Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Tracing the transmission dynamics of HIV-1 CRF55_01B

Abstract

To investigate the genetic diversity, spatiotemporal dynamics, and transmission networks of HIV-1 CRF55_01B epidemic in China. A total of 209 partial pol gene sequences of HIV-1 CRF55_01B were sampled during 2007–2015 from 7 provinces of China. Phylogenetic analyses and trait diffusion process of these sequences were performed using Bayesian methods. Distance-based molecular network analyses were performed to infer putative relationships. Characteristics of genetically linked individuals were analyzed. Our study identified that HIV-1 CRF55_01B likely originated among men who have sex with men (MSM) in Guangdong province in January 2003 (April 2000–April 2005), and that Guangdong province and MSM are major hubs for the spread of the HIV-1 CRF55_01B epidemic in China. A Bayesian Skygrid plot revealed that the effective population size of HIV-1 CRF55_01B experienced increased phase followed by a plateau. All sequences from persons of unknown risk clustered within groups who reported MSM risk. This could be because Chinese MSM may not report such risk due to HIV/AIDS-related stigmatization and discrimination. This study inferred the transmission dynamics of the HIV-1 CRF55_01B epidemic in China at high resolution. The methods developed in this study may be critical for designing effective HIV prevention strategies in China and beyond.

Introduction

The prevalence of HIV-1 among men who have sex with men (MSM) continues to increase in China, especially in its big cities, like Beijing, Shanghai, Guangzhou, Shenzhen, Shenyang, Shijiazhuang1,2. The main HIV-1 subtypes circulating in China are subtype B’ and circulating recombinant forms (CRFs), including CRF01_AE, CRF07_BC, CRF08_BC3. Co-circulation of multiple subtypes of HIV-1 strains among high risk groups, like injecting drug users (IDUs) and MSM, favors the generation of new CRFs4,5. Interestingly, CRF 55_01B was first reported in 2013 among MSM from Changsha city of Hunan province and Dongguan city of Guangdong province in China and it was composed of CRF01_AE and subtype B6. However, the earliest known strain of CRF55_01B was traced back to 2007 in sample from Shenzhen of Guangdong province among MSM7. Now, CRF55_01B is mainly distributed in Guangdong and neighboring provinces in China, and is found across all risk groups7,8. Despite this research, we still have an incomplete understanding the origin and evolutionary history of the CRF55_01B epidemic in China.

In the present study, we employed state-of-the-art methods to define the spatiotemporal dynamics, transmission networks, geographic origins, and migration patterns of CRF55_01B based on 209 partial pol gene sequences of CRF55_01B with known sampling dates (2007–2015) and geographic locations (7 provinces) primarily among MSM in China. We also estimated the maximum posterior probability risk group for each sequence with unknown risk, for the first time, to uncover the key socio-cultural factors (e.g. HIV/AIDS-related stigmatization and discrimination) behind it. Our study provides insights into the origin and evolutionary history of CRF55_01B epidemic in China. The methods developed in this study may be critical for designing effective HIV prevention strategies in China and beyond.

Materials and Methods

Sequence data set

All available partial pol gene sequences of CRF55_01B (2253–3308 nt relative to HXB2) with known sampling year and province of collection were retrieved from the Los Alamos National Laboratory (LANL) HIV Sequence Database (http://www.hiv.lanl.gov). Where multiple sequences were available per individual, only one was selected. Quality Control and RIP v.3.09 from the LANL site were used to analyze the quality and confirm the genotype assignment of all sequences, respectively. Hypermut v2.0 from the LANL site was performed to analysis the hypermutation of all sequences10. For this data set, sequences were aligned using Gene Cutter from the LANL site and then adjusted manually using BioEdit v7.2.511. The final dataset included 209 partial pol gene sequences of CRF55_01B with known sampling year and province between 2007–2015.

