Objective: The identification of Brucella genotypes is essential for epidemiological studies. The whole-genome sequencing is emerging as a novel tool for genetic characterization of infectious microbes. The aim of this study was to genotype Brucella melitensis isolates from Kuwait using whole-genome sequencing and variant analysis of the sequence data. Methods: DNA was purified from 15 heat-inactivated B. melitensis isolates and used to prepare sequencing libraries employing Nextera XT DNA Sample Preparation Kit (Illumina San Diego, CA, USA) and sequenced on a MiSeq (Illumina). The sequence files were aligned to three biovars of B. melitensis, i.e., biovar 1 str. 16M, biovar 2 str. 63/9, and biovar 3 str. Ether. The alignment and variant calling were performed using “bwa-mem” and SAMtools/VCFtools, respectively. Results: The genome size of all the isolates was around 3.3 mega base pairs and resembled B. melitensis biovar 2. Single-nucleotide polymorphisms (SNPs), insertions, and deletions (indels) were spread all over the genome; but 138 SNPs were common among the 14 isolates, supporting the same ancestral origin. A neighbor-joining tree analysis identified isolate 2 as an outlier. In addition, SNPs (2–478) specific to each isolate were also identified, which divided the B. melitensis biovar 2 into two major groups/genotypes. A further analysis showed that the Kuwaiti isolates of the present study shared phylogeny mainly with strains from the Middle Eastern countries. Conclusions: Among the 15 studied isolates from Kuwait, biovar 2 is the most prevalent biovar of B. melitensis. Furthermore, isolate-specific genetic variations were identified, which may be useful in epidemiological investigations.

Highlights of the Study

  • Human brucellosis is a major infectious disease in Kuwait and the other countries of the Middle East.

  • Whole-genome sequencing can provide information about the prevalence of biovars and genotypes circulating in Kuwait, which will be useful in epidemiological investigations.

  • In this study, the whole-genome sequence analyses of Kuwaiti Brucella melitensis isolates identified biovar 2 as the most prevalent biovar, which was further subdivided into two major genotypes, which shared phylogeny with strains from the Middle Eastern countries.

Brucellosis is a highly infectious zoonotic disease caused by Brucella species, which are Gram-negative and facultative intracellular bacteria [1]. The natural hosts of Brucella are domestic animals, e.g., cattle, camels, goats, and sheep, etc. [1]. The infection from animals is transmitted to humans through direct contact as well as consumption of contaminated animal products, including milk and milk products [2]. Human brucellosis is prevalent worldwide and an evidence-based report suggests that the global incidence of human brucellosis is 2.1 million new cases every year [3]. In particular, the Middle East and Central Asia are the regions of the highest incidence of human brucellosis [4]. Furthermore, human brucellosis is among the most reported zoonotic infectious diseases in Kuwait and the countries of the Gulf Cooperation Council (GCC) [5, 6].

Human brucellosis is a painful illness and has a major socioeconomic impact in many countries of the world [2]. Brucella spp. are transmitted through aerosol route as well, which makes them a highly attractive pathogen as a potential warfare agent [7]. Furthermore, the reappearance of clinical symptoms in up to 15% patients has been reported after chemotherapy [8], in some cases even after several decades [9, 10]. The reappearance of symptoms in the treated cases has been usually considered a relapse of the previous infection. However, it is not possible to differentiate between relapse and reinfection in endemic countries by using the classical techniques of culture, serology, and molecular methods like polymerase chain reaction, which can only identify Brucella species [11, 12]. However, to differentiate between relapse and reinfection, further identification of Brucella species at biovar and genotype level is essential [2]. In addition, genotyping methods for subtyping isolates are necessary for allowing epidemiological surveillance and success of eradication programs [1, 13].

In the past, molecular, and genetic approaches have been used for the identification of Brucella species and their genotypes [14, 15]. More recently, whole-genome sequencing (WGS) is emerging as a novel tool for completing genetic characterization of Brucella [16]. A standard approach to identify genotypes from the analysis of whole-genome sequence data, which can be incorporated into high-throughput assays, is the identification of single-nucleotide polymorphisms (SNPs) and insertions and deletions (indels) [17]. Furthermore, WGS and bioinformatic analyses of the sequenced genomes have the advantage of identifying the virulence factors, antibiotic resistance genes, genomic variations, studying evolutionary traits, and making phylogenetic inferences, etc. [18‒20]. Earlier WGS was highly cost intensive; however, with technological advancements and innovative sequencing applications, the prices have reduced drastically [21].

