Microsatellite Signature of Reference Genome Sequence of SARS-CoV-2 and 32 Species of Coronaviridae Family

authors:

avatar Rezwanuzzaman Laskar ORCID 1 , avatar Md Gulam Jilani ORCID 1 , avatar Taslima Nasrin ORCID 1 , avatar Safdar Ali ORCID 1 , *

Aliah University, Kolkata, India

how to cite: Laskar R, Jilani M G, Nasrin T, Ali S. Microsatellite Signature of Reference Genome Sequence of SARS-CoV-2 and 32 Species of Coronaviridae Family. Int J Infect. 2022;9(2):e122019. doi: 10.5812/iji-122019.

Abstract

Background:

Simple sequence repeats (SSRs) are 1 - 6 bp repeat motif sequences present across both prokaryotic and eukaryotic genomes with various clinical implications besides being tools for conservation and evolutionary studies.

Objectives:

Analysis of 33 Coronavirus genomes, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), for incidence, distribution, and complexity of SSRs patterns to understand their role in host divergence and evolution.

Methods:

Full-length genome sequences were extracted from National Center for Biotechnology Information (NCBI). Extraction of microsatellites was done using imperfect microsatellite extractor (IMEx) in “Advanced Mode”. Sequences were aligned with MAFFT v6.861b and the maximum likelihood tree was inferred using RAxML v8.1.20 of the GTR + GAMMA+I model with default specifications.

Results:

A total of 3,442 SSRs and 136 complex sequence repeats (cSSRs) were extracted from the studied 33 genomes. SSR incidence ranged from 82 (CV09) to 144 (CV60). cSSR incidence ranged from 1 (CV42, CV43, CV53) to 11 (CV32). CV61 (SARS-CoV-2) had 107 SSRs and 6 SSRs. Di-nucleotide motifs were the most prevalent followed by tri- and mono-nucleotide motifs. TG/GT was the most represented di-nucleotide motif, followed by CA/AC. In tri-nucleotide SSRs, ACA/TGT was the most represented motif followed by CAA/GTT, whereas in mono-nucleotide SSRs, T was the most observed nucleotide, followed by A. About 94% of SSRs were localized to the coding region. Twenty species, including CV61 (SARS-CoV-2), exhibit mono-nucleotide repeats exclusively in the A/T region, which were clustered in phylogenetic analysis. The sequence similarity of the genomes was assessed through heat map analysis and revealed similar sequences are expectedly placed in proximity on the phylogenetic tree.

Conclusions:

Mono-nucleotide exclusivity to A/T region and SSR genome signature can be a possible basis for predicting the evolution of viruses in terms of host range.

1. Background

The members of the family Coronaviridae are characterized by single-stranded positive-sense RNA genome. They have been named so owing to the resemblance of the structure of virions to a “crown” under the electron microscope (1, 2). Their genome size ranges from 26 to 32 Kb in length and exhibits a wide range of hosts from birds to mammals (3-5). However, their extension to humans as hosts is a recent phenomenon wherein it mostly causes mild respiratory and gastrointestinal problems (6). Some of the earlier known exceptions to this include severe acute respiratory syndrome (SARS) coronavirus in 2002 and Middle East respiratory syndrome (MERS) coronavirus in 2012 (7, 8). A novel human infecting Coronavirus, SARS-CoV-2 was identified from Wuhan, China in December 2019 (9). It exhibited extremely high transmission rates, and patients were reported to suffer from high fever and invasive lesions in lungs (10, 11). As of 11th July 2021, there have been 187,419,263 reported cases and 4,045,647 deaths worldwide (www.worldometers.info/coronavirus/). Of these, 3,08,37,222 cases and 4,08,040 deaths have been reported in India, making it one of the most affected countries in the world (www.mygov.in/covid-19).

Microsatellites or simple sequence repeats (SSRs) are 1 - 6 bp repeat motif sequences present across prokaryotic and eukaryotic genomes with various clinical implications besides being tools for conservation and evolutionary studies (12). Owing to their polymorphic nature and rapid detection protocols, they have been used for multiple plant and animal biotechnological applications (13). These polymorphisms, aided by copy number variations, can act as sites for natural selection and thereon be responsible for evolution (14). This has been studied at different levels of organisms. The study closest to humans reported a persistently smaller number of repeats across all microsatellites in Chimpanzees compared to humans (15). The fact that these sequences can leave an imprint on human evolution makes it worthwhile to assess the impact on viral genomes.

Our previous studies have implied a unique genome signature for each viral genome with implications in the host range as well (16-19). The viral genome provides a very apt candidate to study microsatellites due to their relatively small size, rapid evolution, and simplistic genome features. These SSRs are sources of variations in the genome due to strand slippage and recombination, which can impact different cellular processes like gene expression, chromatin organization and DNA replication (20).

