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A national study of the molecular epidemiology of HIV-1 in Australia 2005–2012

A national study of the molecular epidemiology of HIV-1 in Australia 2005–2012 national study of the molecular epidemiology of HIV-1 in Australia 2005±2012. PLoS ONE 12(5): e0170601. https://doi.org/10.1371/journal. Introduction pone.0170601 Rates of new HIV-1 diagnoses are increasing in Australia, with evidence of an increasing Editor: Chiyu Zhang, Institut Pasteur of Shanghai proportion of non-B HIV-1 subtypes reflecting a growing impact of migration and travel. The Chinese Academy of Sciences, CHINA present study aims to define HIV-1 subtype diversity patterns and investigate possible HIV- Received: September 13, 2016 1 transmission networks within Australia. Accepted: January 6, 2017 Published: May 10, 2017 Methods Copyright:© 2017 Castley et al. This is an open The Australian Molecular Epidemiology Network (AMEN) HIV collaborating sites in Western access article distributed under the terms of the Australia, South Australia, Victoria, Queensland and western Sydney (New South Wales), Creative Commons Attribution License, which permits unrestricted use, distribution, and provided baseline HIV-1 partial pol sequence, age and gender information for 4,873 patients reproduction in any medium, provided the original who had genotypes performed during 2005±2012. HIV-1 phylogenetic analyses utilised author and source are credited. MEGA V6, with a stringent classification of transmission pairs or clusters (bootstrap98%, Data Availability Statement: Sequence data are genetic distance1.5% from at least one other sequence in the cluster). available within GenBank (KY867758-KY872628). Funding: Alison Castley gratefully acknowledges Results the Royal Perth Hospital (RPH) - Medical Research HIV-1 subtype B represented 74.5% of the 4,873 sequences (WA 59%, SA 68.4%, w-Syd Foundation (MRF) for providing funding to complete this study. 73.8%, Vic 75.6%, Qld 82.1%), with similar proportion of transmission pairs and clusters found in the B and non-B cohorts (23% vs 24.5% of sequences, p = 0.3). Significantly more subtype Competing interests: The authors have declared that no competing interests exists. B clusters were comprised of3 sequences compared with non-B clusters (45.0% vs 24.0%, PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 1 / 17 Changing HIV-1 diversity in Australia p = 0.021) and significantly more subtype B pairs and clusters were male-only (88% compared to 53% CRF01_AE and 17% subtype C clusters). Factors associated with being in a cluster of any size included; being sequenced in a more recent time period (p<0.001), being younger (p<0.001), being male (p = 0.023) and having a B subtype (p = 0.02). Being in a larger cluster (>3) was associated with being sequenced in a more recent time period (p = 0.05) and being male (p = 0.008). Conclusion This nationwide HIV-1 study of 4,873 patient sequences highlights the increased diversity of HIV-1 subtypes within the Australian epidemic, as well as differences in transmission networks associated with these HIV-1 subtypes. These findings provide epidemiological insights not readily available using standard surveillance methods and can inform the devel- opment of effective public health strategies in the current paradigm of HIV prevention in Australia. Introduction HIV-1 is highly genetically variable with a continual rapid mutation and recombination asso- ciated with an error-prone and non-proofreading reverse transcriptase activity [1]. There are four distinct HIV-1 groups (M, N, O and P) of which the M group accounts for 90% of infec- tions worldwide. Within the M group there are nine phylogenetically distinct subtypes (A-D, F-H, J and K) along with an increasing number of inter-subtype circulating recombinant forms (CRFs). The main HIV-1 subtypes have distinct geographical associations that can pro- vide useful epidemiological information [2], although there is growing evidence of increasing subtype and inter-subtype HIV-1 genetic diversity in regions previously characterised by spe- cific HIV-1 subtypes [3±6]. Globally, subtype C is the most prevalent and is strongly associated with sub-Saharan African and Indian populations, followed by subtype A (east Africa) and subtype B (western Europe, United States and Australia); these jointly account for 70% of HIV infections [2,7]. Other major subtypes (F, H, J and K) have remained stable at low levels, accounting for around 1% of infections worldwide, whilst subtype D has decreased over time [2]. HIV-1 CRFs account for around 17% of infections worldwide; a 50% increase in the num- ber of total global HIV-1 infections between 2000 and 2007. Unique recombinant forms (URFs) account for approximately 4% of all HIV infections globally, though this proportion can increase to as high as 30% of all new infections in regions where multiple subtypes and CRFs co-circulate, known as recombinant hotspots [8,9]. Although HIV-1 subtype diversity exists in Africa where HIV-1 infection has long been established [2,10,11], recent evidence shows previously geographically-restricted HIV-1 subtypes and CRFs have now migrated to broader regions of the world [2,4±6,12,13]. This increasing global HIV-1 diversity may have important clinical implications given that HIV-1 subtypes have been associated with differences in disease progression [14±16], transmissibility [17,18] susceptibility to antiretroviral therapy [14,19], HIV-1-specific immune responses rele- vant to in vivo infection and vaccine design [20], as well as risk of age-related diseases [21]. Importantly, increased HIV-1 subtype diversity including inter-subtype recombinant forms pose a challenge in HIV diagnostic laboratories, particularly pertaining to HIV-1 RNA assays where the accuracy of results may be influenced by non-B subtype sequence variation [22]. PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 2 / 17 Changing HIV-1 diversity in Australia Australia has a history of strong community engagement, effective public health and clinical management strategies that have contributed to a low national HIV-1 prevalence of ~158/ 100,000 population, with ~26,800 people currently living with HIV [23]. Despite this, the num- ber of new diagnoses has increased by 26% since 2003: this includes 1,236 new cases in 2014, representing a 10% increase over the numbers diagnosed in 2011 [23]. This is set against an overall downward global trend in new HIV-1 diagnoses, most notably in sub-Saharan Africa and also evident in the Asia Pacific region [24]. The Australian HIV epidemic has previously been characterised by a high prevalence of HIV-1 subtype B infection across all risk categories [25±27]. Recently however, there has been a reported increase of imported HIV-1 infection via migration or overseas travel [28] to and from areas where known HIV-1 diversity has been established or from areas where HIV-1 infections are increasing or highly prevalent [24,29,30] and among risk groups other than men who have sex with men (MSM) [29]. Given the ongoing rise in new HIV infections in Austra- lia, investigating HIV-1 subtype distribution may provide valuable information that can inform prevention strategies, while also ensuring that laboratory monitoring is appropriate for the local epidemic. This concept is supported by recent epidemiological studies of HIV-1 sequences that have shown marked increases in the prevalence of non-B subtypes and CRFs [3,4,12] and previously described in previous Australian studies [31±33]. Sequence analysis of the HIV-1 pol region, used for detecting antiretroviral drug resistance, can also be utilised for subtype determination and phylogenetic analysis. These analyses can be used to monitor sequence similarities, follow the introduction of new subtypes, and have increasingly been utilised to characterise transmission clusters and networks in order to trace the global diversity of HIV-1 [34,36,37] and how this has changed over time [38±40] and within different risk groups [41,42]. This methodology has been applied on a global scale to study transmission networks [34] and to investigate the role of travel in the spread of HIV-1 within Europe [6,39], the United States [38] and the United Kingdom [41], therefore providing a better understanding of sequence dynamics to assist real time HIV-1 surveillance and poten- tially prevent further HIV infections. In order to provide a national population-based platform for these analyses, we have estab- lished a collaborative network of all Australian HIV-1 sequencing laboratories, which together provide accredited HIV-1 sequencing for clinical management throughout all Australian states and territories. This network has been identified as the Australian Molecular Epidemiology Network (AMEN). We have performed a retrospective analysis of predominantly baseline (pre-treatment) HIV-1 sequences to determine HIV-1 subtype distribution and phylogenetic structure within Australia during the period 2005±2012, with the aim of supporting rational, evidence-based approaches to prevent, treat and monitor HIV-1 infection within