In this study, whole-genome sequencing of B. melitensis strains isolated from human patients was performed for the identification of biovars. Furthermore, the whole-genome sequence data were analyzed for SNPs and indels to identify variable regions and genotypes.

Brucella Strains and DNA Isolation

A total of 15 Brucella strains, isolated from an equal number of patients, were obtained on culture plates from the Microbiology Laboratory, Farwaniya Hospital (a large secondary hospital serving about 25% population in the country), Kuwait. A loopful of bacterial colonies from each culture plate was suspended into 1 mL sterile phosphate buffered saline (PBS, pH 7.0) and heated at 95°C for 10 min in a water bath. The genomic DNA was purified from the heated specimens using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) [22], The quantities and purities of the isolated DNA were assessed spectrophotometrically by using an Epoch Spectrophotometer (BioTek, Winooski, VT, USA) [23]. In addition, the concentrations of the isolated DNA were also determined by fluorometry using the Qubit dsDNA BR Assay Kit and Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA) [23]. The isolated DNA was stored at −80°C until further use.

Library Preparation and Whole-Genome Sequencing

All DNA samples were normalized to 0.2 ng/µL in a volume of 5 µL (1 ng total) for the library preparation. The Nextera XT DNA Sample Preparation Kit (Illumina, San Diego, CA, USA) was used for preparing the libraries [24]. All samples were subjected to Tagmentation (transposase mediated fragmentation and adapter ligation) and Agencourt AMPure XP magnetic bead-based clean-up (Beckman Coulter, Miami, FL, USA). The purified products were analyzed by Bioanalyzer 2100 using DNA 1000 kit (Agilent, Santa Clara, CA, USA). Prior to multiplexing, the samples were dual indexed (Nextera XT DNA Sample Preparation Index Kit, Illumina Inc.) and normalized employing the Agencourt AMPure XP beads (Fisher Scientific, Loughborough, Leicestershire, UK). Indexed libraries were pooled and sequenced using the MiSeq V2, 300 cycles sequencing kit (Illumina Inc.). Each genome was sequenced using the de novo assembly protocol at a coverage of about 100× selecting 2 × 150 paired end reads.

Post Sequencing Data Processing and Analysis

Raw data were subjected to quality filtering and further processing, as described previously [20]. In brief, the run quality was checked using the MiSeq Reporter analysis software (Illumina). The quality scores of the fastq files obtained through the MiSeq Reporter software were checked using FastQC. Reads were trimmed using FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). The de novo assembly was performed using Velvet 1.2.10. The best Velvet assembly was merged with SPAdes 3.8. Quast was used to check the assembly quality.

Biovar Identification and Variant Analysis

Bionumerics 7.6 software (Applied Maths, Sint-Martens-Latem, Belgium) was used for sequence alignment, biovar identification and variant analysis. The genome sequences of the current study were aligned with B. melitensis biovar 1 str. 16M (GenBank assembly accession no. GCA_000007125.1), B. melitensis biovar 2 str. 63/9 (GenBank assembly accession no. GCA_000182235.1), and B. melitensis biovar 3 str. Ether (GenBank assembly accession no. GCA_000158735.1), downloaded from NCBI (http://www.ncbi.nlm.nih.gov/genome/genomes/943).

The alignment was performed with bwa-mem using default parameters [25], and files were converted to sorted bam format. Variant calling was performed using the Sequence Alignment/Map (SAM) tools [26], and Variant Call Format (VCF) tools [27], with default parameters except that a minimum Phred score of 15 (vcfutils.pl varFilter-Q 15) was set for calling a variant. The final annotated genomes were plotted using CIRCOS to show the genome size, SNPs, and indels [28]. The SNPRelate an R package was used to perform principal component analysis and relatedness analysis using identity-by-descent methods on SNP data [29]. A neighbor joining tree was constructed using genome-wide pairwise distances (Bionumerics v 7.6).

