https://orcid.org/0000-0003-2281-7858
Figures
Abstract
Background
Shigellosis is an acute gastroenteritis infection and one of Ethiopia’s most common causes of morbidity and mortality, especially in children under five. Antimicrobial resistance (AMR) has spread quickly among Shigella species due to inappropriate antibiotic use, inadequacies of diagnostic facilities, and unhygienic conditions. This study aimed to characterize Shigella sonnei (S. sonnei) using whole genome sequence (WGS) analysis in Addis Ababa, Ethiopia.
Methods
The raw reads were quality-filtered and trimmed, and a minimum length of 50bp was retained and taxonomically classified using MiniKraken version 1. The whole genome data were aligned with Antibiotic Resistance Gene (ARG) sequences of the Comprehensive Antibiotic Resistance Database (CARD) by Resistance Gene Identifier (RGI). Plasmids were analyzed using the PlasmidFinder tool version 2.1. Additionally, AMR and virulence genes were screened at the Centre for Genomic Epidemiology (CGE) web-based server.
Results
All isolates in our investigation contained genes encoding blaEC-8 and blaZEG-1. Here, 60.7% of the isolates were phenotypically sensitive to cefoxitin among the blaEC-8 genes detected in the genotyping analysis, whereas all isolates were completely resistant to amoxicillin and erythromycin phenotypically. The study also identified genes that conferred resistance to trimethoprim (dfrA). Plasmid Col156 and Col (BS512) types were found in all isolates, while IncFII and Col (MG828) plasmids were only identified in one isolate.
Conclusion
This study found that many resistant genes were present, confirming the high variety in S. sonnei strains and hence a divergence in phylogenetic relationships. Thus, combining WGS methods for AMR prediction and strain identification into active surveillance may be beneficial for monitoring the spread of AMR in S. sonnei and detecting the potential emergence of novel variations.
Citation: Ayele B, Mihret A, Mekonnen Z, Sisay Tessema T, Melaku K, Nassir MF, et al. (2024) Whole genome sequencing and antimicrobial resistance among clinical isolates of Shigella sonnei in Addis Ababa, Ethiopia. PLoS ONE 19(11): e0313310. https://doi.org/10.1371/journal.pone.0313310
Editor: Muhammad Qasim, Kohat University of Science and Technology, PAKISTAN
Received: August 1, 2024; Accepted: October 23, 2024; Published: November 12, 2024
Copyright: © 2024 Ayele et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All FASTA files that support the findings of this study are available from the supporting information as FASTA files archived that summited in the bio-sample ID: https://www.ncbi.nlm.nih.gov/biosample/42540096-42540132. The following supporting information includes S1 Table: Antimicrobial resistance pattern of Shigella isolated from stool cultures; S2 Table: Performance standards for antimicrobial susceptibility testing of the commonly prescribed antibiotics and additionally can be found at doi: 10.1155/2023/5379881.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Background
Shigella, a Gram-negative bacterium from the Enterobacteriaceae family, is the second most prevalent cause of diarrheal mortality among individuals of all ages and a major cause of diarrheal sickness [1, 2]. More than 200,000 people die from shigellosis each year, with Low -and Middle-Income Countries (LMICs) bearing the majority of this burden [3]. Shigella is primarily transmitted by contaminated food and drink, as well as direct contact between humans. Typically, fever, tiredness, anorexia, and malaise indicate the beginning of a sickness. It can cause moderate diarrhea, severe dysentery with bloody stools, and even mortality from dehydration, particularly in vulnerable individuals such as small children, the elderly, and those with impaired immune systems [4]. Under five children are especially at risk from acute diarrhea, which is commonly defined as having at least three loose stools in 24 hours [5]. Sociocultural factors, poor quality drinking water, lack of formal education, and low hygiene levels are known risk factors for shigellosis, particularly in children under the age of five in developing nations [6, 7]. In comparison to other causes of gastroenteritis, Shigella is a highly contagious microorganism: It only takes 10 bacilli to cause an infection [8]. The most prevalent Shigella species in developed countries is S. sonnei. In contrast, S. flexneri is predominant in developing countries, while S. dysenteriae and S. boydii are rarely isolated [9]. Shigella flexneri (S. flexneri) is dominant in Africa and Asia, although