The HIV-1 epidemic in Asia is mainly caused by CRF01_AE, subtype B and subtype C, alongside additional circulating recombinant forms (CRFs) and unique recombinant forms (URFs) derived from them [1,2]. An interesting fact is that at the epicenter of this continent, the B subtype was separated into two distinct variants. One of them was consistent with the B subtype found in North America, Europe and elsewhere over the world, which is known as the ‘pandemic’ B subtype, whereas the other was divergent and formed a monophyletic group. The latter was termed B-prime (B′) or Thai-B after the country where it was first separated [3–5].
It has since been determined that HIV-1 B′ was closely associated with the early epidemics after 1988 in injecting drug users (IDUs) around the Golden Triangle that represents the main illicit opium-producing areas covering the boundaries of Myanmar, Laos, Thailand and China [3–11], and was rapidly transmitted further all over south-east Asia and China, ultimately spreading into the general population. Although B′ remains prevalent in these regions, few cases of B′ have been reported in other continents and it still coexists with the world pandemic B subtype within Asia. In addition, the circulation of B′ in the same social networks with other subtypes for years fueled the development of recombinant forms of HIV, which are responsible for a growing proportion of new HIV-1 infections in Asia [12–18].
However, in spite of so many localized studies, very little is known about the origin, spread and evolution of B′. Thus, a detailed characterization of the parameters shaping the epidemic, the viral gene flow and evolution of this strain would provide valuable insight regarding the transmission of HIV-1 in Asia as well as aiding in the design of vaccines concentrating on regional epidemics.
The availability of greater numbers of sampled viral sequences combined with increasing computational power makes it possible to retrieve valuable and unique information about the course of viral epidemics from molecular data [19–22]. Furthermore, the coalescent theory of population genetics has been shown to be an effective method for studying the history of a pathogen's population by inferring the transmission dynamics from the genealogy of randomly sampled strains [20,21,23–29]. In this research, we applied this theory to investigate the demographic history of B′ from worldwide and historical perspectives.
The earliest isolated B′ samples were reported to exhibit a distinctive GPGQ motif on the crown region of the V3 loop, whereas the B subtype sequences typically displayed GPGR in this region . We investigated whether these differences remained and whether additional ones existed between the two B subtype variants. We found that although it frequently occurs in the B′, the GPGQ motif is not the most highly conserved and defining difference between the B′ and consensus B subtype. After examination of all the B′ subtype sequences, we report for the first time hallmark mutations of B′ on the p17 and V3 regions, which may assist in vaccine development and study of epidemics of HIV.
Material and methods
Identification of B′ sequences
As the distinction between B′ and B has not been clearly determined, there are few sequences that have been specifically classified as B′ subtype. Thus, we initially downloaded all B subtype sequences covering the p17 or V3 gene regions (one sequence per patient) from the Los Alamos HIV database (http://www.hiv.lanl.gov/content/hiv-db) focusing on samples from Asia where B′ has been documented to be prevalent. Fragments representing known recombinants and sequences from multiple clones were excluded, resulting in a set of 1727 V3 sequences and 317 p17 sequences. Then, following the method by which B′ was first identified, a phylogenetic analysis was performed using PHYLIP 3.65 (PHYLIP; J. Felsenstein, http://evolution.gs.washington.edu/phylip.html) , fastDNAml  and MrBayes V3.1.2  to identify B′ sequences from the raw datasets with confidences as outlined below.
In this analysis, RL42, the first B′ full-length clone , two other B′ full-length clones (CNHN24, LTG0218), and several other B subtype sequences from North America and Europe, for example HXB2, SF33 and CAM1, were used as reference sequences. The test sequences were broken down into smaller groups and compared with these reference sequences. The taxa that provided a consensus, from all three programs, and were closely clustered with the B′ full-length clones with high support were classified as B′; any questionable sequences were excluded. Using this approach, a total of 338 V3 sequences and 147 p17 sequences were collected (supplemental doc file. Accession numbers of all sequences are available from the authors upon request). Several datasets were then compiled from these sequences and manually adjusted according to their codon-reading frame in Se-AL.v2.0 (http://evolve.zoo.ox.ac.uk/). Regions of ambiguity were removed. (All alignments are available from the authors upon request.)