A phylogenetic analysis was conducted with all the B. melitensis genomes available at NCBI genome database from different regions of the world. A total of 302 strains were downloaded; however, only strains of human origin with available country-of-origin information were selected for inclusion in the phylogenetic analysis. These strains (n = 105) were downloaded using NCBI datasets command-line tools (version 16.27.2) using their GCF accessions. To simplify the strain identification, the strains were renamed based on their respective country codes and suffixed with serial numbers derived from ascending order of their GCF accession numbers. The strains from this study were assembled using SPAdes (version 4.0.0) from FASTQ files, applying default settings. The scaffold-level assemblies generated by SPAdes were then compared with NCBI genomes. For whole-genome alignment, these genomes were analyzed using the “nucmer” option in the dRep (version 3.5.0) command-line program. The distance matrix obtained from dRep was subsequently used to construct a dendrogram, employing hierarchical clustering through the “ape” (version 5.8) package in R (version 4.3.3).

The analysis of whole-genome sequence data of all the 15 strains included in the current study revealed their genome sizes to range from 3,217,759 to 3,283,458 base pairs (bp). The 3.2 Mb genome was distributed into two chromosomes (chr)1 and 2 (Fig. 1). SNPs and indels were found dispersed in the entire genome and on both chromosomes. While genetic variations were spread all over the genome, certain regions had a higher number of variations (Fig. 2a, b). The genetic variation density graph showed a hypervariable region between 600 and 700 kb in both the chromosomes, with 10 and 14 mutations per kilobase in chromosome 1 and chromosome 2, respectively (Fig. 2a, b).

Fig. 1.

Circos plot of fifteen clinical isolates of B. melitensis isolates. The outermost circle represents both the chromosomes of B. melitensis (green part of the circle – chromosome 1 and red part of the circle – chromosome 2). The inner circles denote the fifteen strains under current investigation. Each strain is coded by a unique color. The lines in each circle mark the presence of SNPs and indels.

Fig. 1.

Circos plot of fifteen clinical isolates of B. melitensis isolates. The outermost circle represents both the chromosomes of B. melitensis (green part of the circle – chromosome 1 and red part of the circle – chromosome 2). The inner circles denote the fifteen strains under current investigation. Each strain is coded by a unique color. The lines in each circle mark the presence of SNPs and indels.

Close modal
Fig. 2.

Genetic variation density graph for (a) chromosome 1 and (b) chromosome 2 of B. melitensis strains of Kuwait. The x-axis shows the nucleotide length of the chromosomes, and the y-axis represents the number of mutations. Hypervariable regions are marked by the pink bar.

Fig. 2.

Genetic variation density graph for (a) chromosome 1 and (b) chromosome 2 of B. melitensis strains of Kuwait. The x-axis shows the nucleotide length of the chromosomes, and the y-axis represents the number of mutations. Hypervariable regions are marked by the pink bar.

Close modal

SNP and indel counts were obtained upon alignment of SAM files of the present samples with the reference genomes of three biovars downloaded from NCBI. All the samples, except S2, exhibited the least variation with biovar 2. The genetic variation count of S2 was lesser against biovar 2, suggesting resemblance between the two. As the majority of the isolates showed similarity with biovar 2, subsequent comparisons were done using it as a reference genome. Table 1 shows the number of mutations (SNPs and Indels) obtained when the samples were aligned against the reference genome of B. melitensis biovar 2. The number of SNPs was higher than the indels. In addition, we also investigated the presence of common and specific genetic variations in each isolate. A total of 138 genetic variations were found in all the 14 biovar 2 strains, supporting their common ancestral origin (data not shown). Along with the common variations, SNPs and Indels specific to each isolate were also observed (Table 1). The isolate-specific mutations indicated that there were genetic variations present within the individual isolates of biovar 2.

Table 1.

Genetic variations (SNPs and indels) in the genomes of isolates 1–15 after alignment against B. melitensis biovar 2 genome obtained from NCBI

Kuwait isolate No.
123456789101112131415
Total SNPs, n 713 2,446 438 252 552 260 247 453 469 531 455 480 472 240 243 
Specific SNPs, n 478 2,369 305 18 31 84 29 156 18 60 138 
Indels, n 94 192 76 60 85 60 61 77 72 90 71 79 71 59 56 
Kuwait isolate No.
123456789101112131415
Total SNPs, n 713 2,446 438 252 552 260 247 453 469 531 455 480 472 240 243 
Specific SNPs, n 478 2,369 305 18 31 84 29 156 18 60 138 
Indels, n 94 192 76 60 85 60 61 77 72 90 71 79 71 59 56 

The principal component analysis for population substructures in the B. melitensis isolates showed that the first two components account for almost 74% of the variation in the data. A plot of the principal components for the first two principal components is shown in Figure 3. From this figure it is evident that Sample 2 was an outlier among the datasets, while other isolates, distributed into two clusters, were relatively closer. These findings agreed with the SNPs observed in the present investigation.

