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Characterizing intact proviral HIV-1 reservoir size and determinants of reservoir dynamics in African populations by UNIVERSITY OF CAPE TOWN (The Research University TRU)

ABRAHAMS, MELISSA-ROSE

 

Project SummaryAntiretroviral treatment is unable to clear HIV-1 infection because a highly stable latent viral reservoirpersists in the host. Key areas of interest with respect to HIV-1 eradication strategies include latentreservoir establishment, size and make-up. A considerable amount is now known about these key areas insubtype B-infected American men, yet there remains limited knowledge in the most affected population,subtype C-infected South African women, or in African populations in general. Population differences mayexist with respect to reservoir characteristics, and eradication strategies would need to take suchdifferences into account. Characterization of the HIV-1 reservoir in the African context therefore representsa much needed area of attention.This project proposes to firstly address the need for implementation of a high-throughput, accurate reservoirsizing method in South Africa through optimization of the newly developed intact proviral DNA assay (IPDA)for subtype C HIV-1. This method will be applied to more than 200 women from KwaZulu Natal. Reservoirsize in these women will be compared to that of individuals from Ugandan and American cohorts using thesame assay to evaulate reservoir differences across populations. This project will also investigate a role forthe viral factors Nef and the long terminal repeat, which are drivers of immune evasion and genetranscription respectively, in reservoir size and make-up in a subset of these women. Finally, we will explorethe contribution of viral variants from the blood and cervix to the long-lived reservoir in these women usingBayesian evolutionary analyses.We hypothesize that Nef-mediated MHC-I downregulation and LTR activity have independent effects onreservoir size and distribution, and these effects differ according to infecting subtype and study population.This project will allow for comparison of reservoir size across populations using a standardized assay andwill evaluate determinants of size and kinetics of establishment.

 

"Zimbabwe
AfricaUniversity Affairs

Harnessing Data Science for Health Discovery and Innovation in Africa: NIH joins Africa Movement Thinking initiative

We are experiencing an explosion of data relevant to human health, from both biological and non-health sources, that currently exceeds our ability to capture and interpret. This gap applies to every field of biomedical and behavioral research. If we don’t invest in the data itself, applying known and developing new Data Science (DS) approaches, we are wasting an incredible opportunity to improve Human Health. This set of initiatives addresses that gap for Global Health research and Public Health application in low resource settings.

In the next decade, rapid advances in DS, including new approaches to the description, collection, storage, integration, and analysis of large, heterogeneous, structured and unstructured data sets, and new computational methods such as advanced deep learning, digital phenotypes, machine/artificial intelligence, and 3D imaging are expected to transform biomedical and behavioral research and lead to improved health for individuals and populations. Traditional datasets (e.g. national health systems, surveillance, surveys) are becoming deeper and richer while new sources of data based on new technologies and sensors (e.g. social media, geospatial data, mobile phones, wearables, electronic medical records, bioimaging, and genomics) are emerging that may be of greatest value when linked to DS. Progress in development of huge new data sets and advanced methods for mining them underpin advances in diagnostics, technology development, and the potential for precision public health. This initiative examines whether advances in DS developed and/or applied in the African context can be used to spur discoveries and innovations that ultimately promote significant improvements in health for African individuals, communities, and populations. We define DS as “the interdisciplinary field of inquiry in which quantitative and analytical approaches, processes, and systems are developed and used to extract knowledge and insights from increasingly large and/or complex sets of data” (NIH Strategic Plan for Data Science).

While advances in the United States and other high-income countries (HICs) are being actively explored and increasingly supported in both the public and private sectors, applications that are relevant, affordable, acceptable, and scalable in low- and middle-income countries (LMICs) are largely undeveloped. Moreover, in most cases applications and tools cannot simply be adapted from HICs for use in LMICS as they are generated from data collections biased towards European ancestry populations and may not apply to other genetic backgrounds (this is true, for example, for GWAS studies, clinical imaging data used as a basis for machine learning, and major disease biomarkers). However, we can leverage the momentum and knowledge generated in this field in HICs to stimulate new investment, knowledge generation and innovations for LMIC populations.

What is InventXR is solving for?

While advances in the United States and other high-income countries (HICs) are being actively explored and increasingly supported in both the public and private sectors, applications that are: relevant, affordable, acceptable, and scalable in low- and middle-income countries (LMICs) are largely undeveloped. 

  • We focus this initiative in Africa for several reasons: 
    • First, despite recent progress, Africa carries a disproportionate share of the global burden of disease. DS has the potential to significantly impact both quantitative and qualitative research and health on the continent. 
    • Second, the African population is growing faster than other world regions and some African leaders are eager to transition to knowledge-based economies that InventXR infrastructure is well designed for. Extensive mobile phone coverage in Africa has led to major innovations in banking, agriculture, and other sectors and has the potential to “leopard frog” in health care delivery systems, bringing the clinic to the patient through Point of Care (POC) technologies and self-management systems, with applications to rural and underserved populations worldwide. 
    • Third, this initiative is synergistic with and leverages the substantial investments NIH has already made in research and research training in Africa.
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