2. Objectives

In the present study, we analyzed the Coronavirus genomes for incidence, distribution, and complexity of SSRs patterns to understand their role in host divergence and evolution.

3. Methods

3.1. Genome Sequences

As per the classification of International Committee of Taxonomy of Viruses (ICTV) prior to the emergence of SARS-CoV-2, Coronaviruses belonged to Nidovirales order, Coronaviridae family, and Coronavirinae subfamily. From the genera Alphacoronavirus (12 species), Betacoronavirus (12 species), Gammacoronavirus (3 species), Torovirus 1 (species), Bafinivirus (1 species), and others (3 species) were included in the study. Their full-length genomes were extracted from National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/). The rest of the listed species at ICTV were not included in the study because either their full-length genome sequences were not available or due to the absence of their annotation, which was required to assess the distribution of SSRs across coding and non-coding regions. Further, the SARS-CoV-2 sequence from Wuhan, China, was included in the study for comparative purposes. The details of all the sequences used in the study are summarized in supplementary file 1. Though there have been some updates in Coronavirus since the SARS-CoV-2 pandemic, we have used the species as per earlier classification because we aimed to understand what led to the emergence of SARS-CoV-2.

3.2. Microsatellite Extraction

Extraction of microsatellites was done using imperfect microsatellite extractor (IMEx) in “Advanced Mode” with parameters as reported for human immunodeficiency virus (HIV) (21, 22). IMEx can extract microsatellites with repeat motifs of 1 - 6, and hence the present study ranges from mono- to hexa-nucleotide repeat motifs only. The conditions set were type of repeat: Perfect; repeat size: All; minimum repeat number: 6 (Mono), 3 (di), 3 (Tri), 3 (Tetra), 3 (Penta), 3 (Hexa). We also included the study of compound SSR (cSSR), which includes two or more SSRs separated by a distance of dMAX, which was set at 10bp in the study. Since SSR extraction forms the backbone of the study, we cross-checked our extracted SSRs with another software Krait (23), and found the results to be the same as IMEx (Data not shown).

3.3. Statistical Analysis

The extracted raw data were edited on the spreadsheet using data Analysis ToolPak of MS Office Suite v2016. The data for SSR incidence and localizations, along with computation of certain parameters like relative abundance [RA] and relative density [RD], were sorted using Microsoft Excel 2016. Herein, RA: number of microsatellites present/kb of the genome and RD: sequence composed of SSRs/kb of the genome.

3.4. SSR Distribution Across Coding Regions

The IMEx results give the start and end position of the SSRs, whereas the NCBI annotation provides for localization of the genes/coding regions on the genome. The incorporation of SSRs location into the gene is done through incorporation of gene location in SSR file (IGLSF) tool developed by our research group (16).

3.5. Phylogenetic Tree Construction

The construction of phylogenetic tree was performed by aligning the nucleotide sequence with the default parameter of MAFFT v6.861b (24), and the alignment was trimmed by gappyout algorithm of trimAl v1.4.rev6 (25) using the function "build" of ETE3 v3.1.1 (26) as implemented on the GenomeNet (https://www.genome.jp/tools/ete/). We used pmodeltest v1.4 to select evolutionary model that best fits the alignment. The Maximum-Likelihood tree was inferred using RAxML v8.1.20 of the GTR+GAMMA+I model with default specifications (27). The precision of each node of the tree was evaluated using 100 replicates of bootstrap. Graphing of the phylogenetic tree with iTOL (28).

3.6. Heat Map of the Studied Genomes

Pairwise sequence similarity percentages were calculated with an equation SIM%= 100 x (identical position / length of MSA) and unchecked similarity amino acid grouping options using the SIAS server (http://imed.med.ucm.es/Tools/sias.html) from previously aligned multiple sequences. Multiple sequence alignment was performed using MAFFT (v7, online) (https://mafft.cbrc.jp/alignment/server/) with default parameter. The matrix of the similarity percentage was transformed using Morpheus web tool (https://software.broadinstitute.org/morpheus/) to heat maps with Euclidean distance and Pearson correlation metrics (24, 29).

4. Results

4.1. Genome Features

The genome size ranged from around 26,396 bases (CV57) to 31,686 bases (CV48), with an average genome size of 27.6 Kb. The GC% composition ranged from 32.1% (CV32) to 43.2% (CV35) with an average of 39% (Figure 1A). A total of seven species, including CV61 (SARS-CoV-2) have humans as reported hosts. The other hosts included cows, bats, rats, birds, dogs, cats, and fishes (Figure 1A, supplementary files 1 and 2).

Summary of SSR and cSSRs and cSSR% extracted in this study. A, Genome features (Genome size and GC content) and SSR/cSSR incidence across studied genomes. B, Relative abundance and relative density of SSRs and cSSRs. C, cSSR% (percentage of SSRs present as a part of cSSR) across genomes. The incidence and distribution of SSRs follows no pattern across genomes as indicated by the varying peaks of the graph.

