ACoRE: Accurate SARS-CoV-2 genome reconstruction for the characterization of intra-host and inter-host viral diversity in clinical samples and for the evaluation of re-infections.
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ABSTRACT:
SUBMITTER: Marcolungo L
PROVIDER: S-EPMC8028595 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundHigh genetic diversity at both inter- and intra-host level are hallmarks of RNA viruses due to the error-prone nature of their genome replication. Several groups have evaluated the extent of viral variability using different RNA virus deep sequencing methods. Although much of this effort has been dedicated to pathogens that cause chronic infections in humans, few studies investigated arthropod-borne, acute viral infections.Methods and principal findingsWe deep sequenced the complete genome of ten DENV2 isolates from representative classical and severe cases sampled in a large outbreak in Brazil using two different approaches. Analysis of the consensus genomes confirmed the larger extent of the 2010 epidemic in comparison to a previous epidemic caused by the same viruses in another city two years before (genetic distance = 0.002 and 0.0008 respectively). Analysis of viral populations within the host revealed a high level of conservation. After excluding homopolymer regions of 454/Roche generated sequences, we found 10 to 44 variable sites per genome population at a frequency of >1%, resulting in very low intra-host genetic diversity. While up to 60% of all variable sites at intra-host level were non-synonymous changes, only 10% of inter-host variability resulted from non-synonymous mutations, indicative of purifying selection at the population level.Conclusions and significanceDespite the error-prone nature of RNA-dependent RNA-polymerase, dengue viruses maintain low levels of intra-host variability.
| S-EPMC3732279 | biostudies-literature
Project description:The diversity of RNA viruses dictates their evolution in a particular host, community or environment. Here, we reported within- and between-host pH1N1virus diversity at consensus and sub-consensus levels over a three-year period (2015-2017) and its implications on disease severity. A total of 90 nasal samples positive for the pH1N1 virus were deep-sequenced and analyzed to detect low-frequency variants (LFVs) and haplotypes. Parallel evolution of LFVs was seen in the hemagglutinin (HA) gene across three scales: among patients (33%), across years (22%), and at global scale. Remarkably, investigating the emergence of LFVs at the consensus level demonstrated that within-host virus evolution recapitulates evolutionary dynamics seen at the global scale. Analysis of virus diversity at the HA haplotype level revealed the clustering of low-frequency haplotypes from early 2015 with dominant strains of 2016, indicating rapid haplotype evolution. Haplotype sharing was also noticed in all years, strongly suggesting haplotype transmission among patients infected during a specific influenza season. Finally, more than half of patients with severe symptoms harbored a larger number of haplotypes, mostly in patients under the age of five. Therefore, patient age, haplotype diversity, and the presence of certain LFVs should be considered when interpreting illness severity. In addition to its importance in understanding virus evolution, sub-consensus virus diversity together with whole genome sequencing is essential to explain variabilities in clinical outcomes that cannot be explained by either analysis alone.
| S-EPMC7022955 | biostudies-literature
Project description:Technical challenges remain in the sequencing of RNA viruses due to their high intra-host diversity. This bottleneck is particularly pronounced when interrogating long-range co-evolved genetic interactions given the read-length limitations of next-generation sequencing platforms. This has hampered the direct observation of these genetic interactions that code for protein-protein interfaces with relevance in both drug and vaccine development. Here we overcome these technical limitations by developing a nanopore-based long-range viral sequencing pipeline that yields accurate single molecule sequences of circulating virions from clinical samples. We demonstrate its utility in observing the evolution of individual HIV Gag-Pol genomes in response to antiviral pressure. Our pipeline, called Multi-read Hairpin Mediated Error-correction Reaction (MrHAMER), yields >1000s of viral genomes per sample at 99.9% accuracy, maintains the original proportion of sequenced virions present in a complex mixture, and allows the detection of rare viral genomes with their associated mutations present at <1% frequency. This method facilitates scalable investigation of genetic correlates of resistance to both antiviral therapy and immune pressure and enables the identification of novel host-viral and viral-viral interfaces that can be modulated for therapeutic benefit.
| S-EPMC8266615 | biostudies-literature
Project description:We introduce VERSO, a two-step framework for the characterization of viral evolution from sequencing data of viral genomes, which is an improvement on phylogenomic approaches for consensus sequences. VERSO exploits an efficient algorithmic strategy to return robust phylogenies from clonal variant profiles, also in conditions of sampling limitations. It then leverages variant frequency patterns to characterize the intra-host genomic diversity of samples, revealing undetected infection chains and pinpointing variants likely involved in homoplasies. On simulations, VERSO outperforms state-of-the-art tools for phylogenetic inference. Notably, the application to 6,726 amplicon and RNA sequencing samples refines the estimation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution, while co-occurrence patterns of minor variants unveil undetected infection paths, which are validated with contact tracing data. Finally, the analysis of SARS-CoV-2 mutational landscape uncovers a temporal increase of overall genomic diversity and highlights variants transiting from minor to clonal state and homoplastic variants, some of which fall on the spike gene. Available at: https://github.com/BIMIB-DISCo/VERSO.
