Urinary Tract Infections Research Initiative
The Urology Foundation was delighted to invite applications for the first grant call of our Urinary Tract Infections Research Initiative in 2024.
Urinary Tract Infections (UTIs) blight millions of lives and are the second most common reason for prescribing antibiotics, making UTIs one of the contributory causes of the rise in antimicrobial resistance. Around 50% of all women develop a UTI at some stage and an estimated 1 million women in the UK live with chronic UTIs and experience debilitating, consistent pain. In men, UTIs are often associated with infection and inflammation of the prostate gland. Older people are more susceptible to UTIs and UTIs are a significant factor in mortality amongst the elderly population.
The Urology Foundation is committed to driving change in the field of UTIs and to improving the lives of and outlook for patients and their families. UTIs have been identified as one of the research priorities in our Research Strategy 2024 – 2028 and we have recently launched a nurse-led UTI Information Service and Helpline.
This fund was possible thanks to the generosity of a donor.
Projects we are funding
We are delighted to be providing £376,997 of funding for three research projects.
| Principal Investigator | Project Title | Project Title |
|---|---|---|
| Jennifer Rohn, University College London | Using Lactobacillus to Fight Treatment-Resistant Urinary Tract Infection | Urinary tract infections (UTIs) affect around 400 million people globally each year and often recur despite antibiotic treatment. The sheer number of antibiotics prescribed fuels the antibiotic resistance crisis, with these drugs no longer working against many infections. Better treatments are clearly needed. Most UTIs are caused by bacteria called E. coli, but this has largely been studied in mice which don’t accurately reflect the biology of human UTI. To bridge this gap, we developed a human “mini bladder” model to better study UTI. Our team is exploring whether direct bladder application of Lactobacillus—a beneficial bacterium found in yoghurt and healthy bladders—can reduce E. coli’s harmful effects. We aim to test if Lactobacillus alone or alongside antibiotics can prevent UTIs, potentially lowering antibiotic use. If successful, this approach could lead to new Lactobacillus-based probiotic treatments, reducing the burden of UTIs and slowing antibiotic resistance. |
| Stephen Kelly, Queen’s University Belfast | Micro-Mach UTI: Combining microbiome and genomic approaches with machine learning for the early detection and treatment of urinary tract infection | Understanding the bacteria of the urinary tract is key to preventing and managing urinary tract infections (UTIs), especially in patients with catheters or stents, which can promote bacterial growth. Current UTI diagnosis relies on symptoms or dipstick tests, which only detect infection after bacteria multiply, delaying treatment. This project aims to detect UTIs earlier by measuring bacterial DNA in urine, enabling faster, more effective care. Choosing the right antibiotic is also challenging, as traditional lab tests are slow. We propose using machine learning to predict the best antibiotic from bacterial DNA, potentially reducing ineffective treatments and antibiotic resistance. Additionally, we will study the urinary and gut microbiomes to understand which patients are most at risk of UTIs. Samples come from kidney transplant patients, who are highly vulnerable. With expertise in infection research, Queen’s University Belfast is well-placed to lead this work and improve outcomes for patients with UTIs. |
| Maria Vazquez-Montes, University of Oxford | Factors contributing to antibiotic resistance in women who experience multiple UTIs | Bacteria that cause urinary tract infections (UTIs), or cystitis, can develop resistance to antibiotics, rendering treatment less effective. When treating women with UTI, doctors and nurses often make a ‘best guess’ at the right antibiotic to use, because it can take up to three days for a laboratory to grow bacteria in their urine (a urine culture) and test which antibiotics the infection can, and cannot, be killed by. This can lead to treatment failure due to antibiotic resistance, repeated antibiotic use, and increased likelihood of resistance and clinical complications. We aim to use healthcare data to help doctors and nurses decide which antibiotics are likely to treat the infection successfully based on a patient’s demographic and clinical characteristics, symptoms, past test results and resistance profile. This will support better treatment and help prevent the spread of antibiotic resistance, a growing public health challenge. |