# Pattern of the evolution of HIV-1 *env* gene in Côte d'Ivoire

Sery Gonedelé Bi¹, ²*, Didier P Sokouri¹, Kouakou Tiékoura¹, Oulo Alla N'Nan¹, Marcel Lolo¹, Félix Gnangbé¹ & Assanvo SP N'Guetta¹

¹Laboratoire de Génétique, UFR Biosciences, Université Félix Houphouët Boigny, Abidjan, Côte d'Ivoire, 22 BP 582 Abidjan 22;
²Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, 01 BP 1303 Abidjan 01; Sery Gonedelé Bi - Email: gonedelebi@yahoo.fr;
Phone: 00(225) 03 48 89 23; Fax: 00(225) 23 45 12 11; *Corresponding author

Received September 25, 2014; Revised November 11, 2014; Accepted November 12, 2014; Published November 27, 2014

**Abstract:**

Côte d'Ivoire continues to have the highest HIV-1 prevalence rate in West Africa, although the infection number is in constant decline. The external envelope protein of the viruses is a likely site of selection, and responsible for receptor binding and entry into host cells, and therefore constitutes an ideal region with which to investigate the evolutionary processes acting on HIV-1. In this study, we analyse 189 envelope glycoprotein V3 loop region sequences of viruse isolates from 1995 to 2009, from HIV-1 untreated patients living in Côte d'Ivoire, to decipher the temporal relationship between disease diversity, divergence and selection. Our analyses show that the nonsynonymous and synonymous ratio (dN/dS) was lower than 1 for viral populations analysed within 15 years, which showed the sequences did not undergo adequate immune pressure. The phylogenetic tree of the sequences analysed demonstrated distinctly long internal branches and short external branches, suggesting that only a small number of viruses infected the new host cell at each transmission. In addition to identifying sites under purifying selection, we also identified neutral sites that can cause false positive inference of selection. These sites presented form a resource for future studies of selection pressures acting on HIV-1 *env* gene in Côte d'Ivoire and other West African countries.

**Keywords:** HIV-1, *env* gene, diversity, selection, Côte d'Ivoire

**Background:**

Since the first AIDS case detection in Côte d'Ivoire in 1985, the infection number is in constant decline with an actual estimated prevalence of 3.7%. Although this constant decrease, Côte d'Ivoire continues to have the highest HIV-1 prevalence rate in West Africa and 60% of HIV-infected patients are women, most of them of childbearing age [1]. Based on partial polymerase (pol) and/or envelope (env) sequences, the high prevalence of circulating recombinant form CRF02_AG (82%) and cocirculation of subtype A (5%), CRF01_AE (1%), CRF06_cpx (4%), and complex intersubtype recombinants (11%) has been documented in Côte d'Ivoire [2]. One important feature of HIV-1 infection is the diversification and evolution of the viral genome over the course of infection. From all the protein encoding genes, the most variable is the *env* gene. It encodes for the envelope proteins associated with the host cell-HIV interaction [3]. Changes in this highly

conserved residue provide an interesting case of study to test whether selective pressure was altered with the substitution.

Nevertheless, due to their functional relevance, several amino acid residues are extremely conserved among HIV-1 variants. The external envelope protein is a likely site of selection, being targeted by the patient's antibody response [4] and responsible for receptor binding and entry into host cells [5], and therefore constitutes an ideal region with which to investigate the evolutionary processes acting on HIV-1. The long term fate of these abundant genetic changes depends on the interplay of effective population size and natural selection, resulting in an extremely high rate of HIV genomic evolution [6]. Population level process such as selection, migration, population dynamic and recombination shape HIV genetic diversity both among and within hosts [6]. The ratio of nonsynonymous/synonymous substitution rates has proved useful in
investigating molecular adaptation; however, changes in the absolute rates of nonsynonymous and synonymous substitution should provide greater insight [7]. Changes in synonymous substitution rates can reflect changes in generation time or mutation rate, while nonsynonymous rates can also be affected by changes in selective pressure and effective population size. Previous studies of HIV evolution have typically assumed that the rate of neutral or synonymous change (per month or year) is approximately constant among patients [8].