Bayesian Markov Chains Monte Carlo analysis of the genealogical location of the B′ within the B subtype
We downloaded all HIV-1 B subtype env coding regions sampled before 2003 and gag sequences before 2005 from the LANL HIV database. For env and gag, the downloaded B subtype sequences were divided into several sets according to their geographical origin. Three D subtype sequences, five archival Haitian sequences  and RL42 and HXB2 were added to each set of env sequences as reference sequences and MrBayes was used to predict the phylogenetic trees. With the exception of the one Haitian sequence (H2) that was too short to give enough information, the corresponding fragments of the same reference sequences were applied to the gag dataset.
After a series of phylogenetic trees were built, we integrated the topological information from each tree to examine the relationship between B′ and other B subtype clusters from a global perspective. Taking the Haitian sequences, HXB2 and RL42 as reference points, representative env sequences of each cluster of B subtype were extracted and added to the B′ sequences to compile a new dataset Global_env for further analysis. A corresponding representative gag dataset Global_gag was also formed using the same strategy.
Using the three D subtype sequences as an outgroup, the Global_env and Global_gag datasets were analyzed to investigate the genealogical location of B′ at the global level. Both datasets were analyzed by employing a Bayesian Markov Chains Monte Carlo (MCMC) method, as implemented in Bayesian evolutionary analysis by sampling trees (BEAST, V1.4.6; http://evolve.zoo.ox.ac.uk/beast/) , under an uncorrelated log–normal relaxed molecular clock model with a constant Bayesian Skyline coalescent tree prior. This log–normal relaxed-clock model has been previously shown to be accurate and well suited for estimating phylogenetic relationships in viruses .
Reconstruction of the demographic history of B′
Given the earlier HIV sequences were classified based on V3 or p17 region, most of the B′ sequences were only covering a short genetic region and their shared part was even shorter. To make use of more ‘fossil’ sequences while also trying to compile a dataset with sufficient genetic information, we used both the full set of B′_V3 and B′_p17 sequences and a selected dataset of env and gag sequences to perform the demographic history analysis. With a compromise between sequence length and a broad set of geographical location and sample dates, 52 gag sequences and 20 env sequences were selected from the B′_p17 and B′_V3 datasets, respectively.
We first used Modeltest v3.7  to determine the most appropriate nucleotide substitution model for this dataset. The input file for Modeltest, containing a matrix of log-likelihood scores, was generated from the execution of a standard block of Phylogenetic Analysis Using Parsimony (PAUP)*  commands that was provided with the Modeltest package. The four datasets were initially analyzed under a nonparametric model (Bayesian Skyline Plot with uncorrelated log–normal relaxed molecular clock) to directly infer the demographic information within the data. Then, several parametric demographic models were compared to select the model that best describes the epidemiological history of the B′ transmission; they were constant population size, exponential growth, logistic growth and expansion growth. The demographic models were evaluated by the likelihood ratio test from likelihoods calculated by the program GENIE  and the Bayesian factor, which is the ratio of the marginal likelihoods of the two models being compared in a Bayesian framework . The demographic and evolutionary parameters of the epidemic, together with their confidence intervals (CIs), were estimated by Bayesian MCMC inference implemented in the program BEAST.
Identification of signature mutations in the B′ subtype
Because of the limited common region of B′ sequences, we could only analyze the fragments from 1 to 161 of the Gag protein and from 278 to 349 of Env gp160 (HXB2 amino acid sequence position). To identify signature mutations in the B′ sequences, a set of amino acid alignments were generated for subtypes B′ A, B, C, D, 01, F, G, H, J and K from all the sequences covering target gene region downloaded from the LANL HIV database. (All alignments are available from the authors upon request.) For each subtype a consensus sequence was derived, along with the corresponding percentage of each amino acid present at each site. The B′ consensus was considered to be the reference alignment and then compared with each subtype consensus sequence in turn. Only sites in the consensus sequence that contained an amino acid, which was represented at that site in more than 70% of the reference sequences, were considered. All sites in the B′ reference sequence that differed from the other subtype sequences were considered as potential signatures of the B′ subtype and investigated further (software available from authors on request).
