Comparative analysis of the mitochondrial genomes of two fish species Channa striata and Channa punctata using codon usage bias and their evolutionary relationship

doi:10.21203/rs.3.rs-2086098/v1

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Comparative analysis of the mitochondrial genomes of two fish species Channa striata and Channa punctata using codon usage bias and their evolutionary relationship

https://doi.org/10.21203/rs.3.rs-2086098/v1

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Codon usage bias (CUB) occurs when certain codons are utilized more repeatedly than the other synonymous codons for the same amino acid in the coding sequences of genes. The investigation of CUB aids in the understanding of optimal codons, gene expression, protein production and trends of evolution. In our study, CUB was explored for the mitochondrial protein coding genes of Channa striata and Channa punctata, estimating their base contents, identifying over-represented and under-represented codons, and determining the factors contributing to the codon usage bias. The base compositions of the two fishes showed the trend C>T>A>G and the GC composition was in the order GC1>GC3>GC2. The average ENC value in both sets of coding sequences was >35, indicating a lower CUB. The mitochondrial genomes of the two fishes are AT-rich. In Channa striata, 8 codons were found to be over-represented, and 14 codons were under-represented. On the other hand, Channa punctata showed 9 over-represented codons and 18 under-represented codons across the coding sequences in mt-genome. Among the over-represented codons, CTA, ACC, AAA and GAA were found in Channa striataand not in Channa punctata. Whereas, the over-represented codons CTC, CAA, GTC, GAC and GGC were found in Channa punctata and not in Channa striata. The results suggested that both the evolutionary processes viz. selective pressure and mutation governed the codon usage arrangement in the mitochondrial genes of the two freshwater fishes.

Channa striata

Channa punctata

Codon usage bias (CUB)

Evolutionary forces

Freshwater fishes are the vertebrate species that spend all or part of their lives in brackish estuaries or freshwater (such as rivers and lakes) with a salinity of less than 1.05%. Around 42% of all known ichthyospecies are found in fresh water (Das and Antoney, 2010). The freshwater fish species Channa striata and Channa punctata are the members of the same family Channidae. Both the fish species are native to East and Southeast Asia. It can be found from Pakistan's Indus River eastward through India, Sri Lanka, southern part of Nepal, possibly Bangladesh, Bhutan, Southern China and all of Southeast Asia's countries. The length of adult Channa striata ranges between 23.0 cm-65.0 cm, with a mean length of 33.0 cm and weighing 65–955 g. It is commonly found in freshwater areas that flow from lakes and rivers to flooded field; and in the dry season, it returns to permanent bodies of water where it is able to survive by burrowing in the mud. It feeds on water bugs, small fishes and frogs; and during breeding, it attacks anything that moves in the nearby region. The Channa punctata, on the other hand, grows in length of around 15.0 cm; and males can grow up to 31.0 cm. Average weight of this fish species is 11.961 ± 0.1348 g. This is predominately a carnivorous species. This species' favourite food is the yolk flies and fish larvae of other small fish. It feeds on molluscs, crustaceans, semi-digested material, small fishes, insects and occasionally plants in its natural habitat. Its feeding habits vary according to the season (Courtenay Jr and Williams, 2004; Kusek, 2006).

In vertebrates, the mitochondrial genome (mt-genome) is a circular, small cytoplasmic genome (15 to 26 kb) that differs genetically from the nuclear genome. Due to its small size, it is suitable as a molecular marker for species identification and systematic phylogenetic studies. Mt-genomes consist of 22 transfer RNA (tRNA) genes, 2 rRNA genes, 13 protein coding genes and a replication control region. The mt-genome is devoid of introns. The mtDNA genome replicates itself and produces cytochrome oxidase, ATP synthase, and NADH systems in the cell (Uddin et al., 2015).