Differences in the mutational profile among HIV subtypes have been reported [8]. Such high viral genetic diversity among subtypes is involved in difference in the rate of disease progression and response to antiretroviral therapy including the development of resistance [9]. Therefore it is crucial to acquire further knowledge concerning the real significance of

these differences; it may be important to determine strategies of initial treatment for infected individuals. Studying the evolutionary relationship of HIV-1 and characterizing the distinct adaptation patterns in different parts of the HIV-1 genome that interact with the immune system will be key to elucidate how HIV-1 overwhelms the immune system and leads to AIDS [10]. In this study, we present sequence analyses of envelope glycoprotein V3 loop region of viruse isolates from HIV-1 untreated patients living in Côte d'Ivoire, to decipher the temporal relationship between diversity, divergence and selection, in the HIV-1 envelope gene. Understanding the process that determines viral genetic diversity will undoubtedly assist in the struggle against viral infections and will contribute to our knowledge of past epidemiologic events in Côte d'Ivoire.

<figure><img src="image_1.png" /><figcaption>**Figure 1:** Synonymoud and nonsynonimous ratio (dN/dS) plot along HIV-1 *env* gene. Interhost overall dN/dS ratios at all sites within the *env* gene fragments from HIV-1-infected individuals. Box plot showing the mean, standard error and 95% confidence interval for dN/dS ratios obtained for each codon of the HIV-1 alignments. Statistical significance was determined using the Kruskal-Wallis test.</figcaption></figure>

**Methodology:**

*Data sets compilation*

All HIV-1 sequences classified as subtype A derived from Côte d'Ivoire were downloaded from the Los Alamos National Laboratory and GenBank databases. Pseudogenes (as noted in GenBank), clones and sequences with less than 250 bp were excluded from the following analyses. The sequences included in this work were from individuals in the asymptomatic phase

of infection and they were naïve to drug therapy. The description of data sets and the GenBank accession number of each sequence are summarized as supplementary material. The final set included the 189 subtype A sequences described and four non-A sequences, (subtypes B), which were used as out-groups. Fifteen (18) sequences originating from other African countries were also included for phylogenetic comparison: 4 from Mali, 8 from Senegal, and 6 from Congo Democratic
Republic. The sequences were first aligned using the ClustalX program [11]. All sites with deletions and insertions were then excluded in order to preserve the reading frames of the genes.

The final alignment was 406 bp long and is presented as supplementary information.

<figure><img src="image_2.png" /><figcaption>**Figure 2:** Radial phylogeny tree reconstructed by Maximum likelihood methods based on fragment of HIV-1 subtype A *env* V3 loop sequence isolated from HIV-1 untreated patients in Côte d'Ivoire from 1995 to 2009. The tree was generated using the GTR + I + G model of nucleotide substitution. Sequences were named according to their accession number and year of isolation</figcaption></figure>

### Phylogenetic inference

For the maximum likelihood analysis of selection pressures, phylogenetic trees were constructed. We first determined the most appropriate model of nucleotide substitution for each data set using the program jModeltest 2.1 [12]. Models GTR+G, and GTR+I+G were suggested to have better Likelihood scores. Then we reconstructed the phylogenetic tree using the ML method under GTR+G and GTR+I+G methods. We used Markov chain Monte Carlo (MCMC) methods as implemented in BEAST 1.7 to obtain a posterior distribution of trees under an uncorrelated relaxed clock [13]. In order to assess confidence in each of the internal nodes of the constructed phylogeny, a bootstrap resampling (1,000 replicates) of the data using the neighbor-joining method based on maximum-likelihood distances performed with FigTree [14]. To

investigate the diversity change, we inferred between-host mean diversities for the 1995s to 2009s, using the nucleotide diversity, *p*, implemented in MEGA 6 [15] under the GTR+G model again. For Setup Data, the viral sequences obtained from the same year were grouped as one subpopulation. Then, the within-year diversity was calculated by Mean Diversity within Subpopulations, whereas the between-year diversity was calculated by Mean Interpoplational Diversity.

