Comparative Genomic Analysis MERS CoV Isolated from Humans and Camels with Special Reference to Virus Encoded Helicase

Abstract

Middle East Respiratory Syndrome Coronavirus (MERS CoV) is a new emerging viral disease characterized by high fatality rate. Understanding MERS CoV genetic aspects and codon usage pattern is important to understand MERS CoV survival, adaptation, evolution, resistance to innate immunity, and help in finding the unique aspects of the virus for future drug discovery experiments. In this work, we provide comprehensive analysis of 238 MERS CoV full genomes comprised of human (hMERS) and camel (cMERS) isolates of the virus. MERS CoV genome shaping seems to be under compositional and mutational bias, as revealed by preference of A/T over G/C nucleotides, preferred codons, nucleotides at the third position of codons (NT3s), relative synonymous codon usage, hydropathicity (Gravy), and aromaticity (Aromo) indices. Effective number of codons (ENc) analysis reveals a general slight codon usage bias. Codon adaptation index reveals incomplete adaptation to host environment. MERS CoV showed high ability to resist the innate immune response by showing lower CpG frequencies. Neutrality evolution analysis revealed a more significant role of mutation pressure in cMERS over hMERS. Correspondence analysis revealed that MERS CoV genomes have three genetic clusters, which were distinct in their codon usage, host, and geographic distribution. Additionally, virtual screening and binding experiments were able to identify three new virus-encoded helicase binding compounds. These compounds can be used for further optimization of inhibitors.

Middle East Respiratory Syndrome Coronavirus (MERS CoV) is a new emerging virus infection causing lethal pneumonia and high case fatality.^1–3) The infection with MERS CoV has now extended to become a global concern.^4–7) Recently, a serious outbreak of infection was recorded in South Korea and China.^8,9) MERS CoV is a single stranded RNA virus with a relatively large genome of ca. 30 kb.¹⁰⁾ The virus was firstly isolated from humans. Currently there is still much debate in confirming the potential hosts and mode of transmission of the virus. Camels are incriminated as a potential host of the virus, and a considerable number of genomes of MERS CoV from camels were isolated and sequenced.^11–13) Therefore, there are two main sources of MERS CoV genomic data: humans and camels.

The genetic code is comprised of 61 different codons corresponding to the standard 20 amino acids. Two of these amino acids are encoded by one codon (tyrosine (Tyr) and methionine (Met), while the other amino acids are encoded by several codons—up to 6 codons for 1 amino acid. Therefore, alternative codons present within 1 amino acid production codes are called synonymous codons.¹⁴⁾ Codon bias infers the preference of one synonymous codon over the other, and may be over-represented to indicate overuse of a certain codon or rare codons that are rarely used for a certain gene. There are two main forces affecting the composition of a certain genome as well as codon usage patterns: the compositional constraints under mutational pressure and translational-efficiency-driven selection.¹⁵⁾ These two forces are the major driving events for mutations and changes in genomic compositions.^16,17)

The MERS CoV genome is composed of ten open reading frames. At the 5′ end of the genome, there are one large open reading frame (ORF), ORF1ab, which are translated to polyprotein 1ab. The polyprotein 1ab is encoding the viral replicase and sites of interactions with ribosomes. The other 8 ORFs translate to give structural and nonstructural proteins. Despite the public health importance of MERS CoV, research to date has focused on isolation of the virus and tracing its modes of transmission. However, comprehensive analysis of MERS CoV genomic aspects, codon usage bias, and preferred and rare codons are still not determined. Studying codon bias and codon usage patterns could have theoretical and practical applications in understanding the nature of the newly emerged MERS CoV. As a virus, MERS CoV could use the host machinery to produce its own proteins; therefore, it could theoretically have similar codon usage patterns similar to its natural hosts. However, we focus in this study on microbial specific aspects in MERS CoV codon usage to escape the host innate immunity. In this work, the codon usage of the main MERS CoV polyprotein 1ab coding sequence will be investigated. To the best of our knowledge, this is the first study investigating synonymous codons usage and codon usage bias in MERS CoV genome. Furthermore, we compared the codon usage patterns in MERS CoV isolated from either humans (hMERS) or camels (cMERS). Results indicated common and different genetic and codon usage patterns among hMERS and cMERS. Our lab is interested in drug discovery against virus-encoded proteins, so in this study, we targeted non-structural protein 13 (NSP13), which encodes for the MERS CoV helicase. We used virtual screening and fluorescence titration assay to investigate a library of 50000 compounds. For this purpose, we built a structure model for MERS CoV helicase, cloned, expressed, and purified the enzyme. The top candidate hits from the glide docking experiment we then directed to fluorescence titration assay to find potential binding compounds.