| S-EPMC7953447 | biostudies-literature
Project description:Given the absence of universal marker genes in the viral kingdom, researchers typically use BLAST (with stringent E-values) for taxonomic classification of viral metagenomic sequences. Since majority of metagenomic sequences originate from hitherto unknown viral groups, using stringent e-values results in most sequences remaining unclassified. Furthermore, using less stringent e-values results in a high number of incorrect taxonomic assignments. The SOrt-ITEMS algorithm provides an approach to address the above issues. Based on alignment parameters, SOrt-ITEMS follows an elaborate work-flow for assigning reads originating from hitherto unknown archaeal/bacterial genomes. In SOrt-ITEMS, alignment parameter thresholds were generated by observing patterns of sequence divergence within and across various taxonomic groups belonging to bacterial and archaeal kingdoms. However, many taxonomic groups within the viral kingdom lack a typical Linnean-like taxonomic hierarchy. In this paper, we present ProViDE (Program for Viral Diversity Estimation), an algorithm that uses a customized set of alignment parameter thresholds, specifically suited for viral metagenomic sequences. These thresholds capture the pattern of sequence divergence and the non-uniform taxonomic hierarchy observed within/across various taxonomic groups of the viral kingdom. Validation results indicate that the percentage of 'correct' assignments by ProViDE is around 1.7 to 3 times higher than that by the widely used similarity based method MEGAN. The misclassification rate of ProViDE is around 3 to 19% (as compared to 5 to 42% by MEGAN) indicating significantly better assignment accuracy. ProViDE software and a supplementary file (containing supplementary figures and tables referred to in this article) is available for download from http://metagenomics.atc.tcs.com/binning/ProViDE/
| S-EPMC3082859 | biostudies-literature
Project description:The prospect of using whole genome sequence data to investigate bacterial disease outbreaks has been keenly anticipated in many quarters, and the large-scale collection and sequencing of isolates from cases is becoming increasingly feasible. While sequence data can provide many important insights into disease spread and pathogen adaptation, it remains unclear how successfully they may be used to estimate individual routes of transmission. Several studies have attempted to reconstruct transmission routes using genomic data; however, these have typically relied upon restrictive assumptions, such as a shared topology of the phylogenetic tree and a lack of within-host diversity. In this study, we investigated the potential for bacterial genomic data to inform transmission network reconstruction. We used simulation models to investigate the origins, persistence and onward transmission of genetic diversity, and examined the impact of such diversity on our estimation of the epidemiological relationship between carriers. We used a flexible distance-based metric to provide a weighted transmission network, and used receiver-operating characteristic (ROC) curves and network entropy to assess the accuracy and uncertainty of the inferred structure. Our results suggest that sequencing a single isolate from each case is inadequate in the presence of within-host diversity, and is likely to result in misleading interpretations of transmission dynamics--under many plausible conditions, this may be little better than selecting transmission links at random. Sampling more frequently improves accuracy, but much uncertainty remains, even if all genotypes are observed. While it is possible to discriminate between clusters of carriers, individual transmission routes cannot be resolved by sequence data alone. Our study demonstrates that bacterial genomic distance data alone provide only limited information on person-to-person transmission dynamics.
| S-EPMC3967931 | biostudies-literature
Project description:The development of high-throughput genome sequencing enables accurate measurements of levels of sub-consensus intra-host virus genetic diversity and analysis of the role played by natural selection during cross-species transmission. We analysed the natural and experimental evolution of rabies virus (RABV), an important example of a virus that is able to make multiple host jumps. In particular, we (i) analyzed RABV evolution during experimental host switching with the goal of identifying possible genetic markers of host adaptation, (ii) compared the mutational changes observed during passage with those observed in natura, and (iii) determined whether the colonization of new hosts or tissues requires adaptive evolution in the virus. To address these aims, animal infection models (dog and fox) and primary cell culture models (embryo brain cells of dog and fox) were developed and viral variation was studied in detail through deep genome sequencing. Our analysis revealed a strong unidirectional host evolutionary effect, as dog-adapted rabies virus was able to replicate in fox and fox cells relatively easily, while dogs or neuronal dog cells were not easily susceptible to fox adapted-RABV. This suggests that dog RABV may be able to adapt to some hosts more easily than other host variants, or that when RABV switched from dogs to red foxes it lost its ability to adapt easily to other species. Although no difference in patterns of mutation variation between different host organs was observed, mutations were common following both in vitro and in vivo passage. However, only a small number of these mutations also appeared in natura, suggesting that adaptation during successful cross-species virus transmission is a complex, multifactorial evolutionary process.
