A cell-free synthetic biology tool will let researchers study how the hospital superbug *Klebsiella pneumoniae* becomes resistant to antibiotics, without needing to handle the live, dangerous bacteria. This matters because *K. pneumoniae* causes deadly bloodstream infections in hospitals, with mortality rates reaching roughly 50% in some countries. Experts predict that by 2050, antimicrobial resistance (AMR) will kill more people worldwide than cancer and diabetes. Current antibiotics are failing, and the pipeline for replacements is slow and expensive. The project addresses a critical gap: the need for a fast, safe way to predict how resistance evolves and to test new countermeasures. If successful, the research will create a platform that works at low biosafety containment levels and is compatible with laboratory automation. This could dramatically speed up the discovery of new, non-standard antibiotics—including phages and host-directed therapies—that can evade existing resistance mechanisms. The approach is generalisable, meaning it could be expanded to other priority pathogens like the ESKAPE group or tuberculosis. The immediate impact is on the fundamental science of resistance evolution, but the practical payoff would be a faster, cheaper pipeline for novel antimicrobials that could keep routine surgeries and cancer treatments safe from untreatable infections.
View original technical description
Antimicrobial resistance (AMR) is a global health crisis branched over multiple infectious diseases. This problem, if unaddressed, will breach current antibiotic treatments which healthcare systems have relied upon for decades. Experts predict AMR will cause more deaths worldwide than cancer and diabetes by 2050. Currently, this threat is insidious, and affects the immunocompromised and the elderly, particularly in developing countries. However, as this problem speeds up, even everyday wounds or cuts could eventually lead to healthy individuals requiring serious treatment and hospitalisation. Therefore, while more broadly we require a long-term strategy to manage broad-spectrum antibiotic usage as the first line of defence, there is also a need to consider the role of non-standard antimicrobials, phage therapy, and host-directed therapeutics as a countermeasure to fight resistance. Our project concerns the development of a safe cell-free tool to study a specific type of infectious disease-causing bacteria, Klebsiella pneumoniae, and explores a new synthetic biology method to alternative antimicrobials. K. pneumoniae is important since it is a leading cause of hospital-acquired bacteraemia and causes up to a ~50% mortality rate in some countries. Our project will use the latest technological advances that include next-generation DNA sequencing, automation, and cell-free synthetic biology. Overall, our project has three general goals that act as our theme for the proposal: "anticipate, learn, and counter". 1. Anticipate - We need to predict how K. pneumoniae will become resistant to antibiotics. This is important because K. pneumoniae and many other infectious diseases will soon be able to survive all current antibiotic treatments. 2. Learn - We need to study how individual antibiotic resistance mechanisms confer an advantage to K. pneumoniae. 3. Counter - We need to stop antibiotic-resistant K. pneumoniae infections by finding new kinds of non-standard antibiotics, especially ones that can crucially evade or escape current resistance mechanisms. Herein, we provide a cell-free synthetic biology tool that enables us to study a major infectious disease at Containment Level 1, while the system is automation compatible to help speed up the discovery of new antibiotics. Also, our approach is generalisable, and therefore can expand to almost any infectious disease, i.e., ESKAPE pathogens or tuberculosis. Overall, our project is remarkably novel, timely and exciting in its conception and creates a new synergy between the two distinct areas of synthetic biology and infectious diseases. Our project will create a new, fast, and safe way to study how K. pneumoniae becomes resistant to antibiotics, as well as providing a platform to search for novel antibiotics.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know