S. sonnei, the most dominant species in South America, was the predominant isolate in one study in Ethiopia [10]. This variability may depend on different disease epidemiology between study sites. The prevalence of Shigella species reports varies in different studies due to factors that may have occurred during observation and measurement or that are linked to study methods and techniques [11]. Identification of the circulating bacteria is essential for treatment. The disease is complicated by a high rate of drug resistance to the commonly used antibiotic agents in different regions [9]. Antimicrobial resistance (AMR) pattern differs from place to place and between two regions in the same place [12]. The increasing prevalence of multi-drug resistance (MDR) to Shigella species is a serious threat, especially in Ethiopia with health and nutritional problems. There was identified MDR among several Shigella species isolated from acute diarrheal patients [13]. Irrespective of the serogroup/ serotype, most of the strains carried similar genes encoding resistance to specific antimicrobials. Most studies used standard culture to detect Shigella infection, with only a few studies using molecular methods, which can triple the detection rate by detecting lower-burden infections [14]. In Ethiopia, laboratory identification of Shigella species is nearly entirely dependent on culture and biochemical tests. However, one of the issues that diagnostic microbiology laboratories encounter is identifying Shigella strains from E. coli, which may be attributable to the fact that E. coli and all four Shigella species have very tight DNA-DNA connections [15]. Medication resistance based on genome-derived AMR data may be phenotypically susceptible to the linked antimicrobials [16]. Shigella species may now be identified using biochemical tests, and suspected colonies are confirmed by serotyping [15] with commercially available antisera. Serological identification of bacterial strains that create Shigella-like colonies on selective agar plates and biochemical tests show cross-reactivity with other bacteria such as E. coli and Shigella-specific antisera [17]. Polymerase chain reaction (PCR) test on the IpaH gene assay was utilized to identify Shigella species and Intro- invasive E. coli (EIEC) with excellent specificity [18]. However, the approach has no benefit in distinguishing Shigella from EIEC strains due to their identical virulence genes. Almost all molecular approaches fail to observe the evolutionary relationships between Shigella strains obtained within the country and those discovered outside. Because of this researchers are currently switching to whole genome sequencing (WGS) due to its higher resolution compared to previous methods (e.g., pulsed-field gel electrophoresis (PFGE) [19]. The sequencing data also give a higher level of strain differentiation and precision than any subtyping approach previously employed for epidemic identification and investigation. Thus, practically all characterization of Shigella species in the public health laboratory can be replaced by WGS employing commercial and web-based tools, including a serotype identification technique, AMR, plasmid, and virulence prediction tools for the study of Shigella and other microorganisms [20]. Despite the high incidence of shigellosis, there are limited data on the WGS analysis of Shigella species in Ethiopia, necessitating more inquiry. Therefore, this work was intended to investigate the genetic characteristics of S. sonnei utilizing WGS analysis in clinical samples in Addis Ababa, Ethiopia.
Methods
Study sites, sample collection, and bacterial identification
The study was conducted in stored isolates collected from four public health facilities from June 2021 to April 2022. Shigella growth was identified from other lactose-negative suspected colonies on Mac-Conkey agar (MAC) and xylose lysine deoxycholate (XLD) agar plates by unique colony morphology after subculturing and overnight incubation at 37°C. Additionally, biochemical tests were then utilized to identify the Shigella species. Antimicrobial susceptibility testing was also performed using a single-disk diffusion technique described in the previous phase-one study [21].
Serogroups
Shigella serogroups were identified through the slide agglutination method. Shigella species do not produce flagellins or capsular antigens, hence their antigenic characterization is dependent on somatic antigens (O-antigens) using serological methods only. The slide agglutination test with polyvalent commercially available antisera was used to serogroup Shigella isolates.
