# Using strain history to improve prediction of the evolution of antimicrobial resistance in Acinetobacter baumannii > **NIH NIH F31** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $48,974 ## Abstract PROJECT SUMMARY The objective of this proposal is to gain a more complete understanding of the influence of evolutionary history on the predictability of evolution. Understanding predictability and repeatability of evolution has long been a goal of evolutionary biologists. Applying these questions to the field of antimicrobial resistance (AMR) provides the benefit of obtaining clinically relevant results while also studying questions that are fundamental to evolutionary biology. AMR is a rapidly worsening global health issue, with an increasing number of bacterial infections becoming impossible to treat with most drugs. Of serious concern is Acinetobacter baumannii, a nosocomial and highly multidrug resistant (MDR) pathogen. Although resistance is often attained through common pathways, such as increased drug efflux and modifications to the drug target, it remains unclear how evolutionary history affects the evolution of AMR, and to what degree or level resistance evolution is predictable. History (such as genetic relatedness and previous antibiotic exposure) is an underappreciated aspect that influences evolution and can have lasting effects on the genome that alter or constrain paths available for adaptation to antibiotic stress. To address these gaps in knowledge, we propose to use a preexisting collection of A. baumannii clinical isolates. The first aim of this proposal focuses on understanding the genetic and phenotypic differences within these isolates, with special focus on virulence and resistance. Through comparative genomics we will identify sequence similarity as well as pan-genome composition. We will use known causes of resistance, compiled from previous studies in the Cooper Lab and others, to create a genome based multi-locus predictor of resistance phenotypes. We will combine the comparative genomics analysis with results of phenotypic assays for biofilm formation, growth in different conditions, and persistence, to assess any phylogenetic phenotypic patterns. We will verify select gene-phenotype associations through molecular engineering, helping to detangle the genotype- phenotype map of A. baumannii. The second proposed aim will further enhance our knowledge regarding the predictability of resistance evolvability. We will evolve 20 clinical isolates in the presence of the ribosome- targeting drug, tigecycline, and monitor the corresponding fold increase of resistance, with the prediction that initial resistance level will dictate final evolved resistance level. Through whole population, whole genome sequencing, we will be able to assess the level of predictability to which resistance evolution occurs genetically. Completion of this project will not only provide answers to broad evolutionary questions but will also be a crucial step in the fight against the antimicrobial resistance crisis. We will make conclusions as to what level AMR is predictable mechanistically. A greater understanding of predictability will have direct c... ## Key facts - **NIH application ID:** 10974013 - **Project number:** 5F31AI172279-02 - **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH - **Principal Investigator:** Alecia Barbara Rokes - **Activity code:** F31 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $48,974 - **Award type:** 5 - **Project period:** 2023-09-01 → 2025-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10974013 ## Citation > US National Institutes of Health, RePORTER application 10974013, Using strain history to improve prediction of the evolution of antimicrobial resistance in Acinetobacter baumannii (5F31AI172279-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10974013. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*