| S-EPMC6615636 | biostudies-literature
Project description:Purifying selection during dengue viral infection has been suggested as the driving force of viral evolution and the higher complexity of the intra-host quasi-species is thought to offer an adaptive advantage for arboviruses as they cycle between arthropod and vertebrate hosts. However, very few studies have been performed to investigate the viral genetic changes within (intra-host) and between (inter-host) humans in a spatio-temporal scale. Viruses of different serotypes from various countries imported to Taiwan cause annual outbreaks. During 2001-2003, two consecutive outbreaks were caused by dengue virus serotype 2 (DENV-2) and resulted in a larger-scale epidemic with more severe dengue cases in the following year. Phylogenetic analyses showed that the viruses from both events were similar and related to the 2001 DENV-2 isolate from the Philippines. We comprehensively analyzed viral sequences from representative dengue patients and identified three consensus genetic variants, group Ia, Ib and II, with different spatio-temporal population dynamics. The phylodynamic analysis suggested group Ib variants, characterized by lower genetic diversity, transmission rate, and intra-host variant numbers, might play the role of maintenance variants. The residential locations among the patients infected by group Ib variants were in the outer rim of case clusters throughout the 2001-2003 period whereas group Ia and II variants were located in the centers of case clusters, suggesting that group Ib viruses might serve as "sheltered overwintering" variants in an undefined ecological niche. Further deep sequencing of the viral envelope (E) gene directly from individual patient serum samples confirmed the emergence of variants belonging to three quasi-species (group Ia, Ib, and II) and the ancestral role of the viral variants in the latter phase of the 2001 outbreak contributed to the later, larger-scale epidemic beginning in 2002. These findings enhanced our understanding of increasing epidemic severity over time in the same epidemic area. It also highlights the importance of combining phylodynamic and deep sequencing analysis as surveillance tools for detecting dynamic changes in viral variants, particularly searching for and monitoring any specific viral subpopulation. Such subpopulations might have selection advantages in both fitness and transmissibility leading to increased epidemic severity.
| S-EPMC6191158 | biostudies-literature
Project description:Dengue control approaches are best informed by granular spatial epidemiology of these viruses, yet reconstruction of inter- and intra-household transmissions is limited when analyzing case count, serologic, or genomic consensus sequence data. To determine viral spread on a finer spatial scale, we extended phylogenomic discrete trait analyses to reconstructions of house-to-house transmissions within a prospective cluster study in Kamphaeng Phet, Thailand. For additional resolution and transmission confirmation, we mapped dengue intra-host single nucleotide variants on the taxa of these time-scaled phylogenies. This approach confirmed 19 household transmissions and revealed that dengue disperses an average of 70 m per day between households in these communities. We describe an evolutionary biology framework for the resolution of dengue transmissions that cannot be differentiated based on epidemiologic and consensus genome data alone. This framework can be used as a public health tool to inform control approaches and enable precise tracing of dengue transmissions.
| S-EPMC8153871 | biostudies-literature
Project description:BACKGROUND: In computational biology, a novel knowledge has been obtained mostly by identifying 'intra-relation,' the relation between entities on a specific biological level such as from gene expression or from microRNA (miRNA) and many such researches have been successful. However, intra-relations are not fully explaining complex cancer mechanisms because the inter-relation information between different levels of genomic data is missing, e.g. miRNA and its target genes. The 'inter-relation' between different levels of genomic data can be constructed from biological experimental data as well as genomic knowledge. METHODS: Previously, we have proposed a graph-based framework that integrates with multi-layers of genomic data, copy number alteration, DNA methylation, gene expression, and miRNA expression, for the cancer clinical outcome prediction. However, the limitation of previous work was that we integrated with multi-layers of genomic data without considering of inter-relationship information between genomic features. In this paper, we propose a new integrative framework that combines genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression for the clinical outcome prediction as a pilot study. RESULTS: In order to demonstrate the validity of the proposed method, the prediction of short-term/long-term survival for 82 patients in glioblastoma multiforme (GBM) was adopted as a base task. Based on our results, the accuracy of our predictive model increases because of incorporation of information fused over genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression. CONCLUSIONS: In the present study, the intra-relation of gene expression was reconstructed from inter-relation between miRNA and gene expression for prediction of short-term/long-term survival of GBM patients. Our finding suggests that the utilization of external knowledge representing miRNA-mediated regulation of gene expression is substantially useful for elucidating the cancer phenotype.
| S-EPMC3852212 | biostudies-other