DNA extraction, library preparation, and whole genome sequencing(WGS)
The stored isolates were sub-cultured on nutrient agar (NA) media (Oxoid Ltd., Hampshire, UK) at 37°C via overnight incubation and the next day a pure colony was sub-cultured on nutrient broth (NB) (Oxoid Ltd., UK) at 37°C overnight. Then 200 mL was centrifuged at 5000 rpm, after which the sediment was ready for DNA extraction. DNA ex-traction was performed using the Qiagen DNA micro kit (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. DNA concentration was measured in a Qubit 4.0 fluorometer with the Qubit dsDNA HS Assay kit (Thermo-Fisher, Mumbai, MA, USA), and DNA purity was determined using the A260/A280 purity ratio. The target DNA con-centration was at or greater than 10 ng/μL, and the purity ratio was greater than 1.8 but less than 2. Each isolate’s DNA was processed for sequencing using the Nextera Flex DNA Library Preparation Kit (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. Isolates were sequenced using the Illumina NextSeq550 (Illumina, Singapore) technology at the Ethiopian Public Health Institute (EPHI) in conjunction with the Armauer Hansen Research Institute (AHRI). A high-output 300-cycle kit paired-end (PE) 149 bp long reads was produced.
WGS data analysis of S. sonnei strains
The quality of the paired-end raw reads was assessed using the standard FAST-QC tools. Subsequently, trimming and filtering were performed to eliminate sequences from the beginning and end of reads and remove other adapter contamination and reads with low base call quality, utilizing trimmomatic tools. For further analysis, only high-quality paired-end reads with a Phred quality score greater than 20 and a minimum length of 50 base pairs were retained. Species confirmation was accomplished through gold standard Kmer-based taxonomy using MiniKraken version 1 (https://ccb.jhu.edu/software/kraken, (accessed on 22 May 2024)). Alignment was performed using the BWA-MEM algorithm, and mapping quality was evaluated using Samtools stats, assuring a depth of coverage larger than 20× and genome coverage greater than 85% for subsequent analysis. Variants were called using bcftools (http://samtools.github.io/bcftools/, (accessed on 22 May 2024)), with a subsequent filtration of SNPs to construct phylogenetic trees. The phylogenetic tree was constructed using maximum likelihood models with a bootstrap value of 1000 replications.
Determination of AMR, virulence genes of isolates from whole genome data
Using genomic data, we employed an integrative method to identify plasmid, virulence, and antimicrobial resistance (AMR) genes. Firstly, the entire genome sequences were compared against Antibiotic Resistance Gene (ARG) sequences from the Comprehensive Antibiotic Resistance Database (CARD) using the Resistance Gene Identifier (RGI, v5.1.1). Genes exhibiting 90–100% identity and coverage were classified as perfect or strict matches, with any ambiguities excluded. Reference sequences for acquired resistance genes were sourced from CARD, and refined using the AMRFinderplus database. Additionally, genes screened using the ResFinder 2.0 database (https://cge.food.dtu.dk/services/ResFinder, (accessed on 2 June 2024)). PlasmidFinder detected plasmids; VirulenceFinder 1.5 screened virulence gene by CGE web-based server (https://cge.food.dtu.dk/services/VirulenceFinder/, (accessed on 4 June 2024)) with a threshold of 90% identity and 60% minimum length. This methodology ensured comprehensive assessment, aligning reads to a reference database of acquired genes [22, 23].
Quality control
The standard operating procedure (SOP) and Clinical Laboratory Standards Institute (CLSI) were used throughout the procedures. In addition, the sterility of the culture media was checked frequently by incubating the prepared culture media at 37°C overnight and checking for growth. American Type Culture Collection (ATCC) reference strains were used to test the performance. The temperature utilized to store the disks, materials, and regents followed the manufacturer’s instructions. The isolates were held at -80°C, whereas the DNA was kept at -20°C. All DNA and library preparation methods followed standard and manufacturer’s instructions.
Ethics statement
The investigation was conducted using previously archived study samples and received ethical approval from the Jimma University Institute of Health Institutional Review Board (IRB) (IHRP6/1092/2021). AHRI and EPHI also approved the study. As stated in our previous study [21], the mothers/caregivers of the children provided informed, voluntary, written, and signed consent after the study’s purpose was described to them in detail. Parents/guardians of the children were also informed about the confidentiality of the information acquired and their complete right to refuse or withdraw from participation in the research.
Results
Shigella isolates
From 534 stool specimens, 47 (8.8%) Shigella species were identified using culture and biochemical assays, as stated in the earlier phase-one investigation (S1 Table). The age group of one to less than three years old had the highest percentage of infections (5.4%); however, no pathogen was isolated from children under one year. Of the 47 Shigella species, 31 were serologically identified as S. sonnei and 16 as S. flexneri. Of the isolates, 28 were S. sonnei (Fig 1), which were selected for further study utilizing WGS data using the reference strain accession NZ_CP055292.1. All isolates were S. sonneiSE6.1 strains and had Query_Coverage with 87.61 to 92.43.
