Bioinformatic analysis of defective viral genomes in SARS-CoV-2 and its impact on population infection characteristics

Abstract

DVGs (Defective Viral Genomes) are prevalent in RNA virus infections. In this investigation, we conducted an analysis of high-throughput sequencing data and observed widespread presence of DVGs in SARS-CoV-2. Comparative analysis between SARS-CoV-2 and diverse DNA viruses revealed heightened susceptibility to damage and increased sequencing sample heterogeneity within the SARS-CoV-2 genome. Whole-genome sequencing depth variability analysis exhibited a higher coefficient of variation for SARS-CoV-2, while DVG analysis indicated a significant proportion of recombination sites, signifying notable genome heterogeneity and suggesting that a large proportion of assembled virus particles contain incomplete RNA sequences. Moreover, our investigation explored the sequencing depth and DVG content differences among various strains. Our findings revealed that as the virus evolves, there is a notable increase in the proportion of intact genomes within virus particles, as evidenced by third-generation sequencing data. Specifically, the proportion of intact genome in the Omicron strain surpassed that of the Delta and Alpha strains. This observation effectively elucidates the heightened infectiousness of the Omicron strain compared to the Delta and Alpha strains. We also postulate that this improvement in completeness stems from enhanced virus assembly capacity, as the Omicron strain can promptly facilitate the binding of RNA and capsid protein, thereby reducing the exposure time of vulnerable virus RNA in the host environment and significantly mitigating its degradation. Finally, employing mathematical modeling, we simulated the impact of DVG effects under varying environmental factors on infection characteristics and population evolution. Our findings provide an explanation for the close association between symptom severity and the extent of virus invasion, as well as the substantial disparity in population infection characteristics caused by the same strain under distinct environmental conditions. This study presents a novel approach for future virus research and vaccine development.

Keywords: SARS-CoV-2; defective viral genome; genome coverage; mathematical modeling; population infection characteristics; semi-infectious particle; virus evolution.

Figure 1

Biochemical scheme of SARS-CoV-2 life cycle. 22 reactions are illustrated in this diagram.

Figure 2

(A) Depth coverage of DNA bacteriophage (SRR16248203) and SARS-CoV-2 (SRR23175982). The X-axis represents nucleotide positions, while the Y-axis represents the sequencing depth at each corresponding position. The sequencing depth profiles for the DNA bacteriophage (SRR16248203) sample are plotted as solid blue lines, while the sequencing depth profiles for the SARS-CoV-2 (SRR23175982) sample are represented by dashed red lines. (B) Uniformity of NGS data among DNA bacteriophage ( Supplementary Table S1 ) and three major strains of SARS-CoV-2. The distribution of the Coefficient of Variation is represented using box plots, with the mean value for each distribution indicated by a red horizontal line.

Figure 3

(A) A diagram of two types of defective viruses. The term “deletion” refers to the absence of an intermediate segment, while “copy-back” denotes the incorrect recombination resulting from the replication of an intermediate segment, leading to an overall sequence expansion. (B) Analysis for junction distribution of DVGs identified in NSG data. The X-axis represents the Rejoin point, while the Y-axis represents the break point. When the sequence number of the Rejoin point is greater than the break point, it indicates a deletion in defective viral genomes (DVGs). Conversely, when the sequence number of the Rejoin point is smaller than the break point, it denotes copy-backs in DVGs. (C) Analysis of DVGs transformation probability in each genome loci. The X-axis represents the nucleotide position, while the Y-axis represents the probability of each position becoming a break point or rejoin point, i.e., the probability of forming defective viral genomes (DVGs). (D) Comparison of DVGs overall transformation probability among three major strains of SARS-CoV-2. The distribution of the average probability of DVG formation at each nucleotide position for different viral strains is depicted using a box plot, with the mean values indicated by red horizontal lines. The plot reveals consistently low probabilities of DVG formation at individual nucleotide positions across different viral strains, with no significant variation observed.

Figure 4

(A) A diagram of replication error caused by SARS-CoV-2 RNA polymerase. The majority of mismatches were observed to occur at the two ends of long sequence fragments, as revealed by long-read sequencing using third-generation SMRT technology, as indicated by the dashed blue cycles. (B) Proportion of end mutations in the overall SMRT data in three major SARS-CoV-2 strains. The distribution of the proportional probability of mutations at both ends of sequences for different viral strains is depicted using a box plot, with red horizontal lines indicating the average value for each distribution. It can be observed that the proportion for the Omicron strain is significantly lower than that for the Alpha and Delta strains. (C) Proportion of full-matched segments in the overall SMRT data in three major SARS-CoV-2 strains. The distribution of the proportion of sequences perfectly matching the reference genome, as determined by third-generation SMRT data, is depicted using a box plot, with red horizontal lines representing the mean value. From the plot, it can be observed that the Omicron variant exhibits a substantial presence of long sequence fragments with perfect matches, while such sequences are almost absent in the Alpha and Delta variants.

Figure 5

(A) Virus-antibody dynamics after semi-infectious virus infection. (B) Virus-antibody dynamics after full-length virus infection (replication error = 10%). (C) Virus-antibody dynamics after full-length virus infection (replication error = 5%).

Figure 6

Dynamics of virus composition after infection.

Figure 7

(A) protection time against reinfection with full-length virus. (B) protection time against reinfection with semi-infectious virus.

Figure 8

Effects of protection measure on the infection severity at population level.