagenomic sequencing enhances the identification of pathogens associated with acute infection.

The routine identification of pathogens during infection remains challenging because it relies on multiple modalities such as culture and nucleic acid amplification and tests that tend to be specific for very few of an enormous number of possible infectious agents. Metagenomics promises single-test identification, but shotgun sequencing remains unwieldy and expensive or in many cases insufficiently sensitive to detect the amount of pathogen material in a clinical sample. Here we present the validation and application of Castanet, a method for metagenomic sequencing with enrichment that exploits clinical knowledge to construct a broad panel of relevant organisms for detection at low cost with sensitivity comparable to PCR. Castanet targets both DNA and RNA, works with small sample volumes, and can be implemented in a high-throughput diagnostic setting. We used Castanet to analyse plasma samples from 573 patients from the GAinS sepsis cohort and CSF samples from 243 patients from the ChiMES meningitis cohort that had been evaluated using standard clinical microbiology methods, identifying relevant pathogens in many cases where no pathogen had previously been detected. Castanet is intended for use in defining the distribution of pathogens in samples, diseases and populations, for large-scale clinical studies and for verifying the performance of routine testing regimens. By providing sequence as output, Castanet combines pathogen identification directly with subtyping and phylo-epidemiology.

microbiology↗

bioRxiv2019

treeSeg: Testing for Dependence on Tree Structures

Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some given tree. Currently this is addressed on an ad hoc basis, usually requiring the user to decide on an appropriate number of clusters to prune out of the tree to be tested against the response variable. Here we present a statistical method with statistical guarantees that tests for association between the response variable and a fixed tree structure across all levels of the tree hierarchy with high power, while accounting for the overall false positive error rate. This enhances the robustness and reproducibility of such findings. Significance StatementTree like structures are abundant in the empirical sciences as they can summarize high dimensional data and show latent structure among many samples in a single framework. Prominent examples include phylogenetic trees or hierarchical clustering derived from genetic data. Currently users employ ad hoc methods to test for association between a given tree and a response variable, which reduces reproducibility and robustness. In this paper, we introduce treeSeg, a simple to use and widely applicable methodology with high power for testing between all levels of hierarchy for a given tree and the response while accounting for the overall false positive rate. Our method allows for precise uncertainty quantification and therefore increases interpretability and reproducibility of such studies across many fields of science.

bioinformatics↗

bioRxiv2018

Illumina and Nanopore methods for whole genome sequencing of hepatitis B virus (HBV)

Advancing interventions to tackle the huge global burden of hepatitis B virus (HBV) infection depends on improved insights into virus epidemiology, transmission, within-host diversity, drug resistance and pathogenesis, all of which can be facilitated by the large-scale generation of full-length virus genome data. Here we describe advances to a protocol to exploit the circular HBV genome structure, using isothermal rolling-circle amplification to enrich for HBV DNA and to generate concatemeric amplicons containing multiple successive copies of the same genome. We show that this product is suitable for Nanopore sequencing as single reads, as well as for generating short-read Illumina sequences. Nanopore reads can be used to implement a straightforward method for error correction that reduces the per-read error rate, by comparing multiple genome copies combined into a single concatemer and by comparing reads generated from plus and minus strands. Thus we can achieve improved consensus sequencing accuracy of 99.7% and resolve intra-sample sequence variants to form whole-genome haplotypes. The combination of isothermal amplification and Nanopore sequencing offers the longer-term potential to develop point-of-care tests for HBV, which could also be adapted for other viruses.

genomics↗

bioRxiv2018

Interferon lambda 4 impacts broadly on hepatitis C virus diversity.

Type III interferons (IFN-{lambda}) are part of the innate immune response to hepatitis C virus (HCV) infection however the specific role of IFN-{lambda}4 and the nature of the viral adaption to this pressure have not been defined. Here we use paired genome-wide human and viral genetic data in 485 patients infected with HCV genotype 3a to explore the role of IFN-{lambda}4 on HCV evolution during chronic infection. We show that genetic variations within the host IFNL4 locus have a broad and systematic impact on HCV amino acid diversity. We also demonstrate that this impact is larger in patients producing a more active form of IFN-{lambda}4 protein compared to the less active form. A similar observation was noted for viral load. We conclude that IFN-{lambda}4 protein is a likely causal agent driving widespread HCV amino acid changes and associated with viral load and possibly other clinical and biological outcomes of HCV infection.

genomics↗

bioRxiv2017

The immunomodulatory CEA cell adhesion molecule 6 (CEACAM6/CD66c) is a candidate receptor for the influenza A virus

To establish a productive infection in host cells, viruses often use one or multiple host membrane glycoprotein as their receptors. For Influenza A virus (IAV) such a glycoprotein receptor has not been described, to date. Here we show that IAV is using the host membrane glycoprotein CD66c as a receptor for entry into human epithelial lung cells. Neuraminidase (NA), a viral spike protein binds to CD66c on the cell surface during IAV entry into the host cells. Lung cells overexpressing CD66c showed an increase in virus binding and subsequent entry into the cell. Upon comparison, CD66c demonstrated higher binding capacity than other membrane glycoproteins (EGFR and DC-SIGN) reported earlier to facilitate IAV entry into host cells. siRNA mediated knockdown of CD66c from lung cells inhibited virus binding on cell surface and entry into cells. Blocking CD66c by antibody on the cell surface resulted in decreased virus entry. We found CD66c is a specific glycoprotein receptor for influenza A virus that did not affect entry of non-IAV RNA virus (Hepatitis C virus). Finally, IAV pre-incubated with recombinant CD66c protein when administered intranasally in mice showed decreased cytopathic effects in mice lungs. This publication is the first to report CD66c (CEACAM6) as a glycoprotein receptor for Influenza A virus.\n\nSignificance StatementCells are enclosed by a semipermeable membrane that allows selective exchange of biomolecules between cells and their surroundings. A set of specialized proteins in this semipermeable membrane, work like gatekeepers to the cell and regulate entry of these biomolecules. One class of such surface proteins is termed as receptors. Viruses bind to one or more of these receptors and manipulate gatekeepers for their own successful entry into host-cells. A membrane protein that influenza A virus (Flu virus) uses for entry into the cells was not discovered till date. This study reports for the first time, a receptor for influenza A virus, that was sought after by researchers for decades. The viral receptor is a promising target that can be used to inhibit virus entry into host cells.

microbiology↗