Comparison between phenotypic and genotypic AMR profile of Shigella sonnei
Phenotypically, in terms of susceptibility, among the 47 Shigella species, 100%, 93.6%, 80.9%, 72.3%, and 57.5% of the isolates were sensitive to norfloxacin, nalidixic acid, ciprofloxacin, gentamicin, and cefoxitin, respectively. Ampicillin resistance reached 93.6%. However, all isolates were completely resistant to amoxicillin and erythromycin (S1 Table). Here, 7 and 17 isolates showed discordant results for cotrimoxazole (trimethoprim) and cefoxitin, respectively, between the phenotype and genotype (Table 1). The majority were predicted to be susceptible based on genome-derived AMR data. Still, these were phenotypically resistant to the corresponding antimicrobials (S1 Table), so all were classified as major errors (genotypically susceptible but phenotypically resistant).
Resistance to beta-lactams and folate synthesis inhibitors
All of the isolates in our study had genes that encoded beta-lactam drugs, particularly blaEC-8 and blaZEG-1. Here, 60.7% of the isolates were phenotypically sensitive to cefoxitin among the blaEC-8 genes detected in the genotyping analysis. All isolates possessed genes that made them resistant to trimethoprim. Of the genotype resistance isolates, 25% were phenotypically susceptible to trimethoprim. The gene isolates that conferred resistance to trimethoprim had the dfrA gene (Table 1).
Antibiotic resistance genes (ARGs) in S. sonnei
We found 52 types of ARGs in the whole genome data for each S. sonnei isolate. Besides a total of 224 perfect ARG matches, a further 1232 hits were classified as strict and met the criteria of having 100% and 90–99.9%, respectively for coverage and sequential identity. Resistance gene identifier(RGI) criteria were set to predict perfect, strict, and complete genes only, which returned 8 perfect hits and 44 strict hits with no loose hits in each isolate. The resistance mechanism for the perfect RGI criteria includes seven genes cpxA, TolC, mdtE, emrR, emrY, marA, and H-NS involved in antibiotic efflux, and one ARG gene, i.e., PmrF involved in antibiotic target alteration, while marA also involved in reduce permeability to antibiotics in the isolates. The resistance mechanism in strict hit includes 35 genes involved in antibiotic efflux, one gene that has reduced permeability to an antibiotic, one involved in antibiotic inactivation, and thirteen genes involved in antibiotic target alteration (Table 2). All isolates had E. coli AcrAB-TolC with AcrR mutation conferring resistance to ciprofloxacin, tetracycline, and ceftazidime while E. coli AcrAB-TolC with MarR mutations conferring resistance to ciprofloxacin and tetracycline. Additionally, E. coli GlpT with mutation conferring resistance to fosfomycin and E. coli EF-Tu mutants conferring resistance to Pulvomycin were detected. The proportion of resistance mechanisms was calculated based on the ARG diversity. The dominant resistance mechanism of identified ARGs was antibiotic efflux (71.2%), antibiotic target alteration (23.7%), reduced permeability to antibiotics (3.4%), and antibiotic inactivation (1.7%).
Drug class and their ARGs
The finding that ARGs may compromise the efficacy of various antibiotic classes (Table 3) raises some clinical concerns. The identified ARGs may have affected many of the most highly prioritized antibiotics. For human Shigella infection treatment in Ethiopia, diaminopyrimidine(trimethoprim or cotrimoxazole), cephalosporins (cefixime, ceftriaxone), macrolides (azithromycin), penicillins of various categories (ampicillin, amoxicillin), quinolones (nalidixic acid, ciprofloxacin, norfloxacin), aminoglycosides (streptomycin, gentamicin) could be affected by the ARGs identified in the isolates. Antibiotic resistance genes (ARGs) were also discovered, confirming resistance to disinfectants and antiseptics for S. sonnei in perfect hits such as TolC and marA genes. In addition, multiple resistance genes to disinfectants and antiseptics were found in the strict hits, including E. coli acrR with mutation conferring multidrug antibiotic resistance gene.