### Analysis of selective pressures

Codon models of coding sequence evolution were used to detect positive selection operating on the HIV-1 *env* gene. In particular, we were interested in differences in positive selection pressure on the virus from the 1995s to the 2009s. Selective pressures were analyzed using two distinct
approaches that estimate the number of nonsynonymous (dN) and synonymous (dS) at all sites in the sequence alignments. This compares the fit to the data of various models of codon evolution, which differ in the distribution of nonsynonymous and synonymous ratio (dN/dS) among sites and takes into account the phylogenetic relationships of the sequences. HyPhy software [16] was used to generate simulated data under a neutral model with trees generated from the original alignments. The same sequence alignments used as input in the initial analysis were used and one hundred simulated datasets were generated for each alignment. Each simulated dataset was then analyzed using the Dual Model as described above. The minimum value of mean dS across all sliding windows of three adjacent codons, in all of the one hundred simulated datasets, was used as a conservative threshold to identify windows of reduced dS in the observed data. This stringent threshold and a less stringent one that included 95% of the values inferred from the simulated data are shown in the sliding window plots.

<figure><img src="image_3.png" /><figcaption>**Figure 3:** Transitions and transversions versus genetic divergence plots of *env* gene fragment isolated from HIV-1-infected individuals in Côte d'Ivoire (s: transitions, v: transversions). The estimated number of transitions and transversions for each pairwise comparison was plotted against the genetic distance.</figcaption></figure>

### Codon usage analysis

The Relative Synonymous Codon Usage (RSCU) values were calculated for the dataset. The RSCU statistics is calculated by dividing the observed usage of a codon by that expected if all codons were used equally frequently. Thus an RSCU of 1 indicates a codon is used as expected by random usage, RSCU > 1 indicates a codon used more frequently than expected randomly, and RSCU < 1 indicates a codon used less frequently than random. RSCU analysis was conducted using Mega 6 software [15]. Rare codon was computed by the improved implementation in DAMBE [17].

## Results:

### Evolutionary analysis

The nucleotide frequencies are 45.59% (A), 26.29% (T/U), 10.41% (C), and 17.71% (G). The transition/transversion rate

ratios are k1 = 1.729 (purines) and k2 = 8.445 (pyrimidines). The overall transition/transversion bias is R = 1.596. The analysis involved 189 nucleotide sequences. Codon positions included were 1st+2nd+3rd+Noncoding. All ambiguous positions were removed for each sequence pair. There were a total of 82 positions in the final dataset.

<figure><img src="image_4.png" /><figcaption>**Figure 4:** Graph showing the relative codon frequency of portion of HIV-1 subtypes A *env* gene fragment isolated from HIV-1-infected individuals in Côte d'Ivoire. The blue and black color bar indicating codons that are used less than 5% and more than 5% respectively.</figcaption></figure>

### Selection analyses

A faster increase of dS was detected with respect to dN through time, and a slowdown of dN/dS in *env* gene due to a slower increase through time of dN with respect to dS **Table 1** (see supplementary material). The dN/dS ratio of *env* regions fluctuates was less than 1. For the fragment of *env* region analysed, the dN/dS ratio was lower than 1 for viral populations analysed within each of the 15 years, which showed the lowest levels of divergence (**Figure 1**). A low dN/dS ratio indicates that, the sequences did not undergo adequate immune pressure to lead to changes in amino acids. The dN/dS ratio of the years 1997 and 2008 showed a significant difference (P < 0.022) compared to the other years, except for year 2001 and 2006. Both the years 1997 and 2008 showed any significant difference (p = 0.215). The synonymous substitution rate was always significantly higher (P = 0.001, Student's t) than the nonsynonymous substitution rate.