Fig. 3.

Principal component analysis (PCA) of 15 Kuwaiti B. melitensis isolates. PCA plot was generated by identity-by-descent method on SNP data. Colored circles represent individual samples. A legend key for the same has been provided on the right-hand side panel.

Fig. 3.

Principal component analysis (PCA) of 15 Kuwaiti B. melitensis isolates. PCA plot was generated by identity-by-descent method on SNP data. Colored circles represent individual samples. A legend key for the same has been provided on the right-hand side panel.

Close modal

We also obtained a 15 × 15 matrix of genome-wide pairwise distances and constructed a neighbor-joining tree from the distance matrix calculated from the variations and rooted it using isolate 2 as the out-group. From the tree, except isolate 2, all the isolates fell into two closely related groups, i.e., Group 1 (isolates 1, 4, 5, 6, 7, 14, and 15) and group 2 (isolates 3, 8, 9, 10, 11, 12, and 13). As expected, the members of group 1 were phylogenetically closer to each other than the isolates of group 2 (Fig. 4).

Fig. 4.

Neighbor-joining tree of B. melitensis strains (n = 15, S1.bam to S15bam) isolated from Kuwait. The genome-wide distances of SNPs were used to construct the tree in Bionumerics v7.6 software. S = strain, bam = B. melitensis.

Fig. 4.

Neighbor-joining tree of B. melitensis strains (n = 15, S1.bam to S15bam) isolated from Kuwait. The genome-wide distances of SNPs were used to construct the tree in Bionumerics v7.6 software. S = strain, bam = B. melitensis.

Close modal

The phylogenetic analysis of the Kuwaiti strains from this study (n = 15) with 105 global B. melitensis strains revealed that despite the phylogenetic dispersion of Kuwaiti strains throughout the tree, which includes a wide range of strains from multiple countries, one can observe an obvious clustering with strains from the Middle Eastern countries, including Iraq, Syria, and Turkey (Fig. 5).

Fig. 5.

Phylogenetic clustering dendrogram of human-origin B. melitensis strains, constructed using hierarchical clustering with complete linkage. The scale bar on the x-axis represents the percentage dissimilarity between genomes. The Kuwaiti strains of this study are highlighted in dark red. The phylogenetic tree was constructed using whole-genome sequences of B. melitensis strains from Kuwait in this study (n = 15, marked in dark red) and other global strains (n = 105) available at the NCBI genome database, as given in the Materials and Methods.

Fig. 5.

Phylogenetic clustering dendrogram of human-origin B. melitensis strains, constructed using hierarchical clustering with complete linkage. The scale bar on the x-axis represents the percentage dissimilarity between genomes. The Kuwaiti strains of this study are highlighted in dark red. The phylogenetic tree was constructed using whole-genome sequences of B. melitensis strains from Kuwait in this study (n = 15, marked in dark red) and other global strains (n = 105) available at the NCBI genome database, as given in the Materials and Methods.

Close modal

This study was conducted to determine the heterogeneity among the B. melitensis strains isolated from human specimens by identifying the biovars and genotypes through whole-genome sequencing. Some previous attempts included enterobacterial repetitive intergenic consensus-PCR, multilocus sequence typing, and multilocus variable-number tandem-repeat analysis (MLVA)-8, MLVA-11 and MLVA-16, etc. [23] to differentiate between the B. melitensis genotypes. Many studies have shown the potential of MLVA typing for the identification of the genotypes of Brucella species along with their usefulness in the epidemiological monitoring and tracking back the infection source, but this method has limitations because it targets specific regions in the bacterial genome [30]. Whole-genome sequence analysis is a more powerful approach for accurate typing of Brucella spp. because it covers the entire genome of the bacterium and increases the discriminating ability between different strains [30]. Hence, whole genome-based SNP analysis has been suggested to provide improved results for the determination of heterogeneity and identification of genotypes among Brucella spp. [31].