MATERIALS AND METHODS

Genome Sequences, Database Collection and Software Settings

A database of MERS CoV genomes was constructed by searching the GenBank website (Supplementary Table 1). Only full genomes were collected; deficient or incomplete sequences were excluded from our database. Thus, a total number of 238 MERS CoV full genomes were obtained. Nucleotides composition, GC% and AT% were calculated by CLC genomics workbench. CodonW 1.4.2 was used to analyse the codon usage patterns and multivariate statistical analysis. Correlation analysis was performed by GraphPad prism software.

Codons Usage Profiles of MERS CoV Genomes

All genomes were analysed for codon bias through estimation of the following parameters: the frequencies of occurrence of the four nucleotides (A%, G%, C% and T%); the occurrence of GC at the first (GC1) or third position (GC3); and the frequency of occurrence of each nucleotide at the third position of synonymous codons (A3s, T3s, G3s, and C3s).

Relative Synonymous Codons Usage (RSCU)

RSCU stands for the ratio of codons’ observed frequency to their predicted frequency, given that all codons for a specific amino acid are used equally. Codons showing RSCU value of 1 means no bias or the codon usage frequency is similar to the expected value, while codons with RSCU values >1 or <1 are showing positive or negative codon bias, respectively. RSCU is calculated by using the following equation:   

(1)

Where X_ij is the observed number of codon for a certain amino acid and ni is the total number of synonymous codons for the amino acid.

Relative Dinucleotide Frequencies

The occurrence of dinucleotides in a genome can be used as a measure of codon bias by comparing the values of actual to expected dinucleotide frequency. Relative dinucleotide frequency was calculated by using the following formula:   

(2)

Where f(XY) is the observed frequency of dinucleotides and f(X)f(Y) are the frequencies of single nucleotides.

Overpresentation and underpresentation of dinucleotides has values of >1.23 and <0.78, respectively.¹⁸⁾ These values constitute a relative abundance of dinucleotides to their random presentation.

Codon Adaptation Index (CAI)

CAI is a measure of codon usage based on RSCU values relative to the values of a set of a known highly expressed genes. CAI was calculated by using CAI calculator (http://www.umbc.edu/codon/cai/cais.php) by using Homo sapiens as a selected expression system. A set of human genes was selected from the human liver cytochrome gene family.

Effective Number of Codons (ENc)

ENc is a measure of codon bias in a gene and ranges from 20–61.¹⁹⁾ Lower values of ENc indicate severe codon bias in which a lower number of codons is used for each amino acid. In contrast, higher ENc values indicate low or lack of codon bias. As a general rule, ENc value of 35 or lower indicates strong codon bias.²⁰⁾

Estimation of Codon Usage Bias Mediated by Mutational Pressure

ENc plot was used to figure out the effect of mutational pressure on codon bias.²⁰⁾ In ENc plot, ENc values are plotted against GC3. Mutation pressure is associated with ENc values distributed around a standard curve of ENc–GC3 relation. Deviation from the standard curve indicates that other factors are driving the codon bias—e.g., by natural selection.

Codon Usage Bias Mediated by Natural Selection

The codon usage bias mediated by natural selection was investigated by using neutrality plot, codon adaptation index, aromaticity (AROMO) and general average of hydropathicity (GRAVY).²¹⁾

Multivariate Correspondence Analysis (COA)

The codon usage of MERS CoV genomes is analyzed by multivariate correspondence statistical methods. RSCU values were analyzed by CodonW to compare the intragenomic variations. In COA analysis, each gene is geometrically presented in a 59-dimensional vector representing each codon through 59 orthogonal axes.

Correlation Analysis

Pairwise correlation statistics for codon usage indices were performed by GraphPad prism software (GraphPad Inc., U.S.A.).

Building of MERS CoV Helicase Structure Model

The protein sequence of MERS CoV nsp13 was retrieved from the gene bank (accession no. YP_009047224.1). Requests for structure models were submitted to the PHYRE fold recognition engine.²²⁾ The top structure based on sequence alignment was obtained.

Molecular Docking and Screening Compounds Library

The obtained structure of MERS CoV helicase was minimized and optimized for docking run by using Schrodinger glide protein preparation wizard. A library of 50000 compounds was prepared from helicase, antimicrobials, and small molecule protein binding agents were prepared from Zinc database, Selleck and Chembridge commercial compounds suppliers. The compounds were optimized for docking by using Ligprep software. Docking was followed by glide standard docking wizard.