Phylogenetic analyses

To examine the phylogenetic signal for this data set, a likelihood-mapping analysis12 was performed using TREE-PUZZLE v5.3.rc1613. To evaluate the temporal structure for this data set, we performed root-to-tip genetic distance against year of sampling using TempEst v1.514. We then employed a Bayesian phylogenetic approach to estimate the rate of evolution and the time to the most recent common ancestor (tMRCA) for this data using a GTR + G substitution model with an uncorrelated lognormal relaxed-clock model15 and a Bayesian Skygrid coalescent tree prior16 in BEAST v1.8.217. The Markov chain Monte Carlo (MCMC) analysis was run for 500 million steps with sampling every 50,000 steps. Convergence was evaluated by calculating the effective sample sizes (ESSs) of the parameters using Tracer v1.7.118. Trees were summarized as maximum clade credibility (MCC) trees using TreeAnnotator after discarding the first 10% as burn-in, and then visualized in FigTree v1.4.3 (http://tree.bio.ed.ac.uk/software/figtree).

To test the hypothesis that a tip with a province or risk group is more likely to share that discrete-trait with a neighboring adjoining tip than would be expected by chance, we calculated the association index (AI), Fitch parsimony score (PS), and monophyletic clade size (MC) statistics for each discrete-trait using Bayesian Tip-Significance Testing (BaTS) v0.9 beta19, as previous described20. We reject the null hypothesis for a significance level of 0.001, 0.001, and 0.05 for AI, PS, and MC statistics, respectively.

Ancestral reconstructions of discrete traits

We employed a Bayesian phylogenetic method to infer the ancestral discrete traits for this data set. To do this, we modelled two types of traits (e.g. geographic location and risk group) as a diffusion process among discrete states21 in BEAST v1.8.217. Diffusion among discrete traits was modelled using a non-reversible continuous-time Markov chain21. Bayesian stochastic search variable selection was used to identify non-zero migration rates between each pair of traits. In addition, we estimate the expected number of viral migrations using ‘Markov jump counts’ approaches22,23,24 between each pair of traits. The phylogeographic history was visualized using SpreaD3 v0.9.625.

Transmission network reconstruction

We employed HIV TRAnsmission Cluster Engine (HIV-TRACE; www.hivtrace.org)26 to infer transmission network clusters for this data set (e.g. closely related sequences inferring a transmission network). All pairwise distances were calculated and a putative linkage between each pair of two sequences was considered whenever their divergent was ≤0.02 substitutions/site (TN93 substitution model). When calculating pairwise genetic distance, all nucleotide ambiguities were resolved and only sequences with less than 0.2% ambiguities were retained. Multiple linkages were then combined into putative transmission clusters. Clusters comprised of only two linked nodes were identified as dyads. This approach detected clusters of recent transmission in which the clustering viruses are genetically similar, implying a direct or indirect epidemiological connection27.

Results

Social-demographic characteristics of this data set

This data set included 209 sequences of CRF55_01B strains from various risk groups: heterosexuals (Hetero, n = 27), injecting drug users (IDUs, n = 1), MSM (n = 169), and unknown risk (n/a, n = 12). The samples are obtained from 7 Chinese provinces: Anhui (n = 2), Guangdong (n = 166), Guangxi (n = 8), Hebei (n = 10), Hunan (n = 2), Shanghai (n = 18), and Zhejiang (n = 3), with sampling years between 2007 and 2015 (Table 1 and Supplementary Table S1). The main risk groups were MSM (80.86%) and Hetero (12.92%). The samples were primarily from Guangdong (79.43%) and Shanghai (8.6%).

Table 1 Geographic source, sampling year, and risk factor for HIV-1 CRF55_01B strains used in the present study.

Transmission network analysis

To identify putative genetic linkage between 2 individuals, we first performed a sensitivity analysis across a plausible range of genetic distance threshold ranging from 0.1% to 2.0%. We found that transmission clusters started to coalesce and the transmission network lost resolution when the genetic distance >0.2% for this data set (Fig. 1). Therefore, we considered individuals as being genetically linked when the genetic distance between HIV-1 CRF55_01B pol sequences was <0.2%. This allowed us identify 27 clusters that included 98/209 (46.89%) sequences. Clusters ranged in size from 2 to 15 sequences, and 13 (48.15%) had 3 or more sequences (Fig. 2; Supplementary Fig. S1; Supplementary Table S2 and S3). Two clusters included at least 10 sequences, and were considered ‘large’ clusters. Both of the two large clusters included sequences exclusively from Guangdong, ranging in size from 10 to 15 sequences (Fig. 2; Supplementary Table S2 and S3). The largest cluster of 15 sequences included exclusively from MSM. However, the other large cluster of 10 sequences included both from MSM and Hetero, and was a predominantly comprised of sequences from people with ‘Hetero’ risk (Supplementary Fig. S1; Supplementary Table S2 and S3).