### Phylogenetic tree

In the Maximum Likelihood (ML) tree, viral sequences from the same year or from other West African countries do not form a distinct cluster (**Figure 2**). No significant change in sequence diversity was found after nearly two decades of evolution. The reconstructed phylogenetic tree of 211 sequences demonstrated distinctly long internal branches and short external branches, suggesting that only a small number of viruses infected the new host cell at each transmission so that these founder viruses usually are quite different among hosts. These results are compatible with a severe bottleneck at each new infection. The topology of the tree is notable in that the sequences sampled through different times are evenly
distributed among the terminal branches (Figure 2). This
suggests that most of these mutations have occurred
independently and have not been transmitted for sustained
periods of time. Sequences did not cluster according to year or
compartment. Using this model of evolution, the neighbor-
joining tree for the entire data set shows that sequences cluster

predominantly by host individual. Furthermore, no sequences
clustered strongly (bootstrap values 50) with known laboratory
strains of HIV subtype B, an indication of no evidence for
recombination.

<figure><img src="image_5.png" /><figcaption>**Figure 5:** Sliding-window analysis of the cumulative dN/dS across Bayes factor for the event of negative selection at a site along the *env* gene fragment isolated from HIV-1-infected individuals in Côte d'Ivoire.</figcaption></figure>

Phylogenetic Signal and Informativeness

The level of substitution saturation in *env* gene was measured
by comparing the number of transitions and transversions
with the size of the genetic distance for each pair of sequences
(Figure 3). The analysis of these sequences showed that the
amount of substitutions was increasing with the extension of
genetic distance. Only the number of transversions within the
*env* gene showed a lower increasing tendency. However, none
of the plots took the form of a plateau, typical of the state of
saturation with substitutions.

Evolution of env-V3: nucleotide distances

Mean nucleotide distances per year of the *env*-V3 region of the
viral genome of strains A1, are shown in **Table 1**. Overall
nucleotide distances slowly rise over the years. This rise is
mostly accounted for by an increase in synonymous
substitutions, while non-synonymous nucleotide distances are
more constant throughout the period investigated.

Codon usage analysis

Relative synonymous codon usage (RCSU) patterns support
the attenuation hypothesis as well **Table 2** (**see supplementary
material**). Those with the greatest rate of positive change over
time were UUU, UUA, CUG, AUA, GUA, GUG, UCU, UCA,
CCA, ACU, ACA, GCU, GCC, GCA, UAC, CAU, AAU, GAC,
GAA, AGU, AGC, AGA, AGG, GGC, GGG. These changes are
due to simple transition mutations, possibly associated with
some mutational bias. The variation of relative synonymous
codon usage (RSCU) values not only indicated the different
frequency of occurrence of each codon for a given amino acid

in different protein but also revealed the preference of either A
+ U or G + C codon usage as listed in **Table 2**. Preferential
codon usage in the portion of *env* gene analysed indicates that
the codons with A or U at the third position are more preferred
compared to G or C ending codons.

Codon Based Analysis

To examine how the variation of codon usage pattern over
time reflects in the usage of individual codons in HIV-1 *env*
genes in Côte d'Ivoire, the normalized frequency of each codon
in each sequence was compared between the years 1995 and
2009. A graph of codon frequency distribution was plotted to
identify the quantities of rare codons present in each sequence
(Figure 4). Frequency of codon usage with a value of 100
indicates that the codons are highly used for a given amino
acid. Conversely, the frequency of codon usage with a value of
less than 5 is determined as low-frequency codon (blue bars)
which is likely to affect the expression efficiency. Low-
frequency codon are ACG, ACC, AUC, AGA, CCG, CGA,
CCC, CGG, CGC, CGU, CUC, GAU, GCG, GGU, GUC, GUU,
UAU, UCC, UCG, UGC, UGG, UGU, UUC, and UUG,
respectively. This result suggested that the *env* gene analysed
contain a large number of rare codons that may reduce
translational efficiency of the gene. We detected fewer
nonsynonymous substitutions than expected by chance and
dN/dS < 1. Taken together, these results indicate that these
regions are subject to very strong purifying selection. The
location of the midpoints of the window showing negative
selection is given in Figure 5. On average, we detected 68% of
codon sites under negative selection and 28% neutral sites.
BIOINFORMATION

