Molecular Insights into Antimicrobial Resistance Traits of Multidrug Resistant Enteric Pathogens isolated from India

Abstract

Emergence of antimicrobial resistant Gram-negative bacteria has created a serious global health crisis and threatens the effectiveness of most, if not all, antibiotics commonly used to prevent and treat bacterial infections. There is a dearth of detailed studies on the prevalence of antimicrobial resistance (AMR) patterns in India. Here, we have isolated and examined AMR patterns of 654 enteric pathogens and investigated complete genome sequences of isolates from six representative genera, which in aggregate encode resistance against 22 antibiotics representing nine distinct drug classes. This study revealed that ~97% isolates are resistant against ≥2 antibiotics, ~24% isolates are resistant against ≥10 antibiotics and ~3% isolates are resistant against ≥15 antibiotics. Analyses of whole genome sequences of six extensive drug resistant enteric pathogens revealed presence of multiple mobile genetic elements, which are physically linked with resistance traits. These elements are therefore appearing to be responsible for disseminating drug resistance among bacteria through horizontal gene transfer. The present study provides insights into the linkages between the resistance patterns to certain antibiotics and their usage in India. The findings would be useful to understand the genetics of resistance traits and severity of and difficulty in tackling AMR enteric pathogens.

Figure 1

(A) Resistance diversity (i.e. number of antibiotics against which resistance is detected) of the isolates detected from 2009–2015. (B) Variation of the resistance diversities of the individual pathogens detected in this study. The figure shows the progressive increase of antibiotic resistance in isolates till 2011. After 2011, the resistance diversities of the isolates seem to be more or less invariable. The resistance diversities also show significant variations between isolates belonging to different species.

Figure 2

(A) Number of isolates in which resistance is detected for each antibiotic. (B) Detection percentages of resistance to each antibiotic, detected in pre and post 2011. Besides providing an illustration of the pattern of antibiotic resistances across all MDR isolates, the figure also highlights the rise in resistance to certain antibiotics. In contrast, the resistance to ampicillin (a legacy antibiotic) is observed to have decreased post 2011. Many of these trends correlate with the usage of various antibiotics analyzed in previous studies.

Figure 3

(A) Log fold change in the resistance detection to various antibiotics in each of the pathogens (as compared to all others). Only species with more than one isolates (detected in the current study) were included in this analysis. While antibiotics against which resistances are significantly high and significantly low (in the different pathogens) are indicated in blue and yellow stars, respectively. (B) Variation of pairwise Jaccard distances between the resistance profiles of the isolates belonging to the various pathogens. Different species are not only observed to have distinct resistance patterns, but the isolates within species are also observed to vary to different extents in their resistance profiles.

Figure 4

Correlation in the resistance trends of various antibiotics. The figure indicates groups of antibiotic that have similar patterns of resistances across the 11 different pathogens, for which more than 1 isolates were detected in the current study. Two species, P. stuartii and S. dysenteriae, which contained just one isolate were not included in the statistical/association analyses. Correlations with P-value of less than 0.10 corrected for multiple comparisons using Benjamini-Hochberg procedure are highlighted using*.

Figure 5

Antimicrobials resistance encoding genes in the XDR isolates. Bubble sizes correspond to the number of genes detected in the genome of isolates. Subclasses of the resistance function for each category are also mentioned inside the bubble. The picture was drawn to the scale. A, C and D denote serine-β-lactamases, whereas B denotes metallo-β-lactamase. aph = Aminoglycoside phosphotransferase, aac = aminoglycoside acetyltransferase, ant = aminoglycoside nucleotidyltransferase, amt = aminoglycoside resistance methyltransferase, ABC = ATP-binding cassette transporter, RND = Resistance nodulation division, MFS = Major facilitator superfamily, MATE = Multidrug and toxic efflux, SMR = Small multidrug resistance, pmrJLM = Proteus mirabilis polymyxin B resistance encoding operon, pmrBD = Polymixin B resistance genes, TetA = Tetracycline resistance protein A, TetR = Tetracycline resistance regulatory protein R, EFG = Elongation factor G, qnrD = Quinolone resistance, gyr = Gyrase, topII = Type II topoisomerase, cat = chloramphenicol acetyltransferase, fsr = Fosmidomycin resistance protein, sh ble = Bleomycin resistance gene, pac = Puromycin N-acetyl-transferase.

Figure 6

Schematic representation of different antimicrobial resistance encoding genes and their physical association with mobile genetic elements. Genomes of four XDR isolates were scanned to identify the link between resistance genes and mobile element proteins. Description of the resistance genes is provided in the legend of Fig. 5. HP = Hypothetical protein, chrA = Chromate transport protein, padR = Phenolic acid decarboxylase-regulator, MEP = Mobile element protein, OMP = Outer membrane protein, tra = transfer protein, sulA = Sulfonamide resistance gene, DHPS = Dihydropteroate synthase, IstB = Transposon NTP-binding protein, intI = Integrase, TnpA = Transposase, IS = Insertion sequence.