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DOI 10.1007/s10517-017-3910-z
814
Bulletin of Experimental Biology and Medicine, Vol. 163, No. 6, October, 2017
METHODS
Method of Selection of Bacteria Antibiotic Resistance Genes
Based on Clustering of Similar Nucleotide Sequences
I. S. Balashov, V. A. Naumov, P. I. Borovikov, A. B. Gordeev,
D. V. Dubodelov, L. A. Lyubasovskaya, Yu. V. Rodchenko,
A. A. Bystritskii, N. V. Aleksandrova, D. Yu. Trofimov,
and T. V. Priputnevich
Translated from Byulleten’ Eksperimental’noi Biologii i Meditsiny, Vol. 163, No. 6, pp. 784-787, June, 2017
Original article submitted December 9, 2016
A new method for selection of bacterium antibiotic resistance genes is proposed and tested
for solving the problems related to selection of primers for PCR assay. The method implies
clustering of similar nucleotide sequences and selection of group primers for all genes of
each cluster. Clustering of resistance genes for six groups of antibiotics (aminoglycosides,
β-lactams, fluoroquinolones, glycopeptides, macrolides and lincosamides, and fusidic acid)
was performed. The method was tested for 81 strains of bacteria of different genera isolated
from patients (K. pneumoniae, Staphylococcus spp., S. agalactiae, E. faecalis, E. coli, and
G. vaginalis). The results obtained by us are comparable to those in the selection of individual
genes; this allows reducing the number of primers necessary for maximum coverage of the
known antibiotic resistance genes during PCR analysis.
Key Words: antibiotic resistance; clusterization; next-generation sequencing technology;
polymerase chain reaction (PCR)
Resistance of microorganisms to antimicrobial drugs
is a topical problem of modern clinical microbiology. The use of next-generation sequencing (NGS)
technology provides maximum information about the
genome of bacteria isolated from clinical material.
One of the approaches to analyze of this information [6] is de novo assembly of reads with subsequent identification of resistance genes in databases
[9]. This approach is not always convenient, because
whole-genome sequencing of each isolate is required,
which is not always possible for technical and economic reasons.
V. I. Kulakov Research Center for Obstetrics, Gynecology, and Perinatology, Ministry of Health of the Russian Federation, Moscow,
Russia. Address for correspondence: a_gordeev@oparina4.ru.
A. B. Gordeev
In routine practice, PCR test systems that provide
rapid and available diagnostic information are the best
option. The primers for PCR should have high specificity and all target genes (or they should be universal
for a set of similar nucleotide sequences) should be
present to include the maximum number of antibiotic resistance (AR) genes. The search for optimal set
of primers for the detection of AR genes in bacteria
continues [2], since current commercial PCR kits in
real-time for AR assay are limited in the spectrum of
the tested genes or are inaccessible [3].
The goal of this work was the development and
testing of a new method of selection of genes predicting AR of bacteria for further selection of primers
and development of multiplex PCR test systems for
simultaneous identification of a large number of ARrelated genes.
0007­-4888/17/16360814 © 2017 Springer Science+Business Media New York
815
I. S. Balashov, V. A. Naumov, et al.
MATERIALS AND METHODS
The study was conducted on 81 strains of resistant
bacteria of different genus (K. pneumoniae, Staphylococcus spp., S. agalactiae, E. faecalis, E. coli, and
G. vaginalis) that were selected by phenotypic characteristics among strains isolated from patients of the
V. I. Kulakov Research Center for Obstetrics, Gynecology, and Perinatology.
The proposed method consists in clustering of
similar nucleotide sequences and selection of group
primers for all the genes of each cluster.
Pure bacterial cultures were obtained by inoculation of clinical material into nonselective and selective
culture media: 5% blood agar, UriSelect medium (BioRad), Endo agar (State Research Center for Applied
Microbiology and Biotechnology), mannitol-salt agar
(HiMedia Laboratories) and enterococcal agar (BD).
