https://orcid.org/0009-0003-5912-8439
Figures
Abstract
Aeromonas spp. are the opportunistic pathogens that infect both aquatic and terrestrial homeotherms. They were commonly present in aquatic environments, including effluent, tap water, marine, river, and lake, where they are often isolated from aquatic animals, including fish, molluscs, and crustaceans. The Aeromonas infections can cause sepsis, ulcer, and other symptoms, resulting in the death of massive aquatic animals. Therefore, the prevention and control of Aeromonas is of great significance for the healthy development of aquaculture. In this study, we used modern molecular methods to enhance disease control of Aeromonas isolates from freshwater fish in Hebei Province. A total of 130 Aeromonas spp. isolates were isolated from freshwater fish farms in Hengshui, Handan, and Shijiazhuang and all 130 Aeromonas spp. isolates were sequenced for species identification. Of the 130 Aeromonas spp. isolates, 104 isolates were successfully sequenced, and BLAST analysis showed that Aeromonas veronii was predominant in freshwater fish farms in Hebei Province. In addition, 26 antibiotic resistance profiles were obtained from 102 fully cultured isolates among the 104 Aeromonas spp. isolates whose species was primarily identified, and 44 multidrug-resistant bacteria among the 102 isolates were identified using an antibiotic susceptibility test. Using the Multilocus Sequence Typing (MLST) method, 33 out of 44 multidrug-resistant isolates with 14 non-Aeromonas reference strains were selected for phylogenetic and MLST analysis, and all 33 multidrug-resistant isolates were A. veronii. A total of 30 new Sequence Types (STs) were obtained by comparing concatenated sequences (gyrB-groL-gltA-metG-ppsA-recA) on PubMLST website. Furthermore, recombination event analysis detected using RDP5 and ClonalFrameML software 42 and 49 recombination events, respectively, and 22 recombination events were validated by four or more algorithms. Since mutation and recombination events increase clonal diversity and single housekeeping gene sequence alignments are limited for identifying species, we propose the use of multiple concatenated sequence loci to increase discriminatory power. In addition, we propose that the MLST method is an appropriate technique to study and develop the resistance mechanisms of multidrug-resistant Aeromonas and to identify Aeromonas systematically in complex samples obtained from the environment.
Citation: Yu Z, Oh Y, Kim S, Han K, Srikulnath K, Li Q, et al. (2024) Multilocus sequence typing and antibiotic resistance of Aeromonas isolated from freshwater fish in Hebei Province. PLoS ONE 19(3): e0298745. https://doi.org/10.1371/journal.pone.0298745
Editor: Mahmoud Abdel Aziz Mabrok, Suez Canal University, EGYPT
Received: November 16, 2023; Accepted: January 29, 2024; Published: March 27, 2024
Copyright: © 2024 Yu 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: Anyone can access the data for free. Please send queries to 'Molecular Bacteriology Laboratory of Hebei Normal University' (contact via E.mail: hbnumicrobe@163.com) Multilocus sequence typing (MLST) profile data are available from the PubMLST database (https://pubmlst.org/). The data ID is 1038 - 1070 Input the number 1038 - 1070 into the search box to find the MLST profile data in this experiment (https://pubmlst.org/bigsdb?db=pubmlst_aeromonas_isolates&l=1&page=query).
Funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-RS-2023-00275307) and Basic Science Research Capacity Enhancement Project through Korea Basic Science Institute (National research Facilities and Equipment Center) grant funded by the Ministry of Education (Grant No. 2019R1A6C1010033). The funder [NRF-RS-2023-00275307/Grant No. 2019R1A6C1010033] contributed to the decision to publish, manuscript preparation, and recruiting staff.
Competing interests: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-RS-2023-00275307) and Basic Science Research Capacity Enhancement Project through Korea Basic Science Institute (National research Facilities and Equipment Center) grant funded by the Ministry of Education (Grant No. 2019R1A6C1010033).The funder [NRF-RS-2023-00275307/Grant No. 2019R1A6C1010033] contributed to the decision to publish, manuscript preparation, and recruiting staff.