The standard genetic code is different from the mitochondrial genetic code. Standard genetic code comprises of 64 codons. Among them, 61 codons encode twenty common amino acids in the polypeptide chain and the rest codons namely TAG, TGA, TAA are known as the stop codons. But in the mitochondrial genetic code, twenty standard amino acids are coded by 60 sense and synonymous codons and the rest four codons i.e. TAG, TAA, AGG and AGA are known as the stop codons. TGA represents the amino acid Tryptophan in vertebrate mitochondrial genes, but it represents the stop codon in standard genetic code. On the other-hand; ATA represents the amino acid Isoleucine in the standard genetic code, but represents Methionine in the metazoan mitochondrial genome. In general, each amino acid is encoded by multiple codons, so the genetic code is said to be degenerate (Barbhuiya et al., 2021a). Codon usage bias (CUB) arises when some codons are used more frequently than other synonymous ones (Bennetzen and Hall, 1982; Ikemura, 1981). The selection of synonymous codons varies between species, and as a result, the codons appear at varying frequencies in different genes (Uddin, 2017). The study of these differences between genomes and within the same genome contributes to a better understanding of genome evolution in closely related organisms. Synonymous mutations have an effect on mRNA translational efficiency via a variety of cellular mechanisms. CUB controls protein folding, which is required for proper cellular function (Yu et al., 2015).

Two well-known theories were proposed to understand the phenomenon of CUB - (a) the mutation-selection-drift equilibrium model and (b) the neutral theory (Duret and Mouchiroud, 1999; Sharp and Li, 1986; Shields and Sharp, 1987). The neutral theory explains the majority of evolutionary changes that take place at the molecular level and the random genetic drift of mutant alleles that are selectively neutral causes the variations among the species. The selection-mutation-drift model, on the other hand, explains how genetic drift, natural selection, and mutation can all affect CUB. Other factors influencing CUB include gene expression, replication, base skewness, nucleotide base composition, gene length, gene stability, secondary structure, hydrophobicity, translation selection, and dietary nitrogen (Uddin and Chakraborty, 2019). Variation in tRNA pools and disparity in a cell's isochores are important determinants of CUB (Bernardi et al., 1985).

CUB has attracted the interest of some researchers in recent years due to its significant contribution to the understanding of genomic evolution and the generation of information on codon optimization. Codon usage research is critical for predicting and optimising gene expression levels, as well as for detecting lateral gene transfer (Yu et al., 2015).

2.1. Collection of sequences

The coding sequences (cds) of mitochondrial genes of two freshwater fish species were obtained from GenBank (http://www.ncbi.nlm.nih.gov) using accession numbers. For this in silico study, only those coding sequences with no uncertain bases (N) and with proper termination and initiation codons were filtered and considered for further analysis.

2.2. Measures of CUB:

Some of the most important and widely used metrics of CUB as given below were analyzed in this study.

2.2.1. Nucleotide compositional properties

The genes of freshwater fish species were examined for the following characteristics: (i) total base composition (A%, T%, G%, C%) and the composition in the 3rd position of the codon (T3%, A3%, C3%, G3%), (ii) total percentage of guanine and cytosine of synonymous codons in each sequence and also guanine and cytosine percentages of synonymous codons in different codon positions namely GC1%, GC2% and GC3%. We calculated AT skew and GC skew values to better understand the compositional properties of each cds. All calculations were carried out using an in-house PERL programme composed by the supervisor (Prof. Supriyo Chakraborty).

2.2.2. Effective Number of Codons (ENC)

The value of ENC ranges between 20–61 and it is widely used to calculate the CUB of a gene. The ENC value 20 indicates that only one member among the synonymous codons within a family is used to codify the particular amino acid i.e., the existence of the highest codon bias. The ENC value 61 denotes that all the synonymous codons for each amino acid are used equally i.e., little or no bias. While, the ENC value less than or equal to 35 indicates significant level of CUB (Wright, 1990).

ENC is calculated using the F_a value for each amino acid. F_a is calculated as:

$${F}_{a}=\left(na\sum _{i=1}^{k}{p}_{i}^{2}-1\right)/\left({n}_{a}-1)\right.$$

Where 'pi' represents the frequency of the 'i'th codon, 'k' represents the count of synonymous codons for the 'a'th amino acid, and 'na' represents the observed codon value for the amino acid. The mean of the F_a values for each r-fold redundancy class (folds 2, 4, and 6 for amino acids in NCBI's translation Table 2) can be evaluated as:

$$\stackrel{-}{{F}_{r}}=\frac{1}{nRC}\sum _{a\in RC}{F}_{a}$$

Where nRC denotes the total number of amino acids.

