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 (Fà brega et al., 2009; Wong and Kassen, 2011).47 Furt...