Plasmid analysis
Through the PlasmidFinder tool, we identified the presence of position in ref, with their percent identity related to plasmids in S. sonnei strains, as shown in Table 4. Plasmid Col156 and Col (BS512) types were found in all isolates. The IncB/O/K/Z plasmid was discovered in all isolates except B27, B52, B92, and B311; however, the IncFII plasmid incompatibility element and Col (MG828) plasmids were only isolated in isolate B394.
Virulence gene analysis
The existence of virulence genes was investigated using the E. coli database. The detection of virulence factors indicated little variations between strains and no genes producing Shigatoxin. The majority of the isolates included virulence genes such as gad, which were frequently detected, and which are important to the glutamate decarboxylase in the epithelial cells. Isolate B322 had one gad gene, B25, B55, B71, B92, B112, B184, B245, B356, B382, B416, and B510 had three gad genes, and the remaining had two gad genes. Other virulence genes found in the isolates include csgA, fdeC, gad(3), fimH, hlyE(2), nlpl, lpfA, terC(2), yehA, yehB, yehC and yehD, as are shown in Table 5.
Phylogenetic analysis
We sequenced the genomes of the 28 isolates and conducted a single-nucleotide polymorphism (SNP) phylogenetic analysis using the data (Figs 2 and 3). We chose and downloaded the genome sequences of 28 isolates to assess the similarity between strains. We discovered that they were divided into seven major phylogenetic groupings (PGs) and more diverse S. sonnei strains (PG1-PG7). The phylogenetic tree revealed that most strains were clustered in one group with PG6 strains, which included 22 strains, indicating a high level of genomic similarity between them. Six isolates (B26, B52, B382, B129, B311, and B394) deviated from the PG6 group, indicating distinct origins. The phylogenetic tree obtained by mapping our 28 S. sonnei paired-end reads against a reference genome showed the genetic diversity of the strains, with strains B108, B178, and B182 being the most phylogenetically distant. However, strain B394 appeared to be closely related to the reference. Overall, the phylogenetic tree became more diversified and revealed that strains were divided into several distinct groups, implying numerous origins.
Phylogenetic tree based on SNP analysis in CSI Phylogeny 1.4 analyzing 28 S. sonnei genomes from strains obtained at 4 public health facilities in Ethiopia. The tree file with the Newick extension produced by CSI Phylogeny 1.4 was utilized.
The generated tree shows the relatedness of local and global isolates. To perform SNP calling, sequencing data for the nine global S. sonnei strains (red and blue colors) were retrieved from the NCBI SRA and entered into SNVPhyl alongside the local isolates.
Phylogenetic relationships of S. sonnei globally
Single-nucleotide polymorphism (SNP) calling was performed, and a maximum-likelihood tree was constructed to illustrate the relatedness of these S. sonnei; grouping by strain geographic origin was interpreted as the key driver of phylogenetic segmentation. S. sonnei isolated from Uganda varied considerably from the other isolates (Fig 3). Additional branching occurred in one isolate (B394). Most of the remaining 35 isolates from closely related strains formed separate, smaller clusters. However, overall grouping appears to be irrespective of geographic origin, since one large cluster includes both international and local isolates.
Discussion
Shigellosis is the leading cause of death and diarrhea in children under the age of five in underdeveloped countries [24]. Studies on the molecular characterization of Shigella isolates are extremely rare, particularly in underdeveloped countries with limited resources, such as Ethiopia. Although Shigella and E. coli have similar features, there is sometimes a clinical or public health requirement to distinguish between these infections since they have distinct entities in epidemiology and clinical practice. New Shigella strains that do not agglutinate with commercially available antisera are becoming more common [25]. Shigella genome sequencing was previously examined and shown to be divided into seven phylogenetic groupings (PGs) [26, 27]. We selected and downloaded the full genome sequences of 28 isolates to study the similarity between strains, and we observed that they were split into seven major PG and more diversified S. sonneiSE6-1 strains (PG1-PG7). The phylogenetic tree could determine the connection between isolates by constructing clades and branches [22]. In the current study, the phylogenetic tree reveals that most of the S. sonneiSE6-1 strains were associated with PG6 strains, which contained 22 strains and shared a high level of genomic similarity. On the other hand, six isolates branched away from the PG6 group, indicating distinct origins. Generally, the phylogenetic tree was more diversified, demonstrating that strains were classified into numerous unique groups, representing several origins.