Australia and its linked transmission networks. Materials and methods Sample population and dataset collation This study was conducted within the framework of the Australian Molecular Epidemiology Network (AMEN), a collaborative network formed to include HIV laboratory services across Australia (Western Australia, South Australia, Victoria, Queensland and New South Wales: represented in this study by western Sydney). State ethics (Western Sydney Local Health Dis- trict Human Research Ethics Committee, South Australian Human Research Ethics Commit- tee for South Australia, Melbourne Health Human Research Ethics Committee, Pathology Queensland Institutional Review Board for Low and Negligible Risk Research and Royal Perth Hospital Ethics Committee), national ethics (University of Sydney Human Research Ethics PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 3 / 17 Changing HIV-1 diversity in Australia Committee) and governance frameworks were established to include assessment of de-identi- fied HIV-1 sequences where a unique identification number was assigned, along with the noti- fication of each state, the year the sequence was performed, the gender and the age of the patient at the time of sequence. Data collection and analysis was performed on baseline HIV-1 plasma RNA samples analysed throughout Australia from 2005±2012 with the exception of Queensland where sequences from 2007±2012 were provided. In the case where multiple sequences for one patient were identified, the earliest sequence was determined to be the base- line sequence for the analysis. A total of 4,873 sequences were assessed and stratified over four time eras based on the year of sample collection (2005±2006, 2007±2008, 2009±2010 and 2011±2012). HIV-1 subtype determination A contiguous pol sequence was generated spanning the protease (PR, amino acid positions 1±99 in HXB2) and reverse transcriptase region (RT, amino acid positions 20±240 in HXB2) sequences were amplified using in-house techniques (Western Australia [31], South Australia [32], Victoria [33], and western Sydney) or commercially available methods (Queensland; Vir- oseq HIV-1 Genotyping System, Abbott Celera). The HIV-1 subtype determinations were assigned by each state by submitting the fasta files to the calibrated population resistance tool (HIV db program) linked to the Stanford HIV database (http://hivdb.stanford.edu/) with the exception of Victoria who confirmed subtypes by submitting sequences to the Los Alamos database (http://www.hiv.lanl.gov) and the NCBI HIV genotyping tool (http://www.ncbi.nlm. nih.gov/projects/genotyping/). All assays were monitored for quality according to the National Association of Testing Authorities accreditation standards and subjected to quality control procedures and the Royal College of Pathologists of Australasia (RCPA) or alternative quality assurance programs. HIV-1 phylogeny approach: Sequence alignments and processing Data collation and analysis was performed on all de-identified samples at one site (Western Australia). This process involved utilising the BioEdit tool [43] for sequence alignment and assessing sequence quality, while the Molecular Evolutionary Genetics Analysis Version 6 (MEGA V6) phylogenetic tool [44], was used to construct phylogenetic trees and infer clus- tering patterns of similar sequences as previously described [39]. The sequences used in this study have been submitted into GenBank under the following accession identifiers: KY867758-KY872628. Characterisation of sequence similarities pertaining to clusters For computational reasons the sequencing data was divided into B and non-B subtype analysis and in keeping with previous analyses [31±33,39] we removed drug resistance sites then defined clus- tering patterns of similar sequences according to the following conservative approach: (1) paired sequences where two sequences group together according to the criteria where the bootstrap (BS) value was 98% and the genetic distance (GD) between the two sequences was 1.5%; and (2) a ªcluster or networkº where three or more sequences had a BS value of98% and a GD1.5% from at least one other sequence in the cluster. Statistical analysis Statistical analysis of the demographic data was performed using Statistical Package for the Social Sciences version 21.0 (SPSS v21: Armonk, NY: IBM Corp). Data distribution was PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 4 / 17 Changing HIV-1 diversity in Australia assessed for normality then subjected to statistical analysis using T-tests, ANOVA and post hoc tests with correction for multiple comparisons in multivariable analyses. Results were con- sidered statistically significant when p-values <0.05. Results Data distribution within the AMEN HIV-1 cohort The underlying rates of HIV infection in Australia from 2005±2012 are shown for each state (Fig 1). Rates of HIV-1 infection are gradually increasing, overtime, for each state. The baseline HIV-1 sequence contribution from each Australian state from 2005±2012, and the corre- sponding subtype diversity for 4,873 sequences are shown in Table 1. Samples submitted by Victoria (n = 1,668, 34%) and Queensland (n = 1,579, 32%) account for 66% of the total sam- ples for the study while Western Australia (15.5%), Western New South Wales (11%) and South Australia (7.5%) account for 44% of the sequences. Overall, 3631 (74.5%) HIV-1 infected individuals were infected with subtype B while 1,242 (25.5%) were infected with non-B viruses including A, C, D, F, G, CRF01_AE, CRF02_AG subtypes and other inter-subtype recombi- nant forms. Within the subtype B group there was a predominance of males (n = 4,203, 86%; Table 2), with evidence of non-B subtype virus in 19% of males (796/4,203) compared to 67% Fig 1. The number of new HIV-1 diagnoses by year for each Australian jurisdiction outlined in the Australian molecular epidemiology network; NSW = New South Wales, QLD = Queensland, SA = South Australia, VIC = Victoria and WA = Western Australia (Provided by the Kirby Institute [23]). https://doi.org/10.1371/journal.pone.0170601.g001 PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 5 / 17 Changing HIV-1 diversity in Australia Table 1. Contribution from each state toward the AMEN epidemiological and diversity study showing HIV subtype characteristics and baseline sequence information. State Method Year Sequence SUBTYPE ID (assigned from Stanford DB) Hospital/ (n) Service B CRF01_AE C CRF02_AG D Recombinant Other Subtype Subtype Subtype Subtype Subtype Forms Subtypes WA Stanford 2005± 724 427 139 109 17 5 23 4 RPH-DCI dB 2012 SA Stanford 2005± 351 240 38 35 20 1 9 8 SA Health dB 2012 VIC Los 2005± 1668 1261 174 153 10 4 49 17 VIDRL Alamos 2012 dB W-Sydney Stanford 2005± 551 407 43 54 21 3 13 10 Westmead dB 2011 QLD Stanford 2007± 1579 1296 90 124 20 8 34 7 Pathology dB 2012 QLD HIV Reference Lab Total 4873 3631 484 475 88 21 128 46 https://doi.org/10.1371/journal.pone.0170601.t001 of females (436/648, p<0.0001). Females infected with a non-B subtype were on average six years younger than non-B infected males (32.5 years vs 38.5 years; p< 0.001) and 3.8 years younger than subtype B infected females (p<0.001). Females infected with subtype B were approximately three years younger than subtype B infected males (36.3 years vs 39.2 years; p<0.001). HIV-1 subtype diversity distributions from 2005±2012 As shown in Fig 2, a substantial increase in the proportion of HIV-1 non-B subtypes has been demonstrated within each state over time, with Western Australia having the highest Table 2. Demographics for HIV-1 B subtype, non-B subtypes and sequences in a network including age, gender, AMEN centre and sequencing era. Characteristic All sequences B subtype Non-B subtype Sequences in a network (%) Sequences not in a network (%) All Sequences 4873 3631 1242 1135 (23.1) 3738 (76.9) Gender Ð Female 648 212 436 142 (21.9) 506 (78.1) Ð Male 4203 3407 796 991 (23.6) 3212(76.4) Ð Other 22 12 10 2 (13.0) 20 (87.0) Ð Age (Years) 38.3 39 36.4 36.5 38.9 Ð Male 39.2 39.2 38.5 39.6 37.2 Ð Female 33.8 36.3 32.5 31.7 34.3 Ð<18yo (n) 80 23 57 26 54 Ð18yo (n) 4773 3595 1178 1103 3645 AMEN centre Ð WA 724 427 297 178 (24.6) 546 (75.4) Ð SA 351 240 111 70 (19.9) 281 (80.1) Ð VIC 1668 1261 407 439 (26.3) 1229 (73.7) Ð WM NSW 551 407 144 84 (15.2) 467 (84.8) Ð QLD 1583 1296 283 364 (23.1) 1215 (76.9) Sequence Era Ð 2005±2006 809 651 158 119 (14.7) 690 (85.3) Ð 2007±2008 1231 989 242 256 (29.8) 975 (70.2) Ð 2009±2010 1424 1016 408 359 (25.2) 1065 (74.8) Ð 2011±2012 1409 975 434 401 (28.4) 1008 (71.6) https://doi.org/10.1371/journal.pone.0170601.t002 PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 6 / 17 Changing HIV-1 diversity in Australia Fig 2. Australian HIV-1 subtype distribution for 4,873 sequences for B subtype (aqua) and non-B subtypes (grey) over time [2005±2006 (A), 2007±2008 (B), 2009±2010 (C) and 2011±2012 (D)] including the distribution and proportion of HIV-1 subtypes represented in each Australian state (E) from 2005±2012 (blue = B subtype; green = C subtype; tan = CRF01_AE subtype; maroon = CRF02_AG; yellow = D subtype; red = inter-subtype recombination (ISR); light