open access

**Discussion:**

In this study, we contrast the changes in genetic diversity and adaptive evolution of the HIV-1 *env* gene between samples collected during fifteen years. Since HIV-1 is an obligate pathogen on human for replication and assembly, codon usage bias, that affects the translational efficiency, is likely to be subjected to host selection pressure [18]. Thus, codon usage bias can play a significant role in host adaptation of HIV-1. For Côte d'Ivoire, no study has examined the *env* genes at the global scale over a long time period to address this issue.

*Phylogenetic tree*

The *env* sequences analysed indicated that substitution
saturation has not been reached, so that the data can be
expected to provide reliable phylogenetic signal. No significant
change in between-year-sequence diversity was found after
more than a decade of evolution. The reconstructed
phylogenetic tree of 211 sequences demonstrated distinctly
short internal branches and long external branches, suggesting
that a large number of viruses infected the new host cell at
each transmission. Moreover, the viruses that successfully
infected new host cells are not under strong selective pressure
from the host immune system, which does not limited
between-host diversification, as indicated by those large
clusters on the tree. The selective pressure does not
significantly vary between the early and the recent samples.
These samples seem most likely infected with the virus
representing the transmissions between the populations with
different genetic backgrounds. This is supported by the
intermixing of Ivorian strains to these isolated from other
African countries (Senegal, Mali, RDC). Since, Côte d'Ivoire
has for decades been the most important destination for
migrants in West Africa, the exchange of HIV-1 gene pools
between and the populations of Côte d'Ivoire and those of the
neighboring countries may increasingly affect the diversity of
HIV-1 gene pools in Côte d'Ivoire. The data indicate a
maximum intragenotypic subtype A distance of 7.3%, lower
than these reported by Janssens et al. [19] who observed a
maximum intragenotypic subtype A distance of 14.1% in their
limited number of samples collected during 1990±1991 in
Abidjan. It is likely that the intragenotypic distance obtained
by these authors is skewed on the basis of so few years
analysed. The low intragenotypic distance obtained by our
data is supported by a large number of silent (synonymous)
mutations that cause no change in the amino acid sequence.
The viruse sequences were remarkably well conserved at the
amino acid level, both within and among different individuals.

Selective pressure

A low dN/dS ratio indicates that, the sequences did not
undergo adequate immune pressure to lead to changes in
amino acids and hence a reflection of the lower variability in
the env gene when compared. The analyses indicate that the
env sequences analysed are subject to purifying selection
overall and that the derived proteins are not subject to positive
selection favoring diversity at the amino acid level but actually
tend to be conserved evolutionarily. Since the continuity of the
various patients analysed are not known over time, the
changes described may not reflect immune pressure. Indeed,
purifying immune selection dominates evolution of HIV
within hosts, but evolution between hosts is largely decoupled
from within-host evolution [20]. The ratio of 0.701 found in our

study is lower than the ratio of 0.90 found by Yamaguchi-
Kabata and Gojobori [21], and higher than that of 0.68 reported
by Brown & Monaghan [22]. Although we did not analyze the
four variable regions where insertions, deletions, and partial
duplications might be very frequent, we think that the ratio in
this study is realistic. We are aware that changes in the
strength of the immune response may not result in predictable
changes in the dN/dS ratio if the selection coefficient is on the
same order of magnitude as the effective population size and
hence providing only a little information about the status of
the immune system [23].