The global relatedness of S. sonnei identified from Uganda differs significantly from other isolates. In the current study, we observed additional branching in one of the isolates. There was no indication of geographical clustering because the groups contained local and global isolates acquired [23]. Almost all of the remaining 35 isolates from closely related strains formed separate, smaller clusters. However, overall grouping appears to be irrespective of geographic origin, as one large cluster includes both international and local isolates.
Plasmids containing AMR determinants in clinical strains are a serious global concern [28]. The plasmid Col156 and Col(BS512) detection are the genetic components that recognize plasmids containing the colicin E production, immunity, and lysis system [29]. The current investigation revealed that Col156 and Col(BS512) dominated the detected isolates, with 94.81% and 100% identity, respectively. The presence of incompatible plasmids, specifically the IncF plasmid, has been linked to the global emergence of clinically relevant ESBLs and multiple AMR determinants [30]. The IncFII plasmid was only identified in the B394 isolate however IncB/O/K/Z plasmid was detected in most isolates in this finding. Levere and his colleagues discovered that azithromycin resistance was eventually acquired, mostly via IncFII plasmids. Various ESBL genes are carried by different plasmids (IncFII, IncB/O/K/Z) or even incorporated into the chromosome, and they encode resistance to third-generation cephalosporins [31].
The primary goal of this study was to identify genes associated with antibiotic resistance. Consequently, the resistance pattern phenotypic and the genes involved in AMR were examined and contrasted. In contrast to the genetic data, most isolates showed greater phenotypic resistance to antimicrobials tested, as described in the previous study [21]. Since resistance genes are frequently plasmid-mediated, differences may result from some plasmid loss during storage [16]. When phenotypic resistance occurs without gene detection, it suggests that resistance may arise from other mechanisms. On the other hand, genotypes present resistance genes without phenotypic expression, indicating that AMR genes are not expressed [30].
The pathogenesis of Shigella is associated with several virulence factors found in chromosomes or large virulent plasmids that carry genes essential for survival within cells and host cell invasion. Only a few researchers, meanwhile, have tried to describe their molecular virulence profiles. Based on a recent study [30], Shigella virulence genes are linked to several clinical symptoms, including severe stomach discomfort and bloody diarrhea. The authors also emphasized that resistance to more antimicrobials was linked to virulence genes in greater quantities. While analyzing the various genes, it was discovered that the separated virulence genes are involved in pathogenicity. A study [32] found a significant prevalence of pathogenicity-associated islands with the fimH gene, providing evidence for their involvement. Similarly, another study found the existence of specific virulence genes, such as iha, lpfA, yeh(A-D), and gad, controlling adhesion to host cells [33].
This study also found several genes that lead to a high degree of drug class resistance pattern. Among the resistance genes discovered, mdtM and E. coli acrR with the mutation were linked to antibiotic efflux. This finding further demonstrated that the same genes were responsible for multidrug resistance and enabled the organism to withstand stress conditions involving an alkaline environment, higher antibiotic concentrations, and external pH [33]. Furthermore, TolC, mdtE, and H-NS genes in perfect hits, and emrE, gadX, mdtF, CRP,evgS, and evgA genes in strict hits which were observed to be involved in antibiotic efflux, were found to be responsible for causing resistance to macrolide antibiotics [34]. The gene complements of E. coli and Shigella species are very similar, and these species cannot be separated into two groups, since they have a common gene pool in gene phylogenetic trees [35]. The results presented demonstrate that the S. sonnei carried significant amounts of ARGs, and, those genes may also be mobile, having possible consequences on the antibiotic treatment efficacy. As seen above, and as described in other publications [34], there is still a great deal of variation in details that need to be clarified by the interpretation of ARGs. Nonetheless, the possibility that the identified ARGs may impair the efficacy of several antibiotic classes raises clinical concerns. According to the most recent Center for Disease Control (CDC) data on antimicrobial usage in the United States, the most often provided substances are amoxicillin (penam), azithromycin (macrolide), aminoglycoside, amoxicillin, and