Introduction
Aeromonas spp. are known as opportunistic pathogens capable of causing various diseases in aquatic animals, livestock, and humans and are widely distributed in ecosystems encompassing rivers, lakes, oceans, and urban domestic water [1, 2]. Aeromonas spp. possess various virulence factors such as degradative enzymes, surface polysaccharides (capsule, lipopolysaccharide, and glucan), S-layers, iron-binding systems, exotoxins, secretion systems, fimbriae, other nonfilamentous adhesins, and flagella [3, 4]. Various virulence factors of Aeromonas spp. cause immunocompromised organisms through resistance to host defense mechanisms, leading to diseases such as septicemia, ulcers, and visceral erosion and directly damaging host cells, resulting in Aeromonas infection and cell death [5, 6]. In addition, antibacterial and antioxidant molecules in the fish body provide self-defense against invading Aeromonas spp. and virulence factors, but the end product of this is reactive oxygen species (ROS), which can cause host cell damage and stress [7]. Aquatic animals such as fish, shrimp, and amphibians are particularly susceptible to Aeromonas infection due to Aeromonas spp. being the most widespread species in the aquatic environment, which can result in high mortality [8, 9]. Over the past few years, fish diseases caused by Aeromonas spp. have increased, including A. hydrophila, A. sobria, A. veronii and A. caviae [10]. In particular, A. hydrophila and A. veronii cause hemorrhagic diseases in farmed fish, widely infect aquatic animals, and quickly become the epicenter of epidemic outbreaks, resulting in significant economic losses in aquaculture [11–14].
Multiple drug resistance (MDR) has been increased all over the world that is considered a public health threat [15–17]. Several recent investigations reported the emergence of multidrug-resistant bacterial pathogens from different origins that increase the necessity of the proper use of antibiotics [18, 19]. Besides, the routine application of the antimicrobial susceptibility testing to detect the antibiotic of choice as well as the screening of the emerging MDR strains [20, 21]. The occurrence of various diseases caused by Aeromonas spp. have prompted the use of antimicrobial drugs, including aminoglycosides, carbapenems, broad-spectrum cephalosporins, chloramphenicol, quinolones, tetracyclines and other antibiotics [22]. However, bacterial multidrug resistance has become an important research focus due to the regular use of these drugs and other factors contributing to the development of resistance. Some Aeromonas spp. exhibit varying levels of antibiotic resistance, depending primarily on species, distribution, and other factors. In particular, resistance to ampicillin and penicillin is commonly observed in Aeromonas spp. [23, 24]. In fact, some Aeromonas spp. harbor genes encoding β-lactamases, which confer resistance to β-lactam antibiotics such as penicillin, carbapenems, and monocyclic-β-lactams [25]. The research at this stage can guide the rational use of antibiotics in various industries to reduce the probability of resistance development. Therefore, in the context of understanding the molecular mechanisms, it becomes imperative to identify antimicrobial resistance genes associated with phenotypic resistance and analyze the underlying causes of drug resistance in strains.
Accurate classification of Aeromonas spp. is essential to determine whether there is a correlation between the occurrence of Aeromonas infections and the pathogenic mechanisms associated with their host and transmission routes. To elucidate the complex taxonomic relationships between Aeromonas strains, phylogenetic analysis based on the 16S rRNA gene was first applied in large-scale identification of Aeromonas. However, the high degree of conservation of the 16S rRNA gene limited the classification of some species. It has been suggested that relying solely on a single gene to identify the Aeromonas genus may lead unreliable outcomes due to its insufficient phylogenetic power at the species level and limited discriminatory power in some genera [26]. In 1995, it was discovered by Yamamoto that the gyrB gene may be a more appropriate phylogenetic marker for bacterial taxonomy [27].
Multilocus Sequence Typing (MLST) is a widely used bacterial typing method, first proposed in 1998 [28]. MLST approach is based on the sequence polymorphism within internal segments of housekeeping genes and typically involves the PCR amplification and sequencing of 6–11 housekeeping genes. By comparing the resulting sequences and assigning allele numbers, isolates can be assigned sequence types based on unique combinations of allele variants. MLST for classification is characterized by simplicity, high reproducibility, and discriminatory power and can more accurately determine the branching order in the evolution of species [29, 30]. Thus, compared to other typing methods, such as DNA-DNA hybridization, MLST provides a more reliable means of elucidating phylogenetic relationships among taxa [31].