4.2. Incidence of SSRs and cSSRs

A total of 3,442 SSRs and 136 complex sequence repeats (cSSRs) were extracted from the studied 33 genomes. CV61 (SARS-CoV-2) had 107 SSRs and six cSSRs. The SSR incidence ranged from 82 (CV09) to 144 (CV60) with corresponding tract sizes of 667 and 1,284 bases, respectively. Five species had an incidence of 96 SSRs (CV03, CV06, CV33, CV37, and CV55), and their tract size varied from 716, 738, 759, 754, and 762 bases, respectively (Figure 1B, supplementary files 1 and 2).

The cSSR incidence ranged from 1 (CV42, CV43, CV53) to 11 (CV32). The species with a single cSSR had very similar SSR incidence and 91, 93, and 92, respectively (Figure 1A). This gives an initial impression that SSRs clustering happens only after a certain level of incidence has been achieved. However, a closer inspection of the data reveals contrasting facts. CV07, CV09, CV11, and CV12 with 89, 82, 91, and 87 SSRs have 5, 4, 5, and 4 cSSRs, respectively. On the other hand, CV05, CV46, and CV48 with 103, 112, and 122 SSRs have just three cSSRs, respectively (Figure 1A, supplementary files 1 and 3).

In order to understand how the clustering of SSRs behaves in the overall genome, we extracted cSSRs by increasing dMAX to 20, 30, 40, and 50. The limit of 50 was used as its maximum allowed value of dMAX in IMEx and also because beyond that the cSSRs as an entity loses its relevance (Supplementary file 3). The cSSR incidence expectedly increased with increasing dMAX, but the enhancement again failed to entice a pattern reaffirming the uniqueness of SSR genome signature.

4.3. Relative Abundance (RA), Relative Density (RD) and cSSR%

Relative abundance (RA) is the number of microsatellites present per Kb of the genome and is a measure of SSR distribution. It was calculated as RA = Incidence of SSRs/Size of genome (Kb). It ranged from 3.04 (CV09:82 SSRs) to 5.4 (CV60:144 SSRs). The average was 3.61, and CV61 (SARS-CoV-2) was pretty close at 3.57 (Figure 1B, supplementary file 1). Relative density (RD) is the sequence composed of SSRs per Kb of the genome and was calculated as RD = Total length covered by SSRs (bp)/Size of genome in Kb. RD for SSRs ranged from 23.5 (CV09) to 48.16 (CV60), with an average of 28.8. RD for SSRs in CV61 (SARS-CoV-2) was 28.7 (Figure 1B, supplementary files 1 and 2). Similarly, the values for RA and RD for cSSR were calculated. The minimum and maximum RA values of cSSR were 0.033 (CV42, CV43) to 0.37 (CV32). The corresponding range for RD for cSSR was 0.79 (CV53) to 8.85 (CV60). The cSSR RA and RD values for CV61 (SARS-CoV-2) were 0.2 and 4.8, respectively (Figure 1B, supplementary files 1 and 3).

Another aspect of SSR and cSSR interrelation is cSSR%. This was calculated as a percentage of SSRs being a part of cSSR. It is summarized in Figure 1C. Overall, 281 SSRs (8.2%) were present as a part of cSSRs. The cSSR% ranged from 2.15 (CV43) to 16.4 (CV32) and an average of 7.9. If all the genomes followed a universal rule, then a higher cSSR incidence would be accompanied by higher cSSR%. In other words, the more the cSSRs, the greater the chance for SSRs coming together as cSSR. This does happen, but not in a linear manner. For instance, CV28 (122 SSRs), CV30 (121 SSRs). and CV33 (96 SSRs) have seven cSSRs each, but their corresponding cSSR% is 11.5, 11.6, and 15.6, respectively. Thus, a lower SSR incidence can also lead to higher cSSR% with the same cSSR incidence (Figure 1C, supplementary files 1 and 3). This assumes significance as higher SSR density will enhance the polymorphic nature of the genome, thereby fastening evolution (12-14). Hence, some genomes are primed to evolve faster than others.

4.4. Repeat Motif Prevalence as per Size and Their Composition

Subsequently, we assessed the prevalence of SSRs, according to their repeat motif size. Mono- to tri-nucleotide repeat motifs accounted for over 98% (3383) of the extracted SSRs; hence we focused on these primarily. The individual contribution of mono-, di-, and tri-nucleotide motifs was 311, 2086, and 986 SSRs, respectively. This pattern was preserved across genomes, with di-nucleotide motifs being the most prevalent, followed by tri- and mono-nucleotide motifs (Supplementary file 1).

