Pan-genomic and Polymorphic Driven Prediction of Antibiotic Resistance inElizabethkingia

AbstractTheElizabethkingiaare a genetically diverse genus of emerging pathogens that exhibit multidrug resistance to a range of common antibiotics. Two representative species,Elizabethkingia bruunianaandElizabethkingia meningoseptica, were phenotypically tested to determine minimum inhibitory concentrations for five antibiotics. Ultra-long read sequencing with Oxford Nanopore Technologies and subsequentde novoassembly produced complete, gapless circular genomes for each strain. Alignment based annotation with Prokka identified 5,480 features inE. bruunianaand 5,203 features inE. meningoseptica, where none of these identified genes or gene combinations corresponded to observed phenotypic resistance values. Pan-genomic analysis, performed with an additional 19Elizabethkingiastrains, identified a core-genome size of 2,658,537 bp, 32 uniquely identifiable intrinsic chromosomal antibiotic resistance core-genes and 77 antibiotic resistance pan-genes. Using core-SNPs and pan-genes in combination with six machine learning algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84 respectively. Performance on the more challenging multiclass problem for fusidic acid, rifampin and ciprofloxacin resulted in f1 scores of 0.70, 0.75 and 0.54 respectively.