Completed Infection & Immunity Genetics & Molecular Biology

Pathogen Genomics, Phenotype and Immunity (PGPI) & Basic Sciences Programme

In plain English

AI plain-English summary

HIV-1 viruses are evolving faster than the drugs designed to stop them, and this programme will track those changes in real time across entire viral genomes. The problem is stark: HIV drug resistance is undermining antiretroviral therapy, especially in resource-limited settings where patients cycle through limited drug regimens without regular viral load monitoring. Meanwhile, recombinant viruses—genetic mosaics formed when different HIV subtypes swap segments—are becoming more common, making existing treatments less predictable. The programme also investigates HIV superinfection, where a person already infected acquires a second, distinct viral strain, a phenomenon that complicates vaccine design and immune control. If successful, this research will give public health authorities a practical tool: the ability to identify transmission clusters and hotspots through full-length genome sequencing combined with social-epidemiological modelling. That means interventions—like targeted prevention or adjusted drug regimens—can be deployed precisely where they are needed most, rather than blanket approaches. The work also aims to clarify which host and viral factors permit superinfection, a fundamental gap in understanding HIV immunity. This is primarily applied fundamental science. The immediate payoff is better epidemic surveillance and drug resistance management, not a new drug or vaccine. But deeper knowledge of how HIV recombines and reinfects could, over time, reshape how we think about viral persistence and immune evasion—insights that have historically led to unexpected therapeutic breakthroughs.

View original technical description
This programme aims to conduct research that will lead to better understanding of pathogen genomics in order to characterise diseases and epidemics for better control and to investigate virological, immunological and genetic factors required for the development of effective interventions against HIV-1 and other infections. Advantage will be taken of a) well characterised cohorts and biobanks b) Unit track record in intervention trials c) investment in new technologies in genomics, immunological assays and bioinformatics and d) established collaborations. We propose to monitor the HIV epidemic by characterising the circulating HIV-1 subtypes especially in recent infections and use full length genome sequencing to understand better the increasing recombinant viruses using better bioinformatics tools. We will use molecular in combination with social-epidemiological and modelling approaches to provide novel avenues to monitor epidemic trends and transmission dynamics, and to contribute to targeted interventions through the identification of transmission clusters and hotspots. HIV drug resistance (HIVDR) is emerging as one of the most important challenges to ART roll out for both care and prevention, more so in resource limited areas characterized by the use of limited ARV regimens, stock outs, regimen change based on available supply and often limited use of viral load (VL) testing to monitor treatment outcomes. We will expand our drug resistance studies to contribute to improved interventions. HIV superinfection (SI) investigations will utilise a large collection of specimens from high risk populations to study host and viral factors associated with SI.

View the original record at the funder ↗

Researchers

Jesus Salazar-Gonzalez (Co-Investigator)Pietro Pala (Co-Investigator)Pontiano Kaleebu (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Central nervous system compartmentalisation and drug resistance in HIV-1 sub-type C infection
Genetics and biology of drug resistant HIV.
Using iPSC variation to define HIV-1 regulatory networks
MRC Centre for Genomics and Global Health
The investigation of human polymorphisms impacting on HIV/AIDS pathogenesis

Original classification

Intramural

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.