Figure 1
figure 1

Number of clusters and maximum cluster size, as a function of the TN93 distance threshold. Genetic distance at 0.002 substitutions/site was highlighted with a dash line. Color-coded network parameters are shown on the top left.

Figure 2
figure 2

Transmission clusters of HIV-1 CRF55_01B. The structure of inferred CRF55_01B transmission clusters from our data set are illustrated. Nodes (circles) represent connected individuals in the overall network, and putative transmission linkages are represented by edges (lines). Nodes are color coded by the province of origin.

Of the 27 inferred clusters, 84 (85.71%), 7 (7.14%), and 6 (6.12%) included individuals sampled in Guangdong, Hebei, and Shanghai. Notably, 19 (70.37%) and 3 (11.11%) of 27 clusters were comprised of individuals from only Guangdong and Hebei, respectively. 5 (18.52%) of 27 clusters included sequences from two provinces: 4 clusters included sequences from Guangdong and Shanghai, and 1 from Guangdong and Hebei (Fig. 2; Supplementary Table S2 and S3).

We found that 6 clusters (22.22%) included persons reporting both Hetero and MSM, and only 1 cluster (3.70%) included persons only reporting Hetero risk (Supplementary Fig. S1; Supplementary Table S2 and S3).

Of the 27 (12.92%) sequences from individuals identifying as Hetero, 14 (51.85%) clustered with at least one other sequence, and 83 (49.11%) of the 169 sequences from individuals identifying as MSM clustered.

Likelihood-mapping and evolutionary divergence analysis

For this data set, our likelihood-mapping analysis revealed a strong phylogenetic signal (Supplementary Fig. S2). The correlation between root-to-tip distances and sampling year indicated a relative strong temporal signal (R2 = 0.307), with an estimated substitution rate of 1.64 × 10−3 substitutions per site per year and the time to the most recent common ancestor of July 2003 (Supplementary Fig. S3).

Bayesian evolutionary analyses revealed that the evolutionary rate of CRF55_01B was 1.68 × 10−3 substitutions per site per year [95% highest posterior density (HPD) interval: 1.33 × 10−3–2.05 × 10−3), and tMRCA of CRF55_01B was January 2003 (95% HPD interval: April 2000 and April 2005).

We further investigated the estimated population dynamics of CRF55_01B using a Bayesian Skygrid plot, which reflects the changes in effective population size (Ne) over time. The dynamic of the Ne showed two distinct phases: (1) exponential growth (2005–2008 and 2008–2010) followed by (2) declining phase (2011–2013) (Fig. 3).

Figure 3
figure 3

Bayesian Skygrid demographic reconstruction of HIV-1 CRF55_01B. The vertical axis shows the effective number of infections (Ne) multiplied by mean viral generation time (τ). The solid line and shaded region represent the median and 95% credibility interval, respectively, of the inferred Neτ through time.

Phylogenetic association with geographic location and risk group

Based on the AI and PS statistics of geographic location and risk group traits, we rejected the null hypothesis with P < 0.001 (Tables 2 and 3). For the MC statistic of geographic location trait, we also rejected the null hypothesis (P < 0.05), with the exception of the MC (Anhui) and MC (Shanghai) statistics (Table 2). However, for the MC statistic of risk group trait, we accepted the null hypothesis (P > 0.05) (Table 3). Both the transmission network and MC statistic model analyses showed mixed between risk group (e.g. MSM and Hetero).

Table 2 Statistical analysis of province of CRF55_01B sequences used in the present study.
Table 3 Statistical analysis of risk group of CRF55_01B sequences used in the present study.

Dynamics analysis of ancestral discrete traits

Our phylogeographic analysis revealed that the most probable root location of CRF55_01B ancestor was in Guangdong among MSM in January 2003 (posterior state probability = 1.0) (Fig. 4; Supplementary Fig. S4). Our results also revealed that Guangdong acted as a diffusion center to other provinces (Fig. 5). After the introduction of the virus from Guangdong to Shanghai, Shanghai acted as a secondary diffusion province. In addition, there were one bidirectional transition event from the Shanghai to Guangdong (Fig. 5). Most viral transitions between epidemiologically linked provinces were from Guangdong to Shanghai (mean estimate 14.81; 95% HPD interval: 11.70–17.67; Supplementary Fig. S5). Our results also showed that the estimated of the most posterior probability of all of 12 sequences with unknown risk were grouped into MSM ranging from 0.67 to 0.93.