Codon usage biais

Although we are studying changes in codon usage pattern
over a decade, the data were not collected throughout the time
for each host analysed. Hence, our results may represent
outcome of additional and may be even contradictory selective
forces (e.g., effect of anti-retroviral therapies). Such a scenario
can also give rise to results similar to this study. On studying
the codons, Meintjes and Rodrigo [24] found that the early env
sequences displayed a very biased codon usage pattern, where
many codons occurred at very low frequency and the
preferred codons were used at a very high frequency.

Conclusion:

This study is the first that examine the selective pressures that
governed the evolution of the subtypes of HIV-1 in Côte
d'Ivoire, the most affected country in West Africa. No
significant change in the HIV-1 env gene sequences diversity
was found over one decade of evolution. We detected fewer
nonsynonymous substitutions than expected by chance,
indicating that the sequences analyzed are subject to very
strong purifying selection. In addition to identifying sites
under purifying selection, we also identified neutral sites that
can cause false positive inference of selection. These sites
presented form a resource for future studies of selection
pressures acting on HIV-1 env gene in Côte d'Ivoire and other
West African countries.

Acknowledgment:

Authors are grateful to Laboratoire de Génétique at UFR
Biosciences, Université Félix Houphouët Boigny for funding
this research.

**References:**

[1] Toure S *et al*. AIDS. 2008 **22**: 873 [PMID: 18427206]

[2] Toni T *et al*. AIDS Res Hum Retroviruses. 2005 **21**: 667 [PMID: 16060839]

[3] Shioda T et al. Nature. 1991 **349**: 167 [PMID: 1986308]

[4] Moore JP et al. Philos Trans R Soc Lond B Biol Sci. 1993 **342**: 83 [PMID: 7904352]

[5] Wyatt R & Sodroski J, Science. 1998 **280**: 1884 [PMID: 9632381]

[6] Lemey P et al. AIDS Rev. 2006 **8**: 125 [PMID: 17078483]

[7] Seo TK et al. Mol Biol Evol. 2004 **21**: 1201 [PMID: 15014159]

[8] Ganeshan S et al. J Virol. 1997 **71**: 663 [PMID: 8985398]

[9] Frost SD et al. Proc Natl Acad Sci U S A. 2005 **102**: 18514 [PMID: 16339909]

[10] Williamson S *et al*. Mol Biol Evol. 2005 **22**: 456 [PMID: 15509726]

[11] Thompson JD et al. Nucleic Acids Res. 1997 **25**: 4876 [PMID: 9396791]
BIOINFORMATION

open access

[12] Darriba D et al. *Nat Methods*. 2012 *9*: 772 [PMID: 22847109]

[13] Drummond AJ et al. *Mol Biol Evol*. 2012 *29*: 1969 [PMID: 22367748]

[14] Rambaut A (2008) FigTree v1.1.1: Tree figure drawing tool. Available:http://tree.bio.ed.ac.uk/software/figtree/.

[15] Tamura K et al. *Mol Biol Evol*. 2013 *30*: 2725 [PMID: 24132122]

[16] Pond SK & Muse SV, *Mol Biol Evol*. 2005 *22*: 2375 [PMID: 16107593]

[17] Xia X, *Mol Biol Evol*. 2013 *30*: 1720 [PMID: 23564938]

[18] Pandit A & Sinha S, *PLoS ONE*. 2011 *6*: e28889 [PMID: 22216135]

[19] Janssens W et al. *AIDS*. 1994 *8*: 21 [PMID: 8011235]

[20] Keele BF, *Curr Opin HIV AIDS*. 2010 *5*: 327 [PMID: 20543609]

[21] Yamaguchi-Kabata Y & Gojobori T, *J Virol*. 2000 *74*: 4335 [PMID: 10756049]

