Bioinf.nuim.ie

JOURNAL OF VIROLOGY, Feb. 2005, p. 1836–1841 0022-538X/05/$08.00ϩ0 doi:10.1128/JVI.79.3.1836–1841.2005Copyright 2005, American Society for Microbiology. All Rights Reserved.
Evidence for Heterogeneous Selective Pressures in the Evolution of the env Gene in Different Human Immunodeficiency Virus Simon A. A. Travers, Mary J. O’Connell, Grace P. McCormack, and James O. McInerney* Biology Department, National University of Ireland, Maynooth, County Kildare, Ireland Received 30 June 2004/Accepted 10 September 2004 Recent studies have demonstrated the emergence of human immunodeficiency virus type 1 (HIV-1) subtypes
with various levels of fitness. Using heterogeneous maximum-likelihood models of adaptive evolution imple-
mented in the PAML software package, with env sequences representing each HIV-1 group M subtype, we
examined the various intersubtype selective pressures operating across the env gene. We found heterogeneity
of evolutionary mechanisms between the different subtypes with a category of amino acid sites observed that
had undergone positive selection for subtypes C, F1, and G, while these sites had undergone purifying selection
in all other subtypes. Also, amino acid sites within subtypes A and K that had undergone purifying selection
were observed, while these sites had undergone positive selection in all other subtypes. The presence of such
sites indicates heterogeneity of selective pressures within HIV-1 group M subtype evolution that may account
for the various levels of fitness of the subtypes.
It has been hypothesized that human immunodeficiency vi- tion. Recently, more biologically realistic methods have been rus type 1 (HIV-1) may have entered humans in three inde- developed to allow for identification of heterogeneous selec- pendent transmissions of simian immunodeficiency virus from tion pressure across amino acid sites and also heterogeneity infected chimpanzees from which the three HIV-1 M, N, and across both sites and lineages within the phylogeny (32, 33).
O lineages arose (9). Within group M, nine phylogenetically Previous studies (4, 28) focused on searching for positive distinct subtypes have been proposed (subtypes A to D, F to H, selection within the HIV-1 group M subtypes by analyzing each J, and K), with subsubtypes being proposed for subtypes A and subtype independently and identifying amino acid sites with a F (18). Subtype distribution varies worldwide, with subtype B high probability of having undergone positive selection. How- predominating in North America and Europe (15) and subtype ever, Drummond et al. (6), referring to work by Seo et al. (20) C accounting for more than 55% of worldwide infections (7) as an example, suggested that positive selection seems to be a due mainly to its prevalence in Southern and Eastern Africa (1, minor contributor to the overall molecular evolution of HIV-1 2, 14, 17, 26) and India (21) and its increasing prevalence in and that negative (purifying) selection imposed by functional Brazil (22) and China (19). Biological differences including low constraints in HIV-1 is more important than positive selection.
CXCR4 coreceptor usage in subtype C (15), decreased pro- Here, we present an analysis of the likely selective pressures tease susceptibility in subtype G (5), and varying subtype re- that have affected HIV-1 group M env sequences in their di- activity to monoclonal antibodies (13, 25) have been observed versification from the original group M founder virus. We have among the subtypes. The production of broadly neutralizing or carried out this analysis by comparing each individual subtype subtype-specific vaccines requires an in-depth understanding to all other group M subtypes in an attempt to identify amino of the inter- and intrasubtype evolution.