ENC can be calculated as:

$$ENC=\left(\frac{12}{\stackrel{-}{F₂}}\right)+\left(\frac{6}{\stackrel{-}{F₄}}\right)+\left(\frac{2}{\stackrel{-}{F₆}}\right)$$

The symbol F_k in k-fold degenerate amino acids represents the average of F_k values (k = 2, 4 and 6).

2.2.3. Relative Synonymous Codon Usage (RSCU)

This parameter is used mainly to identify frequent codons across genes. It is calculated as the ratio of observed frequency to the expected frequency of codon. If a codon's RSCU value is greater than 1.0, then it indicates that the codon is used more frequently than expected, and if less than 1.0, then it indicates that the codon is used less frequently. Over-represented codon is indicated if the RSCU value is more than 1.6 in the coding sequences, while, an under-represented codon is denoted if the RSCU value is less than 0.6 (Sharp and Li, 1986).

RSCU value can be measured as:

$$RSCU=\frac{Xij}{\frac{1}{ni}\sum _{j=1}^{ni}Xij}$$

Where, ni is the number of codons for the i^th amino acid and Xij is the frequency of the j^th codon for the i^th amino acid. Any Xij with a value of zero is randomly assigned a value of 0.5.

2.2.4. COA (Correspondence analysis)

This computational tool analyses the broad patterns in CUB heterogeneity. Based on the trends, it divides the codons into two ordinates (Shields and Sharp, 1987). For mitochondrial genome, each cds is a 60-dimensional vector, with each vector representing the value of the relative synonymous codon usage (RSCU) of each synonymous codon (total 60 codons). Relative inertia can be used to identify the key trends in the variation of codon usage, which investigates the major forces influencing the CUB based on the location of the genes.

2.2.5. Parity plot (PR2) bias analysis

In this scattered diagram, AT-bias value [A3/ (A3 + T3)] is plotted in the ordinate and the GC-bias value [G3/ (G3 + C3)] is plotted in the abscissa (Sueoka, 1995). Both the coordinates are 0.5 in the plot's centre, with G = C and A = T, indicating that the two evolutionary factors have no effect.

2.2.6. Neutrality Plot

We can use this plot to determine the strength of evolutionary forces (Zhang et al., 2013b). The value of GC12 is contrived on the y-axis, while the value of GC3 is contrived on the x-axis. The slope (regression coefficient) closer to unity indicates that mutational pressure is dominant (Sharp and Li, 1986); when the slope is close to zero; natural selection, rather than mutational pressure, plays a dominant role in the shaping of CUB (Sueoka, 1995).

2.2.7. Mutation responsive index (MRI)

MRI values are used to assess the mutational drift rate in the coding sequences. Positive MRI value indicates the role of directional mutation; while, the negative MRI value indicates the influence of translational selection (Gouy and Gautier, 1982).

2.2.8. Translational selection (P2)

It represents the impact of translational efficiency and the efficiency of the codon-anticodon relationship for a gene. The coding sequence is considered to be favoured by translational selection if the P2 value is > 5; but considered to be not favoured by translational selection if the P2 value is < 5 (Gouy and Gautier, 1982).

2.2.9. GRAVY

General hydropathicity value is the average value of hydropathicity of each amino acid in the encoded protein of each coding sequence. The value of GRAVY ranges between − 2 and + 2, where positive value indicates that the protein is hydrophobic and a negative value is assigned to a protein that is hydrophilic (Kyte and Doolittle, 1982).

2.2.10. Hydrophilicity

This parameter is used to determine the rate at which amino acids in a protein become hydrophilic or hydrophobic. It is generally used to figure out the structure and domain of a protein (Hopp and Woods, 1981).

2.2.11. Aromaticity

It is used to estimate how many aromatic amino acids are present in a translated protein (Lobry and Gautier, 1994). According to previous research, the aromaticity of encoded protein has a significant impact on the codon usage of a gene.

2.2.12 Phylogenetic study

Using the MEGA 11 software, the phylogenetic tree was constructed to evaluate the evolutionary relationship of the mitochondrial genes of the two fish species (Tamura et al., 2021).

2.2.13 Data interpretation

SPSS 16.0 for Windows, a statistical program, was used to determine the correlations of the parameters like base contents, the effective number of codons, skew values and properties of protein with the CUB for this study. To obtain the total codon usage pattern, we used correspondence analysis (COA) and investigated the variation of synonymous codons in compositional distribution using PAST software.

