Characterization of metal(loid)s and antibiotic resistance in bacteria of human gut microbiota from chronic kidney disease subjects

Abstract

Background: Human Gut Microbiota (HGM) is composed of more than one thousand species, playing an important role in the health status of individuals. Dysbiosis (an HGM imbalance) is augmented as chronic kidney disease (CKD) progresses, as loss of kidney function accelerates. Increased antibiotic use in CKD subjects and consumption of nephrotoxic heavy metals and metalloids such as lead, cadmium, arsenic, and mercury in tap water increases the dysbiosis state. Studies in people with stage 3 CKD are complex to carry out, mainly because patients are self-reliant who rarely consult a specialist. The current work focused on this type of patient.

Results: Lead and arsenic-resistant bacteria were obtained from self-reliant (that stands on its own) stage 3 CKD subjects. Pathogen-related Firmicutes and Proteobacteria genus bacteria were observed. Resistance and potentiation of antibiotic effects in the presence of metal(loid)s in vitro were found. Furthermore, the presence of the following genes markers for antibiotic and metal(loid) resistance were identified by qPCR: oxa10, qnrB1, mphB, ermB, mefE1, arr2, sulll, tetA, floR, strB, dhfr1, acrB, cadA2k, cadA3k, arsC, pbrA. We observed a decrease in the number of metal resistance markers.

Conclusions: The presence of cadA and arsC genetic markers of antibiotics and metal(loid)s resistance were detected in samples from stage 3 CKD subjects. Lower gene amplification in advanced stages of CKD were also observed, possibly associated with a decrease in resident HGM during kidney disease progression.

Fig. 1

Selection and synthesis of primers for genetic determinants analysis. Primers were used to amplify resistance genes to metals and antibiotics in DNA isolated from stool samples. For antibiotics analysis, primers were obtained from the literature [30], later an alignment was carried out with all databases of MEGARes [31], CARD [32], and GenBank [33] and evaluated by “in silico” PCR from UGENE [34]. For metal(loid) resistance primer design, principal antibiotic resistance genes belonging to each 6 phyla of HGM were aligned to determine the consensus sequence. Primers were designed with Primer3 software [35, 36]

Fig. 2

Susceptibility to antibiotics in bacteria isolated from stool samples of healthy patients and those with stage 3 chronic kidney disease. Inhibitory zones of bacteria isolated from stool samples were plated on YCFAm agar using the Kirby Bauer method [24]. Paper disks containing gentamicin (10 µg), ampicillin (10 µg), ciprofloxacin (5 µg), cefazolin (30 µg), ceftazidime (30 µg), and gentamicin (120 µg) were placed on previously seeded bacteria on agar plates and incubated at 37 °C for 24 h. Growth inhibition zones were measured and compared with the standardized diameter described by CLSI [25]. ½ MIC of arsenic and lead of E. coli was used to evaluate the effect of metal(loid)s on antibiotic resistance, n = 12. A healthy subjects, B CKD Stage 3 subjects

Fig. 3

Relative abundance of bacteria. Taxonomic profiling of the 16 s rRNA amplicon was performed from colonies that showed antibiotic resistance. Colonies were isolated from healthy and stage 3 CKD subjects and compared in the presence of arsenic, lead, and antibiotics (ampicillin, cefazolin, ceftazidime, ciprofloxacin, and gentamicin) with general or multi-resistance. The abundance of each sample was performed by read count analysis of the phylum (A) and genus (B). C beta-diversity was determined by principal component analysis (PCA) and results sorted by OTUs > 0.2%. Experimental groups are indicated by color and shape, arsenic (green) and lead (orange), healthy subject (triangle) or CKD patients (circle)

Fig. 4

Pie charts indicating antibiotic resistance genes (cold colors) and metal(loid)s (warm colors) analyzed by qPCR of DNA obtained from stool samples. A mefE1, arr2, catB4, strB, dhfr1, floR, tetA, ermB, acrB, qnrB1, cadA3k, arsC, arsA, cadA2k, and pbrA genes were detected in healthy controls. B strB, dhfr1, floR, acrB, arr2, cadA3k, cadA2k, and arsC genes were detected in subjects with stage 3 CKD. C acrB, arr2, qnrB1, strB, dhfr1, floR, ermB, tetA, cadA2k, cadA3k, arsC, and pbrA genes were detected in subjects with stage 4 CKD. D qnrB1, floR, dhfr1, merA, and cadA2k genes were detected in subjects with stage 5 CKD. The graph represents the total appearance of resistance genes in 4 patient samples analyzed by group. The total identified appearances were 96 hits for healthy subjects, 14 hits for stage 3 CKD, 23 hits for stage 4 CKD and 5 hits for stage 5 CKD. CKD groups were compared with healthy controls using Two-Way ANOVA with Dunnett’s test for multiple comparisons (two-tailed) P < 0.0001 (****)

Fig. 5

heat maps showing representative metal(loid)s resistance and antibiotic genes detected in samples from healthy versus CKD (all stages) subjects. A metal(loid)s resistance genes: cadA3k, arsC, cadA2k, arsA, merA and pbrA. For all cases analyzed, there was a tendency exhibit lower number of resistance genes to metal(loid)s, in particular, CKD patients tended to present a higher amount of cadA3k and then arsC compared to other resistance markers. In healthy patients, there was a tendency to present arsC and then cadA2k. B Antibiotic resistance genes observed were: arr2, catB4, qnrB1, strB, dhfr1, floR, tetA, ermB, acrB, and mefE1. The augmented tendency was observed for the dhfr1 gene in patients with CKD. Healthy patients tended to present arr2 and ermB genes, in slightly higher amounts compared to CKD patients. Groups were compared using Student t test, P < 0.0001 (***)