In this study, we isolated 130 Aeromonas spp. from freshwater fish in Hebei Province, China, and determined the predominant species among them. To evaluate the drug resistance profiles of the identified Aeromonas isolates, we performed antibiotic susceptibility testing based on the current operational guidelines of the Clinical and Laboratory Standards Institute (CLSI) [32, 33]. Additionally, we investigated how genotype can influence phenotype by confirming drug resistance genes associated with specific resistance phenotypes. We also applied the Multilocus Sequence Typing (MLST) method to perform phylogenetic typing of the multidrug-resistant Aeromonas discovered in this study and identify new sequence types (STs). Therefore, this study aims to investigate the antibiotic resistance of Aeromonas from aquatic organisms in Hebei Province, China, and verify whether MLST technology is suitable for classifying multidrug-resistant Aeromonas isolated from the environment.
Materials and methods
Sample collection
All the experiments in this study were conducted with the approval of the Animal Ethics Committee of Hebei Normal University (Protocol Number: 198012). Aeromonas often causes disease outbreaks in freshwater cultured fish, resulting in Aeromonas sepsis and ulcerative infection. Some fish have bleeding spots on the body surface and fins [34]. Infected fish eat less and swim on the surface of the pond [35]. Select the fish with insufficient vitality in the pond and pick them up with a small fishing net for body surface sampling and fish were released following sampling. Samples were collected from four common freshwater fish, including grass carp, carp, crucian carp, and koi carp, obtained from freshwater fish farms in Hebei Province (Hengshui, Handan, and Shijiazhuang), China, from April 2020 to August 2021. Use the sterile cotton swab to sample the bleeding points on the body surface or the ulcers around the fins.
Bacterial isolation and extraction of genomic DNA
The samples were aseptically cultured on Aeromonas isolation medium RS medium (Hopebio, Qingdao, China) using the streak plate method at 30°C for 18–24 hours, after that, the sterile inoculation loops were used to pick up the single yellow colonies and streak them on clean RS medium to obtain pure cultures. The purified strains were preserved in 30% glycerol and stored in an ultra-low temperature refrigerator at -80°C for long-term preservation. Genomic DNAs from the pure isolated strains were extracted using the boiling method [36]. Pure isolated strains were inoculated into 1mL of brain heart infusion broth (BHIB) (TOPBIO, Yantai, China) and cultured overnight in a shaker at 180 rpm, at 30°C. The culture was then centrifuged at 10,000 rpm for 2 minutes to collect the bacterial pellet. Subsequently, 1 mL of sterile water was added to resuspend the pellet, followed by another round of centrifugation at 10,000 rpm for 1 min to discard the supernatant. This wash step was repeated 2 times. Bacteria were resuspended by adding 50 uL of sterile water and the suspension was heated in a boiling water bath for 15 min. When the boiling reaction was completed, the boiling lysed samples were in an ice-water bath for 10 minutes and then centrifuged at 20,000 rpm for 2 minutes. After the above steps, the supernatant was collected for genomic DNA and stored in a medical refrigerator at -20°C.
Sequencing of 16S rRNA and gyrB genes
The genomic DNAs were amplified using primer pairs suitable to each target gene for identification, relying on the sequences of the universal bacterial 16S rRNA and gyrB genes. The primer sequences are as follows: universal bacterial 16S rRNA_F forward primer (5’-AGAGTTTGATCCTGGCTCAG-3’), universal bacterial 16S rRNA_R reverse primer (5’-TACGACTTAACCCCAATCG-3’), gyrB_F forward primer (5’-GGGGTCTACTGCTTCACCAA-3’), and gyrB_R reverse primer (5’-CTTGTCCGGGTTGTACTCGT-3’) [37, 38]. Polymerase chain reaction (PCR) was employed for gene amplification, using a 20ul reaction mixture: 1ul of genomic DNA template, 10 uL of 2X SuperMix (TransGen Biotech, Beijing, China), 1 uL each of forward and reverse primers, and 7 uL of ddH2O, resulting in a total volume of 20 uL. Following amplification, 5 uL of the PCR product from each sample was subjected to 1% agarose gel electrophoresis at 120V for 30 min. Samples exhibiting specific bands were documented using a gel imaging system (Bio-Rad, CA, USA) and then sent to GENEWIZ Co., Ltd. for sequencing (GENEWIZ, Suzhou, China).