The purpose of this study was to find the CUB of genes, the similarities between the genes and phylogenetic analysis of the genes in two freshwater fish species i.e. Channa striata and Channa punctata.

3.1. Analysis of CUB

To investigate the degree of CUB, we determined the ENC values for the mitochondrial genes of Channa sriata and Channa punctata fish species. In Channa striata, the values ranged from 39 to 61, with an average of 48.46; and in Channa punctata, the values ranged from 42 to 57, with an average of 48.23. This implies that the average ENC value in both the species is greater than 35, indicating a low CUB (Butt et al., 2014).

In a previous study by Deb et al. (2020), they also found the mean ENC value > 35 in the different genomes of hepadnaviruses, supporting the result of our study in the two fishes (Deb et al., 2020).

3.2. Relative Synonymous Codon Usage (RSCU)

In Fig. 1, we had mentioned the RSCU value of each codon and compared the two mt-genomes. We discovered 8 codons in Channa striata that were over-represented (RSCU value > 1.6), while 14 codons were under-represented (RSCU value < 0.6). While, in Channa punctata, 9 codons were found to be over-represented and 18 codons were under-represented (Table 1).Table 2 shows the preferred codons (> 1) in the mt-genomes of Channa striata and Channa punctata.

Table 1

Over-represented and under-represented codons of *Channa striata* and *Channa punctata*
Channa striata		Channa punctata
Over-represented codons	Under-represented codons	Over-represented codons	Under-represented codons
TCC	TCG	TCC	TCG
CTA	AGT	CTC	AGT
CCC	TTG	CCC	TTG
CGA	CTG	CAA	CTG
ACC	CCG	CGA	TGT
AAA	CAG	GTC	CCG
GCC	CGG	GCC	CAG
GAA	CGT	GAC	CGG
	TGG	GGC	CGT
	ACG		TGG
	AAG		ACG
	GCG		AAT
	GAG		AAG
	GGT		GTG
			GCG
			GAT
			GAG
			GGT

Barbhuiya et al. (2020) previously reported in their study that 29 synonymous codons were frequently used in both sets of obesity genes and housekeeping genes, and these codons mostly ended with base C or G (Chakraborty et al., 2020).

Table 2

Preferred codons of *Channa striata* and *Channa punctata*
Preferred codons
Channa striata	Channa punctata
TCA	TCA
TCC	TCC
TCT	TCT
TTT	AGC
TTA	TTC
CTA	CTA
CTC	CTC
CTT	CTT
CCA	TAC
CCC	CCC
CAC	CCT
CAA	CAC
CGA	CAA
TGA	CGA
ATA	CGC
ATT	TGA
ACA	ATA
ACC	ATT
AAC	ACA
AAA	ACC
GTA	AAC
GCA	AAA
GCC	GTC
GAC	GCA
GAA	GCC
GGA	GAC
GGC	GAA
	GGC

3.3. Compositional properties

Previous CUB findings indicated that the total nucleotide compositional properties of a gene influence synonymous codon usage (Jenkins and Holmes, 2003). The total nucleotide abundance and base composition at the third codon location of the genes in both species were determined in this study (Fig. 2). The bases T (29%) and G (16%) were found in nearly equal proportions in both Channa striata and Channa punctata; however, the proportions of bases A (25% in Channa striata and 24% in Channa punctata) and C (30% in Channa striata and 32% in Channa punctata) were found to be slightly different. The overall GC% contents for Channa striata and Channa punctata were 45.82% and 48.02% respectively, while the overall AT% for both the mt-genomes were 54.18% and 51.98%, indicating that the mitochondrial genes of these two fish species are AT-rich. When comparison was made for the third codon position between the two mt-genomes, it was found that C (34.19% and 38.68%) was most frequent, followed by A (33.37% and 29.27%), T (23.46% and 22.86%), and G (8.98% and 9.18%). The overall GC3 contents were 43.18% and 47.88% for Channa striata and Channa punctata, respectively, and AT3 contents were 56.82% and 52.12%. This indicates that in both the genomes, the 3rd codon position was rich in AT content. GC content across codon position followed the trend GC1 > GC3 > GC2 in the mitochondrial genes of two genomes (Fig. 3). We analysed the correlation of ENC with all nucleotide bases, and the results are represented in the Table 3. From the correlation values we could conclude that base composition had an impact on CUB.