Figure 4
figure 4

Maximum-clade-credibility tree estimated from partial pol gene sequences of HIV-1 CRF55_01B. Nodes are color coded by the most probable geographic location of the descendent branches.

Figure 5
figure 5

Visualization of geographic location transitions for HIV-1 CRF55_01B overlaid on a geographical map. Points are color coded by the geographic location of origin. Lines are color coded by the geographic location of destination.

Discussion

Our evolutionary analyses, based on 209 partial pol gene sequences of HIV-1 CRF55_01B that included province of origin and year of sampling, confirmed that this subtype had spread widely within China (Supplementary Table S1)7,8,28. Our estimates of the evolutionary rate for CRF55_01B was reassuringly consistent across different methods and models. Further, our estimate of the 95% HPD interval of the evolutionary rate for CRF55_01B (1.33 × 10−3–2.05 × 10−3) had a very narrow range and also included in those obtained from previous study7. Our estimate of the 95% HPD interval of tMRCA for CRF55_01B (April 2000–April 2005) was overlapping with, but not similar to, the reported estimates from two previously studies, which ranged from February 1996 to June 20047 and from June 1996 to January 20048. The uncertainty in our estimate is much smaller than those of previous estimates because of the wider temporal span and more provinces of the samples included in our data set.

Coalescent-based demographic inference revealed a rapidly increasing population size for CRF55_01B from initial phase during 2005–2010, consistent with the outbreak during this period of time. However, we also detected a subsequent phase of rapid declining population size during 2011–2013 (Fig. 3). Furthermore, we found that province was indeed having a significant influence on the complex transmission dynamics of CRF55_01B (Table 2). Phylogeny of CRF55_01B was more likely structured by discrete geographic location traits, especially for Guangdong and Hebei provinces. However, phylogeny of CRF55_01B did not structure by risk group traits, especially for MSM and Hetero risk groups, indicating that MSM and Hetero were mixing with each other (Table 3). This may be explained by that many men in China who reported heterosexual exposure were actually bisexual with ongoing risk exposures to men and women, but they did not report their male sexual contact due to HIV/AIDS-related stigmatization and discrimination7. Our results showed that all sequences from persons of unknown risk clustered within groups who reported MSM risk. This could be because Chinese MSM may not report such risk due to HIV/AIDS-related stigmatization and discrimination.

Our phylogeographic reconstruction for this data set demonstrated that the origin of CRF55_01B was located among MSM in Guangdong between April 2000 and April 2005 (Fig. 4; Supplementary Fig. S4). However, we found that Guangdong was the most important hub of dissemination of CRF55_01B outbreak, fueling the origin of new local epidemics in Shanghai and other provinces (Fig. 5). We also identified one bidirectional transition event between Shanghai and Guangdong. These results indicate the complex spatial dynamics of CRF55_01B.

In the present study, we identified 27 highly related clusters within the CRF55_01B outbreak, including two large clusters detected among MSM and heterosexual men and women in Guangdong (Fig. 2; Supplementary Fig. S1; Supplementary Tables S2 and S3). Among the 27 highly related clusters, 22 included sequences from a single province (19 from Guangdong and 3 from Hebei). These clusters ranged in size from 2 to 15 sequences, and included sequences from persons with MSM and heterosexual risk. The other 5 clusters included sequences from two provinces (e.g. Guangdong and Hebei/Shanghai) exclusively from persons with MSM risk. These clusters ranged in size from 2 to 3 sequences. We acknowledge that large clusters are theoretically more likely to include sequences from multiple origins and that these larger clusters may have been identified because of a more intense sampling in a specific region or during a specific period. These results supported a complex scenario of CRF55_01B that was introduced into epidemiologically linked, high-risk groups in China. Such knowledge can be used to help identify new outbreaks of HIV or specific CRFs in near real-time. Since HIV-1 CRF55_01B is primarily exclusive to China, sequences that did not cluster likely represent missed links from inadequate sampling, which is an issue for all molecular epidemiology studies using real world data. Sequences without these links could not be assessed for their origin, therefore we cannot exclude that the possibility that sampling depth hindered our network inferences. Further deep and wide sampling might reveal the presence of additional CRF55_01B clusters. As more sequences are characterized within other provinces of China, more local, regional, and national clusters are likely to emerge, presenting a challenge to HIV control.