Sequence analysis and construction of the phylogenetic analysis
The sequencing results were compared with BLAST on the NCBI website (https://blast.ncbi.nlm.nih.gov/Blast.cgi), and a similarity ≥ 97% were considered reliable, and the result with the highest score was recorded [39]. The 16S rRNA and gyrB sequences were combined and collated separately using the sequence merge function of the TBtools software [40]. Genetic diversity parameters such as G+C content, number of polymorphic loci, Tajima’s D, nucleotide diversity (π), number of segregating sites (θ), number of parsimony informative sites, synonymous substitution rate (Ks), and non-synonymous substitution rate (Ka) were calculated using DnaSP v6.12.03 [41]. The sequences were imported into the MEGA11 software and the nucleotide sequence and amino acid sequence comparison were performed using the MUSCLE algorithm to remove the unaligned sequences [42]. The Kimura 2-parameter (K2P) model was selected for constructing the 16S rRNA and gyrB gene phylogenetic trees, respectively, using the Neighbor-Joining (NJ) and Maximum Likelihood (ML) methods (Bootstrap replication set to 1000) [43].
Antibiotic selection and drug susceptibility test
A total of 13 antibiotics from 10 classes were selected for Kirby-Bauer disk diffusion susceptibility test: Penicillin G: amoxicillin (AMX, 30 ug); Chloramphenicol: florfenicol (FLR, 30 ug); Cephalosporin: cefradine (CED, 30 ug), cefuroxime (CXM, 30 ug), ceftazidime (CAZ, 30 ug), cefepime (FEP, 30 ug); Monocyclic β-lactams: aztreonam (ATM, 30 ug); Carbapenem: meropenem (MEM, 10 ug); Quinolone: ciprofloxacin (CIP, 5 ug); Sulfonamide: co-trimoxazole (SXT, 1.25/23.75 ug (Sulfamethoxazole (SMZ, 1.25 ug) / Trimethoprim (TMP, 23.75 ug)); Aminoglycoside: amikacin (AMK, 30 ug); Polypeptide: polymyxin B (POL, 300units); Tetracycline: tetracycline (TCY, 30 ug) [44, 45]. The above reagents are purchased from Hangzhou Microbial Regent Co., Ltd. (Hangwei, Hangzhou, China).
Quality control strains were selected from Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 27853. The 102 Aeromonas isolates were inoculated on BHI (TOPBIO, Yantai, China) agar overnight and single-colony was picked and resuspended in 0.9% saline. The concentration of Aeromonas was adjusted to 1.5 x 108 CFU/mL according to the McFarland scale, evenly coated on Muller Hinton Agar (Hopebio, Qingdao, China) and incubated at 28°C for 18–24 hours in an inverted position. The diameter of the inhibition zone was recorded using vernier calipers. Refer to CLSI M45-A guidelines to determine the drug-resistant phenotype of Aeromonas isolates (S1 Table) [32].
The diameter of the inhibition zone of different antibiotics was input into the resistance monitoring software WHONET 2020 to obtain the resistance phenotype of the isolates, the resistance rate of different antibiotics, the resistance spectrum (the kind of antibiotic the strain was simultaneously tolerant to), and the type of resistance spectrum (the different drug resistance profiles were named in a certain order) [46]. The diameter of inhibition zone was imported into BioNumerics software for cluster analysis, a cluster tree was drawn, and the species, host, sampling time, place, drug resistance spectrum, drug resistance profile type were added to this cluster tree.
