PROBYDE · Probing the bypassability of genetic constraints in drug-resistance enzymes
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-07-01 → 2023-06-30
- EU contribution
- €160,932
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Probing the bypassability of genetic constraints in drug-resistance enzymes
Antibiotic resistance is estimated to account yearly for 35,000 deaths and a cost of >€1.5 billion in the European Union alone, according to the European Commission. With the slow development of new antibiotics, much recent interest is directed towards evolutionary-based strategies to prevent resistance evolution to preserve the existing arsenal of drugs, mostly by anticipating and curtailing the selective opportunity of drug-resistant mutants. In this spirit, we evolved in the laboratory clinically relevant drug-resistance enzymes and identified the most probable evolutionary paths towards resistance. As a result, our results contribute insights into both fundamental and applied questions about the reproducibility of antibiotic resistance evolution, an aspiration with the potential to improve therapeutic practice ultimately contributing to secure human well-being and to reduce the cost of EU’s healthcare systems. Finally, I should note that the results of this proposal are not amenable to be affected by gender dimension. In brief, we uncovered the potential adaptive pathways available via single step mutations for several drug-resistance enzymes to improve their activity. This information enabled making predictions about antibiotic resistance evolution based on single-gene (in-vitro) observations of repeatability. I also conducted experimental adaptation to antibiotics with a model of E. coli clinical strain carrying the different drug-resistance enzymes. Comparison of the potential versus realized adaptive pathways provided insight into the bypassability of genetic constraints in the drug-resistance enzymes and into the role of mutation biases and GC content in this process. If we were able to predict the most probable new mutants, we could anticipate evolution and design new antimicrobials or inhibitors to tackle the expected new variants.
Data: CORDIS, © European Union
Project objective
The fast evolution of bacterial pathogens towards antibiotic resistance is estimated by 2050 to be killing 10 million people every year. Consequently, much interest is being directed towards finding ways to curb or even arrest this evolutionary process. Of note, sequencing efforts are revealing that many of the genetic changes that drive resistance evolution are often repeatable. Understanding what drives this repeatability is of foremost importance if we ever want to develop interventions aimed at anticipating and preventing the evolution of undesirable variants. Here, I will aim at advancing our understanding of evolutionary repeatability in several clinically-relevant, drug-resistance enzymes spanning a range of GC compositions. I will use this relevant model system to empirically test recent predictions on the roles of mutation bias and GC content in shaping mutational pathways. To this end, I will conduct high-throughput 'in vitro' Directed Evolution experiments to explore the potential adaptive paths available via single step mutations among the drug-resistance enzymes. Next, I will compare these 'in vitro' predictions with the outcomes of highly-parallel antibiotic adaptation experiments conducted with bacterial strains with strong mutation biases (e.g., mutators) carrying the same panel of enzymes. By producing important insights into some of the key determinants of evolutionary repeatability, PROBYDE aspires to form a knowledge base that may help harness evolution not only in bacterial pathogens, but also in other human-relevant systems such as cancer, crop pests and industrial microbes.
Original text from CORDIS.
Participants
- UNIVERSIDAD POLITECNICA DE MADRID · MadridCoordinatorSpain
Links
Data: CORDIS, © European Union