Taken together, our results shed light on the spatial and temporal origins of HIV-1 CRF55_01B, and its mode of spread across comparable geographic areas, and suggest that socio-cultural factors (e.g. HIV/AIDS-related stigmatization and discrimination) should attract enough attention in China and beyond. People’s attitudes and practices concerning HIV/AIDS have an important influence on prevention and control HIV spread. Our results also emphasize the importance of using phylodynamic analyses and transmission networks to provide insights into the role of MSM in the spread of HIV in China. These efforts, combined with epidemiological investigation, are needed to track changes in HIV epidemics. Understanding these epidemic dynamics in real time is increasingly public health importance in terms of guiding prevention efforts.

References

  1. Wu, Z. et al. HIV and syphilis prevalence among men who have sex with men: a cross-sectional survey of 61 cities in China. Clin. Infect. Dis. 57, 298–309, https://doi.org/10.1093/cid/cit210 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zhang, M. et al. A rapidly increasing incidence of HIV and syphilis among men who have sex with men in a major city of China. AIDS Res. Hum. Retroviruses 27, 1139–1140, https://doi.org/10.1089/AID.2010.0356 (2011).

    CAS  Article  PubMed  Google Scholar 

  3. Li, X. et al. Molecular epidemiology of HIV-1 in Jilin province, northeastern China: emergence of a new CRF07_BC transmission cluster and intersubtype recombinants. PLoS One 9, e110738, https://doi.org/10.1371/journal.pone.0110738 (2014).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Li, X. et al. Near full-length genome sequence of a novel HIV-1 recombinant form (CRF01_AE/B) detected among men who have sex with men in Jilin Province, China. AIDS Res. Hum. Retroviruses 30, 701–705, https://doi.org/10.1089/AID.2014.0008 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Li, X. et al. Near full-length genome sequence of a novel HIV type 1 second-generation recombinant form (CRF01_AE/CRF07_BC) identified among men who have sex with men in Jilin, China. AIDS Res. Hum. Retroviruses 29, 1604–1608, https://doi.org/10.1089/AID.2013.0116 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Han, X. et al. Genome Sequences of a Novel HIV-1 Circulating Recombinant Form, CRF55_01B, Identified in China. Genome Announc 1, https://doi.org/10.1128/genomeA.00050-12 (2013).

  7. Zhao, J. et al. Origin and outbreak of HIV-1 CRF55_01B among MSM in Shenzhen, China. J. Acquir. Immune Defic. Syndr. 66, e65–67, https://doi.org/10.1097/QAI.0000000000000144 (2014).

    Article  PubMed  Google Scholar 

  8. Han, X. et al. A Large-scale Survey of CRF55_01B from Men-Who-Have-Sex-with-Men in China: implying the Evolutionary History and Public Health Impact. Sci. Rep. 5, 18147, https://doi.org/10.1038/srep18147 (2015).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. Siepel, A. C., Halpern, A. L., Macken, C. & Korber, B. T. A computer program designed to screen rapidly for HIV type 1 intersubtype recombinant sequences. AIDS Res. Hum. Retroviruses 11, 1413–1416 (1995).

    CAS  Article  Google Scholar 

  10. Rose, P. P. & Korber, B. T. Detecting hypermutations in viral sequences with an emphasis on G–> A hypermutation. Bioinforma. 16, 400–401 (2000).

    CAS  Article  Google Scholar 

  11. Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series 41, 95–98, doi:citeulike-article-id:691774 (1999).

  12. Strimmer, K. & von Haeseler, A. Likelihood-mapping: a simple method to visualize phylogenetic content of a sequence alignment. Proc. Natl Acad. Sci. USA 94, 6815–6819, https://doi.org/10.1073/pnas.94.13.6815 (1997).

    ADS  CAS  Article  PubMed  MATH  Google Scholar 

  13. Schmidt, H. A., Strimmer, K., Vingron, M. & von Haeseler, A. TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. Bioinforma. 18, 502–504, https://doi.org/10.1093/bioinformatics/18.3.502 (2002).

