Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa

Abstract

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance.

Keywords: antibiotic resistance; emerging technologies; genomics; in silico prediction.

Figure 1

Unrooted tree of 390 P. aeruginosa genomes based on SNPs within the core genome as defined by Harvest (100 bootstraps). The total coverage of the core genome among all sequences was 17.5%. Strains are divided into three groups (blue, orange, and green). The number of strains for each group is shown. White circles represent one or more strains that were sequenced by the IPC, while black circles represent publicly available genomes. Adapted with permission from Ref. 5.

Figure 2

Heat map showing unique resistomes for 390 P. aeruginosa strains. The heat map was obtained by performing tblastn searches using the sequences present on the Comprehensive Antibiotic Resistance Database (CARD)7 as queries and the genomes as subjects. Green, perfect match to a sequence in the CARD; red, similar to a sequence in the CARD, within a curated e‐value cutoff (gene specific); and black, absent. The bar plot represents absolute frequency of each resistome. On the left of the heat map, genes, proteins, and specific variants (*) are grouped according to their biological function or the resistance they confer. Adapted with permission from Ref. 5.