We thereon plotted the cumulative SSR contribution of each motif size across genomes wherein, again the trend is followed albeit with a few exceptions. First, CV02, CV08, and CV60 contribute more to the genome SSR composition through tri-nucleotide motifs in spite of higher incidence of di-nucleotide motifs. Secondly, CV32 is the only genome with a higher contribution of SSRs tract size from mono-nucleotide repeat motif than tri-nucleotide motif (Figure 2A). These variations constitute the essence of genome SSR signature.

However, the contribution of repeat motifs to genome function and evolution is dependent on not only its repeat motif size but also composition (30-33). We also looked at the motif composition of extracted SSRs. In the di-nucleotide repeats, TG/GT was most represented with an average of 19 per genome. This was followed by CA/AC with an average incidence of 17 (Figure 2B, supplementary files 1 and 2). In tri-nucleotide SSRs, ACA/TGT was the most represented motif, followed by CAA/GTT, whereas in mono-nucleotide SSRs, T was the most observed nucleotide, followed by A (Figure 2B, supplementary files 1 and 2). This can partly be attributed to the fact that C/G mono-nucleotide repeats are more unstable that A/T repeats (34). Furthermore, their association with transcriptional slippage, codon bias and various diseases in other genomes makes it interesting for viruses as well (35-37).

Nucleotide composition of the incident SSRs. A, SSRs (Mono- to Tri-nucleotide repeat) coverage in genomes. Note the maximum contribution by di-nucleotide motifs across genomes with few exceptions (CV02, CV08, CV60). B, Prevalent motif constituents across mono-, di- and tri-nucleotide motifs. C, SSRs distribution across coding and non-coding regions. D, Distribution of genomes on the basis of mono-nucleotide contribution from A/T region (here shown as AT%). The highest incidence of di-nucleotide repeats makes genome susceptible to variations while motif composition is inclined towards A/T irrespective of size probably due to genome composition.

4.5. Microsatellites in Coding Region

A total of 3,236 SSRs (94%) were localized to the coding region of which 1,806 were present in the polyprotein that encodes for RNA dependent RNA polymerase (RDRP), which was distantly followed by spike protein/glycoprotein with 174/159 SSRs (Figure 2C, supplementary file 1). In order to differentiate between genes of the studied genomes in terms of SSR incidence, we looked into the SSR density (number of SSRs per kb) of individual genes for the genomes. The highest and lowest SSR density in a gene-specific manner has been shown for all these studied genomes in Table 1. The details are represented in supplementary file 4. Non-structural protein has the highest SSR density in all viral genomes with bird as hosts. For those viruses which had humans as hosts, no such pattern was observed. However, spike or surface glycoprotein pivotal for entry into the host cell had the least SSR densities for the incident genomes.

Table 1. SSR Density Range Across Genes of the Studied Genomes
S NGenome IDGene with Highest SSR DensitySSR DensityGene with Lowest SSR DensitySSR Density
1CV32Small membrane protein12.04819Nucleocapsid phosphoprotein1.508296
2CV28ORF1ab polyprotein4.134367
3CV30Hemagglutinin-esterase6.27451Membrane protein1.443001
4CV36Hypothetical protein6.582885Spike glycoprotein2.875817
5CV02Non-structural protein 3a12.65823Membrane protein2.487562
6CV08Membrane protein4.405286Protein 31.474926
7CV04Putative 3a protein13.88889Matrix protein2.534854
8CV61Orf1017.09402Membrane protein (M)1.494768
9CV44Small membrane protein12.04819Membrane glycoprotein1.515152
10CV05Non-structural protein 78.438819Spike protein2.180431
11CV13Hypothetical protein17.3913Spike glycoprotein3.542958
12CV57Nonstructural protein14.49275Membrane protein1.529052
13CV03N protein5.30504Membrane protein (M)1.262626
14CV06Non-structural protein 3a13.88889Non-structural protein 3b2.721088
15CV33Envelop protein (E)3.745318Membrane protein (M)1.455604
16CV55Nonstructural protein6.116208Nucleocapsid phosphoprotein1.888574
17CV53Nonstructural protein20.83333Nucleocapsid phosphoprotein0.952381
18CV11Envelope protein8.888889Nucleocapsid protein0.788022
19CV07Envelope protein12.82051Surface glycoprotein1.703578
20CV14Envelope protein4.329004Putative ORF31.481481
21CV09Spike protein3.149225Nucleocapsid protein0.854701
22CV58Hemagglutinin esterase5.555556Nucleocapsid phosphoprotein3.968254
23CV60Putative nucleocapsid protein8.230453Putative membrane protein1.461988
24CV48Orf 98.714597ORF 5c1.893939
25CV465b protein12.04819Membrane protein2.949853
26CV12Nucleocapsid protein5.279035Spike protein2.16763
27CV35Small membrane protein20.08032Membrane glycoprotein1.508296
28CV34Membrane protein5.822416Hemagglutinin-esterase0.757576
29CV47ORF 5b8.032129N protein2.439024
30CV38Protein (E)8.658009Orf1ab polyprotein (Pp1ab)2.827388
31CV37E protein8.658009Nonstructural polyprotein Pp1ab2.827388

