MARVEL-minimising the emergence and dissemination of HIV-1 drug resistance in Portuguese-speaking African Countries (PALOP): low-cost portable NGS platform for HIV-1 surveillance in Africa

Abstract

Abstract Background HIV-1 infections remain a global public health concern. Scaled-up antiretroviral treatment (ART) is crucial for reducing morbidity and mortality related to HIV/AIDS. The emergence of drug-resistance mutations (DRMs) compromises viral suppression and contributes to the continued HIV-1 transmission. Several reports indicate a recent increase in acquired (ADR) and transmitted (TDR) drug resistance in Africa, probably linked to the lack of implementation of HIV drug resistance (HIVDR) testing and suboptimal treatment adherence. Herein, we will develop a low-cost protocol using third-generation sequencing (Oxford Nanopore Technology) for HIV-1 surveillance in Portuguese-speaking African Countries - PALOP [Angola (AO), Cape Verde (CV), Mozambique (MZ), and Sao Tome & Principe (STP)]. Methods This is a multicentric cross-sectional study that includes around 600 adult patients newly diagnosed with HIV-1 in the PALOP. An epidemiological questionnaire previously validated by our research team will be used to collect sociodemographic and clinical data. Also, whole blood samples will be collected and the plasma samples will be subjected to drug resistance testing using an in-house low-cost NGS protocol. Data analysis will involve bioinformatics, biostatistics and machine learning techniques to generate accurate and up-to-date information about HIV-1 genetic diversity, ADR and TDR. Discussion The implementation of this low-cost NGS platform for HIV-1 surveillance in the PALOP will allow: (i) to increase DRM surveillance capacity in resource-limited settings; (ii) to understand the pattern and determinants of dissemination of resistant HIV-1 strains; and (iii) to promote the development of technical and scientific skills of African researchers for genomic surveillance of viral pathogens and bioinformatics analysis. These objectives will contribute to reinforcing the capacity to combat HIV infection in Africa by optimizing the selection of ART regimens, improving viral suppression, and reducing ADR or TDR prevalence in PALOPs, with relevant implications for public health.

Keywords

• HIV-1
• Drug resistance
• Machine learning
• PALOP
• Africa