Dataset Information

SARS-CoV-2 intra-host single-nucleotide variants associated with disease severity.

ABSTRACT: Variants of severe acute respiratory syndrome coronavirus 2 frequently arise within infected individuals. Here, we explored the level and pattern of intra-host viral diversity in association with disease severity. Then, we analyzed information underlying these nucleotide changes to infer the impetus including mutational signatures and immune selection from neutralizing antibody or T-cell recognition. From 23 January to 31 March 2020, a set of cross-sectional samples were collected from individuals with homogeneous founder virus regardless of disease severity. Intra-host single-nucleotide variants (iSNVs) were enumerated using deep sequencing. Human leukocyte antigen (HLA) alleles were genotyped by Sanger sequencing. Medical records were collected and reviewed by attending physicians. A total of 836 iSNVs (3-106 per sample) were identified and distributed in a highly individualized pattern. The number of iSNVs paced with infection duration peaked within days and declined thereafter. These iSNVs did not stochastically arise due to a strong bias toward C > U/G > A and U > C/A > G substitutions in reciprocal proportion with escalating disease severity. Eight nonsynonymous iSNVs in the receptor-binding domain could escape from neutralization, and eighteen iSNVs were significantly associated with specific HLA alleles. The level and pattern of iSNVs reflect the in vivo viral-host interaction and the disease pathogenesis.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC9728387 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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SARS-CoV-2 intra-host single-nucleotide variants associated with disease severity.

Zhang Yi Y Jiang Ning N Qi Weiqiang W Li Tao T Zhang Yumeng Y Wu Jing J Zhang Haocheng H Zhou Mingzhe M Cui Peng P Yu Tong T Fu Zhangfan Z Zhou Yang Y Lin Ke K Wang Hongyu H Wei Tongqing T Zhu Zhaoqin Z Ai Jingwen J Qiu Chao C Zhang Wenhong W

Virus evolution 20221129 2

Variants of severe acute respiratory syndrome coronavirus 2 frequently arise within infected individuals. Here, we explored the level and pattern of intra-host viral diversity in association with disease severity. Then, we analyzed information underlying these nucleotide changes to infer the impetus including mutational signatures and immune selection from neutralizing antibody or T-cell recognition. From 23 January to 31 March 2020, a set of cross-sectional samples were collected from individua ...[more]

PMID: 36505092

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Project description:Coronavirus disease 2019 (COVID-19) is a lethal disease caused by the coronavirus SARS-CoV-2, which can result in a broad clinical spectrum of respiratory symptoms. While many clinical risk factors such as concomitant chronic diseases play roles in the pathophysiology of COVID-19, genetic predisposition factors have not been widely studied. The aim of this study was, therefore, to evaluate the relationship between some singlenucleotide polymorphisms (SNPs) of the human genes TYK2 and ACE2 and the severity of SARS-CoV-2 infection. Genomic DNA was isolated from 200 SARS-CoV-2-infected individuals with severe (n = 100) or mild (n = 100) disease. Owing to the importance of ACE2 and TYK2 genes in regulating the immune response to SARS-CoV-2 infection, TYK2 gene SNPs, i.e. rs2304255, rs2304256, rs12720270, and rs12720354 and ACE2 rs382746 variants, were genotyped in the samples. To confirm the results, the expression of different TYK2 genotypes was investigated using real-time PCR. The presence of the nucleotide T at the locus rs2304255 was shown to be a risk factor linked to disease severity (OR [95% CI] = 3.2485 [2.1554-4.8961]). Similarly, the presence of A at the locus rs12720354 increased the risk of severity (OR [95% CI]) = 3.9721 [2.6075-6.0509]). In contrast, the presence of A at the loci rs2304256 and rs12720270 was observed to reduce the severity risk (OR [95% CI] = 0.2495 [0.1642-0.3793] and 0.1668 [0.1083-0.2569], respectively). Real-time PCR results also demonstrated that the expression level of TYK2 in samples with the TT genotype of rs2304255 and the AA genotype of rs12720354 and in samples with the GG genotype of rs12720207 was significantly lower than in those with other genotypes. The results of this study suggest that TYK2 SNPs might be utilized to identify individuals who are at risk for severe COVID-19, in order to better manage their health care. It is predicted that the presence of some alleles (T in rs2304255, A in rs12720354, and G in rs12720207) of TYK2 can affect COVID-19 severity by reducing TYK2 expression and thereby affecting the regulatory role of TYK2 in the immune response.