Table 3

Correlation between ENC and base content of genes in *Channa striata* and *Channa punctata*
		A%	T%	G%	C%	A3%	T3%	G3%	C3%	GC%	GC3%
ENC	Channa striata	0.06	0.04	-0.22	0.18	-0.21	0.33	0.06	-0.14	-0.08	-0.54
ENC	Channa punctata	-0.01	-0.04	-0.22	0.24	0.09	-0.14	0.19	-0.07	0.02	-0.09
*Significant at p < 0.01, Significant at p < 0.05

In a study by Barbhuiya et al. (2021) on the CO mitochondrial genes of amphibian orders i.e. Caudata and Gymnophiona, the base frequency was in the order A > T > C > G, but in Anura, the base frequency was in the order A > C > T > G. They also discovered that the total AT content in these genes was more than GC, implying AT richness of CO genes. They observed that the GC content in Anura was the highest; while in Caudata, they found the lowest GC content. From their findings, they concluded that Caudata had the highest AT percent while Anura had the lowest, but they found intermediate GC and AT contents in Gymnophiona among the orders (Barbhuiya et al., 2021b).

3.4. Correspondence analysis (COA)

We performed a correspondence analysis (COA) of the sense codons for the mt genes of Channa striata and Channa punctata (Fig. 4). Both the axes are significant contributors to the overall variation. Axis 1 accounted for 38.68% of the overall variation in Channa striata, while axis 2 accounted for 17.51%. In Channa punctata, axis 1 accounted for 43.05% of the overall variation, while axis 2 accounted for 16.70%. The red dots in Fig. 4 represent AT-ending codons, while the blue dots represent GC-ending codons. A close distribution of bases around the axes in the figures suggests that mutational pressure might have played a role in shaping the CUB of the genes in two mt-genomes (Wei et al., 2014b).

Parvin et al. (2020) in their study on mitochondrial ND genes of Amphibians found that almost all codons were very close to both the axes. This indicated that mutation-induced compositional characteristics may have played a role in determining the CUB. This supports the results of our study of CUB on the mt genes of the two fish species (Barbhuiya et al., 2021a).

3.5. Parity rule 2 plot (PR2)

In PR2 analysis, an equitable distribution of nucleobases across the plot explains the effect of mutation in CUB, whereas a disproportionate distribution depicts the involvement of both natural selection and mutational pressure. The study was carried out to investigate the impact of evolutionary determinants on CUB (Fig. 5). We plotted AT-bias (A3/ A3 + T3) on the vertical axis and GC-bias (G3/ G3 + C3) on the horizontal axis for this research and found an uneven distribution of bases across the plot which revealed that CUB was influenced by both the evolutionary forces (Deb et al., 2018).

Barbhuiyan et al. (2020) in their study discovered an unequal distribution of GC and AT contents in their parity plot analysis of both obesity and housekeeping genes. They reported that not only the mutational pressure, but also the natural selection as an evolutionary force might have an impact in determining the CUB of the mt genes in two species (Chakraborty et al., 2020).

3.6. Interrelationships among the nucleotide compositions

The two evolutionary forces that are primarily responsible for the disparity in codon usage bias are natural selection and mutation pressure (Mazumder et al., 2018a, b). We used Karl Pearson’s method for the correlation analysis of the nucleotide compositional properties to identify the major forces influencing the CUB of genes (Chen, 2013). In our study, we correlated overall nucleotide contents with the nucleotide contents at the 3rd codon position (Table 4) and observed a highly significant correlation at p < 0.01 or 0.05; implying that mutation pressure may have played a role in the CUB of the mt genes in two fish species (Zhang et al., 2013a; Zhao et al., 2007).

