Bacterial Genome Wide Association Studies (bGWAS) and Transcriptomics Identifies Cryptic Antimicrobial Resistance Mechanisms in Acinetobacter baumannii

Abstract

Antimicrobial resistance (AMR) in the nosocomial pathogen, Acinetobacter baumannii, is becoming a serious public health threat. While some mechanisms of AMR have been reported, understanding novel mechanisms of resistance is critical for identifying emerging resistance. One of the first steps in identifying novel AMR mechanisms is performing genotype/phenotype association studies; however, performing these studies is complicated by the plastic nature of the A. baumannii pan-genome. In this study, we compared the antibiograms of 12 antimicrobials associated with multiple drug families for 84 A. baumannii isolates, many isolated in Arizona, USA. in silico screening of these genomes for known AMR mechanisms failed to identify clear correlations for most drugs. We then performed a bacterial genome wide association study (bGWAS) looking for associations between all possible 21-mers; this approach generally failed to identify mechanisms that explained the resistance phenotype. In order to decrease the genomic noise associated with population stratification, we compared four phylogenetically-related pairs of isolates with differing susceptibility profiles. RNA-Sequencing (RNA-Seq) was performed on paired isolates and differentially-expressed genes were identified. In these isolate pairs, five different potential mechanisms were identified, highlighting the difficulty of broad AMR surveillance in this species. To verify and validate differential expression, amplicon sequencing was performed. These results suggest that a diagnostic platform based on gene expression rather than genomics alone may be beneficial in certain surveillance efforts. The implementation of such advanced diagnostics coupled with increased AMR surveillance will potentially improve A. baumannii infection treatment and patient outcomes.

Keywords: AMR; acinetobacter; bioinformatics; genomics; transcriptomics.

Figure 1

A maximum-likelihood phylogeny of Acinetobacter genomes sequenced in this study based on a concatenation of 50, 869 core genome SNPs. Each genome is annotated with its antimicrobial susceptibility profile across 12 drugs. The annotations were visualized with the Interactive tree of life (91). The pair information (1–4) is shown in the middle of the phylogeny.

Figure 2

Screen of selected CARD proteins (31) across all Acinetobacter baumannii genomes sequenced in this study. The heatmap is associated with the blast score ratio (BSR) (79) values of each gene across each genome. The BSR values were visualized with the Interactive tree of life (91).

Figure 3

Gene content comparisons between paired isolates in pair 1 (A), pair 3 (B), and pair 4 (C). All figure panels were generated with genoPlotR (77).