Antimicrobial resistance heterogeneity among multidrug-resistant Gram-negative pathogens: Phenotypic, genotypic, and proteomic analysis

Abstract

Microbes evolve rapidly by modifying their genomes through mutations or through the horizontal acquisition of mobile genetic elements (MGEs) linked with fitness traits such as antimicrobial resistance (AMR), virulence, and metabolic functions. We conducted a multicentric study in India and collected different clinical samples for decoding the genome sequences of bacterial pathogens associated with sepsis, urinary tract infections, and respiratory infections to understand the functional potency associated with AMR and its dynamics. Genomic analysis identified several acquired AMR genes (ARGs) that have a pathogen-specific signature. We observed that bla_CTX-M-15, bla_CMY-42, bla_NDM-5, and aadA(2) were prevalent in Escherichia coli, and bla_TEM-1B, bla_OXA-232, bla_NDM-1, rmtB, and rmtC were dominant in Klebsiella pneumoniae. In contrast, Pseudomonas aeruginosa and Acinetobacter baumannii harbored bla_VEB, bla_VIM-2, aph(3'), strA/B, bla_OXA-23, aph(3') variants, and amrA, respectively. Regardless of the type of ARG, the MGEs linked with ARGs were also pathogen-specific. The sequence type of these pathogens was identified as high-risk international clones, with only a few lineages being predominant and region-specific. Whole-cell proteome analysis of extensively drug-resistant K. pneumoniae, A. baumannii, E. coli, and P. aeruginosa strains revealed differential abundances of resistance-associated proteins in the presence and absence of different classes of antibiotics. The pathogen-specific resistance signatures and differential abundance of AMR-associated proteins identified in this study should add value to AMR diagnostics and the choice of appropriate drug combinations for successful antimicrobial therapy.

Keywords: Gram-negative pathogens; antimicrobial resistance; antimicrobials; genomics; proteomics.

Fig. 1.

(A) Distribution of each pathogenic bacterial genome included in this study depicted by the maximum likelihood 16S rRNA sequence–based phylogenetic analysis. (B) Represents the diverse clinical specimen sources from where these gram-negative pathogens were isolated. The tree scale indicates the number of substitutions per genome per site.

Fig. 2.

Molecular antimicrobial-resistant profile overlaid on the maximum likelihood phylogeny based on 16S, 23S, and 5S rRNA sequences for gram-negative pathogens (n = 203). The tree depicts the difference between the nonfermenters and Enterobacterales along with specimen type, year of isolation, and region. The heat map represents the classification of acquired AMR genes with five major antimicrobials (darker shade represents presence, and lighter shade represents absence). The tree scale indicates the number of substitutions per genome per site.

Fig. 3.

Global phylogeographical analysis of (A) E. coli (n = 995) and (B) K. pneumoniae (n = 776) along with their specimen source, geographical origin, and year of isolation. Phylogroups are mentioned for E. coli. The tree scale indicates the number of substitutions per genome per site.

Fig. 4.

Global phylogeographical analysis of (A) P. aeruginosa (n = 323) and (B) A. baumannii (n = 582) along with their specimen source, geographical origin, and year of isolation. International clones are shown for A. baumannii. The tree scale indicates the number of substitutions per genome per site.

Fig. 5.

Comparison of genetic arrangements for (A) bla_NDM-1 vs. (B) bla_NDM-5 in different organisms to decode diversity in genetic background. Cluster alignments are drawn to scale based using the Clinker tool. Arrows indicate the direction of the open reading frame.