Table 4

Correlation study in *Channa striata* and *Channa punctata* between overall nucleotide contents and nucleotide contents in the third position of codons
	Channa striata				Channa punctata
	A3%	T3%	C3%	G3%	A3%	T3%	C3%	G3%
A	.85**	− .67*	− .82**	.70**	.85**	− .46	− .72**	.49
T	− .85**	.85**	.80**	− .78**	− .81**	.79**	.92**	− .81**
G	− .90**	.73**	.96**	− .85**	− .70**	.76**	.94**	− .85**
C	.89**	− .83**	− .95**	.90**	.57*	− .84**	− .93**	.92**
GC	− .38	− .08	.41	− .15	− .38	− .34	− .03	.307
*Significant at p < 0.01, Significant at p < 0.05

Parvin et al. (2020) discovered a positive, highly significant correlation (p < 0.001) between A3 and A, T3 and T, G3 and G and, GC3 and GC, in an earlier study on codon usage trends in the genes associated with obesity; whereas they found a negative, significant correlation in the majority of the other different nucleotide composition pair. This suggested that the CUB was most likely influenced by mutation pressure. However, there was a significant positive correlation (p < 0.001) between G and C3, GC and T3. This indicated that in determining the CUB of genes that are associated with obesity, natural selection might have played a prominent role. Furthermore, in case of the housekeeping genes, highly significant positive as well as negative correlations were observed between the homogeneous and heterogeneous nucleotides. This indicated that both the evolutionary forces played a role in determining the codon usage pattern of the housekeeping genes (Chakraborty et al., 2020).

3.7. Neutrality plot

It is used to evaluate the effect of evolutionary forces as well as the association of GC3 with GC12 on the mt genes. A negative correlation between GC3 and GC12 (r = -0.19 for Channa striata and r = -0.16 for Channa punctata; p < 0.01) demonstrated the impact of directional mutation across all codons in our study (Sueoka, 1988). Furthermore, for the mt genes of both the species, we plotted GC3 and GC12 on the abscissa and ordinate, respectively, and generated a steady regression graph (Fig. 6). In general, natural selection is considered as more important than the mutational pressure if the regression coefficient (RC) value is less than 0.5; while mutational pressure is more important when the RC value is greater than 0.5. In our study, we discovered the RC values of -0.094 for Channa striata and − 0.075 for Channa punctate; implying that natural selection played a greater role over the mutational pressure in determining the CUB of mt genes in two fish species.

In a study on molecular phylogeny of five mitochondrial genomes of silkworm, Abdoli et al. (2022) found the RC value < 0.5 of GC12 on GC3 in all the 13 mitochondrial genes. This indicated that natural selection dominated over mutational pressure in shaping their CUB (Abdoli et al., 2022).

3.8. Nucleotide skewness

In this experiment, the average AT skew value for Channa striata was − 0.065, and the average GC skew value was − 0.308, whereas in Channa punctata average AT skew was 0.092 and average GC skew was − 0.33. The coding sequences of both mt genomes clearly showed that A and G nucleotides were used less frequently than T and C (Wei et al., 2014a). Previous research on CUB suggested that nucleotide skewness played an important role in determining the CUB of genes or genomes (Deb et al., 2018). We calculated the correlation of ENC with nucleotide skews in Channa striata and discovered a positive correlation of ENC with AT skew (0.002), purine (PU) skew (0.213), and keto skew (0.426), but a negative correlation with GC skew (-0.220), pyrimidine (PY) skew (-0.094), and amino skew (-0.191). On the other hand, in Channa punctata we found positive correlation of purine skew (0.212), keto skew (0.434), but negative correlation of GC skew (-0.237), AT skew (-0.001), py skew (-0.148), and amino skew (-0.251) with ENC. From these findings we could conclude that nucleotide skewness might have affected the CUB of mt genes in two fish species.

Deb et al. (2020) discovered more usage of C over T; and G over A base in their study of hepadnavirus. From the correlation analysis of ENC with nucleotide skews, they found a negative relationship between AT skew and CUB, GC skew, PY skew, PU-PY skew, PU skew and amino skew; while a positive correlation was observed with the keto skew (Deb et al., 2020). Butt et al (2016) recorded higher use of A base over G; and C base over T in skew analysis in their experiment with retroviral genomes (Butt et al., 2016). Chakraborty et al. (2019) found a significant correlation of codon usage with nucleotide skews in Nipah virus genes (Chakraborty et al., 2019).