acid sites whose evolutionary history appears to be unique in The study of the selective pressures governing the evolution terms of selective constraints for that subtype. The identifica- of protein-coding DNA sequences has traditionally been car- tion of such sites yields information as to unique subtype- ried out by comparing dN (nonsynonymous substitutions per specific molecular traits that may also manifest as unique bio- nonsynonymous site) to dS (synonymous substitutions per syn- onymous site), resulting in a dN-to-dS ratio (␻) (see reference31 for a review). An ␻ of Ͼ1 is indicative of positive selection, MATERIALS AND METHODS
an ␻ of 1 indicates neutral evolution, and an ␻ of Ͻ1 indicates Alignments. All available full-length envelope gene sequences were down-
purifying (negative) selection. However, if there is strong pu- loaded from the Los Alamos National Laboratory HIV sequence database rifying selection operating on the majority of amino acid po- (http://hiv-web.lanl.gov) and aligned by using MacClade (12), and neighbor-joining trees were produced for each subtype by using PAUPء (23). A subset of sitions, averaging ␻ over an entire sequence could misleadingly the full-length envelope gene sequences for each subtype was selected by choos- indicate purifying selection for the entire molecule even in the ing as diverse a range of sequences as possible within each subtype based on their presence of a small number of sites undergoing positive selec- spread through the subtype-specific trees (Table 1). Sequences with a largedegree of similarity contain much the same information, whereas divergent se-quences will contain more information about the intrasubtype diversity. Analignment of the representative sequences of each subtype was produced by using * Corresponding author. Mailing address: Bioinformatic and Phar- MacClade (12). Ambiguous regions of the alignment were removed to avoid macogenomics Laboratory, Biology Department, NUI Maynooth, May- possible false detection of positive selection due to alignment of nonhomologous nooth, County Kildare, Ireland. Phone: 353-1-708 3860. Fax: 353-1-708 sites. The resulting env data set contained 40 sequences and was 764 codons in 3845. E-mail: [email protected].
HETEROGENEOUS EVOLUTION BETWEEN HIV-1 GROUP M SUBTYPES TABLE 1. Representative sequences of selected subtypes contrasted with all other subtypes using the branch site-specific models. The branches leading to subtypes A, B, C, D, F, G, H, J, and K were labeled in a sep-arate analysis, as were the branches leading to the A1, A2, F1, and F2 lineages.
Representative sequences (GenBank accession no.) To ensure stable results, each model was run four times using different starting A1 .AF004885, AF457080, AF069673, AF19327, AB098333, ␻ values, and results from the run with the best likelihood score were taken.
Detection of significant sites. Codeml uses a Bayesian approach to infer the
posterior probability that a particular codon in an alignment is in a particular category (i.e., undergoing a specific selective pressure), and generally, codon sites with P values of Ͼ0.95 are accepted as being significantly allocated to that class.
At times, especially with the branch site models, the likelihood ratio test may be F1 .AF077336, AF005494, AJ249238, AY173957 significant, yet no sites allocated to a particular category will consider a P value of Ͼ0.95 as being in that category. The significant result from the LRT indicates G .AF061642, AF061640, AF084936, AF423760 the presence of a class of sites causing significance of the model; however, the Bayesian approach for the identification of these sites has been suggested to be inadequate using the branch-specific models (33). In order to identify these sites causing LRT significance, we used a site-stripping method to remove the siteswith the highest Bayesian posterior probability, and the resulting stripped align-ment was then reanalyzed (using the same models and parameters). This process A phylogenetic analysis of the data was done by using the maximum-likelihood was repeated iteratively until the LRT failed. Sites removed before the LRT failed criterion as implemented with PAUPء (23) using the GTRϩIϩG substitution were taken to be the sites contributing to the significance of the alternate model.
model as selected by Modeltest (16). In order to assess confidence in each of theinternal nodes of the constructed phylogeny, a bootstrap resampling (1,000replicates) of the data using the neighbor-joining method based on maximum- likelihood distances was performed with PAUPء (23). Tests for saturation ofsynonymous sites throughout the phylogenetic tree were performed by using ␻ estimates. All subtype clades in the maximum-likelihood
tree produced from the data were strongly supported by boot- Intersubtype evolutionary analysis. The software program Codeml from the
strapping (Fig. 1), and no saturation of synonymous sites was PAML package (30) was used for evolutionary analysis of the data set. A number observed within the data. For the site-specific models, all LRTs of site-specific models of codon substitution that allow for rate heterogeneityamong sites were employed, namely model 0, model 1, model 2, model 3, model were significant with a P of 0.0005. The biologically more 7, and model 8 (M0, M1, M2, M3, M7, and M8, respectively). The null models realistic models detected positive selection occurring at sites in M0, M1, and M7, with dN-to-dS ratios (␻) limited between 0 and 1, do not allow the data with M3 and M8 allocating 14% of sites with an ␻ of for the existence of positively selected sites. The alternate models M2, M3, and 2.4814 and 12% of sites with an ␻ of 2.58819, respectively.