Drug-resistance genotype and phenotype analysis
Genomic DNA was extracted from 102 Aeromonas isolates using EasyPure® Genomic DNA Kit (TransGen Biotech, Beijing, China) as template and stored in -20°C refrigerator. A variety of resistance gene primers were synthesized by reference: tetracycline resistance genes tetA, tetB, tetE; β-lactam resistance gene TEM; sulfonamide resistance genes sul3, sul2; chloramphenicol resistance genes catA7, floR; aminoglycoside resistance genes Ant(3”)-I; multi-drug transfer gene qacEΔ1, and quinolone resistance gene oqxB [47–51]. The PCR method was used to detect the carriage of various types of resistance genes under this resistance phenotype. Primer information is shown in S2 Table. The detection rate of drug resistant genes was counted, and the resistance phenotype and genotype match rate were calculated to speculate the reasons for the drug resistance of Aeromonas to different kinds of antibiotics.
Multilocus sequence typing
Multilocus sequence typing (MLST) was conducted on 47 strains, comprising 33 multidrug-resistant Aeromonas obtained from prior experiments and 14 reference/type Aeromonas retrieved from the PubMLST database. Genomic DNA from the 33 multidrug-resistant Aeromonas was extracted using the EasyPure® Genomic DNA Kit (TransGen Biotech, Beijing, China), stored at -20°C, and used as templates. The primers for the six housekeeping genes (gyrB, groL, gltA, metG, ppsA, and recA) employed in this study were sourced from a previous literature reference and synthesized by Limibio Biotechnology Co., Ltd. (Limibio, Hefei, China) [52]. Primer details are provided in S2 Table.
Amplification of target genes using polymerase chain reaction (PCR) was perforemd, using a 30 uL reaction mixture: 1.5 uL of genomic DNA template, 15 uL of MonAmpTM 2X Taq Mix (Monad, Wuhan, China), 1.5 uL each of forward and reverse primers, and 10.5 uL of ddH2O, resulting in a total volume of 30 uL. Amplification conditions: Initial denaturation at 94°C for 5 min, followed by 30 cycles of 94°C for 30 sec, 55–60°C (Depending on the melting temperature of different primers) for 30 sec and 72°C for 40 sec, and a final elongation at 72°C for 5 min. Following amplification, 5 uL of the PCR product from each sample was subjected to 1% agarose gel electrophoresis at 120V for 30 min. Samples exhibiting specific bands were documented using a gel imaging system (Bio-Rad, CA, USA) and then sent to Limibio Biotechnology Co., Ltd. (Limibio, Hefei, China) for sequencing.
The allele sequences were submitted to the PubMLST website, and the allele numbers were assigned based on the comparison results. The allelic profiles of the six housekeeping genes for each strain were used to generate a unique allelic profile, which was assigned a sequence type (ST) number and concatenated according to the order of the genes on the chromosome, gyrB (477 bp), groL (510 bp), gltA (495 bp), metG (504 bp), ppsA (537 bp), and recA (561 bp), respectively. The concatenated sequences of these six genes, with a total length of 3084 bp, were analyzed in this study.
The genetic relatedness among the subject strains was investigated using the eBURST online tool (http://eburst.mlst.net/default.asp) to determine the presence of clonal complexes [53]. The eBURST analysis was conducted using various parameters, such as matching of 3–5 allele numbers in the Aeromonas MLST locus, assignment of STs into the same group, and clustering of strains with single (or double) locus differences into clonal complexes (CCs). The genomic relationships were further visualized using GrapeTree (https://pubmlst.org/GrapeTree), and the MLST data of the 33 multidrug-resistant Aeromonas were imported into the software to construct a sequence-based minimal spanning tree [54]. In addition, the data uploaded from China and all data available on the PubMLST website were analyzed separately to investigate the distribution of prevalent Aeromonas clones in China and worldwide.
PhyML 3.0 was used for phylogenetic analysis based on the maximum likelihood method [55]. Sequence comparison was performed using the Clustal Omega online tool (https://www.ebi.ac.uk/Tools/msa/clustalo/) with output format in phylip, and the file was imported into the PhyML online website (http://www.atgc-montpellier.fr/phyml/) for phylogenetic tree construction. The optimal replacement model was determined using the SMS tool (Smart Model Selection) based on the Bayesian Information Criterion (BIC), with the GTR model selected. The standard Bootstrap validation method was chosen, with the value set to 1000. The resulting phylogenetic tree was visualized using iTOL v6 [56].
