Multidrug-resistant ESBL-producing Klebsiella pneumoniae complex in Czech hospitals, wastewaters and surface waters

Abstract

Background: Multidrug-resistant (MDR) bacteria pose a significant challenge to the treatment of infectious diseases. Of particular concern are members of the Klebsiella pneumoniae species complex (KpSC), which are frequently associated with hospital-acquired infections and have the potential to spread outside hospitals via wastewaters. In this study, we aimed to investigate the occurrence and phylogenetic relatedness of MDR KpSC from patients with urinary tract infections (UTIs), hospital sewage, municipal wastewater treatment plants (mWWTPs) and surface waters and to evaluate the clinical relevance of the KpSC subspecies.

Methods: A total of 372 KpSC isolates resistant to third-generation cephalosporins and/or meropenem were collected from patients (n = 130), hospital sewage (n = 95), inflow (n = 54) and outflow from the mWWTPs (n = 63), river upstream (n = 13) and downstream mWWTPs (n = 17) from three cities in the Czech Republic. The isolates were characterized by antimicrobial susceptibility testing and whole-genome sequencing (Illumina). The presence of antibiotic resistance genes, plasmid replicons and virulence-associated factors was determined. A phylogenetic tree and single nucleotide polymorphism matrix were created to reveal the relatedness between isolates.

Results: The presence of MDR KpSC isolates (95%) was identified in all water sources and locations. Most isolates (99.7%) produced extended-spectrum beta-lactamases encoded by bla_CTX-M-15. Resistance to carbapenems (5%) was observed mostly in wastewaters, but carbapenemase genes, such as bla_GES-51 (n = 10), bla_OXA-48 (n = 4), bla_NDM-1 (n = 4) and bla_KPC-3 (n = 1), were found in isolates from all tested locations and different sources except rivers. Among the 73 different sequence types (STs), phylogenetically related isolates were observed only among the ST307 lineage. Phylogenetic analysis revealed the transmission of this lineage from patients to the mWWTP and from the mWWTP to the adjacent river and the presence of the ST307 clone in the mWWTP over eight months. We confirmed the frequent abundance of K. pneumoniae (K. pneumoniae sensu stricto and K. pneumoniae subsp. ozaenae) in patients suffering from UTIs. K. variicola isolates formed only a minor proportion of UTIs, and K. quasipneumoniae was not found among UTIs isolates; however, these subspecies were frequently observed in hospital sewage communities during the first sampling period.

Conclusion: This study provides evidence of the transmission and persistence of the ST307 lineage from UTIs isolates via mWWTPs to surface waters. Isolates from UTIs consisted mostly of K. pneumoniae. Other isolates of KpSC were observed in hospital wastewaters, which implies the impact of sources other than UTIs. This study highlights the influence of urban wastewaters on the spread of MDR KpSC to receiving environments.

Keywords: Klebsiella spp. subspecies; bla CTX−M−15; Urinary tract infections; Wastewater treatment plants.

Fig. 1

Antibiotic resistance and ARGs across sources, samplings and locations. 1 A Heatmap displays differences in antibiotic resistance profiles between locations A, B, and C. Shades of blue indicates resistance to particular antibiotics, the darker the colour is the more isolates were resistant. At location C, only UTIs isolates and isolates from hospital wastewater were collected. 1B Central heatmap with side charts showing differences in antibiotic resistance between the first and second sampling periods. Shades of blue indicates resistance to particular antibiotics, the darker the colour is the more isolates were resistant. During the second sampling period, no isolates from river water were obtained. The upper chart shows the presence of ARGs related to resistance to particular antibiotics. The right side of the chart represents the discrepancy between the presence of ARGs and the phenotypic profile. GP (gene presence) - presence of an antibiotic resistance gene without phenotypic manifestations. The observed resistance of GA (gene absence) to related antibiotics was not supported by its genetic background. Color ramps for the upper chart and ride side chart were created as follows: 0% “white”, 5% “light gray”, 50% “khaki”, 70% “gold”, 99% “goldenred”, and 100% “red”. Sources HU – UTIs isolates, RD – downstream river water, RU – upstream river water, SH – hospital wastewater, SI - mWWTPs inflow, SO – mWWTPs outflow. Antibiotics: AMK, amikacin; AMP, ampicillin; AMS, ampicillin + sulbactam; AZT, aztreonam; CAZ, ceftazidime; CEP, cefepime; CFZ, cefazolin; CIP, ciprofloxacin; CMP, chloramphenicol; COL, colistin; CPS, cefoperazone + sulbactam; CPZ, cefoperazone; CTX, cefotaxime; CXM, cefuroxime; ERT, ertapenem; GEN, gentamicin; MER, meropenem; NET, netilmicin; PIP, piperacillin; PIT, piperacillin + tazobactam; T/S, cotrimoxazole; TET, tetracycline; TGC, tigecycline; TOB, tobramycin

Fig. 2

Phylogenetic distances between different subspecies of KpSC. See legend to distinguish particular subspecies

Fig. 3

Distribution of the most abundant STs among different location (A, B, C)

Fig. 4

Phylogenetic tree of the KpSC isolates based on SNP analysis. See the legend color code for information about location, source, ST,s presence of clinically relevant capsule types, F-type plasmid K7:A-:B-, presence of a single variant of CTX-M-15 beta-lactamase and presence of genes encoding resistance to carbapenems. The colored STs were repeatedly mentioned in the manuscript. Read isolates were coded as follows: sampling period (1, 2), HU- UTIs isolates; RD- downstream river water; RU- upstream river water; SH- hospital wastewater; SI- mWWTPs inflow; SO - mWWTPs outflow, location (A-C), number of isolates cultivated on plates with antibiotics (c – cefotaxime/m – meropenem), variant of isolate (optionally, if two morphologically different colonies were taken from one plate)

Fig. 5

Principal component analysis. Plots provide essential information from a large dataset and transform it into a few principal components, which convey the most variation in the dataset. 4a Loading plot. PC1 is shown horizontally, and PC2 is shown vertically. The values of the vectors on each PC show how much weight they have on that PC. Angles between the vectors express how characteristics correlate with one another, and grouping together suggests a strong positive correlation between variables. 4b PC clustering with marked strain, source, capsule or location. The dots do not represent each isolate; rather, they represent variation and account for the varied influences of the original characteristics