M8 allow for the detection of positive selection by enabling the estimated ␻ to begreater than 1. For each of the site-specific models, all sites in the data set under Purifying selection was observed to have occurred in the ma- examination are allocated to one of the constrained or estimated ␻ values usingmaximum likelihood with the proportion of sites allocated to that category beingdescribed by using P values with p0 pertaining to the proportion of sites allocatedto ␻0, p1 pertaining to the proportion of sites allocated to ␻1, and so on.
Also, branch site-specific models (model A and model B), which allow for rate heterogeneity across sites and across the tree, were employed. Model A com-putes three ␻ values and is an extension of M1 in that it limits the first two ␻values (␻0 and ␻1) to 0 and 1 and allows the final ␻ (␻2), which is estimated, avalue greater than 1. Model B is an extension of M3 in that all three ␻ values areestimated. For both of the branch site models, four proportions of sites areallocated to the data set. p0 is the proportion of sites throughout the alignmentallocated to ␻0 with p1 being the proportion of sites allocated to ␻1. p2 corre-sponds to the proportion of sites with a ␻0 value in the background and a ␻2value in the foreground, while p3 corresponds to the proportion of sites with a ␻1value in the background and a ␻2 value in the foreground. The models that allowfor all parameters to be estimated are more biologically realistic than the onesthat constrain certain parameters since the evolutionary mechanisms operatingwithin a data set are never simple. Constraining certain parameters within theanalysis provides a poor representation of the data, while allowing all parametersto be estimated from the data will be a much better and more realistic repre-sentation of the data.
The significance of the alternate models (whether the alternate model is a significantly better representation of the data than the null model) were tested byusing a likelihood ratio test (LRT) which involves taking twice the difference ofthe log likelihood between the nested models and testing for significance usingthe ␹2 distribution with the degrees of freedom being the difference in thenumber of free parameters between the two models. Models compared in thisstudy using LRT were M0 and M3, M1 and M2, M7 and M8, M1 and model A,and M3 and model B (for more information on the models used, see references32 and 33).
Since the branch site models operate by allowing the user to examine the evolutionary mechanisms occurring in a particular lineage in the tree (the fore-ground) against the other lineages (the background), they provide a uniquemethod of analysis by allowing the selective constraints operating on certainsequences to be compared to the selective constraints operating on all the other FIG. 1. Constructed phylogeny of the env data. Branches labeled in sequences present in the data set. The site-specific models do not allow for this boldface are the branches leading to each subtype lineage analyzed kind of analysis, and therefore, labeling the internal node leading to each HIV-1 in this study. P values for significant branch site models are marked group M subtype allowed comparison of the evolution of that particular subtype (ءء P), as are the bootstrap supports for each subtype lineage.
TABLE 2. Selected sites for each subtypea C (P ϭ 0.01), F1 (P ϭ 0.005), G (P ϭ 0.02), and K (P ϭ 0.02),suggesting that these subtypes contain a category of sites that have evolved differently from the other subtypes. From the branch-specific model, the subtypes’ results fell into two cate-gories. In subtypes C, F1, and G, a proportion of their sites were observed to have undergone positive selection, whereas all other subtypes had undergone purifying selection at thatsite (described herein as category I sites). In subtypes A and K, a proportion of sites was observed to have undergone purifying selection with positive selection having occurred in the other subtypes at those sites (described herein as category II sites).
For subtypes C and F1, two sites each were allocated to cate- gory I, while three sites were identified in this category for subtype G. Six codons were allocated to category II for subtype A, and 45 codons were allocated to category II for subtype K a Category I sites were observed for subtypes C, F1, and G, while category II The significant sites for each subtype were labeled on amino sites were observed for subtypes A and K. Sites are described using the HXB2 acid alignments using the known protein secondary structures b Site undergoing positive selection in at least one HIV-1 group M subtype as for gp120 (11) and gp41 (3) (Fig. 2 and 3).
gp120 structural amino acids. For the gp120 structure (Fig.
c Site undergoing positive selection as determined by Yang (29).
d 2), the majority of sites observed in both category I and cate- Site undergoing positive selection as determined by Yamaguchi-Kabata and gory II were structural sites not directly involved in known e Site undergoing purifying selection as determined by Yamaguchi-Kabata and gp120 functions. Within subtype K, however, a number of the f Site undergoing positive selection as determined by Yang et al (28) for their identified category II gp120 sites are functionally significant.