blue = others). https://doi.org/10.1371/journal.pone.0170601.g002 proportion of baseline non-B sequences in the earliest time era (32.3% in 2005±2006), rising to approximately 48% in the period 2011±2012. Interestingly, the substantial increase in HIV-1 non-B subtypes observed in South Australia, western Sydney and Queensland occurred during 2009±2010 while Victoria showed a steady increase from 2007±2012. Each state was represented by a range of HIV-1 subtypes including B, C, D, CRF01_AE, CRF02_AG subtypes and other inter-subtype recombinant forms (Fig 2). There were propor- tionally more subtype C (15.1%) and CRF01_AE (19.2%) sequences in Western Australia, more subtype B (82%) sequences in Queensland and more CRF02_AG (5.7%) sequences in South Australia than other states. Phylogenetic analysis The phylogenetic trees based on HIV-1 partial pol sequences of subtype B and non-B infec- tions are represented in Fig 3A and 3B respectively. Overall, 1,135 of the 4,873 sequences (23%) were identified within a phylogenetic pair or cluster, with no difference between subtype B and non-B sequences (subtype B in a pair/cluster 829/3,631 (23%), non-B 306/1,242 (24.5%); p = 0.3). From the 419 pairs/clusters there were 286 paired sequences, 115 clusters with 3±5 sequences, 17 clusters with 6±13 sequences and one large cluster of 29 sequences. Analysing these results further in terms of subtype distribution, we found proportionally more potential transmission pairs within the subtype C cohort (77%; Fig 4A) compared to the CRF01_AE or subtype B cohorts (64%; Fig 4B and 47%; Fig 4C). There were also significantly more pairs in the non-B cohort than clusters (3 similar sequences: 94 vs 29, p = 0.003). The subtype B cohort had a significantly higher number of large clusters (4 sequences) compared with the non-B cohort (45% vs 24% p = 0.021). At a national level, the gender distribution within clusters were remarkably different between subtype B, C and CRF01_AE (Fig 5A, Anova: p<0.001). As expected a higher PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 7 / 17 Changing HIV-1 diversity in Australia Fig 3. The phylogenetic tree constructions based on HIV-1 pol sequences for (A) HIV-1 B and (B) non-B subtypes assessed in the AMEN study. Each branch colour represents a state (Western Australia (black), South Australia (red), Victoria (blue), western Sydney (green) and Queensland (maroon). https://doi.org/10.1371/journal.pone.0170601.g003 proportion of subtype B pairs and clusters were male-only (88%) compared to CRF01_AE and subtype C clusters (53% and 17%, respectively). Significantly more subtype C pairs and clusters included males and females (74%) compared to CRF01_AE (39%) and subtype B (11%). The phylogenetic analysis of the subtype B and non-B cohorts was further assessed state by state. Within the subtype B cohort (Fig 5B), most states had high proportions of male-only clusters (>84%), with Victoria registering the highest proportion (95%). The two exceptions were South Australia and western Sydney, with lower proportions of male-only subtype B pairs/clusters (64% and 68% respectively) and correspondingly higher proportions of male- female pairs/clusters (36% and 18%, respectively). When assessing non-B cluster dynamics, Victoria had the largest proportion of male-only non-B clusters (63%) followed by Queensland (44%) (Fig 5C). Conversely, western Sydney, Western Australia and South Australia experienced the highest proportion of non-B male/ female pairs/clusters (73%, 67% and 65% respectively) followed by Queensland and Victoria (49% and 32%, respectively). Analysis of large sub-epidemic HIV-1 clusters In the large sub-epidemic cluster analysis we found five distinct clusters with more than nine patient sequences with closely related sequences represented per cluster. The largest cluster, comprising subtype B, was initially identified in 2008 by three related sequences, increasing to include 29 sequences by 2012. All sequences were from males, including 27 sequences from Western Australia and two from Victoria, with a median age of 47.5 years (range 23±70). The PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 8 / 17 Changing HIV-1 diversity in Australia Fig 4. Differences in the distribution and proportions of pairs and larger cluster size for HIV-1 subtypes; HIV-1 C subtype (A), CRF01_AE subtype (B) and B subtype (C) (excludes singletons). https://doi.org/10.1371/journal.pone.0170601.g004 overall mean genetic difference for this cluster was 0.3% (range, 0.2±1.2%) with a bootstrap value of 99%. Other subtype B clusters included two comprising of 10 sequences, one compris- ing of 11 sequences (these three clusters were all males originating from Victoria) and one sub- type B male/female cluster of ten sequences which included sequences from three states (Western Australia, South Australia and Victoria), with 80% of the sequences notified in South Australia. Factors influencing large HIV-1 networks of similar sequences We employed a multivariate regression analysis to show factors associated with being in a transmission pair or cluster, including factors associated with being in a large cluster (4 sequences). Both analyses excluded children <18 years of age. Factors associated with being part of a cluster (regardless of size) are presented in Fig 6A. At a national level associations were found with younger age (β -0.003; p<0.001), having a sequence performed during later era (β 0.05; p<0.001), being male (β -0.01; p = 0.023) and hav- ing a subtype B infection (β -0.05; p = 0.021). No significant association with the state the sequence originated from was found (β 0.01 p = 0.4). Differences within each state revealed a younger age was associated with being in a cluster for Western Australia, western Sydney and PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 9 / 17 Changing HIV-1 diversity in Australia Fig 5. Gender distribution within clusters according to HIV-1 subtypes (A) and for clusters in each state with B subtype (B) or a non-B subtype (C); W Sydney = Western Sydney, QLD = Queensland, SA = South Australia, VIC = Victoria and WA = Western Australia. https://doi.org/10.1371/journal.pone.0170601.g005 Queensland, while more recent HIV-1 sequencing was a significant factor for being in a cluster for all states besides western Sydney. Victorian males were significantly more likely to be in a cluster while non-B sequences were associated with transmission pairs or small clusters (3 sequences) in Queensland. When factors associated with being in a large cluster (4 sequences) were compared with pairs or clusters of three (Fig 6B), there was a weak association with having a sequence per- formed during a later era (β 0.03; p = 0.05) and a strong association with being male (β 0.12; p = 0.008). In Western Australia, having a subtype B infection (β -0.34; p<0.001) and being sequenced in the current era (β 0.09; p = 0.004) were associated with larger cluster size. Youn- ger age was associated with large cluster size in western Sydney (β -0.01; p = 0.04) while older age was identified as an association in Queensland (β 0.006; p = 0.01). Male gender was a sig- nificant factor in Victoria (β 0.4; p<0.001). Phylogenetic clusters within and between Australian states Monitoring HIV-1 sequence similarities within (intrastate) and across state boundaries (inter- state) was assessed for both subtype B and non-B cohorts (Fig 7), including only the first sequence derived from an individual patient who contributed sequence data in more than one Australian state. There were similar proportions of B and non-B HIV phylogenetic clusters that included sequences from more than one Australian state, increasing from 19.6% (56/285) of paired clusters (20.3% of subtype B pairs versus 18.2% of non-B pairs) to 46.0% (29/63) of clusters comprising >3 sequences (44.7% subtype B clusters >3 in size versus 50% of non-B clusters). Discussion The outcomes of this study highlight an Australian HIV-1 epidemic characterised by an increasing prevalence of non-B infections with an overall expanding subtype diversity. Just over one quarter of all infections were identified as non-B subtypes, which is slightly lower than observed in recent studies undertaken in North America [4], Europe [6] and Belgium [45]. The dominant non-B subtypes identified in Australia are subtype C and CRF01_AE, the PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 10 / 17 Changing HIV-1 diversity in Australia Fig 6. Multivariate regression analysis reveals factors associated with a sequence being classified into (A) a cluster regardless of cluster size or as a singleton or (B) only sequences in larger networks where the sequence size equals 2±3 or4. All results are for >18yo (Coefficient and significance are shown); QLD = Queensland, SA = South Australia, VIC = Victoria and WA = Western Australia. https://doi.org/10.1371/journal.pone.0170601.g006 main HIV-1 strains circulating amongst sub-Saharan African and south-east Asian popula- tions. A recent report has documented that the increase in CRF01_AE infections into previ- ously low prevalence countries can be attributed to travel from