This research develops a rapid, light-based method to study viral fusion, the first step of infection. By applying split NanoLuc technology to HIV, it reveals strain-specific fusion behaviors and unexpected regulatory steps, providing tools that can accelerate responses to future pandemics such as COVID-19.

By stripping Salmonella of its molecular “effectors,” this research identifies interferon gamma as a key immune barrier preventing infection. A small set of SPV genes enables the bacterium to overcome this defense. Understanding these mechanisms reveals new targets for therapies against Salmonella, a major global health threat.

Antibiotic resistance threatens to return medicine to a pre-antibiotic era. This research uses machine learning to study how bacteria balance resistance to antibiotics and bacteriophages. By revealing genetic trade-offs between attack and defense, the work enables smarter combination therapies that exploit bacterial weaknesses and prevent otherwise deadly infections.

Tuberculosis remains deadly despite relying on decades-old antibiotics. This research uses computational methods to identify immune response similarities between TB and other diseases, enabling drug repurposing. By borrowing already approved treatments, this approach aims to restore immune balance, combat drug resistance, and accelerate the development of new TB therapies.

Antibiotic-resistant bacteria like Salmonella cause millions of deaths worldwide. This research explores prohibitin 1, a mitochondrial protein, as an alternative defense. Mouse studies show that higher prohibitin 1 levels protect against bacterial infections, offering a potential non-antibiotic treatment to combat infections and reduce antibiotic resistance.

This research investigates how the human microbiome protects against Streptococcus pneumoniae. Focusing on Streptococcus mitis, it shows how beneficial bacteria detect chemical signals from pathogens and block infection. Understanding when this microbial “security system” succeeds or fails may lead to new strategies for preventing disease.

This research explores how bacteria choose between free-swimming and biofilm lifestyles. Studying Vibrio cholerae reveals that bacterial populations hedge their bets—some cells disperse while others remain protected. This collective decision-making helps bacteria survive threats and plays a key role in infection and transmission.

Variants weaken current COVID vaccines because they target parts of the spike protein that mutate. This project uses nanoparticles displaying engineered versions of the conserved RBD region to steer the immune system toward making broadly protective antibodies. Computational design helps optimize immune targeting, potentially eliminating yearly boosters and protecting against future coronaviruses.

This research uses agent-based modelling (ABM) to simulate infectious disease spread in regions like Nigeria, enabling policymakers to predict outbreaks, test interventions, and allocate limited resources proactively. The low-cost modelling approach supports governments with constrained budgets and offers a sustainable, data-driven tool for preventing large-scale infections and improving global public health.

This research develops a computational method for detecting hidden RNA viruses within existing RNA sequencing datasets. By identifying conserved viral protein signatures, the approach enables large-scale discovery of previously unknown viruses, improving understanding of viral diversity, disease mechanisms, and future opportunities for diagnostics, surveillance, and antiviral treatment development.