Phylogenetic analysis was conducted using MEGA11 software. The MUSCLE algorithm was selected for sequence alignment, removal of unaligned sequences, trimming of redundant bases and gaps, and construction of a phylogenetic tree of the six housekeeping genes and concatenated genes using the Neighbor-Joining (NJ) method. The Kimura 2-parameter (K2P) model was selected with a bootstrap value of 2000. Additionally, a separate phylogenetic tree was constructed for each housekeeping gene and compared with the concatenated gene phylogenetic tree. The sequences of the six housekeeping genes were compared using BLAST on NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi), and all the results were included in the phylogenetic tree as reference data. All 3084 bp concatenated sequences were translated into amino acid sequences using MEGA11 software, and the same method and parameters were used to construct the phylogenetic tree to compare the similarities and differences between the two.
Recombination analysis
The concatenated nucleotide sequences of all 47 strains in this experiment were analyzed for the presence of recombination events using seven algorithms (RDP, GENCONV, BootScan, MaxChi, Chimaera, SiScan, and 3Seq) in the RDP v5.5 software [57]. A recombination event was considered to have occurred if it was supported by more than four algorithms. The clonal relationships between isolates and possible recombination events were inferred using the maximum likelihood method with ClonalFrameML software, which detected the positions of recombination regions on each branch and constructed clonal relationship trees among the 47 strains in this study [58]. Use the R script cfml_results. R provided in ClonalFrameML (https://github.com/xavierdidelot/ClonalFrameML) to generate the graphical output of the analysis.
Statistical analyses
The experimental data was analyzed using SPSS 20 software. Statistical analysis was performed using the Pearson correlation method to test the correlation coefficient between drug-resistance genotype and phenotype. The statistical significance level is set at a two-tailed α-value of 0.05, therefore, p-value less than 0.05 is considered statistically significant Multidrug-resistant strains are resistant to three or more antibiotics [45, 59]. The calculation formula of Multi antibiotic resistant MAR index was calculated by using the follow formula: MAR = Number of antibiotics to which an isolate showed resistance / Total number of antibiotics used in the experiment [60].
Results
Identification of Aeromonas species using 16S rRNA and gyrB gene
In this study, we isolated total of 130 colonies from freshwater fish. Of the 130 samples, we sequenced 104 (80.00%) of the 16S rRNA genes and 76 (58.46%) of the gyrB genes. The sequences were compared by BLAST, and were considered reliable if the similarity of sequencing results for the16S rRNA and gyrB gene ≥ 98% and ≥ 97%, respectively. As a result of confirming the highest score as the final result, the similarity interval after BLAST comparison was 98.95%-100% for the 16S rRNA group and 97.54%-99.96% for the gyrB group. The identification results are as follows, for the 16S rRNA group 76/104 A. veronii (73.08%), 12/104 A. hydrophila (11.54%), 9/104 A. salmonicida (8.65%), 5/104 A. media (4.81%), 1/104 A. caviae (0.96%), and 1/104 A. rivipollensis (0.96%); for the gyrB group 66/76 A. veronii (86.84%), 4/76 A. hydrophila (5.26%), 3/76 A. salmonicida (3.95%), 2/76 A. media (2.63%), and 1/76 A. sobria (1.32%) (Table 1). The identification results of the two groups showed that A. veronii was more predominant and might be the dominant Aeromonas species in freshwater fish farms in Hebei. In the 16S rRNA group, A. veronii was detected in a lower proportion than in the gyrB group, and others were detected in a higher proportion and of more types in the 16S rRNA group. This is because many alignments in the 16S rRNA group showed 100% similarity and the same sequence score, 16S rRNA genes may be more conserved.
Sequence analysis and phylogenetic analysis based on 16S rRNA and gyrB gene
The matched sequences were imported into MEGA11 and DnaSP v6.12.03 to calculate a variety of sequence feature parameters to obtain sequence nucleotide diversity (Table 2). As shown in the Table 2, the number of the single polymorphic sites was significantly higher in gyrB group than the 16S rRNA group. The segregating sites in the gyrB group accounted for 27.29%, much higher than 2.10% in the 16S rRNA group, and the number of parsimony informative sites in the gyrB group was 12.61%, higher than 2.10% in the 16S rRNA group. The average G + C content of the gyrB group was 59.2%, which was clearly distinguishable from the 16S rRNA group of 55.8%.