Asian countries [35,36], with Thailand, Malaysia, China and India recently identified in the top 10 countries where an increase in Australian visitors has been recorded [28]. Phylogenetic analysis found that these non-B infections are more likely to occur within heterosexual transmission networks. The sim- ilar proportion of males and females infected with subtype C in Australia is consistent with the distribution of heterosexual transmissions documented in sub-Saharan Africa [46] while the PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 11 / 17 Changing HIV-1 diversity in Australia Fig 7. The proportion of interstate and intrastate HIV-1 paired sequences, sequences in a network of 3 or networks> 3 (n = # of pairs or networks). https://doi.org/10.1371/journal.pone.0170601.g007 increasing proportion of CRF01_AE infections in Australia amongst both females and males corresponds with south-east Asian patterns of heterosexual transmission and injecting drug use (IDU), as well as increasing MSM transmissions and injecting drug use (IDU) present in south-east Asia [35,47]. This study suggests that there is a growing contribution of migration and travel, both within Australia and overseas, to the increased HIV-1 subtype diversity nationally and within each of the Australian states. This is consistent with evidence of sharp increases in HIV-1 subtype diversity across the United States [4,38] and Europe [3,39±41]. Previous investigations have also recognised the evolution of HIV-1 diversity within Australia, from an early epidemic pre- dominantly characterised by subtype B infection [25] to recent studies showing increasing HIV-1 non-B subtype diversity [31±33]. HIV-1 phylogenetic analysis provides an important objective resource for dynamically assessing new HIV infections and monitoring geographical changes in the global HIV epi- demic. In this study, in which we classified phylogenetic clusters using a conservative approach that required low genetic distance between sequences (1.5%) as well as high bootstrap values (>98%), distinct transmission network patterns for subtype B, C and CRF01_AE were PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 12 / 17 Changing HIV-1 diversity in Australia revealed. This approach has been successfully utilised to monitor both B [6,34,45] and non-B subtype [34,39,45] cluster patterns, however no definite consensus has been established to define clusters. Further sensitivity studies are therefore warranted and could be achieved by including multiple sequences from the same individual, developing criteria from known trans- mission pairs/networks and from sequencing a second HIV-1 gene. As expected, the majority of pairs, small clusters and larger networks (4 sequences) were identified within male-only groups in the subtype B infected cohort, while there was a higher proportion of male-and-female clusters in the subtype C infected cohort compared with CRF01_AE. Evidence of emerging male-only non-B networks in Victoria suggests likely for- ward transmission of these viral subtypes within local MSM transmission networks, while the large proportion of male-female pairs within non-B transmission networks in western Sydney, South Australia and Western Australia suggest limited forward transmission into local networks. The observation of a large subtype B male network, identified in 2008 and crossing state borders to evolve into a network of 29 patients by 2012, is in line with other epidemiological studies [6,45] showing that the size of transmission networks is not normally distributed and tends to involve one large cluster as well as a range of smaller networks. This requires further investigation that acknowledges both the social and behavioural context of HIV-1 transmis- sion as well as the potentially important role of HIV-1 virulence, given a recent US study showed large cluster size was associated with CD4 T cell counts >350 cells/μl and plasma HIV-1 RNA levels >10,000 copies/mL [38]. This observation is also supported by a recent study within Western Australia [31], and can be considered within a broader context of evi- dence that HIV-1 has adapted over time to evade host immunological control [48], with trends towards higher pre-treatment plasma HIV RNA levels over the past 30 years of the HIV epi- demic [49]. Understanding the increase in HIV-1 diversity within Australia is of significant interest to laboratories as inaccurate HIV-1 RNA quantitation of non-B subtypes, mostly due to genetic variation at primer sites, has previously been reported [22]. It is therefore imperative that the evaluation of HIV-1 RNA assays be conducted in light of existing Australian HIV-1 viral diver- sity, to ensure reliable monitoring of HIV disease and treatment. Increased drug resistance has also been shown in non-B subtypes when using integrase inhibitors [50], while concerns regarding the potential failure of NNRTI/NRTI treatment for those infected with subtype C infection has been described [19]. The accurate determination of antiretroviral drug resistance mutations in light of HIV-1 subtype-associated polymorphisms also requires careful consider- ation in the context of increasing non-B subtype diversity [51]. Subtype D infection, ISRs [15,16] and CXCR4 tropism [18] associated with CRF01_AE [52] have all been associated with rapid disease progression, while subtype A has been associated with slower disease progression [53]. These factors may be important for HIV surveillance and patient care in developed countries. To our knowledge this analysis is the first large-scale assessment of HIV-1 subtypes and phylogenetic network patterns in Australia performed over an eight-year period. While this current investigation provides estimates of this HIV-1 genotyping in Australia it does not pro- vide a link between the distribution and evolution data with human behaviour, including high-risk populations. Future investigations are warranted and should be focused on filling this gap as has been achieved in Asia [35]. The study does highlight the key influences of migration and overseas travel on increasing rates of non-B infections, and reinforces the importance of Australia's engagement with regional and global aspects of the HIV epidemic as well as the importance of engagement between Australian state jurisdictions. In this context, we hope that the establishment of the PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 13 / 17 Changing HIV-1 diversity in Australia Australian Molecular Epidemiology Network, and the results from this first national study provide the basis for a greater understanding of the Australian HIV-1 epidemic, and enhances existing national surveillance methods. This information assists in the development of effective laboratory strategies, as well as informing prevention and treatment strategies to influence the HIV epidemic within Australia and beyond. Acknowledgments The AMEN collaborators include the lead author Alison Castley (allison.castley@health.wa. gov.au) and David Nolan from the Department of Clinical Immunology, Royal Perth Hospital; Shailendra Sawleshwarkar and Rick Varma from Western Sydney Sexual Health Centre, West- ern Sydney Local Health District and the Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney; Belinda Herring, Kiran Thapa and Dominic Dwyer from the Centre for Infectious Diseases and Microbiology Laboratory Services, ICPMR-Pathology West (NSW Health Pathology), Westmead Hospital and University of Sydney, Westmead; Doris Chibo from the HIV Characterisation Laboratory, Victorian Infectious Diseases Refer- ence Laboratory, Doherty Institute; Nam Nguyen from the Division of Immunology, HSQ Pathology Queensland Central Laboratory; Karen Hawke from South Australian Health and Medical Research Institute and the School of Medicine, Flinders University: Rodney Ratcliff from Clinic 275, Royal Adelaide Hospital and the Department of Microbiology and Infectious Diseases, SA Pathology; Roger Garsia from the Clinical Immunology Department, Royal Prince Alfred Hospital; and Anthony Kelleher from the Kirby Institute for Infection and Immunity in Society, University of New South Wales. The AMEN collaborators would like to acknowledge George Guelfi for providing the com- puter resources required to perform the large scale phylogenetic analysis assessed in this study; and to Megan Gooey, Jessica De Vries and Helene Johanson for their assistance in collating patient information. Author Contributions Conceptualization: AC DN SS RV AK KH DC NN BH KT DD RR RG. Data curation: AC DN SS RV. Formal analysis: AC DN. Funding acquisition: AC. Investigation: AC DN SS RV AK KH DC NN BH KT DD RR RG. Methodology: AC DN. Project administration: AC DN SS AK. Resources: AC DN SS RV AK KH DC NN BH KT DD RR RG. Software: AC DN. Supervision: DN SS AK. Validation: AC DN SS RV AK KH DC NN BH KT DD RR RG. Visualization: AC DN. Writing ± original draft: AC DN. Writing ± review & editing: AC DN SS RV AK KH DC NN BH KT DD RR RG. PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 14 / 17 Changing HIV-1 diversity in Australia References 1. Roberts JD, Bebenek K, Kunkel T. 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A national study of the molecular epidemiology of HIV-1 in Australia 2005–2012