4.6. Mono-nucleotide Repeat Motif Exclusivity for Hosts

CV61 (SARS-CoV-2) had identical SSR incidence of 107 with CV04 (Feline coronavirus type II). Also, there was no consensus if we compared the SSR incidence for viruses with humans as hosts. Of the six studied species with humans as hosts, three had a higher SSR incidence (CV08:109, CV30:121, CV32:140), while three had lower incidence (CV07:89, CV37:96, CV42:91). Thus, we can say that SSR incidence is not directly associated with host per se. Similarly, the corresponding cSSR incidence, which is representative of SSR clustering also did not reveal any pattern in the six species and was highly divergent from 1 to 11. CV61 (SARS-CoV-2) had six cSSRs (Supplementary file 1).

4.7. Phylogenetic and Similarity Analysis

The phylogenetic analysis of the genomes was subsequently performed to understand the evolutionary aspects. The phylogenetic tree has been represented along with genome features and extracted SSR data in Figure 3. The phylogenetic path in the innermost layer is marked by blue/yellow circular representation of mono-nucleotide distribution. Complete yellow circles represent all mono-nucleotide repeats in the A/T region. The species which had mono-nucleotide repeats in G/C region are represented by blue color in the circles. Such genomes are present in different genera but clustered to each other. A similar distribution is observed for the species with known human hosts.

Phylogenetic analysis of the studied genomes along with genome features like GC content, size, SSR and cSSR incidence. The differences in SSR features within the members of the same genera is suggestive of microsatellites dynamics working at a genome specific level.

Whether or not the phylogenetic tree is a true representation of sequence similarity was accessed through constructing heat map, as shown in Figure 4. Sequences together in the phylogenetic tree do reflect a higher degree of similarity in the heat map. For instance, CV37, CV38, CV42, and CV43 of Betacoronavirus exhibit similarity and are placed adjacently in the heat map. Other sequences also follow the same pattern reaffirming the evolution path of the phylogenetic tree.

Heat map of the studied genomes. The similarity in sequences has been highlighted in red. The heat map corroborates the phylogenetic tree with genomes exhibiting similarity in heat map being placed together on the tree.

5. Discussion

The difference in SSR incidence can be attributed to two aspects. First, variation in copy number of repeat motifs because of more copies of a motif that is present at site in one genome compared to another. Secondly, the size of repeat motifs since if one genome has more tri-nucleotide motifs compared to other di-nucleotide motifs, the former will have a higher tract size for the same number of SSRs. Further, the failure of cSSRs incidence to conform to a rule is a pattern in itself for viral genomes and has been reported earlier as well (16, 17, 19, 38). Thus, each genome carries a unique SSR signature which assumes significance owing to its influence on gene function and genome evolution. If we can understand the underlying message for this SSR signature, predicting and understanding viruses will be easier.

Generally speaking, a higher value of RA will be accompanied by an increase in RD as is clearly observed in Figure 1B. These figures are an average representation of the SSRs of individual genomes. Though their values are in tandem with each other for a genome, they do not necessarily corroborate with SSR/cSSR incidence values. A case in point, CV32, has the highest RA value of cSSR of 0.37 with 11cSSRs, whereas CV60 has maximum RD of 8.85 with just nine cSSRs (Supplementary file 1). This can be explained by two aspects. First, CV32 has a larger genome size of 29,926 bases compared to 26,660 bases of CV60, thus the higher incidence and RA value. Secondly, CV60 has larger cSSR tract size of 236 bases (Nine cSSRs) in contrast to 203 bases (11 cSSRs) for CV32. This, when aided by a smaller genome size, gives CV60 a higher RD value. We thereon ascertained as to how CV60 encompasses more genome as cSSR with lesser incidence. Interestingly, the cSSR composition of the two genomes had one unique difference. Although CV60 cSSRs had multiple tetra- and penta-nucleotide SSR motifs, CV32 had primarily di- and tri-nucleotide SSR motifs as part of cSSR (Supplementary file 3). Thus, CV60 had a higher cSSR tract size with lesser incidence. The variations in RA and RD values indicate that genome SSR signature is unique in its incidence and distribution its composition. The highest incidence of di-nucleotide motifs makes these genomes hot spots for recombination, while tri-nucleotide motifs make them prone to protein dynamics. In the mono-nucleotide repeats, a higher prevalence of A/T repeats can be attributed to two aspects. First, a higher genome content (Average GC% being 39%), and secondly, owing to the instability of G/C repeats, there is negative selection against them. Another study has reported the incidence of AT-rich repeats in Coronavirus genomes and suggested the presence of genic SSRs in the mutation-rich regions of the genome (39). However, when only SARS-CoV-2 genomes were studied, the SSRs were found to be more or less conserved, indicating their role in genome stability (40).