[22] Brown AL & Monaghan P, *AIDS Res Hum Retroviruses*. 1988 *4*: 399 [PMID: 3219234]

[23] Nielsen R & Yang Z, *Genetics* 1998 *148*: 929 [PMID: 9539414]

[24] Meintjes PL & Rodrigo AG, *J Bioinform Comput Biol*. 2005 *3*: 157 [PMID: 15751118]

Edited by P Kangueane

Citation: Gonedelé Bi et al. Bioinformatics 10(11): 671-678 (2014)

**License statement:** This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited
Supplementary material:

<table><caption>**Table 1:** Estimates of Average Codon-based Evolutionary Divergence over HIV-1 *env* gene Sequence Pairs within year in Côte d'Ivoire</caption><thead><tr><th>Year</th><th>N</th><th>d</th><th>dN</th><th>dS</th></tr></thead><tbody><tr><td>1995</td><td>8</td><td>0.039</td><td>0.113</td><td>0.171</td></tr><tr><td>1997</td><td>31</td><td>0.065</td><td>0.132</td><td>0.222</td></tr><tr><td>1998</td><td>21</td><td>0.051</td><td>0.139</td><td>0.205</td></tr><tr><td>1999</td><td>20</td><td>0.036</td><td>0.165</td><td>0.234</td></tr><tr><td>2000</td><td>13</td><td>0.041</td><td>0.168</td><td>0.212</td></tr><tr><td>2001</td><td>5</td><td>0.043</td><td>0.089</td><td>0.112</td></tr><tr><td>2002</td><td>13</td><td>0.035</td><td>0.162</td><td>0.230</td></tr><tr><td>2003</td><td>14</td><td>0.073</td><td>0.153</td><td>0.224</td></tr><tr><td>2004</td><td>15</td><td>0.025</td><td>0.157</td><td>0.241</td></tr><tr><td>2005</td><td>24</td><td>0.046</td><td>0.157</td><td>0.218</td></tr><tr><td>2006</td><td>3</td><td>0.023</td><td>0.180</td><td>0.209</td></tr><tr><td>2008</td><td>8</td><td>0.065</td><td>0.169</td><td>0.301</td></tr><tr><td>2009</td><td>13</td><td>0.042</td><td>0.172</td><td>0.245</td></tr></tbody></table>

The number of synonymous substitutions per synonymous site from averaging over all sequence pairs within each group is shown. The analysis involved 188 nucleotide sequences. All ambiguous positions were removed for each sequence pair. Evolutionary analyses were conducted in MEGA 6 [15]. Mean nucleotide distance were estimated by Maximum Likelihood using the model GTR + I for the entire set HIV-1 envelope glycoprotein V3 loop gene region.