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Abstract

national study of the molecular epidemiology of HIV-1 in Australia 2005±2012. PLoS ONE 12(5): e0170601. https://doi.org/10.1371/journal. Introduction pone.0170601 Rates of new HIV-1 diagnoses are increasing in Australia, with evidence of an increasing Editor: Chiyu Zhang, Institut Pasteur of Shanghai proportion of non-B HIV-1 subtypes reflecting a growing impact of migration and travel. The Chinese Academy of Sciences, CHINA present study aims to define HIV-1 subtype diversity patterns and investigate possible HIV- Received: September 13, 2016 1 transmission networks within Australia. Accepted: January 6, 2017 Published: May 10, 2017 Methods Copyright:© 2017 Castley et al. This is an open The Australian Molecular Epidemiology Network (AMEN) HIV collaborating sites in Western access article distributed under the terms of the Australia, South Australia, Victoria, Queensland and western Sydney (New South Wales), Creative Commons Attribution License, which permits unrestricted use, distribution, and provided baseline HIV-1 partial pol sequence, age and gender information for 4,873 patients reproduction in any medium, provided the original who had genotypes performed during 2005±2012. HIV-1 phylogenetic analyses utilised author and source are credited. MEGA V6, with a stringent classification of transmission pairs or clusters (bootstrap98%, Data Availability Statement: Sequence data are genetic distance1.5% from at least one other sequence in the cluster). available within GenBank (KY867758-KY872628). Funding: Alison Castley gratefully acknowledges Results the Royal Perth Hospital (RPH) - Medical Research HIV-1 subtype B represented 74.5% of the 4,873 sequences (WA 59%, SA 68.4%, w-Syd Foundation (MRF) for providing funding to complete this study. 73.8%, Vic 75.6%, Qld 82.1%), with similar proportion of transmission pairs and clusters found in the B and non-B cohorts (23% vs 24.5% of sequences, p = 0.3). Significantly more subtype Competing interests: The authors have declared that no competing interests exists. B clusters were comprised of3 sequences compared with non-B clusters (45.0% vs 24.0%, PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 1 / 17 Changing HIV-1 diversity in Australia p = 0.021) and significantly more subtype B pairs and clusters were male-only (88% compared to 53% CRF01_AE and 17% subtype C clusters). Factors associated with being in a cluster of any size included; being sequenced in a more recent time period (p<0.001), being younger (p<0.001), being male (p = 0.023) and having a B subtype (p = 0.02). Being in a larger cluster (>3) was associated with being sequenced in a more recent time period (p = 0.05) and being male (p = 0.008). Conclusion This nationwide HIV-1 study of 4,873 patient sequences highlights the increased diversity of HIV-1 subtypes within the Australian epidemic, as well as differences in transmission networks associated with these HIV-1 subtypes. These findings provide epidemiological insights not readily available using standard surveillance methods and can inform the devel- opment of effective public health strategies in the current paradigm of HIV prevention in Australia. Introduction HIV-1 is highly genetically variable with a continual rapid mutation and recombination asso- ciated with an error-prone and non-proofreading reverse transcriptase activity [1]. There are four distinct HIV-1 groups (M, N, O and P) of which the M group accounts for 90% of infec- tions worldwide. Within the M group there are nine phylogenetically distinct subtypes (A-D, F-H, J and K) along with an increasing number of inter-subtype circulating recombinant forms (CRFs). The main HIV-1 subtypes have distinct geographical associations that can pro- vide useful epidemiological information [2], although there is growing evidence of increasing subtype and inter-subtype HIV-1 genetic diversity in regions previously characterised by spe- cific HIV-1 subtypes [3±6]. Globally, subtype C is the most prevalent and is strongly associated with sub-Saharan African and Indian populations, followed by subtype A (east Africa) and subtype B (western Europe, United States and Australia); these jointly account for 70% of HIV infections [2,7]. Other major subtypes (F, H, J and K) have remained stable at low levels, accounting for around 1% of infections worldwide, whilst subtype D has decreased over time [2]. HIV-1 CRFs account for around 17% of infections worldwide; a 50% increase in the num- ber of total global HIV-1 infections between 2000 and 2007. Unique recombinant forms (URFs) account for approximately 4% of all HIV infections globally, though this proportion can increase to as high as 30% of all new infections in regions where multiple subtypes and CRFs co-circulate, known as recombinant hotspots [8,9]. Although HIV-1 subtype diversity exists in Africa where HIV-1 infection has long been established [2,10,11], recent evidence shows previously geographically-restricted HIV-1 subtypes and CRFs have now migrated to broader regions of the world [2,4±6,12,13]. This increasing global HIV-1 diversity may have important clinical implications given that HIV-1 subtypes have been associated with differences in disease progression [14±16], transmissibility [17,18] susceptibility to antiretroviral therapy [14,19], HIV-1-specific immune responses rele- vant to in vivo infection and vaccine design [20], as well as risk of age-related diseases [21]. Importantly, increased HIV-1 subtype diversity including inter-subtype recombinant forms pose a challenge in HIV diagnostic laboratories, particularly pertaining to HIV-1 RNA assays where the accuracy of results may be influenced by non-B subtype sequence variation [22]. PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 2 / 17 Changing HIV-1 diversity in Australia Australia has a history of strong community engagement, effective public health and clinical management strategies that have contributed to a low national HIV-1 prevalence of ~158/ 100,000 population, with ~26,800 people currently living with HIV [23]. Despite this, the num- ber of new diagnoses has increased by 26% since 2003: this includes 1,236 new cases in 2014, representing a 10% increase over the numbers diagnosed in 2011 [23]. This is set against an overall downward global trend in new HIV-1 diagnoses, most notably in sub-Saharan Africa and also evident in the Asia Pacific region [24]. The Australian HIV epidemic has previously been characterised by a high prevalence of HIV-1 subtype B infection across all risk categories [25±27]. Recently however, there has been a reported increase of imported HIV-1 infection via migration or overseas travel [28] to and from areas where known HIV-1 diversity has been established or from areas where HIV-1 infections are increasing or highly prevalent [24,29,30] and among risk groups other than men who have sex with men (MSM) [29]. Given the ongoing rise in new HIV infections in Austra- lia, investigating HIV-1 subtype distribution may provide valuable information that can inform prevention strategies, while also ensuring that laboratory monitoring is appropriate for the local epidemic. This concept is supported by recent epidemiological studies of HIV-1 sequences that have shown marked increases in the prevalence of non-B subtypes and CRFs [3,4,12] and previously described in previous Australian studies [31±33]. Sequence analysis of the HIV-1 pol region, used for detecting antiretroviral drug resistance, can also be utilised for subtype determination and phylogenetic analysis. These analyses can be used to monitor sequence similarities, follow the introduction of new subtypes, and have increasingly been utilised to characterise transmission clusters and networks in order to trace the global diversity of HIV-1 [34,36,37] and how this has changed over time [38±40] and within different risk groups [41,42]. This methodology has been applied on a global scale to study transmission networks [34] and to investigate the role of travel in the spread of HIV-1 within Europe [6,39], the United States [38] and the United Kingdom [41], therefore providing a better understanding of sequence dynamics to assist real time HIV-1 surveillance and poten- tially prevent further HIV infections. In order to provide a national population-based platform for these analyses, we have estab- lished a collaborative network of all Australian HIV-1 sequencing laboratories, which together provide accredited HIV-1 sequencing for clinical management throughout all Australian states and territories. This network has been identified as the Australian Molecular Epidemiology Network (AMEN). We have performed a retrospective analysis of predominantly baseline (pre-treatment) HIV-1 sequences to determine HIV-1 subtype distribution and phylogenetic structure within Australia during the period 2005±2012, with the aim of supporting rational, evidence-based approaches to prevent, treat and monitor HIV-1 infection within Australia and its linked transmission networks. Materials and methods Sample population and dataset collation This study was conducted within the framework of the Australian Molecular Epidemiology Network (AMEN), a collaborative network formed to include HIV laboratory services across Australia (Western Australia, South Australia, Victoria, Queensland and New South Wales: represented in this study by western Sydney). State ethics (Western Sydney Local Health Dis- trict Human Research Ethics Committee, South Australian Human Research Ethics Commit- tee for South Australia, Melbourne Health Human Research Ethics Committee, Pathology Queensland Institutional Review Board for Low and Negligible Risk Research and Royal Perth Hospital Ethics Committee), national ethics (University of Sydney Human Research Ethics PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 3 / 17 Changing HIV-1 diversity in Australia Committee) and governance frameworks were established to include assessment of de-identi- fied HIV-1 sequences where a unique identification number was assigned, along with the noti- fication of each state, the year the sequence was performed, the gender and the age of the patient at the time of sequence. Data collection and analysis was performed on baseline HIV-1 plasma RNA samples analysed throughout Australia from 2005±2012 with the exception of Queensland where sequences from 2007±2012 were provided. In the case where multiple sequences for one patient were identified, the earliest sequence was determined to be the base- line sequence for the analysis. A total of 4,873 sequences were assessed and stratified over four time eras based on the year of sample collection (2005±2006, 2007±2008, 2009±2010 and 2011±2012). HIV-1 subtype determination A contiguous pol sequence was generated spanning the protease (PR, amino acid positions 1±99 in HXB2) and reverse transcriptase region (RT, amino acid positions 20±240 in HXB2) sequences were amplified using in-house techniques (Western Australia [31], South Australia [32], Victoria [33], and western Sydney) or commercially available methods (Queensland; Vir- oseq HIV-1 Genotyping System, Abbott Celera). The HIV-1 subtype determinations were assigned by each state by submitting the fasta files to the calibrated population resistance tool (HIV db program) linked to the Stanford HIV database (http://hivdb.stanford.edu/) with the exception of Victoria who confirmed subtypes by submitting sequences to the Los Alamos database (http://www.hiv.lanl.gov) and the NCBI HIV genotyping tool (http://www.ncbi.nlm. nih.gov/projects/genotyping/). All assays were monitored for quality according to the National Association of Testing Authorities accreditation standards and subjected to quality control procedures and the Royal College of Pathologists of Australasia (RCPA) or alternative quality assurance programs. HIV-1 phylogeny approach: Sequence alignments and processing Data collation and analysis was performed on all de-identified samples at one site (Western Australia). This process involved utilising the BioEdit tool [43] for sequence alignment and assessing sequence quality, while the Molecular Evolutionary Genetics Analysis Version 6 (MEGA V6) phylogenetic tool [44], was used to construct phylogenetic trees and infer clus- tering patterns of similar sequences as previously described [39]. The sequences used in this study have been submitted into GenBank under the following accession identifiers: KY867758-KY872628. Characterisation of sequence similarities pertaining to clusters For computational reasons the sequencing data was divided into B and non-B subtype analysis and in keeping with previous analyses [31±33,39] we removed drug resistance sites then defined clus- tering patterns of similar sequences according to the following conservative approach: (1) paired sequences where two sequences group together according to the criteria where the bootstrap (BS) value was 98% and the genetic distance (GD) between the two sequences was 1.5%; and (2) a ªcluster or networkº where three or more sequences had a BS value of98% and a GD1.5% from at least one other sequence in the cluster. Statistical analysis Statistical analysis of the demographic data was performed using Statistical Package for the Social Sciences version 21.0 (SPSS v21: Armonk, NY: IBM Corp). Data distribution was PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 4 / 17 Changing HIV-1 diversity in Australia assessed for normality then subjected to statistical analysis using T-tests, ANOVA and post hoc tests with correction for multiple comparisons in multivariable analyses. Results were con- sidered statistically significant when p-values <0.05. Results Data distribution within the AMEN HIV-1 cohort The underlying rates of HIV infection in Australia from 2005±2012 are shown for each state (Fig 1). Rates of HIV-1 infection are gradually increasing, overtime, for each state. The baseline HIV-1 sequence contribution from each Australian state from 2005±2012, and the corre- sponding subtype diversity for 4,873 sequences are shown in Table 1. Samples submitted by Victoria (n = 1,668, 34%) and Queensland (n = 1,579, 32%) account for 66% of the total sam- ples for the study while Western Australia (15.5%), Western New South Wales (11%) and South Australia (7.5%) account for 44% of the sequences. Overall, 3631 (74.5%) HIV-1 infected individuals were infected with subtype B while 1,242 (25.5%) were infected with non-B viruses including A, C, D, F, G, CRF01_AE, CRF02_AG subtypes and other inter-subtype recombi- nant forms. Within the subtype B group there was a predominance of males (n = 4,203, 86%; Table 2), with evidence of non-B subtype virus in 19% of males (796/4,203) compared to 67% Fig 1. The number of new HIV-1 diagnoses by year for each Australian jurisdiction outlined in the Australian molecular epidemiology network; NSW = New South Wales, QLD = Queensland, SA = South Australia, VIC = Victoria and WA = Western Australia (Provided by the Kirby Institute [23]). https://doi.org/10.1371/journal.pone.0170601.g001 PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 5 / 17 Changing HIV-1 diversity in Australia Table 1. Contribution from each state toward the AMEN epidemiological and diversity study showing HIV subtype characteristics and baseline sequence information. State Method Year Sequence SUBTYPE ID (assigned from Stanford DB) Hospital/ (n) Service B CRF01_AE C CRF02_AG D Recombinant Other Subtype Subtype Subtype Subtype Subtype Forms Subtypes WA Stanford 2005± 724 427 139 109 17 5 23 4 RPH-DCI dB 2012 SA Stanford 2005± 351 240 38 35 20 1 9 8 SA Health dB 2012 VIC Los 2005± 1668 1261 174 153 10 4 49 17 VIDRL Alamos 2012 dB W-Sydney Stanford 2005± 551 407 43 54 21 3 13 10 Westmead dB 2011 QLD Stanford 2007± 1579 1296 90 124 20 8 34 7 Pathology dB 2012 QLD HIV Reference Lab Total 4873 3631 484 475 88 21 128 46 https://doi.org/10.1371/journal.pone.0170601.t001 of females (436/648, p<0.0001). Females infected with a non-B subtype were on average six years younger than non-B infected males (32.5 years vs 38.5 years; p< 0.001) and 3.8 years younger than subtype B infected females (p<0.001). Females infected with subtype B were approximately three years younger than subtype B infected males (36.3 years vs 39.2 years; p<0.001). HIV-1 subtype diversity distributions from 2005±2012 As shown in Fig 2, a substantial increase in the proportion of HIV-1 non-B subtypes has been demonstrated within each state over time, with Western Australia having the highest Table 2. Demographics for HIV-1 B subtype, non-B subtypes and sequences in a network including age, gender, AMEN centre and sequencing era. Characteristic All sequences B subtype Non-B subtype Sequences in a network (%) Sequences not in a network (%) All Sequences 4873 3631 1242 1135 (23.1) 3738 (76.9) Gender Ð Female 648 212 436 142 (21.9) 506 (78.1) Ð Male 4203 3407 796 991 (23.6) 3212(76.4) Ð Other 22 12 10 2 (13.0) 20 (87.0) Ð Age (Years) 38.3 39 36.4 36.5 38.9 Ð Male 39.2 39.2 38.5 39.6 37.2 Ð Female 33.8 36.3 32.5 31.7 34.3 Ð<18yo (n) 80 23 57 26 54 Ð18yo (n) 4773 3595 1178 1103 3645 AMEN centre Ð WA 724 427 297 178 (24.6) 546 (75.4) Ð SA 351 240 111 70 (19.9) 281 (80.1) Ð VIC 1668 1261 407 439 (26.3) 1229 (73.7) Ð WM NSW 551 407 144 84 (15.2) 467 (84.8) Ð QLD 1583 1296 283 364 (23.1) 1215 (76.9) Sequence Era Ð 2005±2006 809 651 158 119 (14.7) 690 (85.3) Ð 2007±2008 1231 989 242 256 (29.8) 975 (70.2) Ð 2009±2010 1424 1016 408 359 (25.2) 1065 (74.8) Ð 2011±2012 1409 975 434 401 (28.4) 1008 (71.6) https://doi.org/10.1371/journal.pone.0170601.t002 PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 6 / 17 Changing HIV-1 diversity in Australia Fig 2. Australian HIV-1 subtype distribution for 4,873 sequences for B subtype (aqua) and non-B subtypes (grey) over time [2005±2006 (A), 2007±2008 (B), 2009±2010 (C) and 2011±2012 (D)] including the distribution and proportion of HIV-1 subtypes represented in each Australian state (E) from 2005±2012 (blue = B subtype; green = C subtype; tan = CRF01_AE subtype; maroon = CRF02_AG; yellow = D subtype; red = inter-subtype recombination (ISR); light blue = others). https://doi.org/10.1371/journal.pone.0170601.g002 proportion of baseline non-B sequences in the earliest time era (32.3% in 2005±2006), rising to approximately 48% in the period 2011±2012. Interestingly, the substantial increase in HIV-1 non-B subtypes observed in South Australia, western Sydney and Queensland occurred during 2009±2010 while Victoria showed a steady increase from 2007±2012. Each state was represented by a range of HIV-1 subtypes including B, C, D, CRF01_AE, CRF02_AG subtypes and other inter-subtype recombinant forms (Fig 2). There were propor- tionally more subtype C (15.1%) and CRF01_AE (19.2%) sequences in Western Australia, more subtype B (82%) sequences in Queensland and more CRF02_AG (5.7%) sequences in South Australia than other states. Phylogenetic analysis The phylogenetic trees based on HIV-1 partial pol sequences of subtype B and non-B infec- tions are represented in Fig 3A and 3B respectively. Overall, 1,135 of the 4,873 sequences (23%) were identified within a phylogenetic pair or cluster, with no difference between subtype B and non-B sequences (subtype B in a pair/cluster 829/3,631 (23%), non-B 306/1,242 (24.5%); p = 0.3). From the 419 pairs/clusters there were 286 paired sequences, 115 clusters with 3±5 sequences, 17 clusters with 6±13 sequences and one large cluster of 29 sequences. Analysing these results further in terms of subtype distribution, we found proportionally more potential transmission pairs within the subtype C cohort (77%; Fig 4A) compared to the CRF01_AE or subtype B cohorts (64%; Fig 4B and 47%; Fig 4C). There were also significantly more pairs in the non-B cohort than clusters (3 similar sequences: 94 vs 29, p = 0.003). The subtype B cohort had a significantly higher number of large clusters (4 sequences) compared with the non-B cohort (45% vs 24% p = 0.021). At a national level, the gender distribution within clusters were remarkably different between subtype B, C and CRF01_AE (Fig 5A, Anova: p<0.001). As expected a higher PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 7 / 17 Changing HIV-1 diversity in Australia Fig 3. The phylogenetic tree constructions based on HIV-1 pol sequences for (A) HIV-1 B and (B) non-B subtypes assessed in the AMEN study. Each branch colour represents a state (Western Australia (black), South Australia (red), Victoria (blue), western Sydney (green) and Queensland (maroon). https://doi.org/10.1371/journal.pone.0170601.g003 proportion of subtype B pairs and clusters were male-only (88%) compared to CRF01_AE and subtype C clusters (53% and 17%, respectively). Significantly more subtype C pairs and clusters included males and females (74%) compared to CRF01_AE (39%) and subtype B (11%). The phylogenetic analysis of the subtype B and non-B cohorts was further assessed state by state. Within the subtype B cohort (Fig 5B), most states had high proportions of male-only clusters (>84%), with Victoria registering the highest proportion (95%). The two exceptions were South Australia and western Sydney, with lower proportions of male-only subtype B pairs/clusters (64% and 68% respectively) and correspondingly higher proportions of male- female pairs/clusters (36% and 18%, respectively). When assessing non-B cluster dynamics, Victoria had the largest proportion of male-only non-B clusters (63%) followed by Queensland (44%) (Fig 5C). Conversely, western Sydney, Western Australia and South Australia experienced the highest proportion of non-B male/ female pairs/clusters (73%, 67% and 65% respectively) followed by Queensland and Victoria (49% and 32%, respectively). Analysis of large sub-epidemic HIV-1 clusters In the large sub-epidemic cluster analysis we found five distinct clusters with more than nine patient sequences with closely related sequences represented per cluster. The largest cluster, comprising subtype B, was initially identified in 2008 by three related sequences, increasing to include 29 sequences by 2012. All sequences were from males, including 27 sequences from Western Australia and two from Victoria, with a median age of 47.5 years (range 23±70). The PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 8 / 17 Changing HIV-1 diversity in Australia Fig 4. Differences in the distribution and proportions of pairs and larger cluster size for HIV-1 subtypes; HIV-1 C subtype (A), CRF01_AE subtype (B) and B subtype (C) (excludes singletons). https://doi.org/10.1371/journal.pone.0170601.g004 overall mean genetic difference for this cluster was 0.3% (range, 0.2±1.2%) with a bootstrap value of 99%. Other subtype B clusters included two comprising of 10 sequences, one compris- ing of 11 sequences (these three clusters were all males originating from Victoria) and one sub- type B male/female cluster of ten sequences which included sequences from three states (Western Australia, South Australia and Victoria), with 80% of the sequences notified in South Australia. Factors influencing large HIV-1 networks of similar sequences We employed a multivariate regression analysis to show factors associated with being in a transmission pair or cluster, including factors associated with being in a large cluster (4 sequences). Both analyses excluded children <18 years of age. Factors associated with being part of a cluster (regardless of size) are presented in Fig 6A. At a national level associations were found with younger age (β -0.003; p<0.001), having a sequence performed during later era (β 0.05; p<0.001), being male (β -0.01; p = 0.023) and hav- ing a subtype B infection (β -0.05; p = 0.021). No significant association with the state the sequence originated from was found (β 0.01 p = 0.4). Differences within each state revealed a younger age was associated with being in a cluster for Western Australia, western Sydney and PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 9 / 17 Changing HIV-1 diversity in Australia Fig 5. Gender distribution within clusters according to HIV-1 subtypes (A) and for clusters in each state with B subtype (B) or a non-B subtype (C); W Sydney = Western Sydney, QLD = Queensland, SA = South Australia, VIC = Victoria and WA = Western Australia. https://doi.org/10.1371/journal.pone.0170601.g005 Queensland, while more recent HIV-1 sequencing was a significant factor for being in a cluster for all states besides western Sydney. Victorian males were significantly more likely to be in a cluster while non-B sequences were associated with transmission pairs or small clusters (3 sequences) in Queensland. When factors associated with being in a large cluster (4 sequences) were compared with pairs or clusters of three (Fig 6B), there was a weak association with having a sequence per- formed during a later era (β 0.03; p = 0.05) and a strong association with being male (β 0.12; p = 0.008). In Western Australia, having a subtype B infection (β -0.34; p<0.001) and being sequenced in the current era (β 0.09; p = 0.004) were associated with larger cluster size. Youn- ger age was associated with large cluster size in western Sydney (β -0.01; p = 0.04) while older age was identified as an association in Queensland (β 0.006; p = 0.01). Male gender was a sig- nificant factor in Victoria (β 0.4; p<0.001). Phylogenetic clusters within and between Australian states Monitoring HIV-1 sequence similarities within (intrastate) and across state boundaries (inter- state) was assessed for both subtype B and non-B cohorts (Fig 7), including only the first sequence derived from an individual patient who contributed sequence data in more than one Australian state. There were similar proportions of B and non-B HIV phylogenetic clusters that included sequences from more than one Australian state, increasing from 19.6% (56/285) of paired clusters (20.3% of subtype B pairs versus 18.2% of non-B pairs) to 46.0% (29/63) of clusters comprising >3 sequences (44.7% subtype B clusters >3 in size versus 50% of non-B clusters). Discussion The outcomes of this study highlight an Australian HIV-1 epidemic characterised by an increasing prevalence of non-B infections with an overall expanding subtype diversity. Just over one quarter of all infections were identified as non-B subtypes, which is slightly lower than observed in recent studies undertaken in North America [4], Europe [6] and Belgium [45]. The dominant non-B subtypes identified in Australia are subtype C and CRF01_AE, the PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 10 / 17 Changing HIV-1 diversity in Australia Fig 6. Multivariate regression analysis reveals factors associated with a sequence being classified into (A) a cluster regardless of cluster size or as a singleton or (B) only sequences in larger networks where the sequence size equals 2±3 or4. All results are for >18yo (Coefficient and significance are shown); QLD = Queensland, SA = South Australia, VIC = Victoria and WA = Western Australia. https://doi.org/10.1371/journal.pone.0170601.g006 main HIV-1 strains circulating amongst sub-Saharan African and south-east Asian popula- tions. A recent report has documented that the increase in CRF01_AE infections into previ- ously low prevalence countries can be attributed to travel from Asian countries [35,36], with Thailand, Malaysia, China and India recently identified in the top 10 countries where an increase in Australian visitors has been recorded [28]. Phylogenetic analysis found that these non-B infections are more likely to occur within heterosexual transmission networks. The sim- ilar proportion of males and females infected with subtype C in Australia is consistent with the distribution of heterosexual transmissions documented in sub-Saharan Africa [46] while the PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 11 / 17 Changing HIV-1 diversity in Australia Fig 7. The proportion of interstate and intrastate HIV-1 paired sequences, sequences in a network of 3 or networks> 3 (n = # of pairs or networks). https://doi.org/10.1371/journal.pone.0170601.g007 increasing proportion of CRF01_AE infections in Australia amongst both females and males corresponds with south-east Asian patterns of heterosexual transmission and injecting drug use (IDU), as well as increasing MSM transmissions and injecting drug use (IDU) present in south-east Asia [35,47]. This study suggests that there is a growing contribution of migration and travel, both within Australia and overseas, to the increased HIV-1 subtype diversity nationally and within each of the Australian states. This is consistent with evidence of sharp increases in HIV-1 subtype diversity across the United States [4,38] and Europe [3,39±41]. Previous investigations have also recognised the evolution of HIV-1 diversity within Australia, from an early epidemic pre- dominantly characterised by subtype B infection [25] to recent studies showing increasing HIV-1 non-B subtype diversity [31±33]. HIV-1 phylogenetic analysis provides an important objective resource for dynamically assessing new HIV infections and monitoring geographical changes in the global HIV epi- demic. In this study, in which we classified phylogenetic clusters using a conservative approach that required low genetic distance between sequences (1.5%) as well as high bootstrap values (>98%), distinct transmission network patterns for subtype B, C and CRF01_AE were PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 12 / 17 Changing HIV-1 diversity in Australia revealed. This approach has been successfully utilised to monitor both B [6,34,45] and non-B subtype [34,39,45] cluster patterns, however no definite consensus has been established to define clusters. Further sensitivity studies are therefore warranted and could be achieved by including multiple sequences from the same individual, developing criteria from known trans- mission pairs/networks and from sequencing a second HIV-1 gene. As expected, the majority of pairs, small clusters and larger networks (4 sequences) were identified within male-only groups in the subtype B infected cohort, while there was a higher proportion of male-and-female clusters in the subtype C infected cohort compared with CRF01_AE. Evidence of emerging male-only non-B networks in Victoria suggests likely for- ward transmission of these viral subtypes within local MSM transmission networks, while the large proportion of male-female pairs within non-B transmission networks in western Sydney, South Australia and Western Australia suggest limited forward transmission into local networks. The observation of a large subtype B male network, identified in 2008 and crossing state borders to evolve into a network of 29 patients by 2012, is in line with other epidemiological studies [6,45] showing that the size of transmission networks is not normally distributed and tends to involve one large cluster as well as a range of smaller networks. This requires further investigation that acknowledges both the social and behavioural context of HIV-1 transmis- sion as well as the potentially important role of HIV-1 virulence, given a recent US study showed large cluster size was associated with CD4 T cell counts >350 cells/μl and plasma HIV-1 RNA levels >10,000 copies/mL [38]. This observation is also supported by a recent study within Western Australia [31], and can be considered within a broader context of evi- dence that HIV-1 has adapted over time to evade host immunological control [48], with trends towards higher pre-treatment plasma HIV RNA levels over the past 30 years of the HIV epi- demic [49]. Understanding the increase in HIV-1 diversity within Australia is of significant interest to laboratories as inaccurate HIV-1 RNA quantitation of non-B subtypes, mostly due to genetic variation at primer sites, has previously been reported [22]. It is therefore imperative that the evaluation of HIV-1 RNA assays be conducted in light of existing Australian HIV-1 viral diver- sity, to ensure reliable monitoring of HIV disease and treatment. Increased drug resistance has also been shown in non-B subtypes when using integrase inhibitors [50], while concerns regarding the potential failure of NNRTI/NRTI treatment for those infected with subtype C infection has been described [19]. The accurate determination of antiretroviral drug resistance mutations in light of HIV-1 subtype-associated polymorphisms also requires careful consider- ation in the context of increasing non-B subtype diversity [51]. Subtype D infection, ISRs [15,16] and CXCR4 tropism [18] associated with CRF01_AE [52] have all been associated with rapid disease progression, while subtype A has been associated with slower disease progression [53]. These factors may be important for HIV surveillance and patient care in developed countries. To our knowledge this analysis is the first large-scale assessment of HIV-1 subtypes and phylogenetic network patterns in Australia performed over an eight-year period. While this current investigation provides estimates of this HIV-1 genotyping in Australia it does not pro- vide a link between the distribution and evolution data with human behaviour, including high-risk populations. Future investigations are warranted and should be focused on filling this gap as has been achieved in Asia [35]. The study does highlight the key influences of migration and overseas travel on increasing rates of non-B infections, and reinforces the importance of Australia's engagement with regional and global aspects of the HIV epidemic as well as the importance of engagement between Australian state jurisdictions. In this context, we hope that the establishment of the PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 13 / 17 Changing HIV-1 diversity in Australia Australian Molecular Epidemiology Network, and the results from this first national study provide the basis for a greater understanding of the Australian HIV-1 epidemic, and enhances existing national surveillance methods. This information assists in the development of effective laboratory strategies, as well as informing prevention and treatment strategies to influence the HIV epidemic within Australia and beyond. Acknowledgments The AMEN collaborators include the lead author Alison Castley (allison.castley@health.wa. gov.au) and David Nolan from the Department of Clinical Immunology, Royal Perth Hospital; Shailendra Sawleshwarkar and Rick Varma from Western Sydney Sexual Health Centre, West- ern Sydney Local Health District and the Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney; Belinda Herring, Kiran Thapa and Dominic Dwyer from the Centre for Infectious Diseases and Microbiology Laboratory Services, ICPMR-Pathology West (NSW Health Pathology), Westmead Hospital and University of Sydney, Westmead; Doris Chibo from the HIV Characterisation Laboratory, Victorian Infectious Diseases Refer- ence Laboratory, Doherty Institute; Nam Nguyen from the Division of Immunology, HSQ Pathology Queensland Central Laboratory; Karen Hawke from South Australian Health and Medical Research Institute and the School of Medicine, Flinders University: Rodney Ratcliff from Clinic 275, Royal Adelaide Hospital and the Department of Microbiology and Infectious Diseases, SA Pathology; Roger Garsia from the Clinical Immunology Department, Royal Prince Alfred Hospital; and Anthony Kelleher from the Kirby Institute for Infection and Immunity in Society, University of New South Wales. The AMEN collaborators would like to acknowledge George Guelfi for providing the com- puter resources required to perform the large scale phylogenetic analysis assessed in this study; and to Megan Gooey, Jessica De Vries and Helene Johanson for their assistance in collating patient information. Author Contributions Conceptualization: AC DN SS RV AK KH DC NN BH KT DD RR RG. Data curation: AC DN SS RV. Formal analysis: AC DN. Funding acquisition: AC. Investigation: AC DN SS RV AK KH DC NN BH KT DD RR RG. Methodology: AC DN. Project administration: AC DN SS AK. Resources: AC DN SS RV AK KH DC NN BH KT DD RR RG. Software: AC DN. Supervision: DN SS AK. Validation: AC DN SS RV AK KH DC NN BH KT DD RR RG. Visualization: AC DN. Writing ± original draft: AC DN. Writing ± review & editing: AC DN SS RV AK KH DC NN BH KT DD RR RG. PLOS ONE | https://doi.org/10.1371/journal.pone.0170601 May 10, 2017 14 / 17 Changing HIV-1 diversity in Australia References 1. Roberts JD, Bebenek K, Kunkel T. 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