SNPs in Genes ORF7b and S from States with Potential Transfer of SARS-CoV-2 from Odocoileus virginianus to Homo sapiens

Background and Overview

White-tailed deer (Odocoileus virginianus) are common in urban and rural locations across the United States. Their angiotensin-converting enzyme 2 (ACE2) proteins have a high similarity to human (Homo sapien) ACE2 proteins. White-tailed deer populations in North Carolina and Massachusetts could potentially be reservoirs for SARS-CoV-2 (Feng et al., 2023). An animal reservoir occurs when a zoonotic virus is transmitted and circulated within a host population, then transmitted to other animals or humans. There have been cases of White-tailed deer acting as a reservoir for SARS-CoV-2 in Canada. The primary goal of this analysis was to assess the SNPs of the samples from North Carolina and Massachusetts, which are the states with potential transmission of SARS-CoV-2 from White-tailed deer to humans. I have used bash scripting to trim raw data and call variants from SARS-CoV-2 genomes collected from White-tailed deer populations across the United States. I then used R to find the distribution of samples by state, the density of distinct single nucleotide polymorphisms (SNP) in named SARS-CoV-2 genes by state, the density of distinct SNPs in gene ORF7b in North Carolina and Massachusetts, and the comparison of SNP positions in the S gene in North Carolina and Massachusetts. An unequal distribution of samples by state could have led to a misrepresentation of the density of distinct SNPs in named SARS-CoV-2 genes. ORF7b had the highest density of distinct SNPs in both statewide and state specific analysis. North Carolina and Massachusetts samples have identical SNPs at the same position in the ORF7b gene. All the positions of Massachusetts S SNPs align North Carolina S SNPs. However, North Carolina samples have more SNPs in the S gene than Massachusetts do.

Methods

I downloaded the data for this project from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). It contains SARS-CoV-2 genome sequence data (project ID: PRJNA984950). Sequences were checked for quality with fastQC (Brown et al., 2017). These data were cleaned and trimmed using Trimmomatic (Bolger et al., 2014). I then used BWA (Vasimuddin et al., 2019) to map sequences to the Wuhan SARS-CoV-2 reference genome (sequence ID: NC_045512.2). I then called the variants from the mapped reads using vcftools (Danecek et al., 2011). All subsequent analysis and visualization was done with R (v 4.4.1) (Ihaka and Gentleman, 1996). Key packages used included dplyr (Wickham et al., 2023), ggplot2 (Wickham, 2016), and vcfR (Knaus and Grünwald, 2016).

I gave ChatGPT (v GPT-4) (An et al., 2023) prompts to write the script for Figures 1, 2, 3, 4 , and 5 and Tables 1, 2, 3, and 4. I made adjusments to the ChatGPT code to produce the figures in the way I desired.

Results and Discussion

An equal amount of SARS-CoV-2 samples were not taken from each state (Figure 1). States like Tennessee and North Carolina have many more samples than states like Delaware. Therefore, certain states could be more represented when plotting the density of distinct SNPs in named SARS-CoV-2 genes. SNPs were counted by position in genes M, N, ORF10, ORF1ab, ORF3a, ORF6, ORF7a, ORF7b, ORF8, and S. The counted SNPs were then normalized by gene length, which allows the distinct SNPs per unit of gene length to be observed, and grouped by state (Figure 2). Tennessee is seen to have the highest density of SNPs in most genes, however the data also contained many samples from Tennessee. The statewide trends in data can also be seen in specific states. ORF7b has the highest density of SNPs in the samples collected from North Carolina when SNPs are counted by position and normalized by gene length (Figure 3). The same can be said for samples collected from Massachusetts (Figure 4). ORF7b is an accessory protein that induces apoptosis in host cells (Yang et al., 2021). It promotes expression of transcription factors involved in activating cell death. High SNP density in ORF7b for both statewide and state-specific trends could indicate that a change in apoptosis modulation has occurred in these genomes that are not present in the reference genome. Both states have a change from cytosine to thymine at position 27874 in gene ORF7b when compared to the reference genome (Tables 1 & 2). There are more SNPs in the S gene for both North Carolina and Massachusetts (Tables 3 & 4). The higher SNP density but lower SNP count of ORF7b when compared to S is not surprising when gene length is considered. ORF7b is 131 bp in length, while S is 3821 bp in length. The SNPs from Massachusetts samples are found in the same positions as those present in North Carolina samples. However, North Carolina samples have SNPs at positions additional to those found in Massachusetts (Figure 5). The S protein is a SARS-CoV-2 surface protein which has a high affinity for the mammalian ACE2 protein. ACE2 cleavage by a S domain allows for SARS-CoV-2 entry into a mammalian cell. The greater abundance of SNPs in the S gene of North Carolina samples when compared to Massachusetts samples could indicate that the cleavage of the ACE2 protein is different in North Carolina white-tailed deer populations than they are in Massachusetts populations.

An analysis of the SARS-CoV-2 samples from White-tailed deer in states with potential transmission of the virus to humans shows a high SNP density in ORF7b and higher amount of SNPs in S for North Carolina samples. ORF7b modulates apoptosis, while S interacts with ACE2 to allow entry into mammalian cells. The observed differences between the Wuhan SARS-CoV-2 reference and the White-tailed deer alternates in these regions could imply a gain or loss in apoptotic and entry phenotypes.

Figures

Figure 1: Distribution of SARS-CoV-2 samples taken from white-tailed deer by state.

Figure 2: Density of distinct SNPs in named SARS-CoV-2 genes across states. The x-axis represents the SARS-CoV-2 genes in which the SNPs were found. The y-axis represents the density of SNPs found at unique positions on the chromosome. The graph is normalized by the length of gene.

Figure 3: Density of distinct SNPs in named SARS-CoV-2 genes from North Carolina samples. The x-axis represents the SARS-CoV-2 genes in which the SNPs were found. The y-axis represents the density of SNPs found at unique positions on the chromosome. The graph is normalized by the length of gene.

Figure 4: Density of distinct SNPs in named SARS-CoV-2 genes from Massachusetts samples. The x-axis represents the SARS-CoV-2 genes in which the SNPs were found. The y-axis represents the density of SNPs found at unique positions on the chromosome. The graph is normalized by the length of gene.

Figure 5: Position of SNPs occurring in North Carolina and Massachusetts samples. North Carolina SNPs are represented in blue and Massachusetts samples are represented in red.

Tables

Gene	Position	Reference (ref)	Alteration (alt)
ORF7b	27874	C	T

Table 1: Position and nucleotide change in SNP for ORF7b gene in North Carolina samples.

Gene	Position	Reference (ref)	Alteration (alt)
ORF7b	27874	C	T

Table 2: Position and nucleotide change in SNP for ORF7b gene in Massachusetts samples.

Gene	Position	Reference (ref)	Alteration (alt)
S	21618	C	G
S	21846	C	T
S	21987	G	A
S	22028	GAGTTCA	G
S	22326	C	T
S	22329	C	T
S	22736	T	C
S	22747	C	T
S	22917	T	G
S	22995	C	A
S	23202	C	T
S	23403	A	G
S	23481	C	T
S	23604	C	G
S	24165	A	C
S	24208	C	T
S	24287	ATT	AT
S	24340	A	G
S	24410	G	A

Table 3: Position and nucleotide change in SNP for S gene in North Carolina samples.

Gene	Position	Reference (ref)	Alteration (alt)
S	21618	C	G
S	22326	C	T
S	22917	T	G
S	22995	C	A
S	23403	A	G
S	23604	C	G
S	24208	C	T
S	24410	G	A

Table 4: Position and nucleotide change in SNP for S gene in Massachusetts samples.

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