Project description:Variants of concern (VOC) in SARS-CoV-2 refer to viruses whose viral genomes differ from the ancestor virus by ≥3 single-nucleotide variants (SNVs) and that show the potential for higher transmissibility and/or worse clinical progression. VOC have the potential to disrupt ongoing public health measures and vaccine efforts. Still, too little is known regarding how frequently new viral variants emerge and under what circumstances. We report a study to determine the degree of SARS-CoV-2 sequence evolution in 94 patients and to estimate the frequency at which highly diverse variants emerge. Two cases accumulated ≥9 SNVs over a 2-week period and one case accumulated 23 SNVs over 3 weeks, including three nonsynonymous mutations in the spike protein (D138H, E554D, D614G). The remainder of the infected patients did not show signs of intra-host evolution. We estimate that in as much as 2% of hospitalized COVID-19 cases, variants with multiple mutations in the spike glycoprotein emerge in as little as 1 month of persistent intra-host virus replication. This suggests the continued local emergence of variants with multiple nonsynonymous SNVs, even in patients without overt immune deficiency. Surveillance by sequencing for (i) viremic COVID-19 patients, (ii) patients suspected of reinfection, and (iii) patients with diminished immune function may offer broad public health benefits. IMPORTANCE New SARS-CoV-2 variants can potentially disrupt ongoing public health measures and vaccine efforts. Still, little is known regarding how frequently new viral variants emerge and under what circumstances. Based on this study, we estimate that in hospitalized COVID-19 cases, variants with multiple mutations may emerge locally in as little as 1 month, even in patients without overt immune deficiency. Surveillance by sequencing for continuously shedding patients, patients suspected of reinfection, and patients with diminished immune function may offer broad public health benefits.

Project description:BackgroundIn recent years the number of human genetic variants deposited into the publicly available databases has been increasing exponentially. The latest version of dbSNP, for example, contains ~50 million validated Single Nucleotide Variants (SNVs). SNVs make up most of human variation and are often the primary causes of disease. The non-synonymous SNVs (nsSNVs) result in single amino acid substitutions and may affect protein function, often causing disease. Although several methods for the detection of nsSNV effects have already been developed, the consistent increase in annotated data is offering the opportunity to improve prediction accuracy.ResultsHere we present a new approach for the detection of disease-associated nsSNVs (Meta-SNP) that integrates four existing methods: PANTHER, PhD-SNP, SIFT and SNAP. We first tested the accuracy of each method using a dataset of 35,766 disease-annotated mutations from 8,667 proteins extracted from the SwissVar database. The four methods reached overall accuracies of 64%-76% with a Matthew's correlation coefficient (MCC) of 0.38-0.53. We then used the outputs of these methods to develop a machine learning based approach that discriminates between disease-associated and polymorphic variants (Meta-SNP). In testing, the combined method reached 79% overall accuracy and 0.59 MCC, ~3% higher accuracy and ~0.05 higher correlation with respect to the best-performing method. Moreover, for the hardest-to-define subset of nsSNVs, i.e. variants for which half of the predictors disagreed with the other half, Meta-SNP attained 8% higher accuracy than the best predictor.ConclusionsHere we find that the Meta-SNP algorithm achieves better performance than the best single predictor. This result suggests that the methods used for the prediction of variant-disease associations are orthogonal, encoding different biologically relevant relationships. Careful combination of predictions from various resources is therefore a good strategy for the selection of high reliability predictions. Indeed, for the subset of nsSNVs where all predictors were in agreement (46% of all nsSNVs in the set), our method reached 87% overall accuracy and 0.73 MCC. Meta-SNP server is freely accessible at http://snps.biofold.org/meta-snp.

Project description:Long-term infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents a challenge to virus dispersion and the control of coronavirus disease 2019 (COVID-19) pandemic. The reason why some people have prolonged infection and how the virus persists for so long are still not fully understood. Recent studies suggested that the accumulation of intra-host single nucleotide variants (iSNVs) over the course of the infection might play an important role in persistence as well as emergence of mutations of concern. For this reason, we aimed to investigate the intra-host evolution of SARS-CoV-2 during prolonged infection. Thirty-three patients who remained reverse transcription polymerase chain reaction (RT-PCR) positive in the nasopharynx for on average 18 days from the symptoms onset were included in this study. Whole-genome sequences were obtained for each patient at two different time points. Phylogenetic, populational, and computational analyses of viral sequences were consistent with prolonged infection without evidence of coinfection in our cohort. We observed an elevated within-host genomic diversity at the second time point samples positively correlated with cycle threshold (Ct) values (lower viral load). Direct transmission was also confirmed in a small cluster of healthcare professionals that shared the same workplace by the presence of common iSNVs. A differential accumulation of missense variants between the time points was detected targeting crucial structural and non-structural proteins such as Spike and helicase. Interestingly, longitudinal acquisition of iSNVs in Spike protein coincided in many cases with SARS-CoV-2 reactive and predicted T cell epitopes. We observed a distinguishing pattern of mutations over the course of the infection mainly driven by increasing A→U and decreasing G→A signatures. G→A mutations may be associated with RNA-editing enzyme activities; therefore, the mutational profiles observed in our analysis were suggestive of innate immune mechanisms of the host cell defense. Therefore, we unveiled a dynamic and complex landscape of host and pathogen interaction during prolonged infection of SARS-CoV-2, suggesting that the host's innate immunity shapes the increase of intra-host diversity. Our findings may also shed light on possible mechanisms underlying the emergence and spread of new variants resistant to the host immune response as recently observed in COVID-19 pandemic.

Dataset Information

SARS-CoV-2 intra-host single-nucleotide variants associated with disease severity.

Publications

SARS-CoV-2 intra-host single-nucleotide variants associated with disease severity.

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