clavulanic acid (penam, enhanced activity), cephalexin (cephalosporin), and doxycycline (tetracycline) [36]. Furthermore, according to the most recent WHO report on the use of antibiotics worldwide, the most often prescribed oral medications are amoxicillin (penam), ciprofloxacin (fluoroquinolone), sulphamethoxazole, and trimethoprim; in four African countries surveyed, the most often used parenteral medications are ceftriaxone (cephalosporin), gentamicin (aminoglycoside), and benzylpenicillin (penam) [37]. The detection of ARGs may have an impact on several of the most high-priority antibiotics. In the current study, based on the RGI criteria S. sonnei was confirmed resistant to almost all available antibiotics for Shigella species. Shigellosis was initially treated with sulfonamides and tetracycline, then with ampicillin, trimethoprim/sulfamethoxazole, and nalidixic acid [28]. These medications are no longer indicated unless susceptibility is established owing to the advent of resistant strains. The latest WHO report on critically important antimicrobials (CIA) for human Shigella infection treatment includes folate synthesis inhibitors (trimethoprim or cotrimoxazole), cephalosporins (ceftriaxone, cefixime), macrolides (azithromycin), penicillins (ampicillin, amoxicillin), quinolones (nalidixic acid, ciprofloxacin, norfloxacin), and aminoglycosides (streptomycin, gentamicin) [37]. Additionally, disinfectants and antiseptics were used to minimize the spread of the Shigella infection. In line with the study in Egypt [38], the current findings suggested the emergence of S. sonnei exhibited multi-resistance to either antibiotics (especially ESBL-producing strains) or disinfectants.
It is significant to note that all isolates had the gene H-NS, which is critical in the global gene regulation of many bacteria, including this species. The H-NS inhibits the expression of several genes, and its loss enhances AMR while decreasing drug accumulation. Even though this gene is contained in CARD, its functional impact is opposite to that of ARGs [39]. The ARGs not only diminish the efficacy of antibiotic therapy on S. sonnei but are also passed to other pathogenic bacteria in the consumer’s body [40], potentially reducing the efficiency of antibiotic therapy on disorders that include other pathogenic bacteria.
Conclusion
In conclusion, the current investigation improves our understanding of S. sonnei linked with diseases reported to Ethiopian public health authorities and emphasizes the importance of WGS in explaining the phenotypic–genotypic AMR association. All isolates in our investigation contained genes encoding blaEC-8 and blaZEG-1. Here, 60.7% of the isolates were phenotypically sensitive to cefoxitin among the blaEC-8 genes detected in the genotyping analysis, whereas all isolates were completely phenotypically resistant to amoxicillin and erythromycin. The study also identified gene carriers conferring resistance to trimethoprim (dfrA). Plasmid Col156 and Col (BS512) types were found in all isolates. Additionally, the identified ARGs were discovered to be involved in antibiotic target changes, and their overexpression led to decreased permeability and antibiotic efflux. This WGS study found that many genes were present, confirming more variety in S. sonnei strains and hence greater divergence in phylogenetic relationships. Limiting the findings to S. sonnei made characterizing other Shigella species that cause diarrhea more difficult. The study only covered S. sonnei. However, combining WGS approaches for AMR prediction and strain identification into active surveillance may be useful for tracking the spread of AMR in S. sonnei and detecting the possible development of new variants. Furthermore, we recommend that researchers examine the origins of AMR disparities and implement strategies to minimize ARG spread in Ethiopia.
Supporting information
S1 Table. Antimicrobial resistance pattern of Shigella isolated from stool cultures among under five diarrheic children in selected health centers, Addis Ababa (June 2021 to April 2022).
S, Sensitive; I, Intermediate; R, resistant. Notice: any results related to phase one study can be accessible at: Ayele, B.; Mekonnen, Z.; Sisay Tessema, T.; Adamu, E.; Tsige, E.; Beyene, G. Antimicrobial Susceptibility Patterns of Shigella Species among Children under Five Years of Age with Diarrhea in Selected Health Centers, Addis Ababa, Ethiopia. Can. J. Infect. Dis. Med. Microbiol. 2023, 2023(1), 5379881.
https://doi.org/10.1371/journal.pone.0313310.s001
(DOCX)
S2 Table. Performance standards for antimicrobial susceptibility testing of the commonly prescribed antibiotics (adopted from CLSI and EPHI national clinical bacteriology and mycology reference laboratory) (CLSI, 2021).
https://doi.org/10.1371/journal.pone.0313310.s002
(DOCX)
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