viruses Article Molecular Epidemiology of HIV-1 in Ghana: Subtype Distribution, Drug Resistance and Coreceptor Usage Anna Appah 1,2, Charlotte J. Beelen 2 , Don Kirkby 2, Winnie Dong 2, Aniqa Shahid 1,2 , Brian Foley 3 , Miriam Mensah 4, Vincent Ganu 5 , Peter Puplampu 5, Linda E. Amoah 6, Nicholas I. Nii-Trebi 7,*,† , Chanson J. Brumme 2,8,† and Zabrina L. Brumme 1,2,*,† 1 Faculty of Health Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada 2 British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC V6Z 1Y6, Canada 3 Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA 4 Fevers Unit, Department of Medicine, Korle Bu Teaching Hospital, Accra P.O. Box KB 77, Ghana 5 Department of Internal Medicine, Korle Bu Teaching Hospital, Accra P.O. Box KB 77, Ghana 6 Noguchi Memorial Institute for Medical Research, University of Ghana, Accra P.O. Box LG 581, Ghana 7 Department of Medical Laboratory Sciences, School of Biomedical and Allied Health Sciences, University of Ghana, Accra P.O. Box LG 25, Ghana 8 Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada * Correspondence: ninii-trebi@ug.edu.gh (N.I.N.-T.); zbrumme@sfu.ca (Z.L.B.) † These authors contributed equally to this work. Abstract: The greatest HIV-1 genetic diversity is found in West/Central Africa due to the pandemic’s origins in this region, but this diversity remains understudied. We characterized HIV-1 subtype diversity (from both sub-genomic and full-genome viral sequences), drug resistance and coreceptor usage in 103 predominantly (90%) antiretroviral-naive individuals living with HIV-1 in Ghana. Full- genome HIV-1 subtyping confirmed the circulating recombinant form CRF02_AG as the dominant (53.9%) subtype in the region, with the complex recombinant 06_cpx (4%) present as well. Unique recombinants, most of which were mosaics containing CRF02_AG and/or 06_cpx, made up 37% of sequences, while “pure” subtypes were rare (<6%). Pretreatment resistance to at least one drug Citation: Appah, A.; Beelen, C.J.; class was observed in 17% of the cohort, with NNRTI resistance being the most common (12%) and Kirkby, D.; Dong, W.; Shahid, A.; INSTI resistance being relatively rare (2%). CXCR4-using HIV-1 sequences were identified in 23% of Foley, B.; Mensah, M.; Ganu, V.; participants. Overall, our findings advance our understanding of HIV-1 molecular epidemiology in Puplampu, P.; Amoah, L.E.; et al. Ghana. Extensive HIV-1 genetic diversity in the region appears to be fueling the ongoing creation of Molecular Epidemiology of HIV-1 in novel recombinants, the majority CRF02_AG-containing, in the region. The relatively high prevalence Ghana: Subtype Distribution, Drug of pretreatment NNRTI resistance but low prevalence of INSTI resistance supports the use of INSTI- Resistance and Coreceptor Usage. based first-line regimens in Ghana. Viruses 2023, 15, 128. https:// doi.org/10.3390/v15010128 Keywords: HIV; HIV-1; subtype diversity; pretreatment drug resistance; coreceptor usage; molecular Academic Editor: Stephen Barr epidemiology; Ghana Received: 8 December 2022 Revised: 27 December 2022 Accepted: 29 December 2022 1. Introduction Published: 31 December 2022 HIV-1 remains a major global health concern, with Sub-Saharan Africa bearing 70% of the disease burden [1]. An estimated 84.2 million individuals worldwide have acquired HIV-1 since the beginning of the pandemic, with the cumulative death toll from HIV- Copyright: © 2022 by the authors. related illness surpassing 40 million in 2021 [2]. Due to HIV’s extensive mutational and Licensee MDPI, Basel, Switzerland. replicative capacity, its ability to establish lifelong infection, and the pandemic’s large scale, This article is an open access article viral strains have substantially diversified over time, posing challenges to prevention and distributed under the terms and treatment [3–7]. To date, the HIV-1 group M (“pandemic”) strains are classified into ten conditions of the Creative Commons subtypes (A–D, F–H and J–L) and 118 circulating recombinant forms (CRFs), designated Attribution (CC BY) license (https:// when a particular recombinant has been detected in at least three epidemiologically un- creativecommons.org/licenses/by/ linked persons [4,8,9]. The greatest HIV-1 genetic diversity is observed in West/Central 4.0/). Viruses 2023, 15, 128. https://doi.org/10.3390/v15010128 https://www.mdpi.com/journal/viruses Viruses 2023, 15, 128 2 of 23 Africa, primarily due to the virus’ origin in this region [10,11], but this diversity remains somewhat understudied. Note that, from now forward, we use “HIV” interchangeably with “HIV-1” to denote the HIV-1 group M pandemic strain. Ghana reported its first HIV case in 1986 [12]. Since then, national HIV prevalence has remained consistent at ~2% [13,14]. An estimated 342,307 persons were living with HIV in Ghana in 2019 [15,16], the majority (65%) female [17,18], with heterosexual transmission representing the main transmission mode [19,20]. Though Ghana adopted the World Health Organization (WHO)’s “treat all” policy, which offers ART to all people living with HIV (PLWH) irrespective of their CD4+ T-cell count [21], in September 2016, the country fell short of achieving the UNAIDS “90–90–90” goals (where 90% of PLWH would know their status, 90% of those who know their status would be on antiretroviral therapy [ART], and 90% of those on ART would be virally suppressed, by 2020). As of 2018, an estimated 58% of Ghanaian PLWH knew their status, 78% of whom were on ART, and of whom 68% had suppressed viral load on ART [22]. ART use is also shifting in Ghana. Prior to July 2019, first-line regimens featured the nucleoside reverse transcriptase inhibitors (NRTIs) tenofovir + lamivudine (or emtric- itabine) plus a non-nucleoside reverse transcriptase inhibitor (NNRTI), either efavirenz or nevirapine [23,24]. Second line regimens featured two NRTIs plus the boosted protease inhibitor (PI) atazanavir/ritonavir, while integrase strand transfer inhibitors (INSTI) were reserved for third line [23,24]. In July 2019 however, DTG-containing ART was introduced as the preferred first line regimen due to concerns over NNRTI resistance [25]. HIV molecular epidemiology studies in Ghana have been somewhat limited. The circulating recombinant form CRF02_AG dominates in the region, while “pure” subtypes (mainly A and G) as well as complex recombinants such as 06_cpx (a mosaic of subtypes A, G, J and K) and 09_cpx (comprising A- and G-like regions) also circulate at much lower frequencies [26–28]. The vast majority of Ghanaian HIV subtype data however are based on partial polymerase sequences that comprise only 10–15% of the viral genome [8,29], which may not represent the subtype of the full viral genome [30]. In fact, only 31 full-genome HIV sequences from Ghana currently exist in the public domain, all of which were obtained in 2003 or prior [8]. Drug resistance data are also somewhat limited in Ghana. Despite the WHO’s rec- ommendation that routine drug resistance surveillance be conducted in settings where individualized drug resistance genotyping is not standard of care, the last HIV drug resis- tance survey in Ghana undertaken according to WHO guidelines occurred in 2013. Data from research studies however indicate that pretreatment resistance (defined as resistance in persons who discontinued ART more than three months ago without documented viral failure and who are now re-initiating first-line ART, or in treatment-naïve individuals), is increasing. While a study conducted on samples collected in 2003 from treatment-naïve Ghanaian PLWH reported no evidence of transmitted HIV drug resistance (TDR) [31], more recent studies have reported 9%, 11.5% and 33% TDR prevalence in children [32], pregnant women [33] and adults [27], respectively, though the number of individuals genotyped in these reports was relatively small. Acquired drug resistance is also a con- cern [28,32], with studies reporting 25–46% prevalence of the NNRTI resistance mutation K103N and a 39–54% prevalence of the NRTI resistance mutation M184V in persons failing ART [27,28,32,34]. Even fewer studies have investigated coreceptor usage, despite its rele- vance to the use of the HIV entry inhibitor maraviroc, which specifically inhibits viral entry via the CCR5 coreceptor (and is thus only effective in individuals who exclusively harbor CCR5-using HIV) [35]. A small phenotypic study of 27 symptomatic Ghanaian PLWH undertaken in 2007 indicated that CCR5 use predominated [36], but no other studies to our knowledge have investigated HIV co-receptor usage in the region. Data from other global regions also indicates that coreceptor usage distribution differs by HIV subtype [37–39], but this has not been investigated among the diverse subtypes circulating in Ghana. To address these knowledge gaps, we characterized HIV subtype diversity, pretreat- ment drug resistance and coreceptor usage in a cohort of 103 PLWH in Ghana (90% of Viruses 2023, 15, 128 3 of 23 whom were ART-naive and 10% who had discontinued first-line ART at least two years prior), using a combination of Sanger and next-generation sequencing methods. 2. Materials and Methods 2.1. Study Design and Sampling We recruited 103 PLWH (≥16 years) from major HIV care clinics in the Greater- Accra and Central regions of Ghana using purposive sampling in a cross-sectional design (2020–2022). To be eligible for inclusion, participants had to be either ART naïve or must have discontinued first line ART more than 2 years ago without evidence of treatment failure. Whole blood (6 ml) was collected by venipuncture from the forearm into ethylene- diaminetetraacetic acid (EDTA) tubes. Blood was centrifuged the same day at 2000 G for 10 min to obtain plasma, which was stored at −20 ◦C until shipment on dry ice for HIV genotyping. Sociodemographic data, viral load and treatment records were collected by self-report and confirmed through medical records where available. 2.2. Ethics Approval This study was carried out in accordance with ethical regulations for research with human participants in line with the tenets of the Declaration of Helsinki. Each participant provided written informed consent. This study was jointly approved by the Simon Fraser University and Providence Health Care/University of British Columbia Research Ethics Boards in Canada (H19-01947), as well as the Institutional Review Board and the Scientific and Technical Committee of Korle-Bu Teaching Hospital, Accra, Ghana. (KBTH-IRB) 00075/2020. 2.3. HIV Genotyping: RNA Extraction and RT-PCR Amplification Total RNA was extracted from 500 uL blood plasma using the NucliSENS® EasyMag (bioMérieux, Montréal, QC, Canada) according to the manufacturer’s instructions, eluted in 60 ul, and stored at −80 ◦C until reverse transcription PCR (RT-PCR). A positive control (clinical sample) and aliquot of nuclease-free water were included in each extraction run as positive and negative controls, respectively, and carried through all subsequent RT-PCR reactions. The complete HIV coding region was bulk-amplified in five overlapping frag- ments, comprising gag-protease (GAGPR), protease-reverse transcriptase (PRRT), reverse transcriptase–viral protein u (RTVPU), viral protein r-glycoprotein120 (VPR-GP120) and glycoprotein41-negative factor protein (GP41Nef), using primers designed to capture cir- culating HIV diversity in Ghana, in particular subtypes A, G and CRF02_AG [29]. The primary and secondary (backup) primers used for RT-PCR are provided in Supplementary Tables S1 and S2. Note that the PCR primers did not feature unique molecular barcodes (primer IDs). Briefly, cDNA was generated using an HIV sequence-specific reverse primer and NxtScript Reverse Transcriptase by incubating at 42 ◦C for 45 min (Roche Diagnostics, Laval Canada). Nested PCR was then performed using the Expand HiFi system (Roche Diagnostics; Laval, Canada). Thermal cycling conditions for both rounds of PCR were; 94 ◦C for 2 min; 10 cycles of (94 ◦C for 15 s, 55 ◦C for 30 s, 72 ◦C for 2 min); 25 cycles of (94 ◦C for 15 s, 55 ◦C for 30 s and 72 ◦C for 2 min with an additional 5 s per cycle) and a final extension at 72 ◦C for 7 min. Amplicons were visualized on a 1% agarose gel. Samples failing PCR amplification were re-extracted at least twice, and amplification re-attempted using backup primers. 2.3.1. Sanger Sequencing of Pol Regions Amplicons containing protease, reverse transcriptase and integrase regions were bi-directionally sequenced on an ABI Prism 3730xl DNA analyzer (Life Technologies, Burlington, ON, Canada) using the BigDye Terminator v3.1 cycle sequencing kit. Sanger sequencing primers are listed in Supplementary Table S3. Eight sequencing primers were used per amplicon to obtain at least twofold coverage. Chromatograms were called using RECall version 2.28.1, an in-house software that automatically calls bases, trims primer Viruses 2023, 15, 128 4 of 23 sequences, and constructs contiguous consensus sequences [40]. Nucleotide mixtures were automatically called if a subdominant peak of ≥17.5% of the total area of the dominant peak was observed in >50% of sequencing reads covering that position. 2.3.2. Whole HIV Genome Illumina Sequencing and Analysis Samples for which all five overlapping HIV genome-wide RT-PCR reactions yielded amplicons were sequenced by Illumina MiSeq. Amplicon concentrations were normalized, and DNA was purified using AMPure XP magnetic beads (A63880, Beckman Coulter, Mississauga, ON, Canada) to ensure broadly equivalent concentrations of each amplicon. All five amplicons per participant were pooled, quantified using the Invitrogen Quant-iT Picogreen dsDNA assay (P7589, Invitrogen, Carlsbad, CA, USA) and diluted to 1 ng/µL. Libraries were prepared using the Nextera XT DNA Library Preparation Kit (FC-131-1024, Illumina) and Nextera XT Index Kits (FC-131-1002, Illumina) for amplicon tagmentation and dual-index barcoding, respectively. Indexed amplicons were purified with AMPure XP magnetic beads and a final library consisting of all samples pooled together was diluted to 1.3 ng/µL before sequencing on an Illumina MiSeq. FastQ files were processed using the in-house bioinformatics pipeline MiCall (version 7.15) [41,42]. MiCall can assemble viral genomes by either mapping to a set of reference sequences (which, for the present study, consisted of 114 sequences representing all major HIV subtypes as well as CRF02_AG and CRF06_cpx), or by de novo assembly using the Iterative Viral Assembler (IVA) [43] and Haploflow [44] programs. For samples where de novo assembly produced multiple subgenomic contigs, the pipeline assembled these into a full-genome consensus. Here, plurality consensus sequences from the de novo Haploflow pipeline were used as the primary method for HIV genomic reconstruction, with output from the other assembly methods used to resolve challenging regions. For the resistance analyses, MiCall output summarizing amino acid prevalence at all Protease, RT and Integrase codons was used. Residues present at an intra-host prevalence of ≥5% were considered in resistance analyses. 2.4. HIV Subtyping, Phylogenetics, Drug Resistance and Coreceptor Usage Interpretation HIV subtype determination was performed using the Recombinant Identification Pro- gram (RIP3.0) [45–47]. All analyses used a window size of 400 and a 95% confidence thresh- old (CT). To represent the diversity of HIV strains circulating in Ghana, study sequences were queried against a background alignment of 17 sequences comprising the consensus sequences for subtypes A1, A2, A6, B, C, D, F1, F2, G, H, J, CRF01_AE and CRF02_AG, and reference strains A3.SN.01.DDI579, K.CD.97.97ZR_EQTB11, 06_CPX.AU.96.BFP90 and 09_CPX.GH.96.96GH2911 (as no consensus sequences are available for these) [29]. For protease-RT sequences, the consensus (mixture-containing) Sanger sequences were used for subtyping, while for full-genome subtyping the plurality (non-mixture containing) MiSeq consensus sequence was used. Sequences were aligned using MAFFT implemented in HIV Align [48,49], viewed and manually edited in AliView (v1.25). For the protease-RT sequence alignment, 43 codons associated with drug resistance [50] were removed prior to phylogenetic inference so that these residues would not influence tree topology. A maximum likelihood phylogeny was constructed from this alignment using IQTREE [51,52] with automated model selection using ModelFinder [53] and Ultrafast bootstrap option [54]. The tree was visualized and annotated in R (v4.1.2). Drug resistance mutation interpretation was performed using the Stanford University HIV Database Program Algorithm version 9.1 (HIVdb) [55,56]. Briefly, the algorithm assigns a score to each mutation associated with decreased susceptibility to a given antiretroviral drug, as well as to specific combinations of mutations. Summed scores determine the sequence’s degree of reduced susceptibility to each drug, where scores between 0–9 denote full susceptibility, 10–14 denote potential low-level resistance, 15–29 denote low-level resistance, 30–59 denote intermediate resistance, and ≥60 denote high-level resistance to a given drug [56]. Here, we considered a sequence as susceptible if its score was between Viruses 2023, 15, 128 5 of 23 0–14, and as harboring resistance to a particular drug class (PI, NRTI, NNRTI or INSTI) if its score was ≥15 for at least one drug in the class. For all sequences meeting this threshold, we reported all resistance-associated mutations within it, categorizing these as “major” (mutations that, alone, confer a score of ≥15 to any drug) or “minor/accessory” (mutations that, alone, do not confer clinically relevant resistance). HIV coreceptor usage was inferred from the V3 loop region within gp120 envelope (env) sequences obtained by MiSeq, using the geno2pheno (g2p) algorithm [57,58] im- plemented in MiCall. G2p assigns each V3 sequence a “false positive rate” (FPR) value, which represents the likelihood that a CCR5-using virus is misclassified as CXCR4-using. Sequences with low FPR are more likely to be CXCR4-using while those with high FPR are CCR5-using. For each participant, individual complete within-host V3 sequences were reconstructed in MiCall, to generate a list of unique V3 sequences observed per participant. Each of these unique V3 sequences was then interpreted using g2p: as recommended for next-generation sequencing data, unique V3 sequences with FPR <3.5% were denoted as CXCR4-using, while those with FPR ≥3.5% were denoted as CCR5-using. To gener- ate a final coreceptor assignment for each participant, we counted the number of times each unique V3 sequence was observed in the sample (as a proxy for the abundance of this sequence in vivo): a sample was denoted as having CXCR4-using variants if ≥2% of its overall sequences were classified as CXCR4-using; otherwise, it was classified as CCR5-using [59]. 2.5. Statistical Analysis Associations between categorical variables were determined using Fisher’s exact test or chi-squared test where appropriate using Prism v8.4.3 software (GraphPad). For all comparisons, a two-tailed p-value <0.05 was considered statistically significant. 2.6. Accession Numbers GenBank accession numbers for Sanger protease-RT sequences are OP894533–OP894623 while those for Integrase are OP894444–OP894532. Accession numbers for Illumina full- genome HIV consensus sequences are OQ121842–OQ121917. 3. Results 3.1. Cohort Characteristics A total of 103 participants were recruited between 2020–2022 from clinics in major cities in Ghana, namely Accra, Elmina and Komenda (Table 1). Of these, 93 (90%) were ART naïve, while 10 (10%) had discontinued first-line ART with no documented treatment failure at least two years prior. A total of 49 (51%) were female, and the overall cohort median age was 38 (interquartile range; IQR 30–49) years. The main mode of infection was heterosexual contact (79%). HIV plasma viral loads (pVL), available for 27 participants, were a median 5.3 [IQR, 4.5–5.9] log10 HIV RNA copies/mL. CD4+ T-cell counts were not available. Table 1. Participant Characteristics. Sex at birth (n = 96 *) Male, n (%) 47 (49%) Female, n (%) 49 (51%) Age in years (n = 96 *) 38 (30–49) Males, median (IQR) 41 (31.5–48.5) Females, median (IQR) 36 (29–52) ART Status (n = 103) ART Naïve, n (%) 93 (90%) ART previously discontinued, n (%) 10 (10%) Viruses 2023, 15, x FOR PEER REVIEW 6 of 25 Table 1. Participant Characteristics Sex at birth (n = 96 *) Male, n (%) 47 (49%) Female, n (%) 49 (51%) Age in years (n = 96 *) 38 (30–49) Males, median (IQR) 41 (31.5–48.5) Females, median (IQR) 36 (29–52) ART Status (n = 103) ART Naïve, n (%) 93 (90%) ART previously discontinued, n (%) 10 (10%) Infection Risk Group (n = 103) Heterosexual, n (%) 81 (79%) Viruses 2023, 15, 128 6 of 23 Men who have sex with Men, n (%) 1 (1%) Vertical Transmission, n (%) 1 (1%) T a b lSeh1.aCropnst., Needle, n (%) 4 (4%) Unknown/Unsure, n (%) 16 (15%) Infection Risk Group (n = 103) PlasHmetaer vosierxaula ll,ona(d% )(n = 27 *) 81 (79%) Mmeendwihaonh (aIvQe sRex) wLiothgM10e Hn,InV(% R)NA copies/ml 1 (1%) 5.3 (4.5–5.9) Vertical Transmission, n (%) 1 (1%) * SocSihoadrpesm, Noegerdalpe,hnic(% a)nd clinical data were unavailable for som4 (e4% p)articipants; Ns with available data are iUnndkincaowtend/. Unsure, n (%) 16 (15%) Plasma viral load (n = 27 *) 3.2. mSeudbiatynp(IeQ CR)hLaorga1c0tHerIiVzaRtNioAnc oBpaiesse/dm oln Protease-RT Sequ5e.3n(c4e.5s– 5.9) * Sociodemographic and clinical data were unavailable for some participants; Ns with available data are indicated. HIV protease-RT genotyping was successful for 91 participants (88%). As this is the m3.o2.stS ucbotmypme Cohnarlayc tuersiezadti ornegBiaosend foonrP HroIteVas se-uRbTtSyepqiunengc,e ws e began by inferring subtype from these data H(sIeVep mroetetahsoed-RsT).g Uensoitnygp ian gRwIPas wsuicncdesoswfu lsfiozre9 1opf a4r0ti0ci paanndts a(8 c8o%n).fiAdsetnhcisei sththre shold of 95%, 6m0/o9s1t c(6om5.m9%on)l oyfu pserdotreeagsioen-RfoTr sHeqIVuesunbcteysp iwnge,rew eidbeengtainfiebdy iansf eCrrRinFg0s2u_bAtyGp,e wfroitmh the next most frtheeqsueednatt ab(esienegm 0e6th_ocdpsx). (U10si/n9g1;a 1R1I%P ;w Finigduorwe s1iz).e Nofe4x0t0 manodsta pcroenvfiadleenncet wthreerseh oulndique recombi- of 95%, 60/91 (65.9%) of protease-RT sequences were identified as CRF02_AG, with the nnaenxtt mfoorsmt fsre tqhuaetn thbaevineg n0o6_t cypext( b10e/e9n1 ;d1e1%sc;rFiibgeudre i1n) .tNheex ltitmeorsattpurreev,a ilnenctluwderienugn miquoesaics of 06_cpx arnedco mCRbiFna0n2t_fAorGm s(4th.4a%t h)a, vaenndo treycetobmeebnindaensctrsi boefd Ain3t haenldit eCraRtuFr0e2, i_nAclGud (in3g.3m%o)s. aPicusre subtypes G (3o.f30%6_)c,p Ax3a n(d3.C3%RF)0, 2C_ A(2G.2(4%.4)% a)n, dan Bd r(e1c.o1m%b)i nwaenrtse oaflsAo3 oanbdseCrRvFe0d2._ A G (3.3%). Pure subtypes G (3.3%), A3 (3.3%), C (2.2%) and B (1.1%) were also observed. Figure 1. Subtype distribution based on protease-RT sequences. Figure 1. Subtype distribution based on protease-RT sequences Inspection of the protease-RT RIP outputs however indicated that, while some subtype callsIwnesrpeeucntaiomnb iogfu othues (psereoetexamsep-lResTo Rf CIPR Fo0u2t_pAuGtsa nhdoawpeuvrers uinbtdyipceaBtesde qtuheantc, ewinhile some sub- tyFipgeu rcea2lAls, Bw),eorteh eursnwamerebimgouroeuusn c(esretaei ne.xPaamrtpiclipesa notfK CBRHF300-2G_HA’sGs eaqnuden ac epfuorreex saumbptlyepe B sequence contained only two short CRF02_AG regions that met our predefined 95% confidence in Figure 2A,B), others were more uncertain. Participant KBH30-GH’s sequence for exam- threshold, though its RIP plot indicated that it was a likely recombinant of CRF02_AG and p0l6e_ ccpoxn(tFaiignuerde 2oCn).ly two short CRF02_AG regions that met our predefined 95% confidence thresRheolaldte,d thtootuhgish, H itIsV RsuIPbt yppleotc ailnlsdciocualtdedno tthbaetd iet twermasin ae dlikfoerly5 (r5e.5c%o)mobf pinroatneats oe-fR CTRF02_AG and 0s6e_qcupenxc (eFs,igasunreo p2aCr)t.o f the sequence matched any reference sequence at the predefined 95% confidence threshold (Figure 3). These are likely unique recombinants, including a mosaic of subtypes A3 and A1 (participant EHC002-GH; Figure 3A), a mosaic of G and/or CRF02_AG at the 5’ end, with A3 at the 3’ end (KBH77-GH; Figure 3B), a likely recombinant of CRF02_AG and A3 (KBH89-GH; Figure 3C), a mosaic including A-like, G-like, CRF02_AG-like and/or 06_cpx-like sequences (though the overlap in the similarity plots makes classification impossible; KBH47-GH Figure 3D) and a likely recombinant of CRF02_AG and subtype D (KBH29-GH; Figure 3E). Viruses 2023, 15, x FOR PEER REVIEW 7 of 25 Viruses 2023, 15, 128 7 of 23 FFigiguurere 22. .SSuubbttyyppee aassssiiggnnmmeennttss bbaasseedd oonn pprrootteeaassee-R-RTTs esqequuenencecse.sP. aPnaenlesl(sA (A–C–)C: )T: hTehye- ayx-aisxdise ndoetneosttehse th%e s%im siilmariiltayribtyet wbeetewnetehne tphaer ptiacriptiacniptasnetq useeqnuceentcoee taoc heaocfh1 o7fr 1e7fe rreefnecreensecqe useenqcueesn(ceeasc (heaincha idni faf edriefn- t fecroelonrt )covloer)a osvleidr ian gslwidiindgo winodf o40w0 obfa 4se0s0 (bshasoews n(sohnowXna xoins) .XT ahxeisb)a. rTshaet bthaerst oapt tohfee taocph opfl oetacinhd picloatte inthdeicbaetes tthme abtecshti mngatrcehfeinregn rceefesreqnuce nsceequoevnecrea ogvievre an gsiveqenu esneqceuerengceio rneg(lionw (elrowbaerr) baanrd) awndh ewt herththeris thmisa mtchatmche metesetthse th9e5 %95%co cnofindfiednecnecteh trhersehsohlodld(u (upppererb baar)r.).P Paanneell ((A)) A ““purree”” CRFF0022__AG sseeqquueennccee inin ppaartritcicipipaannt tKKBBHH0022-G-GHH. .PPaannele l(B(B) )PPuurere susubbtytyppe eBB inin KKBBHH4488-G-GHH. .PPaanneel l(C(C) )AA ssaammpplele ththaat twwaass cclalassssifiifieedd aass CCRRFF0022__AAGG bbaasseedd oonn ttwwoo sshhoorrtt CCRRFF0022__AAGG rreeggiioonnsst hthaattm meet tt hthee9 59%5%c ocnofinfdiednecneceth trhersehsohl-d, obldu,t bthuat tthisalti kise lliykealrye aco rmecboimnabnint aonf tC oRf FC0R2_FA02G_AanGd a0n6d_ c0p6x_c(ppxa r(tpicairptaicnipt aKnBtH K3B0H-G3H0-)G. H). RWeleatfeudr tthoe rthinisv, eHstIiVg astuebdtypproet ecaalsles- RcoTusldu bntoytp bese pdheytelromgeinneedti cfaolrl y5 ((F5i.g5u%r)e o4f) .pHroetreea,steh-e RfiTv ese“quunecnlacesss,i fiaasb nleo” psaerqtu oefn tchees bseyqRuIePncaer emshatocwhendb aynbyl ureefaerrreonwces ,saenqduetnhceeo artd ethrei npwrehdiec-h fitnheedy 9a5p%pe caorninfidtheenctere tehfrreosmhotlodp (Ftoigbuortet o3m). Tmhaetsceh aerset hliekeolryd uerniinquwe hriecchomthbeyinaarnetsp, rienscelnutde-d ining Fai gmuorsea3ic. EoHf sCu0b0t2y-pGeHs A(F3i ganudre A31A ()pfaerlltiwciipthanint tEhHeCb0ro0a2d-GsHub; cFliagdueref e3aAtu),r ian mg Aos1aiacn odf AG3 asnedq/uoer nCcResF,0i2n_aAnGin atte rtmhee d5’i aetnedp, owsiittiho nAb3e atwt teheen 3A’ 1enadn d(KAB3Hs7u7b-GclHad; eFsi,gcuornes 3isBte),n at wlikitehlyi t rbeceoinmgbainraencto omf bCiRnFan02t_oAf Gth aensed tAw3o (KsuBbHty89p-eGs.HB; FoitghuKreB 3HC7)7, a-G mHos(aFiicg iunrcelu3dBi)nagn Ad-lKikBeH, G8-9- liGkeH, C(FRigFu0r2e_A3CG)-wlikeer eainnda/onr in06te_rcmpxe-dliikaete speoqsuietinocnesb e(tthwoeuegnht hthees uobvteyrplaepA ina nthdeC sRimF0il2a_riAtyG psluobtsc lmadaekse,sc colnasissitfeicnattiwonit himthpeomssbibelien; gKABH/C4R7-FG0H2_ FAiGgurreec o3mD)b ai nadn ats l.ikTehley mreocsotmcobminpanletx oof f CthReFfi02v_eAuGnc alansds isfiuabtlyepseq Du e(nKcBeHs,2K9B-GHH47; -FGiHgu(rFei g3uEr)e. 3 D) clustered close to the internal node giving rise to the CRF02_AG, A and 06_cpx subclades, suggesting that it is a recombinant of these three subtypes. KBH29-GH (Figure 3E) branched off from the internal node giving rise to the subtype B clade, which is consistent with RIP having identified subtype D as the major component of this sequence (subtype D is the most closely related subtype to B). Viruses 2023, 15, x FOR PEER REVIEW 8 of 25 Viruses 2023, 15, 128 8 of 23 FFiigguurree 33.. PPrrootteeaassee--RRTT sseeqquueenncceessw whheereres usubtbytpyepec laclsassifisicfaictiaotniown aws anso tnpoto spsoibslseibalte tahte tphree dperfiendeedfined ccoonnffiiddeennccee tthhrreesshhoolldd.. T hTehye- yax-aisxidse dneonteosteths eth%e %sim siimlarilitayribtyet bweetewnetehne tphaer ptiacirptiacnipt asenqt useenqcueetnocee atcoh each ooff 1177 rreeffeerreennccee sseeqquueenncceess ((eeaacchhi nina ad dififfefreernetncto cloolro)ro)v oevrear sali dsliindginwgi nwdionwdoowf 4o0f0 4b0a0s ebsas(sehso (wshnoownn on XX aaxxiiss)).. TThhee bbaarrss aatt tthhee ttooppo offe eaacchhp ploltotin idnidciactaetteh tehbee bset smt amtcahticnhginrgef reerefenrceensceeq sueeqnuceenocvee rovaegri vae gniven sseeqquueennccee rreeggiioon ((llooweerr bbaarr)) aannddw whhetehtherert htihsism matachtchm emeetsettsh eth9e5 %95c%o ncfiodnefindceentcher etshhroelsdho(uldp p(uerpper bbaarr)).. TThhee RIIP pllotts howeevveerr sshhoowwt htheer erceocmombibniannatncto cmopmopsoitsioitnioans afosl lfowllos:wPsa: nPealn(Ael) (MA)o sMaiocsoafic of ssuubbttypes A3 aannddA A11. .( B(B) M) Mosoasicaiocf oGf aGn da/nodr/oCrR CF0R2F_0A2G_AatGth aet 5t’heen 5d’, ewnidth, Aw3itaht Ath3e 3a’t etnhde. 3P’a ennedl (. CP)anel (LCi)k eLlyikreclyo mrbeicnoamntboifnCanRtF 0o2f_ ACGRaFn0d2_AA3G. P aannedl ( DA)3M. oPsainceiln c(lDud) inMg oAs-aliikce ,iGnc-lliukde,inCgR FA02-_liAkGe,- liGke-like, CaRndF/02o_rA0G6_-clipkxe- laikned/soerq u06e_ncpesx.-lPikaen seelq(Eu)enLcikese.l yParenceol m(Eb)i nLaiknetloyf rCecRoFm02b_inAaGnta onfd CsRuFb0ty2_pAe GD .aTnhde sub- tsyepqeu Den. cTehs ea rseepqureesnecnetse darine pthreesseanmteedo ridne trhaes stahmeye aoprpdeearr aisn tthheeyp ahpyploegaern iyn (tihneF pighuyrleog4)e,nfyro (mint oFpigure 4t)o, fbrootmto mto.p to bottom. We further investigated protease-RT subtypes phylogenetically (Figure 4). Here, the five “unclassifiable” sequences by RIP are shown by blue arrows, and the order in which they appear in the tree from top to bottom matches the order in which they are presented Viruses 2023, 15, x FOR PEER REVIEW 10 of 25 Viruses 2023, 15, 128 9 of 23 Figure 4. Maximum likelihood protease-RT phylogeny. The tree was inferred from 91 protease- Figure 4. Maximum likelihood protease-RT phylogeny. The tree was inferred from 91 protease-RT RT sequences from participants (red symbols) and 21 reference sequences representative of cohort sequences from participants (red symbols) and 21 reference sequences representative of cohort di- verdsiivtye r(s3i teyac(h3 feoarc h7 sfuorbt7ypsuesb;t ybplaecsk; sbylamckboslys)m. Pbholysl)o.gPehnyyl iosg reonoyteids arot omteiddpaotinmt.i dBpluoein atr.roBwluse daernroowtes seqdueennocteess weqituhe nucnecslawssitihfiaubnlcel asussbifityapbeles sbuyb RtyIPpe, sshboywRnIP i,ns hthoew snaminet hoerdsearm freoomrd teorpf rtoom bottotpomto abso Fttiogm- urea s3.F Biglaucrke 3“.>“B lsaycmk b“o>l“s ssyhmowbo klsnoshwonw ekpnidoewmnioelpoigdiecmaliloyl olignikceadll yplaiinrks.e Gd rpeaeinrs a. rGrorwee nshaorwrosw thseh oswe-s quethneces eiqnu eFnigcuerien F2iCg.u rSeca2lCe. iSnc aelestinmeastteidm antuedclenoutcidleeo tsiduebstuitbustiotuntsi opnesrp seirtes.i teA.sAtesrtiesrkisk (s*()* )ininddicicaatete brabnrcahnecsh ewsiwthi tahpappropxroimxiamtea tbeobootsottrsatrpa pvavlualeuse >s7>07. 0. 3.3. SubTtyhpeinpgh ybalosegde noyn aFluslol HcoInVfi Grmeneodmtehsa t KBH30-GH (shown in Figure 2C and indicated in the tree by a green arrow) is likely a novel recombinant comprising CRF02_AG and 06_cpx As the protease-RT fragment represents only ~15% of the total viral genome, it may regions, as it branched near the internal node giving rise to the 06_cpx cluster. In general, noht fouwlleyv reerp, rtheseetnrte ecochoorrrot bsuobratytepde tchoemRpIoPsistuiobnty. pWeec tahllesreinfomreo csatrcraiesdes o(uet. gs.u, bsteyeplea ragneaclylasdise basoefdC oRnF 0fu2l_lA HGIVse gqeuneonmcees smeqautcehnicnegs t(hne =C 7R6,F 7042%_A) Gas RthIPisc iasl tlsh;es geeoladl ssotatnhdeaArd3/ fCorR sFu0b2t_yApG- ingr eicno rmegbiionnans tws aitnhd e0x6te_ncpsixv/eC HRIFV02 d_iAvGersrietcyo. m binants falling within subclades that are inter- meBdaisaeted toont hfuolsle-gceonnotaminei HngIVth seeirqupeanrecnetsa, lthsueb dtyopmeisn)a. nTth seutbretyepaels wo acos nCfiRrFm0e2d_AfoGu,r aktn 5o4w%n preevpaidleenmceio (loFgigicuarlely 5l)i.n Tkehde pnaeixrts imnothste fcroehqouretn(tb lvaacrkia“n>t“s swymerbeo ClsR).F02_AG-containing re- combinants, including CRF02_AG/06_cpx (5.3%), CRF02_AG/A3/A1 (5.3%), CRF02_AG/06_cpx/G (3.9%) and CRF02_AG/A3 (3.9%). In fact, CRF02_AG-containing Viruses 2023, 15, 128 10 of 23 3.3. Subtyping Based on Full HIV Genomes As the protease-RT fragment represents only ~15% of the total viral genome, it may not fully represent cohort subtype composition. We therefore carried out subtype analysis based on full HIV genome sequences (n = 76, 74%) as this is the gold standard for subtyping Viruses 2023, 15, x FOR PEER REVIEWin regions with extensive HIV diversity. 11 of 25 Based on full-genome HIV sequences, the dominant subtype was CRF02_AG, at 54% prevalence (Figure 5). The next most frequent variants were CRF02_AG-containing recombi- nreacnotms, bininclaundtisn rgeCprReFs0e2n_tAedG 3/10.65_%cp oxf (a5l.l3 H%I)V, C gReFn0o2m_AesG s/eAqu3/enAc1e(d5,. 3w%h)e, rCeR tFh0e2 m_AoGst/ c0o6m_cpplxe/x Gge(n3o.9m%e) caonmdpCriRsFed02 r_eAgiGo/nsA o3f( 036.9_%cp).xI, nCfRaFct0,2C_ARFG0,2 s_uAbGty-pcoe nBt aainndin sgurbetcyopme bGin (apnatrstirceippraen-t sKeBnHte3d43-G1.H5%). oOfnallyl HfoIVurg esenqoumeenscseesq rueepnrceesden, wtinhge r“epthuerem” ossutbctoympepsle (x5g%e)n owmereec oidmepntriifsieedd: rthegreioen ssuobfty0p6e_ cGp x(,3C.9R%F)0 a2n_dA Gon, es uBb tsyepqueeBnacen.d Tshuisb tsyupbetyGpe(p Ba rsteicqiupeanncteK dBiHd 3n4o-tG hHa)v.eO hnilgyh four sequences representing “pure” subtypes (5%) were identified: three subtype G (3.9%) similarity to any known subtype B reference strains, nor did it closely match any sequence and one B sequence. This subtype B sequence did not have high similarity to any known previously deposited in HIV LANL nor any Protease-RT or Integrase sequence recently subtype B reference strains, nor did it closely match any sequence previously deposited in isolated at the BC Centre for Excellence in HIV/AIDS where the genotyping was per- HIV LANL nor any Protease-RT or Integrase sequence recently isolated at the BC Centre ffoorrmExedce (lulennpcuebinlisHheIVd /wAoIrDkS), wsuhpepreortthinegg eitnso atuytphienngtiwciatys.p Tehrfeo irdmenedtif(iucantpiounb loisf hae pduwreo srukb),- styuppep oBr stienqguietnscaeu itsh nenottaicbitley., aTsh feeiwd ehnatvifiec baetieonn iodfeantpifuiered siun bWtyepset AB fsreiqcau e[8n]c. eOisnlnyo 1ta%b l(e1,3a osf f2e0w42h) aHvIeVb seeeqnueidnecnesti ffireodmi nGhWaensat iAn fHriIcVa L[8A].NOLn alrye 1s%ub(t1y3peo fB2, a0n42d) aHs IoVf Osecqtoubeenrc 2e0s2f2r,o tmhe GnehiagnhaboinrinHgI VcouLnAtNrieLs ainreclsuudbitnygp TeoBg,oa, nBdenaisn oafnOd cBtuorbkeirn2a0 F2a2s,ot hheadn ereigphobrtoerdin 1d5a%taf raetq >u1e5n%cy frweqerueeindceyn twifieerde biydeSnatnifgieerd, inbyd icSaatninggera, 1in00d%icactoincgo rad 1a0n0c%e a tcothnicsotrhdreasnhcoel da.t Hthoiws ethvreer,shMoilSde.q Hidoewnetvifieerd, M7 iaSdedqi tidioennatlifpieadrt 7ic aipdadnittsiownahlo phaarrtbicoipreadntms uwthatoi ohnasrbthoartedco mnfuetradtieocnrse athseadt csounscfeerp - dteibcrileiatysetdo osunsecoerptmiboilrietya ntoti roentreo voirr aml dorrue gasnatitr5e–tr1o5v%irawl itdhriung-hs oastt 5fr–e1q5u%e nwcyi,ththina-thwosetr efrneo- t qdueetneccyte, dthbayt Swaenrgee rnoset qdueetnecctiendg (bSyu pSapnlegmere nsetaqruyeTnacbinleg S(4S)u. pTphleesme einnctaluryd eTdaobnlee Sp4a)r. tiTchipeasen t in(EclHuCde0d03 o-GneH p) afortricwiphaonmt (bEoHthCS0a0n3g-eGrHan) dfoMr wiSheqomha bdoitdhe nSatinfigeedr tahnedm MajoiSreNq NhaRdT Ii-dreesnitsitfaiendce thmeu mtaatijoorn NE1N3R8ATI-inrerseisvtearnsceet rmanustacrtiiopnta Ese1,3b8uAt winh reerveeMrsieS etrqanadscdriitpiotansael,l ybuidt ewnhtiefireed MMiS2e3q0I , awddhiitcihoncaolnlyfe irdseinnttiefriemde Mdia2t3e0Ir,e wsishtiacnhc ecotnofNerVs PinatenrdmRePdVia,taet r7e.s6i%stawnicteh itno- NhoVsPt parnedv aRlePnVc,e a. tI t 7a.6ls%o winicthluind-ehdossitx pardevdaitlieonncael. Ipta arltsioci ipnacnlutsdfeodr swixh aodmdiStaionngaelr psaerqtuiceipncainntgs fhoard wnhootmid Seanntigfieerd seaqnuyernecsiinstga nhcaedm nuott aitdioenntsi,fibeudt afonryw rehsoismtaMncieS emquitdaetinotnifise, dbuat lfoowr -wabhuonmd ManicSeeqv airdieanntti.fiTehde ase loinwc-laubduenddoannecep avratriciaipnat.n Tt h(KesBeH in10cl-uGdHe)dw ointhe paaMrtiiSciepqa-indte (nKtBifiHed10F-G53HL)m wuitthat aio Mn iinSepqr-oidteeans-e, tiwfihedic hF5c3oLn fmerustlaotwio-nl eivne pl rroesteisatsaen, cwehtoicsha cqouninfearvsi rlo(wSA-lQev),eal tre6s.4is%tawncieth tion -shaoqsutinparevvira l(eSnAcQe.),I t aat ls6o.4i%nc luwditehdintw-hoospta rptirceivpaalnetnsc(eK. BIHt 4a3l-sGo HinacnluddCeHd Ct0w0o3- GpHar)tiwciipthanthtse i(nKtBegHr4a3se-GmHu taatniodn CHC003-GH) with the integrase mutation G140R that confers intermediate resistance to RAL and EVG and high-level resistance to cabotegravir (CAB), at 6.3% and 8.7% within- VViirruusseess 22002233,, 1155,, 1x2 8FOR PEER REVIEW 1186 ooff 2 235 Gho1s4t0 fRretqhuaetnccoiensf.e Irns tinwtoe ramdedditiiaotnearle psaisrttaicnicpeantots R(KABLHa9n4d-GEHV Ganadn KdBhHig9h0--lGevHe)l, MresiSisetqa ndcee- ttoecctaebdo ttheeg rEa1v3i8rA(C mAuBt)a,taiotn6 .i3n% reavnedrs8e. 7t%ranwscitrhipinta-hseo stthafrte cqounefnecrsie lso.wIn-letwveol aRdPdVi trioesniastlapnacre- taitc i5p.3a%nt san(Kd B1H3%94 w-GitHhina-nhdosKt BfrHeq9u0-eGnHcy),, rMesipSeecqtidveetlyec. tFeidnatlhlye, Ein1 3p8aArtimciuptaantti o(nKBinHr7e0v-GerHse) tMrainSsecqr idpetatesecttehda tthcoe n“freervselortwan-lte”v eTl2R15PSV mreusitsattaionnce aastso5.c3i%ateadn dw1it3h% lowwit-hleinv-ehlo rsetsfirsetqanuceen ctyo, rAeZspTe actti av e6l.y4.%F winiatlhlyin, -ihnopsta rfrtiecqiupeanncty(.K BH70-GH) MiSeq detected the “revertant” T215S mutaAtios nsuacshso, cifi arteesdiswtainthcel ogwen-loetvyeplirnegs ihstaadn cbeeeton ApeZrTfoartmae6d. 4b%y wMiitSheiqn -ahnodst aflrle wquitehnicny-.host variaAnstss >u5c%h, hifarde sbiseteann cinecgluendoetdy pinin tgheh aindtebrepernetpaetirofonrsm, tehde boyveMrailSle rqesainstdanalclew pirtehvina-lehnocset vwaoriualndt sh>av5e% bheaedn b2e5e%n, icnocmlupdaerdedin toth 1e7i%nt earsp drettaetrimonins,edth ebyo vSearnagllere. sSipsteacnifciecapllrye,v sailnegnlcee- wcloaussl drehsaivsteabnecen p2r5e%va,lceonmcep aersetidmtaot1es7 %waosudlde theramvein iendcrbeyasSeadn gferor.mSp 1e4c%ifi c(aSlalyn,gseinr)g lteo- c2la3s%s r(eMsisSteaqn)c, ewphrielev adluenacl-ecleasstsim reastiesstawncoeu lpdrehvaavlenincec resatsimedatferos mwo1u4%ld (nSoatn hgaevr)e tcoh2a3n%ge(dM. i Seq), while dual-class resistance prevalence estimates would not have changed. 3.5. Coreceptor Usage 3.5. CWoreec depettoerrmUsinageed HIV coreceptor usage by analyzing individual unique within-host enveWlopeed Vet3e rlomoipn esdeqHueIVncceos rreecceopvteorredu sfarogme b Iylluamnainlyaz sineqguienndciivnigd uoaf lthuen giqpu1e20w rietghiionn-h, ousst- einngv etlhoep geeVno32lpohoepnsoe (qgu2epn)c aelsgorercitohvmer (eFdigfurorem 11Il)l.u Omfi nthae s8e7q upeanrtciicnipgaonftst hfoerg wph12o0mr eggpi1o2n0, usesqinugenthceingge wnoa2sp shuecncoes(sgf2upl,) 6a7l g(7o7ri%th) mha(rFbiogruerde e1x1c).luOsfivtheley8 C7CpRar5t-iucsipinagn tvsafroiranwtsh.o Am fugrpt1h2e0r s1e9q (u2e1n.8c%in)g hwarabsosruedcc ae smsfiuxlt,u6r7e (o7f7 v%ir)uhsaersb coarpeadbelex colfu cseivlle elyntCryC vRi5a- tuhsein CgCvRa5r,i aCnXtsC. RA4 faunrdth/oerr 1b9o(t2h1 c.8o%re)cehpartobrosr.e Idn athmesixet puarertoicfipviarnutsse, sCcXaCpaRb4l-euosfincgel vl iernutsreysv rieaptrheeseCnCteRd5 a, CmXeCdRia4na onfd 2/4o%r b(IoQthR c1o1r–e7c1e%pt)o orfs .thIenirt hweistehipna-hrtoicsitp vainratsl ,pCoXpCulRat4i-ounssi.n Og nveir iunsdeisvridepurael,s eannt eAdRaT mnaeïdviea npaorf- 2ti4c%ipa(InQt,R ha1r1b–o7u1%re)do af pthuerier CwXitChRin4--huossint gv ivrairlapl oppouplualtaiotinosn.. One individual, an ART naïve participant, harboured a pure CXCR4-using viral population. FFiigguurree 1111.. Coorreecceeppttoorru ussaaggeeb baaseseddo nonV V3 3lo loopopse sqeuqeunecnecsegse gneontyoptyepdeuds iunsginIgll uImlluinmainMai SMeqiS.eCqo. rCecoerpe-- tcoerputosra guesawgaes winafse rirnefderuresdin gustihnegg t2hpe agl2gpo raitlghomr.itAhmsa. mAp slaemwpalse dwenaso tdeednaostecdo natsa icnointgaiCnXinCgR C4X-uCsiRn4g- vuasriniagn tvsawriahnetns ≥w2h%eno ≥f2it%s go2f pits cgo2rped scroeraedds rheaaddsa hf aldse ap foaslsitei vpeosraittieve(F rPaRte) (oFfP≤R3) .o5f% ≤.3.5%. Fiinaalllly,, wee iinveessttiigaatteed aassssocciiaattiionss beettweeeen ccorreecceepttorr ussaagee aand eenv ssubttypee ((CRFF0022__AG,,p puureres usbutbytpyep,e0,6 0_6cp_cxpaxn dan“dot h“eort”hedre”t edrmetienremdinuesdin guRsiInPgf rRoImP tfhreomgp 1th2e0 MgpiS1e2q0 cMoniSseeqn scuosnsseenqsuuesn sceeq) uinen8c5ep) einrs 8o5n speforsrownhs ifcohr wehiscuhc wcees ssfuucllcyesssefquulleyn sceeqdutehneceednt tihree gepn1ti2r0e rgepg1io20n .reOgvioenra. lOl,vwereaollb, swerev oebdsenrovestda tnisot isctaaltliystsicigalnlyifi sciagnntifaiscsaonct iaastisooncibaetitowne beentwcoereenc ecpotroer- ucesapgtoera unsdasgueb atnypde s(uCbhtyi-psqeu (aCrheid-spq=ua0r.4e7d) .pT =h 0e.4o7n)e. Tcahsee oonf ep ucareseC oXfC pRu4reu sCaXgCe Rw4a suosabgseer wveads ionbasepravretdic iinp aan pt awrtitichipCaRnFt 0w2_itAh GC.RF02_AG. 4. Discussion 4. Discussion We characterized HIV subtype diversity (using both protease-RT and full-genome HIV We characterized HIV subtype diversity (using both protease-RT and full-genome sequences), drug resistance and predicted coreceptor usage in a cohort of predominantly HIV sequences), drug resistance and predicted coreceptor usage in a cohort of predomi- (90%) ART-naïve persons in Ghana. Though our cohort was relatively modest in size, nantly (90%) ART-naïve persons in Ghana. Though our cohort was relatively modest in participant characteristics were nevertheless consistent with the epidemiology of HIV isnizGe,h paanrat.icOipuarntc ochhoarrtacctoemrisptriicsse dwselrieg hntelyvemrtohreelefsesm caolnessisthteannt mwaitlhe st,hceo nespiisdteenmtiwoliotghyt hoef oHvIeVr- irnep Grehsaennat.a tOiounr ocfofheomrta cleosmapmroisnegd PsLliWghHtlyg lmoboarlel yfe(mUaNleAsI DthSanes mtimalaetse, sctohnastis5t4e%nt owf iatlhl PthLeW oHveirn-r2e0p2re1swenetraetiwono mofe fnemanadlegs iarlms o[2n]g), PanLdWiHn sgulobb-Saallhya (rUanNAAfIrDicSa e[s6t2im–6a4t]e,si nthclaut d5i4n%g Gofh aalnl PaL[1W6,H27 i,n6 52]0,2in1 wpaerrtei cwuolamr.enC oanndsi sgtiernlst [w2]i)t,h anpdre ivni osuubs-rSeaphoarrtasnf rAofmricGa h[6a2n–a64[1],9 i,n27cl,6u6d]-, tihneg dGomhainnaa n[t1m6,2o7d,e65o]f, trina npsmaritsisciuolnari.n Coounrsciostheonrtt wwaitshh eptreervoisoeuxsu arle, paonrdtst hferocmoh oGrthaagnea d[1is9t,r2i7b,u66ti]o, nthwe adsocmominpaanrta mbloedtoe roefc etrnatnsstmudisiseisoinn itnh eoruerg cioonho[2rt5 ]w. as heterosexual, and the cohort age distribution was comparable to recent studies in the region [25]. Viruses 2023, 15, 128 17 of 23 Our results confirm that protease-RT-based HIV subtyping, though routinely per- formed, does not fully capture HIV subtype diversity in regions with high population-level HIV diversity, such as Ghana [30]. Though both protease-RT and full-genome HIV subtyp- ing identified CRF02_AG as the dominant variant in Ghana, protease-RT-based subtyping overestimated CRF02_AG prevalence by over 10% relative to whole-genome sequencing (66% vs. 54%, respectively). Indeed, overall concordance between protease-RT and full- genome-based HIV subtyping was only 63%, where discordant calls were attributable to additional recombinant complexity that either occurred outside of protease-RT, or that could not be resolved within this sub-genomic region at our predefined confidence threshold. Full-genome HIV subtyping also revealed a large proportion of novel recombinants that have not previously been described, including mosaics of CRF02_AG and/or cpx_06 along with other subtypes, that together made up nearly 37% of full-genome sequences in our cohort. Of note, most of these recombinants had unique breakpoints, indicating that they had arisen independently and were not the result of shared transmission within the cohort. Importantly, our estimate of 54% CRF02_AG prevalence based on full-genome se- quencing is substantially lower than that currently reported for Ghana (as of mid-November 2022, the Los Alamos HIV database estimates CRF02_AG prevalence at 78%; with 1254 of 1609 Ghanaian sequences being CRF02_AG [8]). This discrepancy is not due to our use of full-genome (rather than subgenomic) subtyping, as even our protease-RT-based subtyping estimated CRF02_AG prevalence at 66%. Instead, our results indicate that HIV genetic diversity in Ghana may be substantially higher than current estimates: specifically, that “pure” CRF02_AG prevalence is considerably lower than currently reported, while the prevalence of novel recombinants is considerably higher. Of note, CRF02_AG is estimated to be the most prevalent HIV recombinant strain globally (7.7%) [4], despite its relative restriction to West Africa [67]. Though the rea- sons for CRF02_AG’s spread are unclear (and could largely be due to founder effects), a 2004 study from Ghana reported that asymptomatic individuals with CRF02_AG had fivefold higher viral loads than those with other subtypes, suggesting a replicative advan- tage [68], a hypothesis that is supported by a recent report suggesting that CRF02_AG has a higher in vitro replicative capacity relative to its parental subtypes [69]. Regardless, our frequent observance of CRF02_AG along with unique recombinants, many of which contain CRF02_AG, is consistent with the ongoing generation and spread of HIV recombinant forms which now make up 23% of HIV infections globally [4]. Indeed, the high prevalence of URFs observed in this study is consistent with previous reports from Ghana [30,34,70]. High URF prevalence in the region is likely attributable to multiple factors, including high HIV subtype diversity in West Africa as well as socio-epidemiological factors. Due to the stigma associated with HIV, many individuals remain unaware of their status, and barriers to treatment access remain [71,72]. There are also high levels of migration, including among populations at increased risk of HIV [73]. Together, these factors contribute to high rates of multiple or superinfection [70], which increases the likelihood that novel recombinants will form. Our results also enhance our understanding of pretreatment drug resistance in Ghana. Using Sanger sequencing, which can reliably detect minority HIV variants at a threshold of about 20–25% of the within-host viral population, and is still widely used for HIV drug resistance genotyping globally [74,75], we observed a pretreatment drug resistance preva- lence of 17% (16/94). This total included 9 individuals (9.6%) with resistance to one or more drugs used in recommended first- or second-line regimens. NNRTI resistance was by far the most commonly observed type of resistance, at 12% prevalence. Specifically, we observed three instances of the major resistance mutations K103N (commonly selected in persons receiving EFV or NVP [76,77] and whose presence increases the probability of virological failure of common NNRTI-based WHO first-line regimens [78,79]) and V108I. We also ob- served two instances each of Y188L and E138A, and single occurrences of K101E (observed in tandem with Y188L in an ART-naive person), G190A and P225H (observed in tandem Viruses 2023, 15, 128 18 of 23 with K103N in an ART-naive individual). NRTI, PI and INSTI resistance was less common, observed at 4.4%, 1% and 2.2% prevalence, respectively. The relatively low prevalence of INSTI resistance supports the recent shift towards use of INSTI-based regimens as first-line therapy in Ghana [25]. Most cases of pretreatment resistance were limited to single-class resistance. Dual-class pretreatment resistance was uncommon (2.3%), and no participant exhibited triple or quadruple-class resistance. Of note, Illumina sequencing identified an additional seven individuals harboring minority (5–15% within-host prevalence) variants that were not detected by Sanger sequencing, including 2 cases where a minority variant was associated with high-level resistance (e.g., G140R in KBH43-GH which leads to high level CAB resistance). Nevertheless, the high concordance between the two sequencing methods demon- strates the continued relevance of Sanger sequencing for drug resistance genotyping. Though the detection of low-abundance resistance mutations in this population is notable, the relevance of these mutations to treatment outcomes remains unclear. While some prior studies have demonstrated associations between low-abundance (<15% within-host prevalence) mutations—in particular minority NNRTI resistant variants [80]—and poorer virologic outcomes in ART-naïve individuals, other studies have failed to demonstrate any impact on clinical outcomes [81,82]. The impact of minority variants on PI- or INSTI-based regimens has not been established. Further studies are required to elucidate the impact of low-abundance variants on antiretroviral treatment outcomes, and the potential added benefit of incorporating deep-sequencing approaches for HIV drug resistance into routine clinical management or population-level surveillance [83]. While CCR5-using viruses are preferentially transmitted and typically predominate during early infection [38], available data suggest that 6–18% of individuals in early in- fection may harbor CXCR4-using variants [84,85]. Broadly consistent with this, 23% of study participants harbored CXCR4-using variants, though most would have likely already reached the chronic phase of infection at study enrolment, despite their ART-naive status. Coreceptor usage may also differ between subtypes and CRFs [38,86]. Intriguingly, a study undertaken in neighboring Guinea Bissau reported 86% CXCR4 tropism in 111 CRF02_AG sequences from participants in late stage infection [87], suggesting that CXCR4 usage may occur more frequently in CRF02_AG, particularly as the infection progresses. In the present study, however, we did not observe any association between HIV subtype and coreceptor usage. That said, when comparing coreceptor usage findings across the literature, it is important to keep in mind that direct comparisons cannot always be made, since different studies use different methods, interpretation algorithms and cutoffs. Our study has some limitations. Sociodemographic data were collected by self- report, as were data on treatment history. Date of HIV infection, prior ART regimen (for the ART experienced subset) and CD4+ T-cell counts data were not available, while plasma viral loads were available for less than one-third of the cohort. HIV sequences were bulk-amplified without the use of unique molecular identifiers, so our estimates of within-host drug resistance mutation prevalence, as well as our estimates of within-host X4 co-receptor usage prevalence, should be interpreted with caution as they may not reflect true within-host variant prevalence. We note however that the g2p cutoffs that we used to identify within-host X4 sequences were those that were defined in the original study that validated deep V3 sequencing as an accurate method to genotypically infer HIV-1 co-receptor usage, a study that also did not employ unique molecular identifiers during HIV genotyping [59]. As coreceptor usage was inferred from unique V3 loop sequences excised from env-gp120 sequences rather than direct amplification of the much smaller V3 loop region, it is possible within-host V3 diversity was underestimated as full gp120 amplification may have been less efficient. The g2p algorithm has also been reported to be less sensitive in some non-B subtypes including CRF02_AG [88,89], which could impact coreceptor usage predictions. Viruses 2023, 15, 128 19 of 23 5. Conclusions Our study of HIV-1 subtype diversity (from full viral genomes), drug resistance and coreceptor usage is the first of its kind to be undertaken for Ghana. We demonstrated that CRF02_AG is the dominant subtype in circulation (54%), with unique recombinant forms containing CRF02_AG, cpx_06 and/or other subtypes also present at considerable (nearly 37%) prevalence. This frequent observation of unique recombinant forms strongly suggests that HIV-1 superinfection is not uncommon [90] and this is leading to the ongoing genera- tion of novel complex recombinant viruses in the region. This highlights the importance of public education on HIV prevention measures, the importance of regular HIV testing, and the expansion of antiretroviral treatment to reduce disease progression and transmission risk. Our characterization of 17% pretreatment drug resistance prevalence (including 12% pretreatment resistance to NNRTIs) in this mainly ART-naïve cohort contributes important data to guide population-level HIV treatment recommendations and supports the recent decision to transition to dolutegravir-based first line regimens. Ultimately, our findings underscore the importance of continued HIV molecular surveillance in resource-limited regions to inform treatment strategies to improve the health of people living with HIV. Supplementary Materials: The following supporting information can be downloaded at https:// www.mdpi.com/article/10.3390/v15010128/s1. Table S1: Primary PCR Primers; Table S2: Alternate PCR Primers; Table S3: Sanger Sequence Primers for Polymerase Region; Table S4: Clinically Relevant Drug Resistance Mutations Observed by Illumina Sequencing. Author Contributions: Conceptualization: A.A., N.I.N.-T. and Z.L.B.; methodology: A.A. and W.D.; software: C.J.B. (Charlotte J. Beelen), D.K.; formal analysis: A.A.; resources: M.M., L.E.A., B.F., V.G. and P.P.; data curation: A.A.; writing—original draft preparation: A.A.; writing—review and editing: A.A., N.I.N.-T., C.J.B. (Chanson J. Brumme) and Z.L.B.; visualization: A.A. and A.S.; supervision: Z.L.B., C.J.B. (Chanson J. Brumme) and N.I.N.-T.; project administration: A.A., P.P., V.G., N.I.N.-T. and Z.L.B.; funding acquisition: Z.L.B. Manuscript draft review; all authors. All authors have read and agreed to the published version of the manuscript. Funding: This study was funded by the Canadian Institutes for Health Research [PJT-148621]. A.A was supported by a Queen Elizabeth II Diamond Jubilee Scholarship, a program managed through a unique partnership of Universities Canada, the Rideau Hall Foundation (RHF) and Canadian universities. A.S. is supported by a Doctoral Award from the Canadian Institutes of Health Research. Z.L.B is supported by a Scholar Award from the Michael Smith Foundation for Health Research. Institutional Review Board Statement: This study was jointly approved by the Simon Fraser Univer- sity and Providence Health Care/University of British Columbia Research Ethics Boards in Canada (H19-01947), as well as the Institutional Review Board and the Scientific and Technical Committee of Korle-Bu Teaching Hospital, Accra, Ghana. (KBTH-IRB) 00075/2020. Informed Consent Statement: This study was carried out in accordance with ethical regulations for research with human participants. Each participant provided written informed consent. Data Availability Statement: Sequence data have been deposited in GenBank. GenBank accession numbers for Sanger protease-RT sequences are OP894533–OP894623 while those for Integrase are OP894444–OP894532. Accession numbers for Illumina full-genome HIV consensus sequences are OQ121842–OQ121917. Acknowledgments: We are grateful to the research administrative assistant, Adjoa Obo-Akwa, the Biomedical Laboratory Scientist, John Mensah Tosenu and the counselling team, all of the Fevers Unit of the Korle Bu Teaching Hospital for their immense support during participant recruitment, data assembly and sample collection. We thank Yurou Sang and Helena Louie for assistance with logistics and shipment. We are also grateful to the British Columbia Centre for Excellence in HIV/AIDS for the support. We thank the participants of the study, without whom this research would not have been possible. Conflicts of Interest: The authors declare no conflict of interest. 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