Understanding the Impacts of Sub-Inhibitory Concentrations and Clinical Use of Beta-lactam Antibiotics on the Evolution of Beta-lactamase Resistance Genes
- Author(s): Mira, Portia Mae
- Advisor(s): Barlow, Miriam
- Meza, Juan C
- et al.
Antibiotic resistance continues to be a major challenge we face today. Scientists and medical professionals are competing against a microbial evolutionary time bomb. The alarming increase in the number of deaths caused by multi-drug resistant infections (1) and the decrease in development of reliable treatment regimens is disturbing. Historically, most studies focus on the effects of fatal concentrations of antibiotics on the evolution of antibiotic resistance (2). Focusing on high antibiotic concentrations limits our understanding of antibiotic resistance and how its evolution is established. Especially since it has been shown that there is a greater selection of resistant bacteria at sub-lethal concentrations of antibiotics (3). Our main goal is to further investigate the impacts of sub-inhibitory concentrations of antibiotics on antibiotic resistance evolution. To do this, we studied two genes that confer resistance to β-lactam antibiotics,blaTEM-50 and blaTEM-85. We created adaptive landscapes from each of the 16 alleles of every combination of the four amino acid substitutions in each gene using bacterial growth rates as a measurement of fitness. We have shown that the topography of these adaptive landscapes depend on the type, and concentration, of the β-lactam antibiotic treatment (4). We also developed a rational design of antibiotic treatment plans based on mathematical models of the adaptive landscape data. We found that by cycling between structurally similar antibiotics, there is a 60%-100% probability of returning to a more-susceptible state. This is a favorable result for laying a foundation to use antibiotic cycling to help alleviate the effects of antibiotic resistance, which has recently shown promising (5) (6). Furthermore, we investigated the evolution of resistance within a local hospital by studying the trends in resistant phenotypes of patient isolates (7). We found there was no significant trend in antibiotic resistance occurring in the hospital, and suspect that the community contributes the majority of the selective pressures leading to multidrug resistant pathogens. Using our novel mathematical models, we were able to successfully predict the resistance genes that were present in the hospital and, by using genomic sequencing data; we confirmed the presence of these resistance genes. These studies show that sub-inhibitory concentrations of antibiotics, present in the environment, accelerate the diversity of antibiotic resistance genes. Also, we found that antibiotics used within the hospital do not impact the evolution of antibiotic resistance within the hospital. Altogether, we have 1) shown that sub-lethal concentrations of β-lactam antibiotics have an effect on the evolution of β-lactamase resistance genes, 2) developed mathematical models that can be used to lay a foundation for antibiotic cycling, and 3) developed a tool for hospitals to assess the transmission of antibiotic resistance trends using phenotypic data