Species identification of the isolated cultures was conducted on a Vitek 2-Compact automatic bacteriological analyzer (BioMerieux) and by using MALDI-TOF
technique on a Autoflex III mass spectrometer with
MALDI-Biotyper 3.0 software (Bruker Daltonics).
The antibiotic sensitivity tests for isolated cultures
were performed by phenotypical methods: by discdiffusion method and by determining minimal inhibitory concentration of the drug on a Vitek 2 Compact
30 automatic bacteriological analyzer. The results were
evaluated in accordance with the EUCAST Breakpoint
table 5.0 recommendations.
We performed NGS analysis for all strains of bacteria. Genomic DNA was isolated from fresh cultures
containing at least 10 million cells by lysis with lysozyme and proteinase K followed by DNA extraction
with phenol-chloroform mixture. DNA libraries were
prepared with Ion Xpress Plus Fragment Library Kit
and Ion Xpress Barcode adapters 1-96 kits (Thermo
Fisher Scientific). The quality of the libraries was
controlled on a Bioanalyzer 2100 with High Sensitivity DNA Kit (Agilent Technologies). Ion OneTouch
Template Kit (Thermo Fisher Scientific) was used for
emulsion PCR and sphere enrichment. Sequencing
was performed on an Ion PGM Torrent platform with
Ion Sequencing Kit and 316v2 chips (Thermo Fisher
Scientific). All the stages starting from preparation of
libraries were conducted in accordance with manufacturer’s protocols.
The search among known AR associated genes
was performed in the ResFinder database [8] and
699 of 821 unique AR-related genes were selected
that were responsible for resistance to antibiotics of
6 classes (aminoglycosides, β-lactams, fluoroquinolones, glycopeptides, macrolides and lincosamides,
and fusidic acid) and grouped in accordance with the
targeted antibiotic. For each group of genes, hierar-
chical clustering of similar nucleotide sequences was
performed on the basis of similarity matrix of nucleotide sequences.
Contig assembly was carried out on the basis of
the data obtained from whole-genome sequencing as
well as the assessment of coverage together with alignment for the nucleotide sequences of the genes on the
ResFinder. The total length of the contigs (more than
500 nucleotide pairs) adjusted to the reference length
for the given type of microorganisms was taken further
as a correction value n.
For E. coli strains, no genes with nucleotide coverage proportion >0.5 were detected. The values >1
are due to normalization (the average value of the
covered nucleotides to the genes more than the average in the genome).
For each gene in the samples, nucleotide coverage
was evaluated; the value <50% was interpreted as the
absence of the gene in the sample.
For all samples, the presence of genes from each
cluster was evaluated. The association of genes belonging to the cluster with phenotypic response to the
tested antibiotic was evaluated using the Cohen κ coefficient. A similar algorithm was used to search for the
association of single genes with phenotypic resistance.
The results of evaluation of the association for genes
belonging to the cluster with the phenotypic response
to the tested antibiotic were compared with similar
results for single genes.
The correction of p-values to the plurality of hypotheses was conducted with FDR method (the expected fraction of false deviations). Contigs assembly was
carried out using SPAdes software [1]; sequence alignment was performed using BWA software [4]; nucleotide coverage and proportion of covered nucleotides
were evaluated using SAMtools [5] and BEDtools
software [7]. The statistical analysis was performed
using R platform including Biostrings, stats, Dynamic
Tree Cut, irr, and ggplot2 packages.
RESULTS
The results of NGS are shown in Table 1. Clustering
of similar nucleotide sequences yielded from 1 (for
fusidic acid) to 17 (for β-lactams) clusters in each
group of genes (Table 2).
