Simon J. Watson

Human infection with MERS coronavirus after exposure to infected camels, Saudi Arabia, 2013.

We investigated a case of human infection with Middle East respiratory syndrome coronavirus (MERS-CoV) after exposure to infected camels. Analysis of the whole human-derived virus and 15% of the camel-derived virus sequence yielded nucleotide polymorphism signatures suggestive of cross-species transmission. Camels may act as a direct source of human MERS-CoV infection.

Transmission and evolution of the Middle East respiratory syndrome coronavirus in Saudi Arabia: a descriptive genomic study

Summary Background Since June, 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) has, worldwide, caused 104 infections in people including 49 deaths, with 82 cases and 41 deaths reported from Saudi Arabia. In addition to confirming diagnosis, we generated the MERS-CoV genomic sequences obtained directly from patient samples to provide important information on MERS-CoV transmission, evolution, and origin. Methods Full genome deep sequencing was done on nucleic acid extracted directly from PCR-confirmed clinical samples. Viral genomes were obtained from 21 MERS cases of which 13 had 100%, four 85–95%, and four 30–50% genome coverage. Phylogenetic analysis of the 21 sequences, combined with nine published MERS-CoV genomes, was done. Findings Three distinct MERS-CoV genotypes were identified in Riyadh. Phylogeographic analyses suggest the MERS-CoV zoonotic reservoir is geographically disperse. Selection analysis of the MERS-CoV genomes reveals the expected accumulation of genetic diversity including changes in the S protein. The genetic diversity in the Al-Hasa cluster suggests that the hospital outbreak might have had more than one virus introduction. Interpretation We present the largest number of MERS-CoV genomes (21) described so far. MERS-CoV full genome sequences provide greater detail in tracking transmission. Multiple introductions of MERS-CoV are identified and suggest lower R0 values. Transmission within Saudi Arabia is consistent with either movement of an animal reservoir, animal products, or movement of infected people. Further definition of the exposures responsible for the sporadic introductions of MERS-CoV into human populations is urgently needed. Funding Saudi Arabian Ministry of Health, Wellcome Trust, European Community, and National Institute of Health Research University College London Hospitals Biomedical Research Centre.

Spread, Circulation, and Evolution of the Middle East Respiratory Syndrome Coronavirus

ABSTRACT The Middle East respiratory syndrome coronavirus (MERS-CoV) was first documented in the Kingdom of Saudi Arabia (KSA) in 2012 and, to date, has been identified in 180 cases with 43% mortality. In this study, we have determined the MERS-CoV evolutionary rate, documented genetic variants of the virus and their distribution throughout the Arabian peninsula, and identified the genome positions under positive selection, important features for monitoring adaptation of MERS-CoV to human transmission and for identifying the source of infections. Respiratory samples from confirmed KSA MERS cases from May to September 2013 were subjected to whole-genome deep sequencing, and 32 complete or partial sequences (20 were ≥99% complete, 7 were 50 to 94% complete, and 5 were 27 to 50% complete) were obtained, bringing the total available MERS-CoV genomic sequences to 65. An evolutionary rate of 1.12 × 10−3 substitutions per site per year (95% credible interval [95% CI], 8.76 × 10−4; 1.37 × 10−3) was estimated, bringing the time to most recent common ancestor to March 2012 (95% CI, December 2011; June 2012). Only one MERS-CoV codon, spike 1020, located in a domain required for cell entry, is under strong positive selection. Four KSA MERS-CoV phylogenetic clades were found, with 3 clades apparently no longer contributing to current cases. The size of the population infected with MERS-CoV showed a gradual increase to June 2013, followed by a decline, possibly due to increased surveillance and infection control measures combined with a basic reproduction number (R0) for the virus that is less than 1. IMPORTANCE MERS-CoV adaptation toward higher rates of sustained human-to-human transmission appears not to have occurred yet. While MERS-CoV transmission currently appears weak, careful monitoring of changes in MERS-CoV genomes and of the MERS epidemic should be maintained. The observation of phylogenetically related MERS-CoV in geographically diverse locations must be taken into account in efforts to identify the animal source and transmission of the virus. MERS-CoV adaptation toward higher rates of sustained human-to-human transmission appears not to have occurred yet. While MERS-CoV transmission currently appears weak, careful monitoring of changes in MERS-CoV genomes and of the MERS epidemic should be maintained. The observation of phylogenetically related MERS-CoV in geographically diverse locations must be taken into account in efforts to identify the animal source and transmission of the virus.

Specific Capture and Whole-Genome Sequencing of Viruses from Clinical Samples

Whole genome sequencing of viruses directly from clinical samples is integral for understanding the genetics of host-virus interactions. Here, we report the use of sample sparing target enrichment (by hybridisation) for viral nucleic acid separation and deep-sequencing of herpesvirus genomes directly from a range of clinical samples including saliva, blood, virus vesicles, cerebrospinal fluid, and tumour cell lines. We demonstrate the effectiveness of the method by deep-sequencing 13 highly cell-associated human herpesvirus genomes and generating full length genome alignments at high read depth. Moreover, we show the specificity of the method enables the study of viral population structures and their diversity within a range of clinical samples types.

Viral population analysis and minority-variant detection using short read next-generation sequencing

RNA viruses within infected individuals exist as a population of evolutionary-related variants. Owing to evolutionary change affecting the constitution of this population, the frequency and/or occurrence of individual viral variants can show marked or subtle fluctuations. Since the development of massively parallel sequencing platforms, such viral populations can now be investigated to unprecedented resolution. A critical problem with such analyses is the presence of sequencing-related errors that obscure the identification of true biological variants present at low frequency. Here, we report the development and assessment of the Quality Assessment of Short Read (QUASR) Pipeline (http://sourceforge.net/projects/quasr) specific for virus genome short read analysis that minimizes sequencing errors from multiple deep-sequencing platforms, and enables post-mapping analysis of the minority variants within the viral population. QUASR significantly reduces the error-related noise in deep-sequencing datasets, resulting in increased mapping accuracy and reduction of erroneous mutations. Using QUASR, we have determined influenza virus genome dynamics in sequential samples from an in vitro evolution of 2009 pandemic H1N1 (A/H1N1/09) influenza from samples sequenced on both the Roche 454 GSFLX and Illumina GAIIx platforms. Importantly, concordance between the 454 and Illumina sequencing allowed unambiguous minority-variant detection and accurate determination of virus population turnover in vitro.

Respiratory Tract Samples, Viral Load, and Genome Fraction Yield in Patients With Middle East Respiratory Syndrome

Abstract Background. Analysis of clinical samples from patients with new viral infections is critical to confirm the diagnosis, to specify the viral load, and to sequence data necessary for characterizing the viral kinetics, transmission, and evolution. We analyzed samples from 112 patients infected with the recently discovered Middle East respiratory syndrome coronavirus (MERS-CoV). Methods. Respiratory tract samples from cases of MERS-CoV infection confirmed by polymerase chain reaction (PCR) were investigated to determine the MERS-CoV load and fraction of the MERS-CoV genome. These values were analyzed to determine associations with clinical sample type. Results. Samples from 112 individuals in which MERS-CoV was detected by PCR were analyzed, of which 13 were sputum samples, 64 were nasopharyngeal swab specimens, 30 were tracheal aspirates, and 3 were bronchoalveolar lavage specimens; 2 samples were of unknown origin. Tracheal aspirates yielded significantly higher MERS-CoV loads, compared with nasopharyngeal swab specimens (P = .005) and sputum specimens (P = .0001). Tracheal aspirates had viral loads similar to those in bronchoalveolar lavage samples (P = .3079). Bronchoalveolar lavage samples and tracheal aspirates had significantly higher genome fraction than nasopharyngeal swab specimens (P = .0095 and P = .0002, respectively) and sputum samples (P = .0009 and P = .0001, respectively). The genome yield from tracheal aspirates and bronchoalveolar lavage samples were similar (P = .1174). Conclusions. Lower respiratory tract samples yield significantly higher MERS-CoV loads and genome fractions than upper respiratory tract samples.

