Parallel evolution of tobramycin resistance across species and environments

Abstract

words)13 An important problem in evolution is identifying the genetic basis of how different species adapt 14 to similar environments. Understanding how various bacterial pathogens evolve in response to 15 antimicrobial treatment is a pressing example of this problem, where discovery of molecular 16 parallelism could lead to clinically useful predictions. Evolution experiments with pathogens in 17 environments containing antibiotics combined with periodic whole population genome 18 sequencing can be used to characterize the evolutionary dynamics of the pathways to 19 antimicrobial resistance. We separately propagated two clinically relevant Gram-negative 20 pathogens, Pseudomonas aeruginosa and Acinetobacter baumannii, in increasing 21 concentrations of tobramycin in two different environments each: planktonic and biofilm. 22Independent of the pathogen, populations adapted to tobramycin selection by parallel evolution 23 of mutations in fusA1, encoding elongation factor G, and ptsP, encoding phosphoenolpyruvate 24 phosphotransferase. As neither gene is a direct target of this aminoglycoside, both are relatively 25 novel and underreported causes of resistance. Additionally, both species acquired antibiotic-26 associated mutations that were more prevalent in the biofilm lifestyle than planktonic, in electron 27 transport chain components in A. baumannii and LPS biosynthesis enzymes in P. aeruginosa 28 populations. Using existing databases, we discovered both fusA1 and ptsP mutations to be 29 prevalent in antibiotic resistant clinical isolates. Additionally, we report site-specific parallelism of 30 fusA1 mutations that extend across several bacterial phyla. This study suggests that strong 31 selective pressures such as antibiotic treatment may result in high levels of predictability in 32 molecular targets of evolution despite differences between organisms' genetic background and 33 environment.The notion that evolution can be forecasted at the level of phenotype, gene, or even 36 amino acid is no longer a fantasy in the post-genomic era (Lässig et al., 2017). If we 37 acknowledge that most forecasting efforts rely on history to anticipate the future, the explosive 38 growth of whole-genome sequencing (WGS) sets the stage to resolve evolutionary phenomena 39 in action and suggest the next selected path. Among the best examples, bacterial populations 40 exposed to strong selection like antibiotics and analyzed by WGS are likely to identify gene 41 regions that produce resistance (Ahmed et al., 2018a; Cooper, 2018; Feng et al., 2016; Palmer 42 and Kishony, 2013). Repeated instances of the same antibiotic selection may enrich the same 43 types of mutations and ultimately enable some measure of predictability (Ibacache-Quiroga et 44 al., 2018; Wong et al., 2012). For instance, we can be confident that exposure of many bacteria 45 to high doses of fluoroquinolones like ciprofloxacin may select for substitutions in residues 83 or 46 87 of the drug target, DNA gyrase A (Fàbrega et al., 2009; Wong and Kassen, 2011).47 Furt...

Comparative Evolutionary Patterns of Burkholderia cenocepacia and B. multivorans During Chronic Co-infection of a Cystic Fibrosis Patient Lung

“…It remains important to understand why LPS is under selection in B. cenocepacia or B. multivorans . While the most common explanation invokes selection for immune evasion, it is also possible that LPS modifications are adaptations to biofilm growth ( Traverse et al, 2013 ) or antibiotic resistance ( Scribner et al, 2020 ). Another explanation for loss of OAg is that it increases Bcc survival in phagocytic eukaryotic cells such as amoebae, epithelial cells and human macrophages ( Saldias et al, 2009 ; Saldias and Valvano, 2009 ; Kotrange et al, 2011 ; Maldonado et al, 2016 ).…”

Experimental evolution to identify undescribed mechanisms of resistance to a novel cationic peptide antibiotic

“…To identify mechanisms of resistance to WLBU2, we propagated the laboratory strains of A. baumannii ATCC 17978 and P. aeruginosa PA14 that had no prior exposure to WLBU2. We followed a protocol successfully used previously with several pathogens and different antibiotics, in which large populations of bacteria are treated with increasing concentrations of antibiotic over 12 days (Hernando-Amado et al, 2019; Santos-Lopez et al, 2019, 2020; Scribner et al, 2020; Trampari et al, 2019). We serially passaged both strains (bottleneck of ~10 7 cfu) in WBLU2 concentrations initially at half of the Minimum Inhibitory Concentration (MIC) and increasing two-fold every three days, up to four times the MIC.…”

“…We propagated the same number of populations in the absence of WLBU2 to distinguish between adaptation to the environment and WLBU2-related mutations. Knowing that biofilm formation can influence the evolution of resistance (Ahmed et al, 2018; Santos-Lopez et al, 2019; Scribner et al, 2020), we implemented this regimen in both planktonic and biofilm cultures (see http://evolvingstem.org and (Santos-Lopez et al, 2019; Scribner et al, 2020) for details of the biofilm propagation). To test the general capability of the experimental design for selecting populations resistant to cationic peptides, we also propagated populations of A. baumannii in each lifestyle with increasing concentrations of polymyxin B.…”

“…We focused on instances of gene-level parallel (repeated) evolution as strong candidates, because mutations in the same gene found in independently derived lineages provide strong evidence of selection on this trait. Further, the large population size and high mutation supply empowers selection to enrich the most beneficial genotypes (Bailey et al, 2015; Ibacache-Quiroga et al, 2018; Lieberman et al, 2011; Papkou et al, 2020; Scribner et al, 2020; Toprak et al, 2011; Vogwill et al, 2014).…”

Experimental Evolution In Vivo To Identify Selective Pressures during Pneumococcal Colonization

“…To infer the genealogical structure of each population and visualize changes in lineage frequencies, we used the lolipop software package we developed that identifies linked genotypes and ancestry from shared mutation trajectories over time (37). These results are then displayed as Muller diagrams that integrate both frequency and ancestry ( Fig.…”