<table><caption>**Table 2:** Synonymous codons usage pattern in the coding sequences. Optimal codons were identified based on differences in relative synonymous codon usage. Frequency per thousand bases: use frequency per thousand bases in identified high-confidence coding sequences from partial HIV-1 *env* gene (P < 0.05).</caption><thead><tr><th>Codon</th><th>Count</th><th>RSCU</th><th>Codon</th><th>Count</th><th>RSCU</th><th>Codon</th><th>Count</th><th>RSCU</th><th>Codon</th><th>Count</th><th>RSCU</th></tr></thead><tbody><tr><td>UUU(F)</td><td>1.5</td><td>1.89</td><td>UCU(S)</td><td>2.9</td><td>2.06</td><td>UAU(Y)</td><td>0.2</td><td>0.34</td><td>UGU(C)</td><td>0.1</td><td>0.41</td></tr><tr><td>UUC(F)</td><td>0.1</td><td>0.11</td><td>UCC(S)</td><td>0</td><td>0.01</td><td>UAC(Y)</td><td>1.2</td><td>1.66</td><td>UGC(C)</td><td>0.2</td><td>1.59</td></tr><tr><td>UUA(L)</td><td>3.2</td><td>2.46</td><td>UCA(S)</td><td>1.5</td><td>1.04</td><td>UAA(*)</td><td>4.3</td><td>1.62</td><td>UGA(*)</td><td>0.5</td><td>0.2</td></tr><tr><td>UUG(L)</td><td>0.7</td><td>0.58</td><td>UCG(S)</td><td>0</td><td>0.01</td><td>UAG(*)</td><td>3.2</td><td>1.19</td><td>UGG(W)</td><td>1.3</td><td>1</td></tr><tr><td>CUU(L)</td><td>0.2</td><td>0.17</td><td>CCU(P)</td><td>0.6</td><td>1.24</td><td>CAU(H)</td><td>2.2</td><td>1.21</td><td>CGU(R)</td><td>0</td><td>0.07</td></tr><tr><td>CUC(L)</td><td>0.1</td><td>0.11</td><td>CCC(P)</td><td>0</td><td>0.07</td><td>CAC(H)</td><td>1.4</td><td>0.79</td><td>CGC(R)</td><td>0</td><td>0.13</td></tr><tr><td>CUA(L)</td><td>1.7</td><td>1.33</td><td>CCA(P)</td><td>1.2</td><td>2.58</td><td>CAA(Q)</td><td>3.6</td><td>1.02</td><td>CGA(R)</td><td>0.1</td><td>0.18</td></tr><tr><td>CUG(L)</td><td>1.7</td><td>1.34</td><td>CCG(P)</td><td>0</td><td>0.1</td><td>CAG(Q)</td><td>3.5</td><td>0.98</td><td>CGG(R)</td><td>0</td><td>0.09</td></tr><tr><td>AUU(I)</td><td>3.9</td><td>1.3</td><td>ACU(T)</td><td>1.7</td><td>0.91</td><td>AAU(N)</td><td>4.9</td><td>1.38</td><td>AGU(S)</td><td>2.6</td><td>1.83</td></tr><tr><td>AUC(I)</td><td>1</td><td>0.34</td><td>ACC(T)</td><td>0.4</td><td>0.21</td><td>AAC(N)</td><td>2.2</td><td>0.62</td><td>AGC(S)</td><td>1.5</td><td>1.04</td></tr><tr><td>AUA(I)</td><td>4.1</td><td>1.36</td><td>ACA(T)</td><td>5.2</td><td>2.75</td><td>AAA(K)</td><td>3.7</td><td>1.35</td><td>AGA(R)</td><td>1.5</td><td>4.38</td></tr><tr><td>AUG(M)</td><td>2.4</td><td>1</td><td>ACG(T)</td><td>0.2</td><td>0.13</td><td>AAG(K)</td><td>1.8</td><td>0.65</td><td>AGG(R)</td><td>0.4</td><td>1.14</td></tr><tr><td>GUU(V)</td><td>0.6</td><td>0.35</td><td>GCU(A)</td><td>0.3</td><td>0.37</td><td>GAU(D)</td><td>0.2</td><td>0.13</td><td>GGU(G)</td><td>1</td><td>0.65</td></tr><tr><td>GUC(V)</td><td>0.1</td><td>0.05</td><td>GCC(A)</td><td>0.8</td><td>1.08</td><td>GAC(D)</td><td>3.4</td><td>1.87</td><td>GGC(G)</td><td>1.7</td><td>1.13</td></tr><tr><td>GUA(V)</td><td>3.5</td><td>2.15</td><td>GCA(A)</td><td>1.8</td><td>2.34</td><td>GAA(E)</td><td>4.1</td><td>1.24</td><td>GGA(G)</td><td>1.2</td><td>0.77</td></tr><tr><td>GUG(V)</td><td>2.4</td><td>1.45</td><td>GCG(A)</td><td>0.2</td><td>0.22</td><td>GAG(E)</td><td>2.5</td><td>0.76</td><td>GGG(G)</td><td>2.2</td><td>1.45</td></tr></tbody></table>