Cloning of MERS CoV nsp13

MERS CoV polyprotein (accession no. JX869059.2) was used to extract the coding sequence of MERS CoV encoded helicase. The coding region for MERS CoV helicase was obtained by gene synthesis (nucleotides no. 16208 to 18001). The coding region was then subcloned into pET28a(+) vector. The obtained plasmid was used to transform competent Escherichia coli JM109 cells.

Expression and Purification of MERS CoV Helicase

E. coli cultures were grown in 2 L of LB medium containing kanamycin (50 µg/mL) and shaken until OD600=0.5. Induction of expression was by Isopropyl-D-thiogalactoside (IPTG) to a final concentration of 0.4 mM, and shaking was continued for 12 h at 15°C. Extraction was in buffer A composed of 25 mM Tris–HCl pH 7.2 containing 150 mM NaCl. Purification was by 2-step chromatography. At first, talon metal affinity resin was used to isolate our histidine (His)-tagged protein. A second purification step was using gel filtration chromatography on sephacryl 200HR column. The purity of bands was confirmed by conventional protein electrophoresis.

Compounds Binding Assay

Fluorescence quenching assay was done to measure the compounds binding potency. The top 4 compounds with lowest binding energy from docking studies were purchased and dissolved in dimethyl sulfoxide (DMSO) at 10 mM concentration. Twenty micromolar of protein solution were prepared in the extraction buffer. Fluorescence titration experiment was run at 30°C. The compounds solution was added gradually to the enzyme solution and the fluorescence intensity was recorded. Intrinsic fluorescence was measured by excitation at 295 nm, and emission spectra were traced from 300–450 nm. The fluorescence intensities were measured in the presence and absence of compounds. The binding constant was measured according to the following equation:   

(3)

where F0 and F are the measured fluorescence intensities in the absence and presence of compounds, n is the number of binding sites and Q is the concentration of compounds. The plot of lg((F0−F)/F) versus lg[Q] yields lg K as the intercept and n as the slope.

RESULTS AND DISCUSSION

MERS CoV Genomes Nucleotides Content

A total of 238 complete genomes of MERS CoV were used to estimate their nucleotide composition (149 from humans and 89 from camels). Comparative analysis of nucleotides composition reveals little difference between hMERS and cMERS (Table 1). The most abundant nucleotide was T (32.6%), while C was the lowest (20.2%). This indicates that MERS CoV is using more AT nucleotides in its genome with AT% of 58.81%. Furthermore, we investigated the nucleotide position more critically by comparison of nucleotides at the third position (NT3s) in a codon (A3s, T3s, G3s and C3s). There was a difference in NTs among hMERS and cMERS. T3s were the highest (0.39) in hMERS CoV. In contrast, A3s was the highest in cMERS CoV (0.35), followed by T3s (0.34). Furthermore, the rank of NT3s frequencies were T3s>A3s>G3s=C3s and A3s>T3s>G3s>C3s in hMERS and cMERS, respectively. cMERS showed GC3s frequency 3% higher than hMERS. This implies more relative similarity of cMERS to human codons pool, which is mostly has higher GC% over AT%.

Table 1. Analysis of Nucleotides Contents in 238 MERS CoV Genomes

		cMERS	hMERS
Frequencies of nucleotides	Nucleotide	Camel	Human
	Adenine (A)	0.26	0.26
	Cytosine (C)	0.20	0.20
	Guanine (G)	0.21	0.21
	Thymine (T)	0.33	0.33
	C+G	0.41	0.41
	A+T	0.58	0.58
NT3s	T3s	0.34	0.39
	C3s	0.24	0.25
	A3s	0.35	0.33
	G3s	0.30	0.25
Codon usage indices	CAI	0.505	0.503
	Nc	55.5	56.39
	GC3s	0.4	0.43
	GC1	0.43	0.43
	L_sym	8949	8873
	L_aa	9389	9370
	Gravy	0.32	0.56
	Aromo	0.12	0.11

Frequencies of nucleotides, nucleotides at the third position of codons (NT3s) and codon usage indices were calculated by codonW software.

Codon Adaptation Index (CAI)

CAI is specifically important for viruses as MERS CoV. Viruses do not encode their specific tRNA and depend on host machinery for their replication. Therefore, virus codons could adapt the host codon requirements. The results indicate that cMERS and hMERS are not fully adapted to human codons requirements, as CAI is about 0.5 (Table 1). Due to the lack of information about the highly expressed genes in camels, we were not able to discuss CAI of MERS CoV isolates in camels.

