HIV subtype diversity worldwide.

PURPOSE OF REVIEW: 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. RECENT FINDINGS: 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. SUMMARY: 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

Short Communication: Choosing the Right Program for the Identification of HIV-1 Transmission Networks from Nucleotide Sequences Sampled from Different Populations.

HIV-TRAnsmission Cluster Engine (HIV-TRACE) and Cluster Picker are some of the most widely used programs for identifying HIV-1 transmission networks from nucleotide sequences. However, choosing between these tools is subjective and often a matter of personal preference. Because these software use different algorithms to detect HIV-1 transmission networks, their optimal use is better suited with different sequence data sets and under different scenarios. The performance of these tools has previously been evaluated across a range of genetic distance thresholds without an assessment of the differences in the structure of networks identified. In this study, we tested both programs on the same HIV-1 pol sequence data set (n?=?2,017) from three Ugandan populations to examine their performance across different risk groups and evaluate the structure of networks identified. HIV-TRACE that uses a single-linkage algorithm identified more nodes in the same networks that were connected by sparse links than Cluster Picker. This suggests that the choice of the program used for identifying networks should depend on the study aims, the characteristics of the population being investigated, dynamics of the epidemic, sampling design, and the nature of research questions being addressed for optimum results. HIV-TRACE could be more applicable with larger data sets where the aim is to identify larger clusters that represent distinct transmission chains and in more diverse populations where infection has occurred over a period of time. In contrast, Cluster Picker is applicable in situations where more closely connected clusters are expected in the studied populations

Virological outcome of patients with HIV drug resistance attending an urban out-patient clinic in Uganda: a need for structured adherence counselling and third line treatment options

BACKGROUND HIV drug resistance and suboptimal adherence are the main reasons for treatment failure among HIV-infected individuals. As genotypic resistance testing is not routinely available in resource-limited settings such as Uganda, data on transmitted and acquired resistance is sparse. METHODS This observational follow-up study assessed the virological outcomes of patients diagnosed with virological failure or transmitted HIV drug resistance in 2015 at the adults' out-patient clinic of the Infectious Diseases Institute in Kampala, Uganda. Initially, 2430 patients on antiretroviral therapy (ART) underwent virological monitoring, of which 190 had virological failure and were subsequently eligible for this follow-up study. Nine patients diagnosed with transmitted drug resistance were eligible. In patients with a viral load > 1000 copies/mL genotypic resistance testing was done. RESULTS Of 190 eligible patients, 30 (15.8%) had either died or were lost to follow-up. A total of 148 (77.9%) were included, of which 98 had had a change of ART regimen, and 50 had received adherence counselling only. The majority was now on 2-line ART (N=130, 87.8%). The median age was 39 years (interquartile range: 32-46) and 109 (73.6%) were female. Virological failure was diagnosed in 29 (19.6%) patients, of which 24 (82.8%) were on 2-line ART. Relevant drug resistance was found in 25 (86.2%) cases, of which 12 (41.3%) carried dual and 7 (24.1%) triple drug resistance. CONCLUSION Two years after initial virological failure, most patients followed up by this study had a successful virological outcome. However, a significant proportion either continued to fail or died or was lost to follow-up

Effect of HIV-1 subtypes on disease progression in rural Uganda: a prospective clinical cohort study.

OBJECTIVE: We examined the association of HIV-1 subtypes with disease progression based on three viral gene regions. DESIGN: A prospective HIV-1 clinical cohort study in rural Uganda. METHODS: Partial gag, env and pol genes were sequenced. Cox proportional hazard regression modelling was used to estimate adjusted hazard ratios (aHRs) of progression to: CD4≤250, AIDS onset and death, adjusted for sex, age and CD4 count at enrolment. RESULTS: Between 1990 and 2010, 292 incident cases were subtyped: 25% had subtype A, 45% had D, 26% had A/D recombinants, 1% had C and 4% were other recombinant forms. Of the 278 incident cases included in the disease progression analysis, 62% progressed to CD4≤250, 32% to AIDS, and 34% died with a higher proportion being among subtype D cases. The proportions of individuals progressing to the three endpoints were significantly higher among individuals infected with subtype D. Throughout the study period, individuals infected with subtype D progressed faster to CD4≤250, adjusted HR (aHR), (95% CI) = 1.72 (1.16-2.54), but this was mainly due to events in the period before antiretroviral therapy (ART) introduction, when individuals infected with subtype D significantly progressed faster to CD4≤250 than subtype A cases; aHR (95% CI) = 1.78 (1.01-3.14). CONCLUSIONS: In this population, HIV-1 subtype D was the most prevalent and was associated with faster HIV-1 disease progression than subtype A. Further studies are needed to examine the effect of HIV-1 subtypes on disease progression in the ART period and their effect on the virological and immunological ART outcomes

Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda:Implications for Epidemic Control

Phylogenetic inference is useful in characterising HIV transmission networks and assessing where prevention is likely to have the greatest impact. However, estimating parameters that influence the network structure is still scarce, but important in evaluating determinants of HIV spread. We analyzed 2017 HIV pol sequences (728 Lake Victoria fisherfolk communities (FFCs), 592 female sex workers (FSWs) and 697 general population (GP)) to identify transmission networks on Maximum Likelihood (ML) phylogenetic trees and refined them using time-resolved phylogenies. Network generative models were fitted to the observed degree distributions and network parameters, and corrected Akaike Information Criteria and Bayesian Information Criteria values were estimated. 347 (17.2%) HIV sequences were linked on ML trees (maximum genetic distance ≤4.5%, ≥95% bootstrap support) and, of these, 303 (86.7%) that consisted of pure A1 (n = 168) and D (n = 135) subtypes were analyzed in BEAST v1.8.4. The majority of networks (at least 40%) were found at a time depth of ≤5 years. The waring and yule models fitted best networks of FFCs and FSWs respectively while the negative binomial model fitted best networks in the GP. The network structure in the HIV-hyperendemic FFCs is likely to be scale-free and shaped by preferential attachment, in contrast to the GP. The findings support the targeting of interventions for FFCs in a timely manner for effective epidemic control. Interventions ought to be tailored according to the dynamics of the HIV epidemic in the target population and understanding the network structure is critical in ensuring the success of HIV prevention programs

The Molecular Epidemiology and Transmission Dynamics of HIV Type 1 in a General Population Cohort in Uganda

The General Population Cohort (GPC) in south-western Uganda has a low HIV-1 incidence rate (25 years (aOR = 1.52; 95% CI, 1.16-2.0) and being a resident in the GPC (aOR = 6.90; 95% CI, 5.22-9.21). Phylogeographic analysis showed significant viral dissemination (Bayes Factor test, BF > 3) from the GPC without significant viral introductions (BF < 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

A large population sample of African HIV genomes from the 1980s reveals a reduction in subtype D over time associated with propensity for CXCR4 tropism

We present 109 near full-length HIV genomes amplified from blood serum samples obtained during early 1986 from across Uganda, which to our knowledge is the earliest and largest population sample from the initial phase of the HIV epidemic in Africa. Consensus sequences were made from paired-end Illumina reads with a target-capture approach to amplify HIV material following poor success with standard approaches. In comparisons with a smaller 'intermediate' genome dataset from 1998 to 1999 and a 'modern' genome dataset from 2007 to 2016, the proportion of subtype D was significantly higher initially, dropping from 67% (73/109), to 57% (26/46) to 17% (82/465) respectively (p < 0.0001). Subtype D has previously been shown to have a faster rate of disease progression than other subtypes in East African population studies, and to have a higher propensity to use the CXCR4 co-receptor ("X4 tropism"); associated with a decrease in time to AIDS. Here we find significant differences in predicted tropism between A1 and D subtypes in all three sample periods considered, which is particularly striking the 1986 sample: 66% (53/80) of subtype D env sequences were predicted to be X4 tropic compared with none of the 24 subtype A1. We also analysed the frequency of subtype in the envelope region of inter-subtype recombinants, and found that subtype A1 is over-represented in env, suggesting recombination and selection have acted to remove subtype D env from circulation. The reduction of subtype D frequency over three decades therefore appears to be a result of selective pressure against X4 tropism and its higher virulence. Lastly, we find a subtype D specific codon deletion at position 24 of the V3 loop, which may explain the higher propensity for subtype D to utilise X4 tropism

Employing phylogenetic tree shape statistics to resolve the underlying host population structure

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 ([Formula: see text]) 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 [Formula: see text] using SVM-polynomial classifier

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data.

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

Rates of HIV-1 virological suppression and patterns of acquired drug resistance among fisherfolk on first-line antiretroviral therapy in Uganda.

OBJECTIVES: We 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: We 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