Bayesian Markov Chains Monte Carlo analysis of the genealogical location of the B′ within the B subtypes
To investigate the origin of B′, we performed an analysis on representative datasets of B subtype env sequences sampled before 2003 and gag sequences sampled before 2005, with a compromise between a wide sampling date range and a realistic size for the dataset, using a Bayesian MCMC method that simultaneously estimates genealogical relationships and the time of the most recent common ancestors (TMRCA). The resulting tree (Fig. 1) predicts with high support that the D subtype group is located outside the B subtype, and the Haitian sequences occupy the basal position of the B subtype and can be regarded as being closest to the common B subtype ancestor. Notably, among the B subtype sequences, the B′ sequences can be easily recognized as they form a monophyletic group both distinct and distant from the other B subtype sequences with the highest possible posterior value (P = 1). The resulting Global_gag tree shows similar results to the Global_env tree.
When the C2V3 region and p17 region of the same datasets used above were analyzed following the same procedure, the D subtype could still be located correctly, but few details within the B subtype could be established. This is most probably due to insufficient genetic information as a consequence of the short sequence length. However, even within these relatively poorly resolved trees, the B′ sequences remained tightly clustered with the highest support (supplemental Fig. S).
Although some sequences appeared to be more closely associated with the strongly clustered B′ sequences, the relationship was not supported by high clade credibility, thus we were unable to clearly identify a sister cluster in the B subtype. It was also noted that a single B subtype sequence named 5127_86  sampled in the United States in 1986 appeared to cluster comparatively stably with the B′ group (Fig. 1 and supplemental Fig. S), which might provide some insight into the origin of B′ subtype, but there is little available information about this patient.
The origin of the B subtype global epidemic was also estimated. According to the env dataset, the time of the TMRCA of the B/D subtype was calculated to be 1951 (1945–1958 95% CI), the origin of B subtype was 1966 (1964–1969 95% CI), and the emergence of B′ was estimated to be 1984 (1982–1987 95% CI). Although comprising sequences from different patients, the Global_gag dataset provided a similar estimation (Fig. 1).
Demographic history of B′
To infer the demographic history of B′, an analysis was performed using a general time-reversible substitution model with a γ-distributed rate variation and a proportion of invariable sites, as suggested by the Modeltest. Then, different models were compared to interpret the demographic information in each of the four B′ datasets (supplemental doc file). The nonparametric reconstruction of the epidemic history with appropriate confidence limits is illustrated in Fig. 2, showing change in the effective number of infections. All the estimations about the beginning of B′ epidemic from whole B′ V3 and p17 datasets and selected B′ env and gag dataset are consistent (Table 1), regardless of their difference in chronological and geographical spread (supplemental doc file).
Molecular signatures of B′ subtype
We also compared the consensus sequences from each subtype with the consensus sequence for the B′ subtype to try and identify mutations that were unique to B′. This is shown in Fig. 3. Eight and nine distinct signature mutation sites unique to B′ were found in the p17 and V3 regions, respectively. Notably, these differences existed in almost all of the B′ samples, regardless of when and where the samples were collected, and thus they appear to be characteristic of the B′ subtype (supplemental Table S1 and Table S2).
The emergence of B′ was sudden and forced people to recognize within-subtype variety of HIV-1 at the nucleonic  or even antigenic [40,41] level. Although phylogenetically distinguishable, the difference between B′ and typical B subtype has never been clearly determined and many of the B′ sequences have merely been classified as belonging to the B subtype; thus, it took us a great deal of effort to collect a reliable set of B′ sequences. After retrieving and verifying this set, we found that B′ was mainly prevalent in south-east Asia and China. The majority of B′ sequences were found in Thailand, Myanmar and China, and sporadic incidences of B′ were found in other countries such as Malaysia, Singapore, India, Indonesia and Cambodia. This finding is consistent with many previously published epidemiology reports [2–11,42–47].
Each of the different datasets used in this study indicate a similar origin of B′ around 1985. Given the documented explosive epidemic of B′ is in 1988, the epidemic of B′ seems to have been established in about 3 years. Compared with other epidemics (e.g. the HIV-1 C subtype , HIV-1 B subtype in Haiti and USA [21,25]), the transmission of B′ was very fast. This is consistent with findings from earlier studies of the Asian HIV-1 epidemic situation, which suggest that B′ is closely associated with initial phase of the blood-born HIV-1 transmission in Asia, which involved the epidemic among IDUs in south-east Asia and China [3–11,42,47–50] and the epidemic in paid blood donors (PBDs) in China [11,43,46]. HIV is more efficiently transmitted by virus-polluted injecting equipment than other forms of transmission, because it enables the virus to bypass the body tissues and HIV can pass directly into the blood. This is the reason why IDUs and PBDs are the most vulnerable populations under the threat of HIV. In addition, because they may also pass on HIV infection sexually , creating a ‘critical mass’ of infections within sexual networks; even wherever the numbers of people injecting drugs are relatively small, their contribution to the overall HIV epidemic in a country can be considerable. Modeling shows that in situations wherein HIV has remained low for years despite low condom use, a sharp rise in HIV infection among drug injectors could ‘kick-start’ an HIV epidemic that may otherwise have taken many decades to develop . Our result highlights that an epidemic of HIV in IDUs can be established in an astonishingly short period.