Relative Synonymous Codon Usage (RSCU) Analysis

RSCU analysis confirms that MERS CoV is using more AT in its genome as well as in the preferred codons (Table 2). RSCU values of codons were divided into three categories: i) negatively biased or underrepresented codons with RSCU values lower than 0.6; ii) unbiased or represented codons with RSCU values between 0.6 and 1.6; and iii) overrepresented or positively biased codons with RSCU values above 1.6. The results indicated common and different preferred codons in cMERS and hMERS. Five amino acids (20%), valine, threonine, alanine, arginine and lysine, showed different preferred codons (Table 2). The majority of underrepresented codons (RSCU value <0.6) in cMERS CoV were containing G3s/C3s, while the majority of represented codons were ending with A/T. With regard to overrepresented codons, cMERS were containing 2 overbiased codons for arginine (AGA and AGG), while hMERS were containing only 1 codon (AGG). The codon positive bias observed in MERS CoV genomes coincides with similar high representation of AGA and AGG in the pandemic influenza virus H1N1, and H3N2.²¹⁾ This result indicates that the usage of preferred codons is mostly affected by compositional constraints and mutational pressure. More than 76% of the preferred codons (29–30 codons) were containing A3s/T3s, indicating the general preference of MERS CoV for using A/T in genomic composition as well as in the third position of the preferred codons.

Table 2. Average RSCU Values from 149 hMERS and 89 cMERS Genomes

The preferred codon for every amino acid is displayed in bold. Diagonal line is placed in cells with negative codon bias. Highlighted cells are over biased codon.

Dinucleotide Frequencies

The pattern of codon usage can be determined by the influence on dinucleotide frequency in the genome. Therefore, we estimated the relative abundance of 16 dinucleotides in the MERS CoV genome (Fig. 1). The results indicated that MERS CoV is similar to other RNA viruses, as it shows low CpG dinucleotide frequency.²³⁾ In order to check the effect of low CpG frequency on codon bias, the RSCU values of the 8 codons containing CpG were checked. Interestingly, there was a difference in the number of underrepresented codons containing CpG in cMERS and hMERS. cMERS showed 4 underrepresented biased codons containing CpG, which is lower than hMERS (6 codons). The low CpG relative frequency in MERS CoV is regarded as a way to escape human innate immunity, as unmetylated CpG is considered to be a pathogen signature.^24,25) The low relative CpG is therefore considered as a low immunity signal and minimizes the host’s immune response against the virus. It is well known that virus evolution and infection of new hosts is governed by changes in viral genome composition and changes in virus-related immune factors. In our analysis, we reveal that cMERS showing lower RSCU values of CpG containing codons. In contrast, the hMERS showed higher numbers of underrepresented CpG codons. This indicates that the MERS CoV adapted innate immune response by showing lower number of CpG.

Fig. 1. Relative Dinucleotides Frequency among Human (hMERS) and Camel (cMERS) Isolates of MERS CoV

The frequencies are calculated by CodonW software. The values were obtained by comparing the values of actual to expected dinucleotide frequency.

The ENc

The ENc values for cMERS and hMERS were 56.38 and 55.5, respectively. Both of the virus strains’ ENc values were showing a similar range from 53.5 to 56.6. The estimated average ENc values for MESR CoV is higher than similar RNA viruses, which falls in the range from 38.9 to 55.5.²⁶⁾ The high ENc value in MERS CoV indicates successful virus replication and relatively high conserved genomic structure of MERS CoV. The lowest number of ENc is 20 and means extreme bias in codon usage, where only one codon is used for every amino acid. Our results reveal that MERS CoV is using diverse codons for every amino acids, and there is little bias in codon usage. This may be partly influenced by the large size of the MERS CoV genome, which reaches up to 30 kb compared with other viruses. In addition, the high ENc value is a feature of RNA viruses and helps these viruses in rapid replication in a host environment with variable codon preferences.²⁶⁾

ENc-Plot

When GC3s is plotted against ENc values, we can draw conclusions as to the role of compositional constraints or mutational pressure on the RSC and codon bias. If all points of GC3s are lying on or just below the standard curve, then codon biases are affected mostly by mutational bias. In both cMERS and hMERS, all of the plotted points lie under the standard curve. This indicates that compositional constraints under mutational pressure is not the only force affecting codons bias in MERS CoV genome, and that other factors such as natural selection and host-related factors should also be considered. Furthermore, there was a strong positive correlation between ENc and GC3, r=0.94 for cMERS and 0.77 for hMERS (Fig. 2). This indicates the influence of compositional constraints on codon usage bias in MERS CoV.

Fig. 2. ENc Plot of Human (hMERS) and Camel (cMERS) Isolates of MERS CoV

The effective number of codons is plotted against the frequency of GC at the third position of codons. The calculated frequencies (dots) are compared to the expected ENc–GC3s relation (line).

Neutrality Plot

Neutrality of evolution is assessed by the neutrality plot to estimate the influence of mutation and selection on codon bias. Genes located at the slope of unity means significant correlation of GC3 with GC12, and this gene is under neutral mutation by random selection. Directional mutation pressure is assumed when the slope is below the unity and near the X-axis. There was a moderate to high correlation of GC3 with GC12 in cMERS (r=0.78). In contrast, hMERS showed a low correlation between GC3 and GC12 in hMERS (r=0.27). The regression slopes were 0.123 and 0.0498 for cMERS and hMERS, respectively (Fig. 3). Therefore, there is relative neutrality and directional mutational pressure of 12.3% for cMERS and 4.9% for hMERS. This indicates a general high influence of directed mutational pressure, which is more prominent in camel strains of MERS CoV. Since directed mutations are in response to environmental stresses, the high directed mutations in cMERS correlates with the living of camels in extremely different harsh external environment. These external environmental stresses influence the mutations in cMERS virus to adapt living in these conditions.

Fig. 3. Neutrality Plot of Human (hMERS) and Camel (cMERS) Isolates of MERS CoV

The frequency of GC nucleotides at the first or second position of codons (GC) is plotted against the frequency of GC at the third position of codons (GC3).

Natural Selection and Codon Usage Bias

Linear regression analysis was estimated between Gravy and Aromo with ENc, GC and GC3 (Table 3). Gravy is a measure of an amino acid’s hydropathic index, while Aromo implies the frequencies of hydrophobic amino acids. For Gravy, there was significant positive correlation with ENc, GC, and GC3. In contrast, Aromo showed significant positive correlation with GC only. This variable correlation data indicates a role of natural selection in codon bias, but not as the major contributor.

Table 3. Summary of Correlation Analysis between ENc, GC3s, GC, Gravy and Aromo n both of cMERS and hMERS

cMERS
	ENc	GC3s	GC	Gravy
GC3s	0.97***
GC	0.91***	0.78***
Gravy	0.79***	0.91***	0.46***
Aromo	0.21	−0.03	0.60***	−0.44**
hMERS
	ENc	GC3s	GC	Gravy
GC3s	0.88***
GC	0.67***	0.27**
Gravy	0.52***	0.86***	−0.25**
Aromo	0.03	−0.44*	0.70***	−0.83***

The data were analysed by GraphPad Prism sofwtare. *** p<0.001 ** p<.01 * p<0.05.

COA

COA of RSCU was performed to evaluate the codon usage pattern of MERS CoV genomes in which synonymous codons are distributed in multidimensional space. At first, the relative and cumulative data inertia were plotted over the first 20 factors of COA. In Figs. 4 to 7, data from different genetic and geographic sources were plotted against axes of principle component analysis (Axes 1 and 2) from COA. Axes 1 and 2 represent the major trends in data variations and imply the highest and the second major influencing factors of codon usage bias. The first axis indicated 93.2 and 79.8% of data inertia was the major contributor to variations in cMERS and hMERS, respectively (data not shown). The following factors were minimally contributing to the variations; therefore, we selected the first 2 axes for further analysis. The rarely used codons on the first axis were GUG, ACG, CCG, GCG, CUG, AGG, GGG, and UCG in hMERS and ACG, CCG, GCG, GUG, GGG, AGG, UCG, and AAC in cMERS. Therefore, by COA analysis G3s codons, CpG codons in particular constitute most of the rare codons in either camel or human isolates of MERS CoV. In COA analysis, the G3s codons were mostly occupying the left part of the first axis and constituted the unfavorable codon usage pattern (Fig. 4). In contrast, T3s were the major constituent of the right part, representing the overused codons and the highest frequency of the used nucleotides.

Fig. 4. Correspondence Analysis of Human (hMERS) and Camel (cMERS) Isolates of MERS CoV Based on the Nucleotides Composition

Axes 1 and 2 represent the most important factors affecting nucleotides composition. The frequencies of nucleotides at the third position of codons is given as dots.

COA analysis reveals more a prominent evolution of the virus isolated in camels as reflected in lower variability and lower dispersion around the first axis, as well as a prominent increase in U contents. In contrast, the codons from human isolates of MERS CoV were more dispersed around the first axis, with the presence of U, C and A among the favorable nucleotides. This indicates more variability among hMERS than cMERS. Based on this result, we postulated that the hMERS might be in a period of adaptation and evolution in the human cellular environment. In further investigation of this notice, the positions of various MERS CoV were plotted against the first and second axis of COA. COA analysis of cMERS reveals the presence of 3 genetic clusters of cMERS viruses. Within each cluster the sequences are highly similar, and little difference is found. hMERS were also dividable into 3 clusters, but with marked differences and lower degrees of similarity (Fig. 5). COA analysis of combined cMERS and hMERS reveals 6 completely different clusters, 3 clusters for every group of MERS CoV strains. In addition, there was no overlap between these clusters. This might indicate a real difference between hMERS and cMERS in terms of their origins and epidemiological aspects. One striking feature is that for each genetic cluster of hMERS, there is a similar more advanced corresponding cluster of cMERS. This may indicate more initial infection and development of MERS CoV in camels, where it adapted mammalian cells and then developed enough to infect humans.

Fig. 5. Correspondence Analysis of Human (hMERS, Upper Panel) and Camel (cMERS, Middle Panel) Isolates of MERS CoV Based on the Isolates Genomes

The lower panel represent the grouped data of hMERS and cMERS. The figure illustrates three different clusters of hMERS and cMERS.

The data were reanalysed based on the location or country of hMERS CoV genomes (Fig. 6). The strains cluster in the previously described 3 genetic clusters in the isolates from Saudi Arabia, Jordan and South Korea. The Chinese and Qatar isolates were located in one cluster, and the Emirates isolates shared two genetic clusters. The isolates from Saudi Arabia were then reanalysed for the specific location of isolates in the different Saudi towns or regions (Fig. 7). The first genetic cluster can be termed the western cluster as it includes isolates from Jeddah and Bisha. The second cluster was the mid-north cluster for isolates from Wadi aldawaser, Buraiydah and HafrAlbatin. The third is the mid-eastern cluster and is present in Riyadh and Alahsa. The Alahsa and Riyadh region showed the presence of three different clusters. The recent Chinese isolates are located with the mid-eastern genetic cluster. In addition, the recent South Korean isolates were representing all three genetic clusters in Saudi Arabia. The isolates from the Arabian Gulf countries Emirates, Qatar and Oman shared most of its isolates with the mid-eastern genetic MERS CoV cluster.

Fig. 6. Correspondence Analysis of Human (hMERS) and Camel (cMERS) Isolates of MERS CoV Based on the Geographic Regions

The data from genomes isolated from different countries were plotted against the two main axes of correspondence analysis.

Fig. 7. Correspondence Analysis of Human (hMERS) and Camel (cMERS) Isolates of MERS CoV Based on the Distribution in Different Saudi Towns

The data from genomes isolated from different Saudi towns were plotted against the two main axes of correspondence analysis.

In addition to the rMERS isolates and their geographic distribution, the COA data were analysed for the date of isolation and its relation to the genetic rMERS CoV clusters. All three genetic clusters contained strains from different years starting from 2012. This means that three genetic clusters of MERS CoV probably existed prior to the initial discovery of the virus.

Screening of Helicase Binding Compounds

A library of 50000 small molecules was used to search for MERS CoV helicase binding agents. The top four compounds were purchased and used in fluorescence titration assays. Two of the four compounds were found to bind to MERS CoV helicase (Table 4, Fig. 8). The binding constant was calculated in the range from 4.8×10³ to 2.1×10⁵ M⁻¹. These compounds can be used for further studies of MERS CoV helicase activity.

Fig. 8. Compound 1 Docked into the Binding Site of MERS CoV Helicase Model

The figure is generated by Schrodinger Glide software.

Table 4. Docking Scores and Binding Constant of Top Compounds by Fluorescence Titration

The structures were drawn by Chemdraw software.

CONCLUSION

MERS CoV causes severe fatal respiratory diseases. To the best of our knowledge, this is the first study of codon usage and codon usage bias in the MERS CoV genome. Natural selection and mutation pressure are two forces affecting the shaping of the genome. The results from this study indicate certain degrees of interaction between these two forces in shaping the MERS CoV genome. The high AT content of MERS CoV indicates compositional bias exits. When codon usage bias is derived by mutational pressure only, then the frequencies of the four nucleotides could be the same at the codon’s third position. In our study, there was a lower representation of CpG nucleotides and a higher preference for T3s, indicating that the presence of forces other than mutational pressure are shaping the MERS CoV genome. Selection pressure is evident from host immune response—e.g., by selection of lower CpG-containing genomes. MERS CoV has a slight codon bias supported by high ENc and medium CAI. This might be derived by selection pressure against viruses unable to adapt to the codon usage requirement. Neutrality plots indicated that mutational bias is the most effective force affecting MERS CoV codon usage and shaping of the genome. Finally, from a library of 50000 compounds, we were able to identify two compounds that can bind to MERS CoV helicase, one of them with medium-to-high binding potency. This data can be used for further optimization of a specific inhibitor.

Acknowledgment

This work is supported by Deanship of scientific research, King Faisal University, Grant number 160285. URL: https://www.kfu.edu.sa/en/deans/research/pages/home-new.aspx.

Conflict of Interest

The authors declare no conflict of interest.

Supplementary Materials

The online version of this article contains supplementary materials.

REFERENCES

• 1) Assiri A, Al-Tawfiq JA, Al-Rabeeah AA, Al-Rabiah FA, Al-Hajjar S, Al-Barrak A, Flemban H, Al-Nassir WN, Balkhy HH, Al-Hakeem RF, Makhdoom HQ, Zumla AI, Memish ZA. Epidemiological, demographic, and clinical characteristics of 47 cases of Middle East respiratory syndrome coronavirus disease from Saudi Arabia: a descriptive study. Lancet Infect. Dis., 13, 752–761 (2013).
• 2) 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., 367, 1814–1820 (2012).
• 3) Alfuwaires M, Altaher A, Kandeel M. Molecular Dynamic Studies of Interferon and Innate Immunity Resistance in MERS CoV Non-Structural Protein 3. Biol. Pharm. Bull., 40, 345–351 (2017).
• 4) Mailles A, Blanckaert K, Chaud P, Van der Werf S, Lina B, Caro V, Campese C, Guéry B, Prouvost H, Lemaire X. First cases of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infections in France, investigations and implications for the prevention of human-to-human transmission, France, May 2013. Middle East Respiratory Syndrome Coronavirus (MERS-CoV), 12, 19 (2013).
• 5) Reusken CB, Messadi L, Feyisa A, Ularamu H, Godeke GJ, Danmarwa A, Dawo F, Jemli M, Melaku S, Shamaki D, Woma Y, Wungak Y, Gebremedhin EZ, Zutt I, Bosch BJ, Haagmans BL, Koopmans MP. Geographic distribution of MERS coronavirus among dromedary camels, Africa. Emerg. Infect. Dis., 20, 1370–1374 (2014).
• 6) Devi JP, Noraini W, Norhayati R, Kheong CC, Badrul A, Zainah S, Fadzilah K, Hirman I, Hakim SL, Hisham AN. Laboratory-confirmed case of Middle East respiratory syndrome coronavirus (MERS-CoV) infection in Malaysia: preparedness and response, April 2014. Euro Surveill., 19, pii20797 (2014).
• 7) Bialek SR, Allen D, Alvarado-Ramy F, Arthur R, Balajee A, Bell D, Best S, Blackmore C, Breakwell L, Cannons A, Brown C, Cetron M, Chea N, Chommanard C, Cohen N, Conover C, Crespo A, Creviston J, Curns AT, Dahl R, Dearth S, DeMaria A, Echols F, Erdman DD, Feikin D, Frias M, Gerber SI, Gulati R, Hale C, Haynes LM, Heberlein-Larson L, Holton K, Ijaz K, Kapoor M, Kohl K, Kuhar DT, Kumar AM, Kundich M, Lippold S, Liu L, Lovchik JC, Madoff L, Martell S, Matthews S, Moore J, Murray LR, Onofrey S, Pallansch MA, Pesik N, Pham H, Pillai S, Pontones P, Pringle K, Pritchard S, Rasmussen S, Richards S, Sandoval M, Schneider E, Schuchat A, Sheedy K, Sherin K, Swerdlow DL, Tappero JW, Vernon MO, Watkins S, Watson J. First confirmed cases of Middle East respiratory syndrome coronavirus (MERS-CoV) infection in the United States, updated information on the epidemiology of MERS-CoV infection, and guidance for the public, clinicians, and public health authorities—May 2014. MMWR. Morbidity and mortality weekly report, 63, 431–436 (2014).
• 8) Su S, Wong G, Liu Y, Gao GF, Li S, Bi Y. MERS in South Korea and China: a potential outbreak threat? Lancet, 385, 2349–2350 (2015).
• 9) Cowling BJ, Park M, Fang VJ, Wu P, Leung GM, Wu JT. Preliminary epidemiologic assessment of MERS-CoV outbreak in South Korea, May-June 2015. Euro Surveill., 20, 7–13 (2015).
• 10) Kandeel M, Elaiziz M, Kandeel A, Altaher A, Kitade Y. Association of host tropism of Middle East syndrome coronavirus with the amino acid structure of host cell receptor dipeptidyl peptidase 4. Acta Virol., 58, 359–363 (2014).
• 11) Chu DK, Poon LL, Gomaa MM, Shehata MM, Perera RA, Abu Zeid D, El Rifay AS, Siu LY, Guan Y, Webby RJ, Ali MA, Peiris M, Kayali G. MERS coronaviruses in dromedary camels, Egypt. Emerg. Infect. Dis., 20, 1049–1053 (2014).
• 12) Kandeil A, Shehata MM, El Shesheny R, Gomaa MR, Ali MA, Kayali G. Complete genome sequence of Middle East respiratory syndrome coronavirus isolated from a dromedary camel in Egypt. Genome Announcements, 4, e00309-16 (2016).
• 13) Sabir JS, Lam TT, Ahmed MM, Li L, Shen Y, Abo-Aba SE, Qureshi MI, Abu-Zeid M, Zhang Y, Khiyami MA, Alharbi NS, Hajrah NH, Sabir MJ, Mutwakil MH, Kabli SA, Alsulaimany FA, Obaid AY, Zhou B, Smith DK, Holmes EC, Zhu H, Guan Y. Co-circulation of three camel coronavirus species and recombination of MERS-CoVs in Saudi Arabia. Science, 351, 81–84 (2016).
• 14) Nakamura Y, Gojobori T, Ikemura T. Codon usage tabulated from international DNA sequence databases: status for the year 2000. Nucleic Acids Res., 28, 292–292 (2000).
• 15) Karniychuk UU. Analysis of the synonymous codon usage bias in recently emerged enterovirus D68 strains. Virus Res., 223, 73–79 (2016).
• 16) Sharp PM, Li W-H. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res., 15, 1281–1295 (1987).
• 17) Sharp PM, Matassi G. Codon usage and genome evolution. Curr. Opin. Genet. Dev., 4, 851–860 (1994).
• 18) Chen Y, Chen Y-F. Analysis of synonymous codon usage patterns in duck hepatitis A virus: a comparison on the roles of mutual pressure and natural selection. VirusDisease, 25, 285–293 (2014).
• 19) Uddin A, Mazumder TH, Choudhury MN, Chakraborty S. Codon bias and gene expression of mitochondrial ND2 gene in chordates. Bioinformation, 11, 407–412 (2015).
• 20) Shi S-L, Jiang Y-R, Yang R-S, Wang Y, Qin L. Codon usage in α-baculovirus and β-baculovirus hosted by the same insect species is weak, selection dominated and exhibits no more similar patterns than expected. Infect. Genet. Evol., 44, 412–417 (2016).
• 21) Kumar N, Bera BC, Greenbaum BD, Bhatia S, Sood R, Selvaraj P, Anand T, Tripathi BN, Virmani N. Revelation of Influencing Factors in Overall Codon Usage Bias of Equine Influenza Viruses. PLOS ONE, 11, e0154376 (2016).
• 22) Kelley L, Bennett-Lovsey R, Herbert A, Fleming K. Phyre: Protein Homology/analogY Recognition Engine. Structural Bioinformatics Group, Imperial College, London, UK (2011).
• 23) Atkinson NJ, Witteveldt J, Evans DJ, Simmonds P. The influence of CpG and UpA dinucleotide frequencies on RNA virus replication and characterization of the innate cellular pathways underlying virus attenuation and enhanced replication. Nucleic Acids Res., 42, 4527–4545 (2014).
• 24) Krieg AM. The role of CpG motifs in innate immunity. Curr. Opin. Immunol., 12, 35–43 (2000).
• 25) Gursel M, Gursel I. Development of CpG ODN Based Vaccine Adjuvant Formulations. Vaccine Design: Methods and Protocols, Vaccines for Veterinary Diseases, 2, 289–298 (2016).
• 26) Jenkins GM, Holmes EC. The extent of codon usage bias in human RNA viruses and its evolutionary origin. Virus Res., 92, 1–7 (2003).