The results of clustering showed that the number of
genes in the majority of clusters did not exceed 14. For
β-lactams, 5 clusters including 20-179 genes were detected. This is due to high polymorphism of the genes of
the blaSHV, blaTEM, blaCMY, blaCTX-M, and blaVIM
gene families. Smaller fragmentation of glycopeptides
was due to the heterogeneity of sequences that was
higher than for other groups. For fusidic acid, clustering
was low effective due to low number of known resis-
816
Bulletin of Experimental Biology and Medicine, Vol. 163, No. 6, October, 2017 METHODS
TABLE 1. Results of NGS Analysis
E. coli
E. faecalis
G. vaginalis
K. pneumoniae
P. aeruginosa
S.
agalactiae
S.
aureus
S. epidermidis
9
6
6
19
11
10
4
16
Average coverage, readings
8.17
11.93
28.99
8.16
11.49
6.8
49.37
36.57
Length of contigs/length
of reference (n)
0.993
1.076
1.009
0.980
1.046
0.331
0.880
0.995
Number of found genes (median)
0
32.5
3
18
38
9.5
37
25.5
Average part of coated
nucleotides in the genes
—
0.769
0.888
0.793
0.816
1.812
1.004
0.970
Parameter
Number of strains
TABLE 2. Distribution of Genes during Clustering Stage
Drug
Number of genes
Number of clusters
Number of genes in clusters [Min; Max]
Aminoglycosides
61
15
[2; 10]
β-Lactams
530
17
[2; 179]
Fluoroquinolones
21
3
[2; 14]
Glycopeptides
24
8
[2; 5]
Macrolides
61
15
[2; 10]
Lincosamides
2
1
[2]
tance genes. During evaluation of association between
single genes and clusters, only elements with κ≥0.5 and
p<0.05 after correction after multiple comparisons were
selected. The results of evaluation of the genotype and
phenotype association are given in Table 3 (the data
are presented only for antibiotics, for which significant
results were obtained).
For erythromycin and carbapenems, the clustering
of gene sequences made it possible to reach κ>0.5,
while for single genes no significant result was received. For ampicillin, clusters showed better association than with single genes. For amikacin, there
were no clusters for a significant evaluation of AR.
It should be noted that for vancomycin, the results of
test of single genes and clusters coincide because of
the co-presentation of the same genes from clusters in
the samples.
The method of clusterization was applied for solving the task of selection of a set of nucleotide sequences, to which it is then proposed to select primers that
allow the identification of a wide range of bacterial
resistance genes for the six groups of antibiotics (aminoglycosides, β-lactams, fluoroquinolones, glycopeptides, macrolides and lincosamides, and fusidic acid).
For fusidic acid this method has no significant advantages, as only 2 resistance genes (fusB and far1) are
known. However, in the case of the other five groups,
the number of clusters in each group (from 3 to 17)
was significantly lower than the number of genes
(from 21 to 530). The method of clustering of similar
nucleotide sequences allowed reducing the number
of nucleotide sequences required for full coverage of
genes by primers during PCR analysis, from 699 (for
unique genes) to 59 (for clusters). The analysis of the
correspondence of the phenotypic response with a set
of genes and a set of clusters showed similar results
for genes and for clusters, which gives us grounds to
TABLE 3. Assessment of the Genotype and Phenotype
Association for Single Genes and Clusters
The maximum value of κ Cohen
Drug
single genes
clusters
Amikacin
0.68
—
Ampicillin
0.6
0.68
Amoxicillin/clavulanic
acid
0.76
0.76
Third generation
of cephalosporins
0.76
—
—
0.54
Vancomycin
0.78
0.78
Erythromycin
—
0.54
Carbapenems
817
I. S. Balashov, V. A. Naumov, et al.
state that all useful information about genes at the application of clustering is preserved.
Thus, testing of the proposed method showed that
the use of clustering during molecular-genetic testing of strains for the presence of AR yielded results
comparable to the results obtained of testing of single
genes. In addition, clustering based on similarity of
nucleotide sequences reduced the number of primers
required to maximum coverage of known AR genes
during real-time multiplex PCR and increased availability of the analysis for health care institutions.
The work was performed within the framework of
the Agreement with the Ministry of Science and Education of the Russian Federation No. 14.607.21.0019
“Development of molecular and genetic test systems
for evaluation of the pathogenicity and resistance of
nosocomial and opportunistic pathogens in mothers
and newborns (code 2014-14-579-0001-065).
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