The distribution of SSRs across coding and non-coding regions often exhibits a bias toward the coding region (16, 17, 19, 41). This is primarily because the genome of viruses is predominantly coding. However, the analysis is always required to give an insight into which part of the coding genome is more prone to mutation, selection, and eventually evolution. This may be accompanied by enhanced pathogenesis and virulence. The fact that two most densely populated genes in terms of SSRs (RDRP and Spike protein) are quintessential for virus infection affirms the ongoing viral evolution aided by SSRs. Also, spike protein having fewer SSRs suggests a restrictive measure on host evolution. Further, the lack of any pattern in gene SSR density conforms to unique genome SSR signature.

Previously, we have reported a prevalence of G/C mono-nucleotide repeat motifs (90%) in Mycobacteriophages with broad host range (16). However, the trend reverses when we analyze viruses with humans as hosts. Herein, an exclusive contribution of mono-nucleotide repeats from the A/T region has been observed in human or related species as hosts in Polyomaviruses (18). The distribution of mono-nucleotide repeats across A/T and G/C regions of the genomes studied revealed interesting results. Twenty species, including CV61 (SARS-CoV-2) exhibit mono-nucleotide repeats exclusively in the A/T region (Figure 2D, supplementary file 1). This means even a single mono-nucleotide repeat is not localized in these genomes in the G/C region of the genome. Four out of the six species with known human hosts CV07, CV08, CV30, and CV32 also follow the pattern. The two deviations to this in the study (CV37, CV42) are suggestive of multiple players in host determination, which is understandable (Figure 5). The presence of these A/T repeats in Coronavirus genomes should not be confused with poly A tailing associated with these viruses. This is because poly A addition is characterized by the presence of hexamer sequence in the genome (42). Therefore, we hypothesize that this bias in the incidence of mono-nucleotide repeats can serve as a marker for predicting the course of viral host divergence. The present study comprehensively analyzed the diversity of microsatellites across Coronavirus genomes with the perspective of SARS-CoV-2 and constant monitoring of how the accruing mutations in SARS-CoV-2 impact the SSR profile will help us evaluate the contribution of microsatellites in viral evolution.

Correlation between mono-nucleotide A/T repeat incidence and host. Studied species of Coronaviridae arranged in decreasing order of mono-nucleotide repeats (left to right, Blue representing 100% or mono-SSRs exclusive to the A/T region). The corresponding hosts are also mentioned. Since a direct relation does not exist, multiple factors deciding viral host is expected.

5.1. Conclusions

Each genome has its SSR signature, which attributes variation or stability in terms of evolution. The observed results in Coronaviruses suggest similarity as well as differences with other viruses. While no pattern of incidence and localization of SSRs in the coding region have been predominantly observed in other viral genera, the observations were deviant from others when it came to correlation with host divergence. Thus, the unique microsatellite signature of viral genomes can be a predictive and understanding tool for viral hosts’ divergence and evolution.

Acknowledgements

References

  • 1.

    Weiss SR, Navas-Martin S. Coronavirus pathogenesis and the emerging pathogen severe acute respiratory syndrome coronavirus. Microbiol Mol Biol Rev. 2005;69(4):635-64. doi: 10.1128/MMBR.69.4.635-664.2005. [PubMed: 16339739]. [PubMed Central: PMC1306801].

  • 2.

    Lai MM, Cavanagh D. The Molecular Biology of Coronaviruses. Adv Virus Res. 1997;48:1-100. doi: 10.1016/s0065-3527(08)60286-9. [PubMed: 9233431]. [PubMed Central: PMC7130985].

  • 3.

    Su S, Wong G, Shi W, Liu J, Lai ACK, Zhou J, et al. Epidemiology, Genetic Recombination, and Pathogenesis of Coronaviruses. Trends Microbiol. 2016;24(6):490-502. doi: 10.1016/j.tim.2016.03.003. [PubMed: 27012512]. [PubMed Central: PMC7125511].

  • 4.

    Cavanagh D. Coronavirus avian infectious bronchitis virus. Vet Res. 2007;38(2):281-97. doi: 10.1051/vetres:2006055. [PubMed: 17296157].

  • 5.

    Ismail MM, Tang AY, Saif YM. Pathogenicity of turkey coronavirus in turkeys and chickens. Avian Dis. 2003;47(3):515-22. doi: 10.1637/5917. [PubMed: 14562877].

  • 6.

    Wevers BA, van der Hoek L. Recently discovered human coronaviruses. Clin Lab Med. 2009;29(4):715-24. doi: 10.1016/j.cll.2009.07.007. [PubMed: 19892230]. [PubMed Central: PMC7131583].

  • 7.

