Background
South Africa has the largest public antiretroviral therapy (ART) programme in the world. We assessed temporal trends in pretreatment HIV-1 drug resistance (PDR) in ART-naïve adults from South Africa.
Methods
We included datasets from studies conducted between 2000 and 2016, with HIV-1
pol
sequences from more than ten ART-naïve adults. We analysed sequences for the presence of 101 drug resistance mutations. We pooled sequences by sampling year and performed a sequence-level analysis using a generalized linear mixed model, including the dataset as a random effect.
Findings
We identified 38 datasets, and retrieved 6880 HIV-1
pol
sequences for analysis. The pooled annual prevalence of PDR remained below 5% until 2009, then increased to a peak of 11·9% (95% confidence interval (CI) 9·2-15·0) in 2015. The pooled annual prevalence of non-nucleoside reverse-transcriptase inhibitor (NNRTI) PDR remained below 5% until 2011, then increased to 10.0% (95% CI 8.4–11.8) by 2014. Between 2000 and 2016, there was a 1.18-fold (95% CI 1.13–1.23) annual increase in NNRTI PDR (p < 0.001), and a 1.10-fold (95% CI 1.05–1.16) annual increase in nucleoside reverse-transcriptase inhibitor PDR (p = 0.001).
Interpretation
Increasing PDR in South Africa presents a threat to the efforts to end the HIV/AIDS epidemic. These findings support the recent decision to modify the standard first-line ART regimen, but also highlights the need for broader public health action to prevent the further emergence and transmission of drug-resistant HIV.
Source of Funding
This research project was funded by the South African Medical Research Council (MRC) with funds from National Treasury under its Economic Competitiveness and Support Package.
Disclaimer
The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
Background
South Africa has the largest public antiretroviral therapy (ART) programme in the world. We assessed temporal trends in pretreatment HIV-1 drug resistance (PDR) in ART-naïve adults from South Africa.
Methods
We included datasets from studies conducted between 2000 and 2016, with HIV-1
pol
sequences from more than ten ART-naïve adults. We analysed sequences for the presence of 101 drug resistance mutations. We pooled sequences by sampling year and performed a sequence-level analysis using a generalized linear mixed model, including the dataset as a random effect.
Findings
We identified 38 datasets, and retrieved 6880 HIV-1
pol
sequences for analysis. The pooled annual prevalence of PDR remained below 5% until 2009, then increased to a peak of 11·9% (95% confidence interval (CI) 9·2-15·0) in 2015. The pooled annual prevalence of non-nucleoside reverse-transcriptase inhibitor (NNRTI) PDR remained below 5% until 2011, then increased to 10.0% (95% CI 8.4–11.8) by 2014. Between 2000 and 2016, there was a 1.18-fold (95% CI 1.13–1.23) annual increase in NNRTI PDR (p < 0.001), and a 1.10-fold (95% CI 1.05–1.16) annual increase in nucleoside reverse-transcriptase inhibitor PDR (p = 0.001).
Interpretation
Increasing PDR in South Africa presents a threat to the efforts to end the HIV/AIDS epidemic. These findings support the recent decision to modify the standard first-line ART regimen, but also highlights the need for broader public health action to prevent the further emergence and transmission of drug-resistant HIV.
Source of Funding
This research project was funded by the South African Medical Research Council (MRC) with funds from National Treasury under its Economic Competitiveness and Support Package.
Disclaimer
The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
Background:
Transmitted drug resistance (TDR) remains a significant threat to Human
immunodeficiency virus (HIV) infected patients that are not exposed to antiretroviral treatment. Although,
combined antiretroviral therapy (cART) has reduced deaths among infected individuals,
emergence of drug resistance is gradually on rise.
Objective:
To determine the drug resistance mutations and subtypes of HIV-1 among pre-treatment
patients in the Eastern Cape of South Africa.
Methods:
Viral RNA was extracted from blood samples of 70 pre-treatment HIV-1 patients while
partial pol gene fragment amplification was achieved with specific primers by RT-PCR followed by
nested PCR and positive amplicons were sequenced utilizing ABI Prism 316 genetic sequencer.
Drug resistance mutations (DRMs) analysis was performed by submitting the generated sequences
to Stanford HIV drug resistance database.
Results:
Viral DNA was successful for 66 (94.3%) samples of which 52 edited sequences were obtained
from the protease and 44 reverse transcriptase sequences were also fully edited. Four major protease
inhibitor (PI) related mutations (I54V, V82A/L, L76V and L90M) were observed in seven patients
while several other minor and accessory PIs were also identified. A total of 11(25.0%) patients
had NRTIs related mutations while NNRTIs were observed among 14(31.8%) patients. K103N/S,
V106M and M184V were the most common mutations identified among the viral sequences. Phylogenetic
analysis of the partial pol gene indicated all sequences clustered with subtype C.
Conclusions:
This study indicates that HIV-1 subtype C still predominates and responsible for driving
the epidemic in the Eastern Cape of South Africa with slow rise in the occurrence of transmitted
drug resistance.
Background:
Effective global antiretroviral vaccines and therapeutic strategies depend on the diversity, evolution, and epidemiology of their various strains as well as their transmission and pathogenesis. Most viral disease-causing particles are clustered into a taxonomy of subtypes to suggest pointers toward nucleotide-specific vaccines or therapeutic applications of clinical significance sufficient for sequence-specific diagnosis and homologous viral studies. These are very useful to formulate predictors to induce cross-resistance to some retroviral control drugs being used across study areas.
Objective:
This research proposed a collaborative framework of hybridized (Machine Learning and Natural Language Processing) techniques to discover hidden genome patterns and feature predictors, for HIV-1 genome sequences mining.
Method:
630 human HIV-1 genome sequences above 8500 bps were excavated from the National Center for Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov) for 21 countries across different continents, Antarctica exempt. These sequences were transformed and learned using a self-organizing map (SOM). To discriminate emerging/new sub-strain(s), the HIV-1 reference genome was included as part of the input isolates/samples during the training. After training the SOM, component planes defining pattern clusters of the input datasets were generated, for cognitive knowledge mining and subsequent labelling of the datasets. Additional genome features including dinucleotide transmission recurrences, codon recurrences, and mutation recurrences, were finally extracted from the raw genomes to construct output classification targets for supervised learning.
Results:
SOM training explains the inherent pattern diversity of HIV-1 genomes as well as inter- and intra-country transmissions in which mobility might play an active role, as corroborated by literature. Nine sub-strains were discovered after disassembling the SOM correlation hunting matrix space attributed to disparate clusters. Cognitive knowledge mining separated similar pattern clusters bounded by a certain degree of correlation range, discovered by the SOM. A Kruskal-Wallis rank-sum test and Wilcoxon rank-sum test showed statistically significant variations in dinucleotide, codon, and mutation patterns.
Conclusion:
Results of the discovered sub-strains and response clusters visualizations corroborate existing literature, with significant haplotype variations. The proposed framework would assist in the development of decision support systems for easy contact tracing, infectious disease surveillance, and studying the progressive evolution of the reference HIV-1 genome.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
