Background: Little is known about prevalence of drug resistance among HIV-infected Ugandans, a setting with over 15 years of public sector access to antiretroviral therapy (ART) and where virological monitoring was only recently introduced. Setting: This study was conducted in the adults' out-patient clinic of the Infectious Diseases Institute, Kampala, Uganda. Methods: HIV genotyping was performed in ART-naive patients and in treatment-experienced patients on ART for ≥6 months with virological failure (≥1000 copies/mL). Results: A total of 152 ART-naive and 2430 ART-experienced patients were included. Transmitted drug resistance was detected in 9 (5.9%) patients. After a median time on ART of 4.7 years [interquartile range: 2.5–8.7], 190 patients (7.8%) had virological failure with a median viral load of 4.4 log10 copies per milliliter (interquartile range: 3.9–4.9). In addition, 146 patients had a viral load between 51 and 999 copies per milliliter. Most patients with virological failure (142, 74.7%) were on first-line ART. For 163 (85.8%) ART-experienced patients, genotype results were available. Relevant drug-resistance mutations were observed in 135 (82.8%), of which 103 (63.2%) had resistance to 2 drug classes, and 11 (6.7%) had resistance to all drug classes available in Uganda. Conclusion: The prevalence of transmitted drug resistance was lower than recently reported by the WHO. With 92% of all patients virologically suppressed on ART, the prevalence of virological failure was low when a cutoff of 1000 copies per milliliter is applied, and is in line with the third of the 90-90-90 UNAIDS targets. However, most failing patients had developed multiclass drug resistance.
The General Population Cohort (GPC) in south-western Uganda has a low HIV-1 incidence rate (<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 2003-2015 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 (≥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, 1.06-1.54), being >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.
ABSTRACT Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping of cohorts with diverse HLA properties. Here we build on the concept of using two key parameters, diversity metric of the HLA profile of individuals within a population and consideration of sequence diversity in the context of an individual’s CD8 T-cell immune repertoire to assess the HIV proteome for defined regions of immunogenicity. Using this approach, Analysis of HLA adaptation and functional immunogenicity data enabled the identification of regions within the proteome that offer significant conservation, HLA recognition within a population, low prevalence of HLA adaptation and demonstrated immunogenicity. We believe this unique and novel approach to vaccine design that, in combination with in vitro functional assays, offers a bespoke pipeline for expedited and rational CD8 T-cell vaccine design for HIV and potentially other pathogens with the potential for both global and local coverage.
During 2006–2007, transmitted human immunodeficiency virus (HIV) drug resistance (TDR) among drug-naive women with newly diagnosed HIV infection and likely to be recently infected when attending antenatal clinics in Entebbe was found to be <5% with use of the World Health Organization (WHO) survey method. Using the same method, we attempted to classify TDR among women who seroconverted during 2008–2010 and who were identified from a cohort of recently infected sex workers in Kampala, Uganda. TDR mutations were identified using the 2009 WHO TDR mutations list. The WHO survey method could not be used to classify TDR because the necessary sample size was not reached during the survey period. However, a point prevalence estimate of 2.6% (95% confidence interval, 0.07%–13.8%) nonnucleoside reverse-transcriptase inhibitor TDR was determined.
HIV care programs in resource-limited settings have hitherto concentrated on antiretroviral therapy (ART) access, but HIV drug resistance is emerging. In a cross-sectional study of HIV-positive adults on ART for ≥6 months enrolled into a prospective cohort in Uganda, plasma HIV RNA was measured and genotyped if ≥1000 copies/ml. Identified Drug resistance mutations (DRMs) were interpreted using the Stanford database, 2009 WHO list of DRMs and the IAS 2014 update on DRMs, and examined and tabulated by ART drug classes. Between July 2013 and August 2014, 953 individuals were enrolled, 119 (12.5%) had HIV-RNA ≥1000 copies/ml and 110 were successfully genotyped; 74 (67.3%) were on first-line and 36 (32.7%) on second-line ART regimens. The predominant HIV-1 subtypes were D (34.5%), A (33.6%) and Recombinant forms (21.8%). The commonest clinically significant major resistance mutations associated with the highest levels of reduced susceptibility or virological response to the relevant Nucleoside Reverse Transcriptase Inhibitor (NRTI) were; the Non-thymidine analogue mutations (Non-TAMS) M184V—20.7% and K65R—8.0%; and the TAMs M41L and K70R (both 8.0%). The major Non-NRTI (NNRTI) mutations were K103N—19.0%, G190A—7.0% and Y181C—6.0%. A relatively nonpolymorphic accessory mutation A98G—12.0% was also common. Seven of the 36 patients on second line ART had major Protease Inhibitor (PI) associated DRMS including; V82A—7.0%, I54V, M46I and L33I (all 5.0%). Also common were the accessory PI mutations L10I—27%, L10V—12.0% and L10F—5.0% that either reduce PI susceptibility or increase the replication of viruses containing PI-resistance mutations. Of the 7 patients with major PI DRMs, five had high level resistance to ritonavir boosted Lopinavir and Atazanavir, with Darunavir as the only susceptible PI tested. In resource-limited settings, HIV care programs that have previously concentrated on ART access, should now consider availing access to routine HIV viral load monitoring, targeted HIV drug resistance testing and availability of third-line ART regimens.
Background With the scale-up of antiretroviral therapy (ART), monitoring programme performance is needed to maximize ART efficacy and limit HIV drug resistance (HIVDR). Methods We implemented a WHO HIVDR prospective survey protocol at three treatment centers between 2012 and 2013. Data were abstracted from patient records at ART start (T1) and after 12 months (T2). Genotyping was performed in the HIV pol region at the two time points. Results Of the 425 patients enrolled, at T2, 20 (4.7%) had died, 66 (15.5%) were lost to follow-up, 313 (73.6%) were still on first-line, 8 (1.9%) had switched to second-line, 17 (4.0%) had transferred out and 1 (0.2%) had stopped treatment. At T2, 272 out of 321 on first and second line (84.7%) suppressed below 1000 copies/ml and the HIV DR prevention rate was 70.1%, just within the WHO threshold of ≥70%. The proportion of participants with potential HIVDR was 20.9%, which is higher than the 18.8% based on pooled analyses from African studies. Of the 35 patients with mutations at T2, 80% had M184V/I, 65.7% Y181C, and 48.6% (54.8% excluding those not on Tenofovir) had K65R mutations. 22.9% had Thymidine Analogue Mutations (TAMs). Factors significantly associated with HIVDR prevention at T2 were: baseline viral load (VL) <100,000 copies/ml [Adjusted odds ratio (AOR) 3.13, 95% confidence interval (CI): 1.36–7.19] and facility. Independent baseline predictors for HIVDR mutations at T2 were: CD4 count <250 cells/μl (AOR 2.80, 95% CI: 1.08–7.29) and viral load ≥100,000 copies/ml (AOR 2.48, 95% CI: 1.00–6.14). Conclusion Strengthening defaulter tracing, intensified follow-up for patients with low CD4 counts and/or high VL at ART initiation together with early treatment initiation above 250 CD4 cells/ul and adequate patient counselling would improve ART efficacy and HIVDR prevention. The high rate of K65R and TAMs could compromise second line regimens including NRTIs.
Emerging highly transmissible viral infections such as SARS-CoV-2 pose a significant global threat to human health and the economy. Since its first appearance in December 2019 in the city of Wuhan, Hubei province, China, SARS-CoV-2 infection has quickly spread across the globe, with the first case reported on the African continent, in Egypt on February 14th, 2020. Although the global number of COVID-19 infections has increased exponentially since the beginning of the pandemic, the number of new infections and deaths recorded in African countries have been relatively modest, suggesting slower transmission dynamics of the virus on the continent, a lower case fatality rate, or simply a lack of testing or reliable data. Notably, there is no significant increase in unexplained pneumonias or deaths on the continent which could possibly indicate the effectiveness of interventions introduced by several African governments. However, there has not yet been a comprehensive assessment of sub-Saharan Africa’s (SSA) preparedness and response to the COVID-19 pandemic that may have contributed to prevent an uncontrolled outbreak so far. As a group of early career scientists and the next generation of African scientific leaders with experience of working in medical and diverse health research fields in both SSA and resource-rich countries, we present a unique perspective on the current public health interventions to fight COVID-19 in Africa. Our perspective is based on extensive review of the available scientific publications, official technical reports and announcements released by governmental and non-governmental health organizations as well as from our personal experiences as workers on the COVID-19 battlefield in SSA. We documented public health interventions implemented in seven SSA countries including Uganda, Kenya, Rwanda, Cameroon, Zambia, South Africa and Botswana, the existing gaps and the important components of disease control that may strengthen SSA response to future outbreaks.
Background The General Population Cohort (GPC) in Southwestern Uganda is a low-risk population with low HIV incidence rates (<1%). Despite several interventions for close to 30 years, new cases of HIV continue to emerge. We set out to use phylogenetics and patients' demographic data to understand the HIV transmission dynamics in this population to inform prevention. Methods A total of 2049 pol sequences of participants diagnosed from 2003–2015 were included in this analysis; pol sequences were from GPC (n=1049), Central Uganda (n=800) and Eastern Uganda (n=200). Phylogenetic analysis was used to identify transmission networks. The demographic and clinical characteristics of the transmission clusters were analysed. Results The overall subtype distribution was: A (45%), C (3%), D (40%) and others (12%). The subtype distribution by region was for GPC: A (41%), C (2%), D (45%) and others (12%). For Central: A (49%), C (4%), D (35%) and others (12%). Eastern: A (60%), C (3%), D (24%) and others (13%). We identified 233 transmission clusters (cluster size variation 2–10) that comprised of 559 (27%) of the 2049 participants. The majority of clusters comprised transmission pairs (n=186) and triplets (n=30). The majority (∼60%) of the 233 clusters was from the GPC and all 13 large clusters (≥5) were also from the GPC. A significant number of clusters (n=25, 11%) was formed between individuals from different geographic locations. Participants in transmission networks were associated with high-risk sexual behaviour: low condom use, high alcohol use, and partner change even with known HIV-positives. Conclusions The transmission networks identified among individuals from the GPC and other populations or geographic regions may imply HIV introductions from outside communities. This suggests that HIV introductions into communities are common and account for a substantial number of new infections in the GPC. HIV prevention efforts should therefore target the broader communities beyond the GPC.
Around 2.5 million HIV-infected individuals failing first-line therapy qualify for boosted protease inhibitor (bPI)-based second-line therapy globally. Major resistance mutations are rarely present at treatment failure in patients receiving bPI and the determinants of failure in these patients remain unknown. There is evidence that Gag can impact PI susceptibility. Here, we have sequenced Gag-Protease before and following failure in 23 patients in the SARA trial infected with subtypes A, C, and D viruses. Before bPI, significant variation in Protease and Gag was observed at positions previously associated with PI exposure and resistance including Gag mutations L449P, S451N, and L453P and Protease K20I and L63P. Following PI failure, previously described mutations in Protease and Gag were observed, including those at the cleavage sites such as R361K and P453L. However, the emergence of clear genetic determinants of therapy failure across patients was not observed. Larger Gag sequence datasets will be required to comprehensively identify mutational correlates of bPI failure across subtypes.
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