In an epidemic that spreads rapidly through a population, the evolutionary rate of HIV is relatively low  and, thus, the diversity of virus is limited, indicating that the molecular character of the founder virus of the epidemic can be preserved on a comparatively large scale. We observed more conserved amino acid regions in B′ and CRF01_AE, which have been transmitted rapidly in Asia than in other subtypes. The signature sites of B′ we identified exist widely among the sequences, regardless of their sampling date and location, which provides insight into the molecular characteristics of the ancestor of B′. These results in turn confirmed our identification of B′ sequences as almost all of these sequences exhibited a majority of these motifs, in sharp contrast to the B subtype sequences (supplemental Table S1 and Table S2). Although only the p17 and V3 regions were analyzed, it is likely that additional signatures exist in other regions, and the amino acid difference between B′ and typical B subtype should be taken into consideration in epidemic investigation and vaccine design.
The complex drug traffic provides routes for various HIV strains to co-circulate in IDUs. As the drug-use and sexual networks intermix in the IDUs, the molecular epidemic situation in this group is changing. After less than a decade, the IDUs in Thailand, Myanmar, Malaysia and China experienced an increase of recombinant forms of HIV, and the dominant position of B′ in those populations had been replaced. The recombinant forms originate from two sources: the CRF01_AE that used to be prevalent in infections via sexual transmission expanded into the IDUs [5,40,41,47,54–56]; new recombinant forms that had been emerging in this group because of the co-circulation of B′ and different subtypes [12,15–17,45,54,57–61]. These new recombinant forms are playing increasingly important roles in the epidemic in Asia. In addition to the role played by B′ in the IDUs–FSWs (female sex workers) driven HIV-1 epidemic in Asia, the B′ remains a dominant subtype of HIV-1 prevalence in rural China as a consequence of illegal blood collection [11,43,46]. However, its impact is no longer restricted to former PBDs, as B′ was transmitted further into the general population through the established pattern of sexual and vertical transmission.
Our finding is the first in-depth investigation of the HIV-1 B′ subtype at a global level, which investigates both the genetic and transmission aspects, providing a valuable reference for HIV scientists, public health officials and HIV vaccine designers.
Explanation: Although our results indicate that B′ represents a variant of subtype B, for consistency with previously published research, and for clarity we refer to this variant as subtype B′ or B′ throughout the article.
The authors would like to thank Xiao Tong for helpful suggestions regarding data interpretation.
Authors contribution: R.Y., X.D., and S.R. conceived the original idea. X.D. and S.R. designed the experiment. X.D. collected the data and performed the genealogical and demographic analysis. S.R. developed the software and performed amino acid analysis. H.L. set up the analysis platform. X.D., S.R., R.Y., and Y.S. contributed to the writing of the article.
Sponsorship: This research was supported by the One Hundred Intellectual Project of The Chinese Academy of Sciences (0702291YC1), National Basic Research 973 Program of China: 2005CB522903, and National Basic Research 973 Program of China: 2006CB504200.
Conflicts of interest: The authors have declared that there is no conflict of interest.
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B' is always reported to be so distant from other B subtype sequences that its relation with B subtype and other HIV-1 subtypes seems obscure and it triggers the wildest guess about its origin. To illustrate the relationship between B' and other HIV-1 subtypes, we downloaded two reference sequences suggested by the Human Retroviruses and AIDS compendium for each HIV-1 subtype from the Los Alamos HIV database. Adding four B' sequences to the selected reference sequences, a full-length alignment was created. MrBayes, V3.1.2 was then used to build a reference tree. We ran two independent chains, sampled every 100th generation, and 250 samples of each run were discarded as burn-in. Our phylogenetic analysis of the full-length B'+ reference sequences alignment showed that although formed a monophyletic group supported by highest posterior value (P = 1), B' still has a closer association with B subtype than with other subtypes (Fig. 1).
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