Amino acid sites 295N, 297T, and 334S correspond to a cluster g Site undergoing positive selection as determined by Yang et al (28) in a of nonlinear sites located on the outer domain of gp120 asso- separate analysis of subtypes A, B, and C.
ciated with the binding of the 2G12 antibody (25). Twenty-sixresidues spanning six segments of the gp120 molecule are in- jority of sites (86% for M3 and 88% for M8) through the env volved in direct contact with the host cell CD4 receptor (11), one of which (474D) was identified in category II for subtype The branch site models were implemented to detect any K. Sites 305K, 306R, and 322K, also identified as category II sites that have evolved uniquely to a particular subtype when sites in subtype K, are sites directly involved in or adjacent to compared to the other subtypes. The branch site results were sites directly involved in the switch from the CXCR4 to the significant for the branches leading to subtypes A (P ϭ 0.02), FIG. 2. Positions of category I sites (identified by ͉ for subtype F) and category II sites (identified by ∧ and ء for subtypes A and K, respectively) across the gp120-coding sequence. Secondary protein structures are marked above their coding sequences. Site positions are described using theHXB2 reference sequence.
HETEROGENEOUS EVOLUTION BETWEEN HIV-1 GROUP M SUBTYPES FIG. 3. Positions of category I sites (identified by ϳ for subtype C) and category II sites (identified by ∧ and ء for subtypes A and K, respectively) across the gp41-coding sequence. Secondary protein structures are marked above their coding sequences. Site positions are describedusing the HXB2 reference sequence.
gp41 structural amino acids. Within gp41, many of the iden-
amino acid at that site in the radiation of the subtype. These tified category I and category II sites are located in the C- amino acid changes were radical, with large physiochemical terminal transmembrane region for which, as yet, there is no distances between them when compared using the Grantham three-dimensional structure. Only one category I site was iden- indices (10). For example, within subtype C, position 665 in the tified (665K, subtype C) within the known gp41 ectodomain gp41 flexible linker region contains a serine, while a lysine is (Fig. 3) and was located in the crucial flexible linker region that present in all other subtypes. Previous studies (33, 34) have connects the ectodomain to the transmembrane region. Within shown that functional shifts in a protein are often associated category II, only one subtype A amino acid site (641L) and six with amino acids that exhibit evidence of positive selection.
subtype K amino acid sites for were identified within the Therefore, it is possible that these positively selected amino acid changes, observed here in HIV-1 subtypes, may also haveinduced functional change. However, further analysis of the DISCUSSION
effects on viral fitness and structure by the observed replace-ments is needed.
We have conducted an analysis of the intersubtype evolution There was little correlation between category I sites and of HIV-1 group M subtypes that identify groups of sites that amino acid sites identified in other studies (4, 27–29) as hav- have been subject to different selective constraints in the lin- ing undergone strong selective pressures. Previous intersub- eages leading to each subtype. The use of evolutionary models type studies (4, 28) examined group M subtypes independently, that incorporate different rates over different lineages allowed looking at selective pressures within each subtype. In this study, for the detection of sites undergoing evolutionary constraints we have examined the selective pressures of the branches lead- unique to a lineage within the data that would not have beendetected in any other method of analysis. Two categories of ing to each subtype compared to those of all other lineages.
sites were observed: first, sites that have experienced positive Sites we have identified as undergoing positive selection in one selection in a particular subtype when the same site has expe- subtype compared to all other subtypes may seem to be un- rienced purifying selection in all other subtypes (category I); dergoing purifying selection when only the subtype itself is ex- and second, sites that have experienced purifying selection in amined due to the conserved nature of these sites within a one subtype while other subtypes have experienced positive selection at that site (category II). Sites with a high probability Category II amino acid sites. Both subtype A and subtype K
of being in category I were identified within subtypes C, F1, contain amino acid sites that have been under pressure to and G, while sites with a high probability of being in category retain their current state, while these sites are under pressure II were identified within subtypes A and K.
to change in all other subtypes. While a small number (six) of Category I amino acid sites. Upon sequence examination,
such sites was identified for subtype A, a much larger number category I sites are generally composed of amino acids that are (45) was identified for subtype K. This finding may indicate a conserved in the selected subtype but different in all other substantial difference between the selective pressures govern- subtypes. This indicates positive selection for this particular ing the evolution of subtype K and those of all other subtypes.
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