3.9. Role of translational selection (P2)

P2 values are used to determine whether or not the genes are influenced by translational selection. We evaluated the mean P2 value of the genes in Channa striata (0.40) and Channa punctata (0.47). Mean P2 value less than 0.5 indicated that translational selection was less important across two genomes (Chakraborty et al., 2019). Correlation analysis of the ENC and P2 values revealed a negative relationship (-0.207) in Channa striata and a positive correlation (0.017) in Channa punctata; implying a positive relationship between CUB and translational selection in Channa punctata (Deb et al., 2020).

Chakraborty et al. (2019) calculated the MRI and P2 values in their CUB study on Nipah virus, indicating a significant role of the translational selection and mutational pressure (Chakraborty et al., 2019). Deka et al. (2019) discovered a lower level of translational efficiency in the influenza A virus’s matrix protein genes (M1 and M2) (Deka et al., 2019). In their study of the latent-stage genes in Epstein-Barr virus, Karlin et al. (1990) reported that de-optimized codons compete less with the host's cell translation machinery (Karlin et al., 1990).

3.10. The mutation responsive index (MRI)

MRI is used to calculate the effects of translational and mutational selection on CUB (Chakraborty et al., 2019). Presence of mutational pressure is indicated when the MRI value is positive, whereas, the presence of translational selection is indicated if the MRI value is negative. Channa striata had a mean MRI value of 1.14 in our study, while Channa punctata had a mean MRI value of -1.10. It revealed that lateral mutation pressure acted in the mt-genome of Channa striata, while translational selection influenced the mt-genome of Channa punctata.

Chakraborty et al. (2017) in one of their study discovered that translational selection outperformed mutational pressure in four Bungarus species, with average MRI values of -2.58 (B. candidus), -8.74 (B. flaviceps), -0.19 (B. fasciatus), and − 1.44 (B. multicinctus) (Chakraborty et al., 2017). In contrast, Deb et al. (2020) in their study discovered an average MRI value of 0.47, which showed a strong effect of mutation pressure on the CUB of the hepadnavirus genome (Deb et al., 2020).

3.11. General hydropathicity value (GRAVY)

In our study, we found a positive GRAVY (Grand average hydropathy) score (0.725 in Channa striata and 0.735 in Channa punctata) of the encoded proteins in two mt-genomes which revealed that the mitochondrial proteins of the two fish species are all hydrophobic in nature. This could be due to the maintenance of the biological functions in two fish species (Abdoli et al., 2022).

Previous research also found that amino acids like cysteine, threonine and alanine are more abundant in the proteins encoded by ATP6 and ATP8 genes in mammals, fish and bird (Uddin et al., 2020).

3.12. Phylogenetic analysis of the mitochondrial genes of the two fish species

The results of the phylogenetic clustering of the mitochondrial genes of Channa striata and Channa punctata are presented in Fig. 7. In this cluster analysis we found that each of the genes falls in the same cluster. So we could conclude from this clustering analysis that any particular mt gene in both the fishes might have originated from the same ancestral gene.

Acknowledgments

The authors are thankful to Assam University, Silchar.

Funding Information

The work was unfunded.

Ethical statement

Not Applicable. This study is based on DNA sequence analysis and the data were retrieved from publicly available databases.

Conflict of interests

The authors have declared that no conflict of interest exists for this research work.

Declaration of interest

None.

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Comparative analysis of the mitochondrial genomes of two fish species Channa striata and Channa punctata using codon usage bias and their evolutionary relationship

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Abstract

Figures

1. Introduction

2. Methodology

2.1. Collection of sequences

2.2. Measures of CUB:

2.2.1. Nucleotide compositional properties

2.2.2. Effective Number of Codons (ENC)

2.2.3. Relative Synonymous Codon Usage (RSCU)

2.2.4. COA (Correspondence analysis)

2.2.5. Parity plot (PR2) bias analysis

2.2.6. Neutrality Plot

2.2.7. Mutation responsive index (MRI)

2.2.8. Translational selection (P2)

2.2.9. GRAVY

2.2.10. Hydrophilicity

3. Results And Discussions

3.1. Analysis of CUB

3.2. Relative Synonymous Codon Usage (RSCU)

3.3. Compositional properties

3.4. Correspondence analysis (COA)

3.5. Parity rule 2 plot (PR2)

3.6. Interrelationships among the nucleotide compositions

3.7. Neutrality plot

3.8. Nucleotide skewness

3.9. Role of translational selection (P2)

3.10. The mutation responsive index (MRI)

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