Precisely calculating relative fitness advantage (s) for diverse mutants that provide drug resistance to better inform treatment models
Authors: Daphne Newell 1,2; Kara Schmidlin 2; Rachel Eder 1,2; Michael Hinczewski 3; Jacob Scott 3,4,5; Kerry Geiler-Samerotte 1,2
Affiliations: 1) School of Life Sciences, Arizona State University, Tempe, AZ; 2) Center for Mechanisms of Evolution, Biodesign Institute, Arizona State University, Tempe, AZ; 3) Department of Physics, Case Western Reserve University, Cleveland, OH; 4) Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH; 5) Case Western Reserve University School of Medicine, Cleveland, OH
Keywords: Genomic selection/prediction
Drug resistance in pathogens is a major global health concern. Thus, there is great interest in modeling the behavior of drug resistant mutations; for example, how quickly they will rise to high frequency within a population, and whether they come with fitness tradeoffs that can form the basis of treatment strategies. These models of how resistant mutations behave often depend on precise measurements of the relative fitness advantage (s) for each resistant mutation, as well as the strength of the fitness tradeoff that each mutation suffers in other contexts. Previous studies often determine the maximum growth rate of each mutant individually to calculate s. More recent experiments use direct competitions in batch culture to study which strains perform better in different environments. In many cases, a direct competition experiment is more clinically relevant, as microbes with different resistant mutations often compete within the same patient.
Precisely quantifying s helps us create better, more accurate models of how mutants act in different treatment strategies. For example, P. falciparum acquires antimalarial drug resistance through a series of mutations to a single gene. Prior work in yeast expressing this P. falciparum gene demonstrated that mutations come with tradeoffs. Computational work has demonstrated the possibility of a treatment strategy which first enriches for a particular resistant mutation, which then makes the population grow poorly once the drug is removed. This treatment strategy still requires knowledge of s and how it changes when multiple resistant mutants are competing with one another across various drug concentrations.
Here, we precisely quantified s in varying drug concentrations for five resistant mutants, each of which provide varying degrees of drug resistance to antimalarial drugs. This was accomplished using DNA barcodes to label each strain, allowing the mutants to be pooled together for direct competition in different concentrations of drug. This will provide data that can make the models more accurate, potentially facilitating more effective drug treatments in the future.