    CAS  Article  Google Scholar 

  14. Rambaut, A., Lam, T. T., Max Carvalho, L. & Pybus, O. G. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus evolution 2, vew007, https://doi.org/10.1093/ve/vew007 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88, https://doi.org/10.1371/journal.pbio.0040088 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Gill, M. S. et al. Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci. Mol. Biol. Evol. 30, 713–724, https://doi.org/10.1093/molbev/mss265 (2013).

    CAS  Article  PubMed  Google Scholar 

  17. Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973, https://doi.org/10.1093/molbev/mss075 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Syst. Biol. 67, 901–904, https://doi.org/10.1093/sysbio/syy032 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Parker, J., Rambaut, A. & Pybus, O. G. Correlating viral phenotypes with phylogeny: accounting for phylogenetic uncertainty. Infection, Genet. evolution: J. Mol. Epidemiol. Evolut. Genet. Infect. Dis. 8, 239–246, https://doi.org/10.1016/j.meegid.2007.08.001 (2008).

    CAS  Article  Google Scholar 

  20. Li, X. et al. Tracing the epidemic history of HIV-1 CRF01_AE clusters using near-complete genome sequences. Sci. Rep. 7, 4024, https://doi.org/10.1038/s41598-017-03820-8 (2017).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS computational Biol. 5, e1000520, https://doi.org/10.1371/journal.pcbi.1000520 (2009).

    ADS  MathSciNet  CAS  Article  Google Scholar 

  22. Minin, V. N. & Suchard, M. A. Counting labeled transitions in continuous-time Markov models of evolution. J. Math. Biol. 56, 391–412, https://doi.org/10.1007/s00285-007-0120-8 (2008).

    MathSciNet  Article  PubMed  MATH  Google Scholar 

  23. Minin, V. N. & Suchard, M. A. Fast, accurate and simulation-free stochastic mapping. Philos. Trans. R. Soc. London. Ser. B, Biol. Sci. 363, 3985–3995, https://doi.org/10.1098/rstb.2008.0176 (2008).

    Article  Google Scholar 

  24. O’Brien, J. D., Minin, V. N. & Suchard, M. A. Learning to count: robust estimates for labeled distances between molecular sequences. Mol. Biol. evolution 26, 801–814, https://doi.org/10.1093/molbev/msp003 (2009).

    CAS  Article  Google Scholar 

  25. Bielejec, F. et al. SpreaD3: Interactive Visualization of Spatiotemporal History and Trait Evolutionary Processes. Mol. Biol. evolution 33, 2167–2169, https://doi.org/10.1093/molbev/msw082 (2016).

    CAS  Article  Google Scholar 

  26. Kosakovsky Pond, S. L., Weaver, S., Leigh Brown, A. J. & Wertheim, J. O. HIV-TRACE (TRAnsmission Cluster Engine): a Tool for Large Scale Molecular Epidemiology of HIV-1 and Other Rapidly Evolving Pathogens. Mol. Biol. Evol. 35, 1812–1819, https://doi.org/10.1093/molbev/msy016 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Wertheim, J. O. et al. Social and Genetic Networks of HIV-1 Transmission in New York City. PLoS Pathog. 13, e1006000, https://doi.org/10.1371/journal.ppat.1006000 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Zhao, J. et al. The dynamics of the HIV epidemic among men who have sex with men (MSM) from 2005 to 2012 in Shenzhen, China. Sci. Rep. 6, 28703, https://doi.org/10.1038/srep28703 (2016).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by a grant from National Natural Science Foundation of China (No. 31470268) to Prof. Yi Li. Dr. Smith was supported for this work by the San Diego Center for AIDS Research (NIH AI036214). The authors would like to thank Prof. Philippe Lemey, Prof. Simon Y. W. HO, Prof. Marcia L. Kalish, Prof. Brian Foley and Dr. Gytis Dudas for their technical support.

Author information

Authors and Affiliations

Authors

Contributions

X.L. conceived and designed the study, performed the experiments, and drafted the manuscript. X.L. and A.C. analyzed the data. X.L., D.S., A.C., H.L., Z.L., Y.L. and J.Z. interpreted data and provided critical comments. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Yi Li or Xingguang Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zai, J., Liu, H., Lu, Z. et al. Tracing the transmission dynamics of HIV-1 CRF55_01B. Sci Rep 10, 5098 (2020). https://doi.org/10.1038/s41598-020-61870-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-020-61870-x

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing