Search

Introduction Because next-generation sequencing (NGS) can detect minority mutations that have been linked to treatment failure but are missed by population Sanger sequencing, it has the potential to enhance HIV treatment monitoring. Comparing NGS based on the Illumina platform to Sanger sequencing, we assessed the clinical importance of NGS in HIV-1 medication resistance testing. Methods In this retrospective case-control study, 167 people with matched Sanger sequencing data underwent HIV genotypic analysis using an Illumina-Miseq. These included 122 time-matched controls from the same cohort who had viral suppression at 12 months, and 45 patients with virologic failure at 12 months. Results NGS identified all major HIV drug resistance mutations detected by Sanger sequencing and revealed additional major mutations M184V and K65R that increased the resistance profile to antiretroviral therapy used in this cohort. Abacavir's HIV drug resistance score increased 60-fold, Zidovudine's by 25-fold and to Emtricitabine/Lamivudine by 90-fold. Overall, 108/167 (64.7%) of our subjects had minority DRMs at baseline. K70E, M184V, Y115F, and K70R were among the NRTI minority SDRMs discovered, whereas K103N, Y181C, and K101E were NNRTI minority SDRMs. PI minority SDRMs were also detected in 4 individuals. Being female (p = 0.005) and having a CD4 p = 0.029) were associated with minority mutations. Minority surveillance DRMs expanded the HIV drug resistance profiles of individuals. A higher frequency of baseline minority mutations correlated with a higher viral load count at end point (p ). Conclusions NGS identified pre-treatment minority variants linked to increased viral load count and enhanced resistance to NNRTIs and NRTIs, and it detected more major mutations than Sanger sequencing. Being female and having low CD4 count were associated with presence of minority mutations. NGS could be used to create drug resistance profiles for people receiving HIV-1 ART, allowing clinicians to use both major and minor mutation profiles to inform treatment choices and so increase the effectiveness of the currently available antiretroviral medication. This may be crucial if the UNAIDS 95-95-95 targets are to be met and if we are to eliminate HIV/AIDS as a public health issue by 2030.

Background Host population structure is a key determinant of pathogen and infectious disease transmission patterns. Pathogen phylogenetic trees are useful tools to reveal the population structure underlying an epidemic. Determining whether a population is structured or not is useful in informing the type of phylogenetic methods to be used in a given study. We employ tree statistics derived from phylogenetic trees and machine learning classification techniques to reveal an underlying population structure. Results In this paper, we simulate phylogenetic trees from both structured and non-structured host populations. We compute eight statistics for the simulated trees, which are: the number of cherries; Sackin, Colless and total cophenetic indices; ladder length; maximum depth; maximum width, and width-to-depth ratio. Based on the estimated tree statistics, we classify the simulated trees as from either a non-structured or a structured population using the decision tree (DT), K-nearest neighbor (KNN) and support vector machine (SVM). We incorporate the basic reproductive number ($$R_0$$ R 0 ) in our tree simulation procedure. Sensitivity analysis is done to investigate whether the classifiers are robust to different choice of model parameters and to size of trees. Cross-validated results for area under the curve (AUC) for receiver operating characteristic (ROC) curves yield mean values of over 0.9 for most of the classification models. Conclusions Our classification procedure distinguishes well between trees from structured and non-structured populations using the classifiers, the two-sample Kolmogorov-Smirnov, Cucconi and Podgor-Gastwirth tests and the box plots. SVM models were more robust to changes in model parameters and tree size compared to KNN and DT classifiers. Our classification procedure was applied to real -world data and the structured population was revealed with high accuracy of $$92.3\%$$ 92.3 % using SVM-polynomial classifier.

HIV drug resistance (HIVDR) is of increasing health concern, especially among key populations. We investigated the prevalence of virological suppression (VS), prevalence and correlates of HIVDR in HIV-infected women, enrolled in a high-risk cohort. We enrolled 267 women initiated on first-line antiretroviral therapy (ART) between 2015 and 2018. Participants' plasma samples were analyzed for HIV RNA viral load (VL) and genotypic resistance testing was performed on those with VL nonsuppression (defined as VL ≥1,000 copies/mL). We used the Stanford HIVDR database-algorithm to assess HIVDR mutations and logistic regression to assess risk factors for VL nonsuppression and HIVDR. We observed an overall VS prevalence of 76.0% (203/267) and detected respective acquired drug resistance prevalence to non-nucleoside reverse transcriptase inhibitors (NNRTIs) and nucleoside reverse transcriptase inhibitors (NRTIs) of 81.3% [confidence interval (CI) 67.4-91.1] and 45.8% (CI 31.4-60.8) among the 48 successfully genotyped VL nonsuppressors. NNRTI mutations were observed in 81.3% (39/48) of the genotyped participants and 45.8% (22/48) had both NRTI and NNRTI mutations. The mutation K103N was detected in 62.5% (30/48) of participants, 41.7% (20/48) had M184V/I, 14.6% had K65R, and 12.5% (6/48) had thymidine analog mutations (TAMs). None of the analyzed potential risk factors, including age and duration on ART, was significantly correlated with VL nonsuppression or HIVDR. Although high levels of NNRTI mutations support the transition to dolutegravir, the presence of NRTI mutations, especially TAMs, may compromise dolutegravir-based regimens or other second-line ART options. The moderate VS prevalence and high HIVDR prevalence therefore call for timely ART switching and intensive adherence counseling.

Summary Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%. Availability and implementation QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm. Supplementary information Supplementary data are available at Bioinformatics Advances online.

ObjectivesWe examined virological outcomes, patterns of acquired HIV drug resistance (ADR), correlates of virological failure (VF) and acquired drug resistance among fisherfolk on first-line ART.MethodsWe enrolled 1169 adults on ART for a median duration of 6, 12, 24, 36 and ≥48 months and used a pooled VL testing approach to identify VF (VL ≥1000 copies/mL). We performed genotyping among VF cases and determined correlates of VF and ADR by logistic regression.ResultsThe overall virological suppression rate was 91.7% and ADR was detected in 71/97 (73.2%) VF cases. The most prevalent mutations were M184V/I (53.6%) for NRTIs and K103N (39.2%) for NNRTIs. Thymidine analogue mutations were detected in 21.6% of VF cases while PI mutations were absent. A zidovudine-based ART regimen, duration on ART (≥24 months) and secondary/higher education level were significantly associated with VF. A nevirapine-based regimen [adjusted OR (aOR): 1.87; 95% CI: 0.03–0.54)] and VL ≥10000 copies/mL (aOR: 3.48; 95% CI: 1.37–8.85) were ADR correlates. The pooling strategies for VL testing with a negative predictive value (NPV) of ≥95.2% saved US $20320 (43.5%) in VL testing costs.ConclusionsWe observed high virological suppression rates among these highly mobile fisherfolk; however, there was widespread ADR among those with VF at the first VL testing prior to intensive adherence counselling. Timely treatment switching and adherence support is recommended for better treatment outcomes. Adoption of pooled VL testing could be cost effective, particularly in resource-limited settings.

To provide a summary of the current data on the global HIV subtype diversity and distribution by region. HIV is one of the most genetically diverse pathogens due to its high-mutation and recombination rates, large population size and rapid replication rate. This rapid evolutionary process has resulted in several HIV subtypes that are heterogeneously globally distributed.Subtype A remains the most prevalent strain in parts of East Africa, Russia and former Soviet Union countries; subtype B in Europe, Americas and Oceania; subtype C in Southern Africa and India; CRF01_AE in Asia and CRF02_AG in Western Africa. Recent studies based on near full-length genome sequencing highlighted the growing importance of recombinant variants and subtype C viruses.The dynamic change in HIV subtype distribution presents future challenges for diagnosis, treatment and vaccine design and development. An increase in recombinant viruses suggests that coinfection and superinfection by divergent HIV strains has become more common necessitating continuous surveillance to keep track of the viral diversity. Cheaper near full-length genome sequencing approaches are critical in improving HIV subtype estimations. However, missing subtype data and low sequence sampling levels are still a challenge in some geographical regions. VIDEO ABSTRACT: http://links.lww.com/COHA/A14.

The General Population Cohort (GPC) in south-western Uganda has a low HIV-1 incidence rate (&lt, 1%). However, new infections continue to emerge. In this research, 3796 HIV-1 pol sequences (GPC: n = 1418, non-GPC sites: n = 1223, Central Uganda: n = 1010 and Eastern Uganda: n = 145) generated between 2003&ndash, 2015 were analysed using phylogenetic methods with demographic data to understand HIV-1 transmission in this cohort and inform the epidemic response. HIV-1 subtype A1 was the most prevalent strain in the GPC area (GPC and non-GPC sites) (39.8%), central (45.9%) and eastern (52.4%) Uganda. However, in the GPC alone, subtype D was the predominant subtype (39.1%). Of the 524 transmission clusters identified by Cluster Picker, all large clusters (&ge, 5 individuals, n = 8) involved individuals from the GPC. In a multivariate analysis, clustering was strongly associated with being female (adjusted Odds Ratio, aOR = 1.28, 95% CI, 1.06&ndash, 1.54), being &gt, 25 years (aOR = 1.52, 95% CI, 1.16&ndash, 2.0) and being a resident in the GPC (aOR = 6.90, 95% CI, 5.22&ndash, 9.21). Phylogeographic analysis showed significant viral dissemination (Bayes Factor test, BF &gt, 3) from the GPC without significant viral introductions (BF &lt, 3) into the GPC. The findings suggest localized HIV-1 transmission in the GPC. Intensifying geographically focused combination interventions in the GPC would contribute towards controlling HIV-1 infections.

Recombination is an important feature of HIV evolution, occurring both within and between the major branches of diversity (subtypes). The Ugandan epidemic is primarily composed of two subtypes, A1 and D, that have been co-circulating for 50 years, frequently recombining in dually infected patients. Here, we investigate the frequency of recombinants in this population and the location of breakpoints along the genome. As part of the PANGEA-HIV consortium, 1,472 consensus genome sequences over 5 kb have been obtained from 1,857 samples collected by the MRC/UVRI & LSHTM Research unit in Uganda, 465 (31.6 per cent) of which were near full-length sequences (>8 kb). Using the subtyping tool SCUEAL, we find that of the near full-length dataset, 233 (50.1 per cent) genomes contained only one subtype, 30.8 per cent A1 (n = 143), 17.6 per cent D (n = 82), and 1.7 per cent C (n = 8), while 49.9 per cent (n = 232) contained more than one subtype (including A1/D (n = 164), A1/C (n = 13), C/D (n = 9); A1/C/D (n = 13), and 33 complex types). K-means clustering of the recombinant A1/D genomes revealed a section of envelope (C2gp120-TMgp41) is often inherited intact, whilst a generalized linear model was used to demonstrate significantly fewer breakpoints in the gag–pol and envelope C2-TM regions compared with accessory gene regions. Despite similar recombination patterns in many recombinants, no clearly supported circulating recombinant form (CRF) was found, there was limited evidence of the transmission of breakpoints, and the vast majority (153/164; 93 per cent) of the A1/D recombinants appear to be unique recombinant forms. Thus, recombination is pervasive with clear biases in breakpoint location, but CRFs are not a significant feature, characteristic of a complex, and diverse epidemic. 1. Introduction 2. Methods 2.1 Sample collection 2.2 Sequencing and alignment 2.3 Subtyping 2.4 Identification of transmitted breakpoints 2.5 Recombination pattern classification 2.6 Breakpoint and genome location model framework 3. Results 3.1 Subtype distribution 3.2 Identification of CRFs and transmitted breakpoints 3.3 Recombinant groupings 3.4 Breakpoint distribution 4. Discussion

Across sub-Saharan Africa, key populations with elevated HIV-1 incidence and/or prevalence have been identified, but their contribution to disease spread remains unclear. We performed viral deep-sequence phylogenetic analyses to quantify transmission dynamics between the general population (GP), fisherfolk communities (FF), and women at high risk of infection and their clients (WHR) in central and southwestern Uganda. Between August 2014 and August 2017, 6185 HIV-1 positive individuals were enrolled in 3 GP and 10 FF communities, 3 WHR enrollment sites. A total of 2531 antiretroviral therapy (ART) na&iuml, ve participants with plasma viral load &gt, 1000 copies/mL were deep-sequenced. One hundred and twenty-three transmission networks were reconstructed, including 105 phylogenetically highly supported source&ndash, recipient pairs. Only one pair involved a WHR and male participant, suggesting that improved population sampling is needed to assess empirically the role of WHR to the transmission dynamics. More transmissions were observed from the GP communities to FF communities than vice versa, with an estimated flow ratio of 1.56 (95% CrI 0.68&ndash, 3.72), indicating that fishing communities on Lake Victoria are not a net source of transmission flow to neighboring communities further inland. Men contributed disproportionally to HIV-1 transmission flow regardless of age, suggesting that prevention efforts need to better aid men to engage with and stay in care.

Although fishing communities (FCs) in Uganda are disproportionately affected by HIV-1 relative to the general population (GP), the transmission dynamics are not completely understood. We earlier found most HIV-1 transmissions to occur within FCs of Lake Victoria. Here, we test the hypothesis that HIV-1 transmission in FCs is isolated from networks in the GP. We used phylogeography to reconstruct the geospatial viral migration patterns in 8 FCs and 2 GP cohorts and a Bayesian phylogenetic inference in BEAST v1.8.4 to analyse the temporal dynamics of HIV-1 transmission. Subtype A1 (pol region) was most prevalent in the FCs (115, 45.1%) and GP (177, 50.4%). More recent HIV transmission pairs from FCs were found at a genetic distance (GD)

Dried blood spots (DBS) are an alternative specimen type for HIV drug resistance genotyping in resource-limited settings. Data relating to the impact of DBS storage and shipment conditions on genotyping efficiency under field conditions are limited. We compared the genotyping efficiencies and resistance profiles of DBS stored and shipped at different temperatures to those of plasma specimens collected in parallel from patients receiving antiretroviral therapy in Uganda. Plasma and four DBS cards from anti-coagulated venous blood and a fifth card from finger-prick blood were prepared from 103 HIV patients with a median viral load (VL) of 57,062 copies/ml (range, 1,081 to 2,964,191). DBS were stored at ambient temperature for 2 or 4 weeks or frozen at −80°C and shipped from Uganda to the United States at ambient temperature or frozen on dry ice for genotyping using a broadly sensitive in-house method. Plasma (97.1%) and DBS (98.1%) stored and shipped frozen had similar genotyping efficiencies. DBS stored frozen (97.1%) or at ambient temperature for 2 weeks (93.2%) and shipped at ambient temperature also had similar genotyping efficiencies. Genotyping efficiency was reduced for DBS stored at ambient temperature for 4 weeks (89.3%, P = 0.03) or prepared from finger-prick blood and stored at ambient temperature for 2 weeks (77.7%, P < 0.001) compared to DBS prepared from venous blood and handled similarly. Resistance profiles were similar between plasma and DBS specimens. This report delineates the optimal DBS collection, storage, and shipping conditions and opens a new avenue for cost-saving ambient-temperature DBS specimen shipments for HIV drug resistance (HIVDR) surveillances in resource-limited settings.