SARS-CoV-2 exhibits intra-host genomic plasticity and low-frequency polymorphic quasispecies

Karamitros, Timokratis; Papadopoulou, Gethsimani; Bousali, Maria; Mexias, Anastasios; Tsiodras, Sotirios; Mentis, Αndreas

doi:10.1101/2020.03.27.009480

Cited by 47 publications

(70 citation statements)

References 53 publications

(57 reference statements)

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“…These results demonstrate the existence of several mutated positions across the genome leading to relatively low percentage of identical sites (62 to 64%). However, the level of genetic diversity is relatively low, as shown by the high percentage of pairwise identity (>99%), suggesting that most mutations only occur in a few genomes, in line with other studies[18][19][20].…”

supporting

confidence: 88%

CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2

Carneiro¹,

Pereira

2020

Preprint

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The ability to detect the SARS-CoV-2 in a widespread epidemic is crucial for screening of carriers and for the success of quarantine efforts. Methods based on real-time reverse transcription polymerase chain reaction (RT-qPCR) and sequencing are being used for virus detection and characterization. However, RNA viruses are known for their high genetic diversity which poses a challenge for the design of efficient nucleic acid-based assays. The first SARS-CoV-2 genomic sequences already showed novel mutations, which may affect the efficiency of available screening tests leading to false-negative diagnosis or inefficient therapeutics.Here we describe the CoV2ID(http://covid.portugene.com/), a free database built to facilitate the evaluation of molecular methods for detection of SARS-CoV-2 and treatment of COVID-19.The database evaluates the available oligonucleotide sequences (PCR primers, RT-qPCR probes, etc.) considering the genetic diversity of the virus. Updated sequences alignments are used to constantly verify the theoretical efficiency of available testing methods. Detailed information on available detection protocols are also available to help laboratories implementing SARS-CoV-2 testing.

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supporting

confidence: 88%

CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2

Carneiro¹,

Pereira

2020

Preprint

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“…ORF1a and ORF1b encode the replicase machinery [7]. We account for differences in gene size by normalizing variants to kilobase gene length (variants / kb-gene-length – “v/kbgl”) [10]. The highest density of variants was reported in smaller genes like envelope, ORF7a, and ORF10 ( S2 Table ).…”

Section: Resultsmentioning

confidence: 99%

“…Though limited, in-vivo studies of SARS-CoV-2 show low-frequency variants are detectable within individual hosts and are likely due to random fluctuations in allele frequencies. One study highlights an excess of nonsynonymous variants compared to synonymous variants among these low-frequency variants, consistent with the possibility of ongoing diversifying selection in SARS-CoV-2 viruses [10, 11]. Another recent study by Liu and colleagues highlights a deletion in the Spike gene at nucleotide (nt) positions 23,585–23,599, encoding QTQTN, that flanks the polybasic cleavage site in S1/S2.…”

Section: Introductionmentioning

confidence: 89%

Limited SARS-CoV-2 diversity within hosts and following passage in cell culture

Moreno

Braun

et al. 2020

Preprint

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Since the first reports of pneumonia associated with a novel coronavirus (COVID-19) emerged in Wuhan, Hubei province, China, there have been considerable efforts to sequence the causative virus, SARS-CoV-2 (also referred to as hCoV-19) and to make viral genomic information available quickly on shared repositories. As of 30 March 2020, 7,680 consensus sequences have been shared on GISAID, the principal repository for SARS-CoV-2 genetic information. These sequences are primarily consensus sequences from clinical and passaged samples, but few reports have looked at diversity of virus populations within individual hosts or cultures. Understanding such diversity is essential to understanding viral evolutionary dynamics. Here, we characterize within-host viral diversity from a primary isolate and passaged samples, all originally deriving from an individual returning from Wuhan, China, who was diagnosed with COVID-19 and subsequently sampled in Wisconsin, United States. We use a metagenomic approach with Oxford Nanopore Technologies (ONT) GridION in combination with Illumina MiSeq to capture minor within-host frequency variants ≥1%. In a clinical swab obtained from the day of hospital presentation, we identify 15 single nucleotide variants (SNVs) ≥1% frequency, primarily located in the largest gene – ORF1a. While viral diversity is low overall, the dominant genetic signatures are likely secondary to population size changes, with some evidence for mild purifying selection throughout the genome. We see little to no evidence for positive selection or ongoing adaptation of SARS-CoV-2 within cell culture or in the primary isolate evaluated in this study.Author SummaryWithin-host variants are critical for addressing molecular evolution questions, identifying selective pressures imposed by vaccine-induced immunity and antiviral therapeutics, and characterizing interhost dynamics, including the stringency and character of transmission bottlenecks. Here, we sequenced SARS-CoV-2 viruses isolated from a human host and from cell culture on three distinct Vero cell lines using Illumina and ONT technologies. We show that SARS-CoV-2 consensus sequences can remain stable through at least two serial passages on Vero 76 cells, suggesting SARS-CoV-2 can be propagated in cell culture in preparation for in-vitro and in-vivo studies without dramatic alterations of its genotype. However, we emphasize the need to deep-sequence viral stocks prior to use in experiments to characterize sub-consensus diversity that may alter outcomes.

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“…Notably, a group studying sequence variants within patients reported limited evidence of intrahost variation, though they cautioned that the results were preliminary and could be the result of limited data [20,21].…”

Section: Introductionmentioning

confidence: 99%

Genetic Grouping of SARS-CoV-2 Coronavirus Sequences using Informative Subtype Markers for Pandemic Spread Visualization

Zhao

Sokhansanj

Malhotra

et al. 2020

Preprint

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We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies. However, identifying viral subtypes in real-time is challenging: SARS-CoV-2 is a novel virus, and the pandemic is rapidly expanding. Viral subtypes may be difficult to detect due to rapid evolution; founder effects are more significant than selection pressure; and the clustering threshold for subtyping is not standardized. We propose to identify mutational signatures of available SARS-CoV-2 sequences using a population-based approach: an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Through ISM compression, we find that certain distant nucleotide variants covary, including non-coding and ORF1ab sites covarying with the D614G spike protein mutation which has become increasingly prevalent as the pandemic has spread. ISMs are also useful for downstream analyses, such as spatiotemporal visualization of viral dynamics. By analyzing sequence data available in the GISAID database, we validate the utility of ISM-based subtyping by comparing spatiotemporal analyses using ISMs to epidemiological studies of viral transmission in Asia, Europe, and the United States. In addition, we show the relationship of ISMs to phylogenetic reconstructions of SARS-CoV-2 evolution, and therefore, ISMs can play an important complementary role to phylogenetic tree-based analysis, such as is done in the Nextstrain project. The developed pipeline dynamically generates ISMs for newly added SARS-CoV-2 sequences and updates the visualization of pandemic spatiotemporal dynamics, and is available on Github at https://github.com/EESI/ISM and via an interactive website at https://covid19-ism.coe.drexel.edu/.

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SARS-CoV-2 exhibits intra-host genomic plasticity and low-frequency polymorphic quasispecies

Cited by 47 publications

References 53 publications

CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2

CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2

Limited SARS-CoV-2 diversity within hosts and following passage in cell culture

Genetic Grouping of SARS-CoV-2 Coronavirus Sequences using Informative Subtype Markers for Pandemic Spread Visualization

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