    Peiris JS, Guan Y, Yuen KY. Severe acute respiratory syndrome. Nat Med. 2004;10(12 Suppl):S88-97. doi: 10.1038/nm1143. [PubMed: 15577937]. [PubMed Central: PMC7096017].

  • 8.

    Zaki AM, van Boheemen S, Bestebroer TM, Osterhaus AD, Fouchier RA. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N Engl J Med. 2012;367(19):1814-20. doi: 10.1056/NEJMoa1211721. [PubMed: 23075143].

  • 9.

    Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382(8):727-33. doi: 10.1056/NEJMoa2001017. [PubMed: 31978945]. [PubMed Central: PMC7092803].

  • 10.

    Chan JF, Yuan S, Kok K, To KK, Chu H, Yang J, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395(10223):514-23. doi: 10.1016/s0140-6736(20)30154-9.

  • 11.

    Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. doi: 10.1016/s0140-6736(20)30183-5.

  • 12.

    Bhargava A, Fuentes FF. Mutational dynamics of microsatellites. Mol Biotechnol. 2010;44(3):250-66. doi: 10.1007/s12033-009-9230-4. [PubMed: 20012711].

  • 13.

    Madesis P, Ganopoulos I, Tsaftaris A. Microsatellites: evolution and contribution. Methods Mol Biol. 2013;1006:1-13. doi: 10.1007/978-1-62703-389-3_1. [PubMed: 23546780].

  • 14.

    Haasl RJ, Payseur BA. Microsatellites as targets of natural selection. Mol Biol Evol. 2013;30(2):285-98. doi: 10.1093/molbev/mss247. [PubMed: 23104080]. [PubMed Central: PMC3548306].

  • 15.

    Kwong M, Pemberton TJ. Sequence differences at orthologous microsatellites inflate estimates of human-chimpanzee differentiation. BMC Genomics. 2014;15:990. doi: 10.1186/1471-2164-15-990. [PubMed: 25407736]. [PubMed Central: PMC4253012].

  • 16.

    Alam CM, Iqbal A, Sharma A, Schulman AH, Ali S. Microsatellite Diversity, Complexity, and Host Range of Mycobacteriophage Genomes of the Siphoviridae Family. Front Genet. 2019;10:207. doi: 10.3389/fgene.2019.00207. [PubMed: 30923537]. [PubMed Central: PMC6426759].

  • 17.

    Alam CM, Singh AK, Sharfuddin C, Ali S. Genome-wide scan for analysis of simple and imperfect microsatellites in diverse carlaviruses. Infect Genet Evol. 2014;21:287-94. doi: 10.1016/j.meegid.2013.11.018. [PubMed: 24291012].

  • 18.

    Laskar R, Jilani MG, Ali S. Implications of genome simple sequence repeats signature in 98 Polyomaviridae species. 3 Biotech. 2021;11(1):35. doi: 10.1007/s13205-020-02583-w. [PubMed: 33432281]. [PubMed Central: PMC7787124].

  • 19.

    Singh AK, Alam CM, Sharfuddin C, Ali S. Frequency and distribution of simple and compound microsatellites in forty-eight Human papillomavirus (HPV) genomes. Infect Genet Evol. 2014;24:92-8. doi: 10.1016/j.meegid.2014.03.010. [PubMed: 24662441].

  • 20.

    Li YC, Korol AB, Fahima T, Beiles A, Nevo E. Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Mol Ecol. 2002;11(12):2453-65. doi: 10.1046/j.1365-294x.2002.01643.x. [PubMed: 12453231].

  • 21.

    Mudunuri SB, Nagarajaram HA. IMEx: Imperfect Microsatellite Extractor. Bioinformatics. 2007;23(10):1181-7. doi: 10.1093/bioinformatics/btm097. [PubMed: 17379689].

  • 22.

    Chen M, Tan Z, Zeng G, Zeng Z. Differential distribution of compound microsatellites in various Human Immunodeficiency Virus Type 1 complete genomes. Infect Genet Evol. 2012;12(7):1452-7. doi: 10.1016/j.meegid.2012.05.006. [PubMed: 22659082].

  • 23.

    Du L, Zhang C, Liu Q, Zhang X, Yue B, Hancock J. Krait: an ultrafast tool for genome-wide survey of microsatellites and primer design. Bioinformatics. 2018;34(4):681-3. doi: 10.1093/bioinformatics/btx665. [PubMed: 29048524].

  • 24.

    Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772-80. doi: 10.1093/molbev/mst010. [PubMed: 23329690]. [PubMed Central: PMC3603318].

  • 25.

    Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25(15):1972-3. doi: 10.1093/bioinformatics/btp348. [PubMed: 19505945]. [PubMed Central: PMC2712344].

  • 26.

    Huerta-Cepas J, Serra F, Bork P. ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data. Mol Biol Evol. 2016;33(6):1635-8. doi: 10.1093/molbev/msw046. [PubMed: 26921390]. [PubMed Central: PMC4868116].