Evolutionary dynamics of local pandemic H1N1/2009 influenza virus lineages revealed by whole-genome analysis

ABSTRACT Virus gene sequencing and phylogenetics can be used to study the epidemiological dynamics of rapidly evolving viruses. With complete genome data, it becomes possible to identify and trace individual transmission chains of viruses such as influenza virus during the course of an epidemic. Here we sequenced 153 pandemic influenza H1N1/09 virus genomes from United Kingdom isolates from the first (127 isolates) and second (26 isolates) waves of the 2009 pandemic and used their sequences, dates of isolation, and geographical locations to infer the genetic epidemiology of the epidemic in the United Kingdom. We demonstrate that the epidemic in the United Kingdom was composed of many cocirculating lineages, among which at least 13 were exclusively or predominantly United Kingdom clusters. The estimated divergence times of two of the clusters predate the detection of pandemic H1N1/09 virus in the United Kingdom, suggesting that the pandemic H1N1/09 virus was already circulating in the United Kingdom before the first clinical case. Crucially, three clusters contain isolates from the second wave of infections in the United Kingdom, two of which represent chains of transmission that appear to have persisted within the United Kingdom between the first and second waves. This demonstrates that whole-genome analysis can track in fine detail the behavior of individual influenza virus lineages during the course of a single epidemic or pandemic.

Universal Amplification, Next-Generation Sequencing, and Assembly of HIV-1 Genomes

ABSTRACT Whole HIV-1 genome sequences are pivotal for large-scale studies of inter- and intrahost evolution, including the acquisition of drug resistance mutations. The ability to rapidly and cost-effectively generate large numbers of HIV-1 genome sequences from different populations and geographical locations and determine the effect of minority genetic variants is, however, a limiting factor. Next-generation sequencing promises to bridge this gap but is hindered by the lack of methods for the enrichment of virus genomes across the phylogenetic breadth of HIV-1 and methods for the robust assembly of the virus genomes from short-read data. Here we report a method for the amplification, next-generation sequencing, and unbiased de novo assembly of HIV-1 genomes of groups M, N, and O, as well as recombinants, that does not require prior knowledge of the sequence or subtype. A sensitivity of at least 3,000 copies/ml was determined by using plasma virus samples of known copy numbers. We applied our novel method to compare the genome diversities of HIV-1 groups, subtypes, and genes. The highest level of diversity was found in the env, nef, vpr, tat, and rev genes and parts of the gag gene. Furthermore, we used our method to investigate mutations associated with HIV-1 drug resistance in clinical samples at the level of the complete genome. Drug resistance mutations were detected as both major variant and minor species. In conclusion, we demonstrate the feasibility of our method for large-scale HIV-1 genome sequencing. This will enable the phylogenetic and phylodynamic resolution of the ongoing pandemic and efficient monitoring of complex HIV-1 drug resistance genotypes.

Analysis of high-depth sequence data for studying viral diversity: a comparison of next generation sequencing platforms using Segminator II

BackgroundNext generation sequencing provides detailed insight into the variation present within viral populations, introducing the possibility of treatment strategies that are both reactive and predictive. Current software tools, however, need to be scaled up to accommodate for high-depth viral data sets, which are often temporally or spatially linked. In addition, due to the development of novel sequencing platforms and chemistries, each with implicit strengths and weaknesses, it will be helpful for researchers to be able to routinely compare and combine data sets from different platforms/chemistries. In particular, error associated with a specific sequencing process must be quantified so that true biological variation may be identified.ResultsSegminator II was developed to allow for the efficient comparison of data sets derived from different sources. We demonstrate its usage by comparing large data sets from 12 influenza H1N1 samples sequenced on both the 454 Life Sciences and Illumina platforms, permitting quantification of platform error. For mismatches median error rates at 0.10 and 0.12%, respectively, suggested that both platforms performed similarly. For insertions and deletions median error rates within the 454 data (at 0.3 and 0.2%, respectively) were significantly higher than those within the Illumina data (0.004 and 0.006%, respectively). In agreement with previous observations these higher rates were strongly associated with homopolymeric stretches on the 454 platform. Outside of such regions both platforms had similar indel error profiles. Additionally, we apply our software to the identification of low frequency variants.ConclusionWe have demonstrated, using Segminator II, that it is possible to distinguish platform specific error from biological variation using data derived from two different platforms. We have used this approach to quantify the amount of error present within the 454 and Illumina platforms in relation to genomic location as well as location on the read. Given that next generation data is increasingly important in the analysis of drug-resistance and vaccine trials, this software will be useful to the pathogen research community. A zip file containing the source code and jar file is freely available for download from http://www.bioinf.manchester.ac.uk/segminator/.

Virus genomes reveal factors that spread and sustained the Ebola epidemic

The 2013–2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic ‘gravity’ model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics.