¹Department of Biological Science and Engineering, MANIT Bhopal (M.P.) India

²Department of Applied Mechanics, MANIT Bhopal (M.P.) India

Corresponding author Email: anjumune@gmail.com

Article Publishing History

Introduction

Louping ill virus (LIV) is a tick-born member of the genus Flavivirus in Flaviviridae family. It is a positive single stranded, 40-50 nm RNA virus whose genome comprises a single open reading frame (ORF) that is approximately 11 kb in length (Grard et al.,2007;Jeffries et al., 2014). The ORF encodes a polyprotein that consists of three structural and seven non-structural proteins. The virus show high degree of genetic homology to tick-borne encephalitis virus (TBEV) of the same family (McGuire et al., 1998; Jiang et al., 1993). It is mainly transmitted by ticks and the primary vector is Ixodes ricinus (Dobler
et al., 2010).LIV mainly causes febrile illness in sheep, cattle, horse, pigs and some other animals that may eventually result in fatal encephalitis.

Sheep are the most important reservoir host for LIV. The disease is dominantly detected in animals from upland areas of British Isles (Gao et al., 1997) though the disease is also reported in Scotland, Ireland, and northern England where the tick vector Ixodes ricinus is found. Infection with LIV was first reported in sheep of Basque region of northern Spain in 1987 (Gonzalez et al., 1987). Most of the cases of LI infection occur in spring / early summer when ticks are common. In endemic areas morbidity and mortality depends upon animal’s immune status, concurrent infection and other factors. All age group of animal get infected by it and once encephalitis is developed the case fatality rate goes up to 50%. The mortality rate is even higher in animals that are less than two years old. Currently, there is no specific treatment for LIV with only supportive therapies being helpful to some extent (Hyde et al., 2007 Mansfield et al., 2015 Butt et al., 2016).

The molecular sequence data started to be accumulated nearly 20 years ago. It was observed that the genetic code is redundant and most amino acids can be translated by more than one codon (Wang et al., 2011). This redundancy is a key factor regulating the efficiency and accuracy of protein production.Alternative codons within the same group that encode the same amino acid are often called ‘synonymous’ codons. These codons are not randomly selected within and between genomes. This is referred to as ‘codon usage bias’ (CUB). CUB are widespread across the tree of life and are influenced by mutation pressure, natural or translational selection, secondary protein structure, replication, selective transcription, hydrophobicity and hydrophilicity of the protein, and the external environment (Xiang et al., 2015 Butt et al., 2016 Mune et al., 2017).

As viruses are intracellular pathogens they have to co-evolve with host molecular mechanisms. The interplay between the codon usage of the virus and its host is expected to affect the overall viral survival, fitness, evasion of the host immune system and evolution. The knowledge of the codon usage of viruses can provide information about their molecular evolution and extend our understanding of the regulation of viral gene expression. This may also offer significant improvement in vaccine design for which the efficient expression of viral proteins may be required to generate immunity (Tao et al., 2009 Velazquez et al., 2016). To gain insight into the characteristics of the viral genome and evolution, the codon usage patterns of the three components of transmission cycle, namely – the virus (LIV), vector (Ixodes ricinus), and hosts (Sheep (Ovis aries), Pig (Sus scrofa) and cattle (Bos taurus)) were investigated in our study.

Materials and Methods

Sequence Data

The complete genome sequences were downloaded from the National Centre for Biotechnology (NCBI) database (http: //www.ncbi.nlm. nih.gov) in FASTA format. The detailed information (accession numbers, country, sequence length etc.) of the selected genomes were listed [Table. S1]. Open reading frames (ORF) of all the genomic sequences were identified by using NCBI ORF finder (https://www.ncbi.nlm.nih.gov/orffinder/). The host (Ovis aries, Sus scrofa and Bos taurus) and vector (Ixodes ricinus) codon usage were obtained from the Codon Usage Data Base (CUD).

Table 1: Nucleotide composition analysis of LIV genome (IR: Ixodes ricinus, SS: Sus scrofa, OA: Ovis aries, BT: Bos taurus)
Accession no.	U	C	A	G	U3	C3	A3	G3	AU	GC	AU3	GC3	GC12	ENC	CAIIR	CAISS	CAIOA	CAIBT
NC_001809.1	20.72	22.67	24.47	32.13	18.57	26.53	20.85	34.06	45.19	54.81	39.41	60.59	30.29	53.88	0.658	0.622	0.691	0.711
Y07863	20.72	22.67	24.47	32.13	18.57	26.53	20.85	34.06	45.19	54.81	39.41	60.59	30.29	53.88	0.658	0.622	0.691	0.711
KT224354.1	20.81	22.48	24.57	32.14	18.62	26.47	20.67	34.23	45.38	54.62	39.30	60.70	30.35	54.12	0.657	0.623	0.687	0.711
KP144331.1	20.61	22.59	24.60	32.20	18.16	26.68	20.94	34.23	45.21	54.79	39.09	60.91	30.45	53.89	0.658	0.624	0.689	0.711
KJ495985	20.81	22.48	24.58	32.13	18.62	26.47	20.67	34.23	45.39	54.61	39.30	60.70	30.35	54.15	0.657	0.623	0.687	0.710
KF056331.1	20.74	22.54	24.45	32.27	18.48	26.56	20.59	34.38	45.19	54.81	39.06	60.94	30.47	53.93	0.658	0.622	0.690	0.711
Avg.	20.74	22.57	24.52	32.17	18.50	26.54	20.76	34.20	45.26	54.74	39.26	60.74	30.37	53.97	0.658	0.623	0.689	0.711
Std. D	0.0722	.0890	.0652	.0561	.1780	.0756	.1361	.1234	.0961	.0961	.1532	.1532	.0766	.1261	.0005	.0008	.0018	.0004

Codon Usage Analysis

The overall frequency of occurrence of the nucleotides (A %, C %, U %, and G %) was calculated along with the frequency of each nucleotide at the third site of the synonymous codons (A₃, C₃, U₃ and G₃).Also the overall GC, AU and GC₃ content were calculated using MEGA7 software to investigate the compositional properties of coding region of LIV. To investigate the codon usage pattern, the RSCU (Relative synonymous codon usage) values for synonymous codons were calculated according to the published equation (Sharp et al., 1986). The stop codons (UAA, UAG and UGA) and AUG for Met, UCG for Try were not introduced into the RSCU analysis. Further, ENC (Effective number of codon) values were calculated to measure the magnitude of codon usage bias in the coding sequences of viral genome. The ENC value ranges from 20 (when only one synonymous codon is chosen by the corresponding amino acid) to 61 (when all synonymous codons are used equally). A low ENC value indicates a strong codon usage bias (Wright et al., 1990; Zhang et al., 2011 Butt et al., 2013).

The CAI (Codon adaptation index) was used to estimate the adaptation of LIV to its host and vector codons. CAI values range from 0 to 1. A higher CAI score for a given gene indicates more similarity between its codon usage and the predefined reference set, using the CAIcal approach (available at: http://genomes.urv.es/CAIcal) (Puigbo et al., 2008).

Results and Discussion

Synonymous Codon Usage In Liv

The preference for one type of codon over another can be greatly influenced by the nucleotide composition of genome. We first analysed nucleotide composition and observed that the nucleotides A and G were higher and followed by C and U (Table 1) The LIV genome is rich with G content having a mean value of 32.17. For a better understanding we analysed nucleotide composition at third position of codon and observed the dominance of G₃ nucleotide with a mean value of 34.20. Even the percentage of dinucleotide with G is higher compared to dinucleotide with other nucleotides (respective mean values for GC, AU, GC₃ and AU₃ being 54.74, 45.26, 60.74, and 39.26).

To investigate the extent of codon usage bias, the ENC values among LIV genome were calculated. An average value of 53.97 represents stable ENC value (ENC > 40) (Mune et al., 2017) which suggests that the genomic composition of LIV is conserved. The result shows that the codon usage of LIV is slightly biased and mainly affected by the nucleotide composition. To further understand the codon usage pattern, the analysis of ENC – plot (ENC value V/s GC₃ content) was carried out. It is observed that all points lie below the expected curve (Fig.1). This implies that the codon usage bias is mainly affected by nucleotide composition (in other words – by mutation pressure).

Figure 1: Graph showing the relationship between the effective number of codons (ENC) and GC content of the third codon position (GC3).The curve indicates the expected codon usage if GC compositional constraints alone account for codon usage bias

To further explore the codon usage preferential optimization and adaptation of LIV in relation to its vector and hosts CAI analysis was performed. CAI values were calculating keeping Ixodes ricinus, Ovis aries,Sus scrofa and Bos taurus codon usage as a reference set. A mean CAI value of 0.658 was obtained for the LIV ORFs in relation to primary vector Ixodes ricinus codon usage reference set and mean CAI values of 0.623, 0.689 and 0.711 were obtained for the LIV ORFs in relation to host pig , sheep and cattle (Ovis aries,Sus scrofa and Bos taurus) codon usage reference set respectively. In this study we found a tendency for higher CAI values indicating lower efficiency of translation. A comparison between vector and host indicated a lower CAI for LIV in relation to pig, which leads to lower efficiency of protein synthesis in pig. This suggests that the interplay of codon usage between LIV and its hosts may influence viral fitness, survival and evolution.

To investigate the codon usage pattern of virus, an RSCU analysis was performed for the 59 sense codons (Table.2). In LIV among the 18 most abundantly used codons, 12 were G/C-ended (five G-ended, seven C-ended) and the remaining six were A/U-ended ( five A-ended and one U-ended).

Table 2: The relative synonymous codon usage patterns of LIV, its host (cattle, sheep and pig) and primary transmission vector (Ixodes ricinus)
AA	Codon	Pathogen	Vector	Host
		louping ill	Ixodes ricinus	Cattle	Sheep	Pig
Phe	UUU	0.88	0.66	0.85	0.94	0.79
	UUC	1.12	1.34	1.15	1.06	1.21
Leu	UUA	0.18	0.16	0.38	0.24	0.32
	UUG	1.11	0.75	0.71	0.49	0.67
	CUU	0.90	1.08	0.7	0.74	0.65
	CUC	1.2	1.40	1.26	1.83	1.35
	CUA	0.37	0.26	0.36	0.24	0.33
	CUG	2.24	2.45	2.59	2.46	2.68
Ile	AUU	0.71	0.85	0.98	0.63	0.91
	AUC	1.36	1.79	1.57	1.74	1.67
	AUA	0.93	0.36	0.45	0.63	0.42
Val	GUU	0.7	0.68	0.64	0.46	0.57
	GUC	1.1	1.36	1.01	0.91	1.07
	GUA	0.29	0.35	0.4	0.36	0.34
	GUG	1.92	1.61	1.95	2.27	2.03
Ser	UCU	0.69	0.76	1.04	0.91	0.99
	UCC	0.81	1.54	1.37	1.28	1.5
	UCA	1.11	0.48	0.79	0.48	0.73
	UCG	0.64	0.83	0.39	0.28	0.39
	AGU	1.17	0.69	0.87	1.48	0.77
	AGC	1.58	1.70	1.53	1.58	1.62
Pro	CCU	0.96	0.75	1.08	1.26	1.05
	CCC	0.98	1.70	1.39	1.29	1.46
	CCA	1.36	0.96	1	1.03	0.94
	CCG	0.7	0.98	0.53	0.42	0.56
Thr	ACU	0.75	0.68	0.89	0.78	0.83
	ACC	1.18	1.71	1.55	2.05	1.68
	ACA	1.29	0.82	1.01	0.78	0.92
	ACG	0.77	1.00	0.56	0.38	0.57
Ala	GCU	1.06	1.07	1	1.18	0.96
	GCC	1.11	2.69	1.71	1.55	1.8
	GCA	1.12	0.84	0.8	0.9	0.74
	GCG	0.72	0.95	0.48	0.37	0.5
Tyr	UAU	0.61	0.45	0.79	0.72	0.73
	UAC	1.39	1.59	1.21	1.28	1.27
His	CAU	0.75	0.50	0.75	1.08	0.7
	CAC	1.25	1.75	1.25	0.92	1.3
Gln	CAA	0.66	0.60	0.46	0.57	0.44
	CAG	1.34	1.16	1.54	1.43	1.56
Asn	AAU	0.68	0.55	0.81	0.49	0.79
	AAC	1.32	1.07	1.19	1.51	1.21
Lys	AAA	0.79	0.65	0.78	0.68	0.76
	AAG	1.21	1.04	1.22	1.32	1.24
Asp	GAU	0.8	0.54	0.84	0.66	0.8
	GAC	1.2	1.40	1.16	1.34	1.2
Glu	GAA	0.69	0.91	0.78	0.75	0.72
	GAG	1.31	1.02	1.22	1.25	1.28
Cys	UGU	1.04	0.57	0.85	0.72	0.79
	UGC	0.96	1.62	1.15	1.28	1.21
Arg	CGU	0.38	0.75	0.49	0.82	0.44
	CGC	0.94	1.59	1.17	1.15	1.31
	CGA	0.53	0.80	0.68	0.89	0.6
	CGG	0.64	1.04	1.32	0.86	1.29
	AGA	1.78	0.83	1.14	1.12	1.12
	AGG	1.74	1.62	1.2	1.16	1.23
Gly	GGU	0.66	0.78	0.64	0.92	0.57
	GGC	0.82	2.01	1.43	1.33	1.46
	GGA	1.51	1.31	0.95	1.05	0.91
	GGG	1.02	0.67	0.99	0.71	1.05

To determine the potential influences of the vector and host on the codon usage pattern of the LIV, the RSCU pattern of LIV coding sequence were correlated with those of Ixodes ricinus (vector) and pig, sheep and cattle (hosts) (Fig.2).All the 18 most abundantly used codons of vector and host were G/C ending (In Ixodes ricinus twelve C-ended and six G-ended, Pig thirteen C-ended and five G-ended, cattle twelve C-ended and six G-ended, and in sheep eleven C-ended codons six G-ended codons and one U-ended codon) we observed a common pattern of preference towards G/C-ended codons in vector and host. An analysis of over and under – represented codons showed that for LIV 4 out of 18 preferred codons (CUG for Leu, GUG for Val and AGA and GGA for Arg) in Ixodes ricinus 11 out of 18 preferred codons (CUG for Leu, AUC for Ile, GUG for Val, AGC for Ser, CCC for Pro, ACC for Thr, GCC for Ala, CAC for His, UGC for Cys, AGG for Arg and GGC for Gly), in cattle 3 out18 preferred codons (CUG for Leu, GUG for Val and GCC for Ala), in sheep 5 out of 18 preferred codons (CUG and CUC for Leu, AUC for Ile, GUG for Val and ACC for Thr), and in pig 6 out of 18 preferred codons (CUG for Leu, AUC for Ile, GUG for Val, AGC for Ser and ACC for Thr, GCC for Gly) had RSCU value >1.6, whereas the remaining preferred codons had RSCU values >0.6 and <1.6. CUG for Leu and GUG for Val are common over represented codons in virus vector and hosts.

Figure 2: Comparative analysis of relative synonymous codon usage (RSCU) patterns between virus, vector and three hosts (cattle, sheep and pig).

None of the preferred codons were under-represented (RSCU<0.6). UUA and CUA for Leu and GUA for Val are common underrepresented codons in virus, vector and hosts. Interestingly, a mixture of coincidence and antagonism was observed in the codon usage pattern as LIV showed no complete coincidence or complete antagonism to any of the patterns of its vector and host. Among the 18 most abundantly used codons, the ratio of coincident/antagonist preferred codon was 12:6 between virus vector and hosts.

Conclusion

Our analysis has provided an insight into codon usage pattern of LIV virus and its relationship with host and vector. We observed that the codon usage bias of LIV is slightly biased which reflects that the key role played by mutation pressure and natural selection. Our observations suggest that codon usage of LIV is an evolutionary process However, a more comprehensive analysis with higher sample sizes is needed as this study and subsequent analysis is based on a relatively small sample size.

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