B. melitensis has been divided into three biovars using biochemical methods and whole-genome sequence analysis [30]. Biovar analysis of Brucella isolates has been suggested to trace-back infection and in epidemiological investigations [32]. Our results of whole-genome sequence comparisons with the standard B. melitensis strains of biovar 1 (B. melitensis str. 16M), biovar 2 (B. melitensis str. 63/9 and biovar 3 (B. melitensis str. Ether) showed that 14 strains were biovar 2 and one strain biovar 1. These results suggest that biovar 2 could be the predominant biovar in patients served by the Farwania Hospital in Kuwait. Nevertheless, the limited number of isolates used in the present investigation cannot be ignored. The studies for biovar identification of B. melitensis in human infections have shown that Biovar 1 and 3 are the predominant biovars in many countries of the world [33‒35]. Although, B. melitensis biovar 2 has also been reported in some countries [33, 35]; however, to our knowledge, there is no report to show that B. melitensis biovar 2 was predominant in human brucellosis. In the present investigation we report B. melitensis biovar 2 to be prevalent in the Farwania Hospital of Kuwait with a limited number of isolates studied (n = 15).

The biovar determination of Brucella isolates has been suggested to be useful for epidemiological investigations [33]. However, biotyping does not allow for accurate trace-back investigations and is also unable to determine the source of infections. Recent reports suggest that further identification of Brucella biovars should be done at genotype level using SNP analysis [30]. Whole-genome sequence data can be mapped to a reference genome to identify SNPs, which correspond to the individual mutations in a genome. It has been suggested that SNPs can be compared across several genomes and the differences can be used to generate phylogenetic trees that provide information about the relatedness among the genomes [30]. These individual mutations provide the basis for making every organism unique among a set of nearly identical sequences. While these mutations only occur rarely, they can be several and enough to provide data for making comparisons among various genomes and the differences can then be used to create phylogenetic trees. SNP analysis of B. melitensis provides quantitative profiles of mutations and could facilitate a better understanding of the pathogen-specific pathology in human populations [30]. Our results showed that the 14 B. melitensis biovar 2 strains could be further divided into two major genotypes.

In addition to SNP based phylogeny, a comparative analysis based on whole genomes of globally sequenced B. melitensis strains may also help in tracing their origins [36]. Our results from the phylogenetic analysis suggest that despite the phylogenetic dispersion of Kuwaiti strains throughout the tree, which includes a wide range of strains from multiple countries, one can observe an obvious clustering of strains from the Middle Eastern countries. These results agree with the population structure of different nationalities in Kuwait and support our previous investigation wherein the MLVA biotypes of most B. melitensis strains were closer to the countries of the Middle East [23].

The biovar 2 of B. melitensis is the most prevalent biovar in the studied isolates obtained from the Farwaniya Hospital, Kuwait. Furthermore, isolate-specific genetic variations were associated with each isolate. This information may be useful in the epidemiological investigations and outbreaks of brucellosis in humans and animals in Kuwait. However, the study should be expanded in future studies to include a larger number of isolates from different hospitals to identify the biovars and genotypes circulating in Kuwait.

The authors would like to thank Dr. Farhat Habib, Scientist, IISER Pune, India, for his guidance in data analysis.

The study was approved by the Ethics Committee of Health Sciences Center and the project funding body (Approval No. MI04/15), Kuwait University.

The authors have no conflicts of interest to declare.

This study was supported by the Research Sector Grants MI04/15 and SRUL02/13 of Kuwait University.

A.S. Mustafa and W. Alfouzan conceptualized the study. A.S. Mustafa, M.W. Khan, and N. Habibi designed the experimental protocols and analyzed the data. M.W. Khan and N. Habibi performed the experiments. A.S. Mustafa was responsible for funding acquisition. All authors approved the final manuscript and contributed to the writing of the manuscript.

The raw sequences have been deposited to the National Center for Biotechnology Information (NCBI) website and can be accessed under the Accession No. PRJNA1100291 (SRR28680384 to SRR28680398). The reviewers’ link for the same is https://dataview.ncbi.nlm.nih.gov/object/PRJNA1100291?reviewer=fumphfq2u3neck075je7ubda73.

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