  • 27.

    Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312-3. doi: 10.1093/bioinformatics/btu033. [PubMed: 24451623]. [PubMed Central: PMC3998144].

  • 28.

    Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47(W1):W256-9. doi: 10.1093/nar/gkz239. [PubMed: 30931475]. [PubMed Central: PMC6602468].

  • 29.

    Starruss J, de Back W, Brusch L, Deutsch A. Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology. Bioinformatics. 2014;30(9):1331-2. doi: 10.1093/bioinformatics/btt772. [PubMed: 24443380]. [PubMed Central: PMC3998129].

  • 30.

    Gu T, Tan S, Gou X, Araki H, Tian D. Avoidance of long mononucleotide repeats in codon pair usage. Genetics. 2010;186(3):1077-84. doi: 10.1534/genetics.110.121137. [PubMed: 20805553]. [PubMed Central: PMC2975279].

  • 31.

    Qi WH, Yan CC, Li WJ, Jiang XM, Li GZ, Zhang XY, et al. Distinct patterns of simple sequence repeats and GC distribution in intragenic and intergenic regions of primate genomes. Aging (Albany NY). 2016;8(11):2635-54. doi: 10.18632/aging.101025. [PubMed: 27644032]. [PubMed Central: PMC5191860].

  • 32.

    Coenye T, Vandamme P. Characterization of mononucleotide repeats in sequenced prokaryotic genomes. DNA Res. 2005;12(4):221-33. doi: 10.1093/dnares/dsi009. [PubMed: 16769685].

  • 33.

    Zou C, Lu C, Zhang Y, Song G. Distribution and characterization of simple sequence repeats in Gossypium raimondii genome. Bioinformation. 2012;8(17):801-6. doi: 10.6026/97320630008801. [PubMed: 23139588]. [PubMed Central: PMC3488841].

  • 34.

    Harfe BD, Jinks-Robertson S. Sequence composition and context effects on the generation and repair of frameshift intermediates in mononucleotide runs in Saccharomyces cerevisiae. Genetics. 2000;156(2):571-8. doi: 10.1093/genetics/156.2.571. [PubMed: 11014807]. [PubMed Central: PMC1461279].

  • 35.

    Tats A, Tenson T, Remm M. Preferred and avoided codon pairs in three domains of life. BMC Genomics. 2008;9:463. doi: 10.1186/1471-2164-9-463. [PubMed: 18842120]. [PubMed Central: PMC2585594].

  • 36.

    Gatchel JR, Zoghbi HY. Diseases of unstable repeat expansion: mechanisms and common principles. Nat Rev Genet. 2005;6(10):743-55. doi: 10.1038/nrg1691. [PubMed: 16205714].

  • 37.

    Baranov PV, Hammer AW, Zhou J, Gesteland RF, Atkins JF. Transcriptional slippage in bacteria: distribution in sequenced genomes and utilization in IS element gene expression. Genome Biol. 2005;6(3):R25. doi: 10.1186/gb-2005-6-3-r25. [PubMed: 15774026]. [PubMed Central: PMC1088944].

  • 38.

    George B, Mashhood Alam CH, Jain SK, Sharfuddin CH, Chakraborty S. Differential distribution and occurrence of simple sequence repeats in diverse geminivirus genomes. Virus Genes. 2012;45(3):556-66. doi: 10.1007/s11262-012-0802-1. [PubMed: 22903752].

  • 39.

    Satyam R, Jha NK, Kar R, Jha SK, Sharma A, Kumar D, et al. Deciphering the SSR incidences across viral members of Coronaviridae family. Chem Biol Interact. 2020;331:109226. doi: 10.1016/j.cbi.2020.109226. [PubMed: 32971122]. [PubMed Central: PMC7505113].

  • 40.

    Siddiqe R, Ghosh A. Genome-wide in silico identification and characterization of Simple Sequence Repeats in diverse completed SARS-CoV-2 genomes. Gene Rep. 2021;23:101020. doi: 10.1016/j.genrep.2021.101020. [PubMed: 33521382]. [PubMed Central: PMC7835092].

  • 41.

    Alam CM, Singh AK, Sharfuddin C, Ali S. In-silico analysis of simple and imperfect microsatellites in diverse tobamovirus genomes. Gene. 2013;530(2):193-200. doi: 10.1016/j.gene.2013.08.046. [PubMed: 23981776].

  • 42.

    Peng YH, Lin CH, Lin CN, Lo CY, Tsai TL, Wu HY. Characterization of the Role of Hexamer AGUAAA and Poly(A) Tail in Coronavirus Polyadenylation. PLoS One. 2016;11(10). e0165077. doi: 10.1371/journal.pone.0165077. [PubMed: 27760233]. [PubMed Central: PMC5070815].

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