Nucleotide diversity per site (π) and the average number of nucleotide differences per site (θ) were significantly higher in the gyrB group compared to the 16S rRNA group (Table 2). Where D value = 0, the actual variant type is similar to the predicted variant type; D value > 0, no or less rare alleles; D value < 0, more rare alleles. The 16S rRNA group with a D value of 1.31 > 0 undergoes equilibrium selection or population contraction and population variation decreases, while the gyrB group with a D value of -1.55 < 0 undergoes population expansion and population variation increases. The ratio of the non-synonymous substitution rate to the synonymous substitution rate (Ka/Ks) is used to determine whether the gene is under selection pressure.
In this experiment, Ka/Ks = 0.88 in the 16S rRNA group tends to be close to 1, and is under natural selection pressure, indicating that the 16S rRNA genes are quite stable in the evolutionary process. In contrast, Ka/Ks = 0.036 in the gyrB group was under purifying selection, indicating that the gyrB gene tended to specialize during evolution. Ka/Ks was consistent with the results of Tajima’s D test and the results of polymorphic loci and parsimony informative loci, all of which proved that the gyrB gene had higher diversity, and therefore the gyrB gene was more suitable for interspecific identification of Aeromonas spp.
We reconstructed phylogenetic trees using Neighbor-Joining (NJ) and Maximum Likelihood (ML) methods. As shown in Fig 1A and 1B, four A. veronii strains (21619, 21618–2, 2021wx5, and 21616) clustered with A. salmonicida reference strain in the 16S rRNA phylogenetic tree (Fig 1A and 1B). A. salmonicida strain 20813 clustered with A. veronii as a single clade. A. media and A. rivipollensis reference strains clustered together with the Aeromonas hydrophila branch. No significant associations were found between strains and hosts in either phylogeny, suggesting that Aeromonas can be commonly found in various freshwater fish species.
The phylogenetic tree generated by the MEGA11 software was modified using iTOL v6 software to add the corresponding sampling time and host information for each sequence, and to facilitate the distinction between the two genes, gyrB group strain name followed by the letter g and 16S rRNA group added the letter S. The lavender dots represent the bootstrap value size. (A) Neighbor-joining phylogenetic tree of 16S rRNA gene. (B) Maximum Likelihood phylogenetic tree of 16S rRNA gene. (C) Neighbor-joining phylogenetic tree of gyrB gene. (D) Maximum Likelihood phylogenetic tree of gyrB gene.
In the gyrB phylogeny, each strain was clustered into one group with its corresponding reference strain. Strain 21622 was identified and clustered with A. sobria, but was clustered with A. salmonicida in the 16S rRNA phylogenetic trees (Fig 1). Strains 2021WX8 and 2021WX3 were classified as A. salmonicida and A. hydrophila, respectively, in the 16S rRNA phylogenetic trees, but clustered with A. veronii in the gyrB phylogenetic trees. Strain 20901, 20709, and 20503 were classified as A. hydrophila in the 16S rRNA phylogenetic trees, but clustered with A. veronii in the gyrB phylogenetic trees. Interestingly, A. schubertii clustered with A. veronii in two different sets of trees. The reference strain of A. culicicola clusters with A. veronii, supporting two reference strains being considered synonymous.
Antibiotic susceptibility test and drug resistance profile
For phenotypic analysis of antibiotic susceptibility and drug resistance, we measured the zone inhibition using 13 common antibiotics on 102 fully cultured Aeromonas isolates according to the CLSI-2020 guidelines. The rate of drug resistance to amoxicillin was the highest (100%), followed by tetracycline (18.6%) and cefradine (13.7%), The proportion of moderate resistance to co-trimoxazole, meropenem and florfenicol was 4.9%, 8.8% and 9.8%, respectively. It is sensitive to cefuroxime, ceftazidime, cefepime, aztreonam, and polymyxin B (Table 3).
The analysis of the antimicrobial resistance spectrum of Aeromonas showed resistance to 11 of the 13 tested antibiotics, with ceftazidime and cefepime showing no resistance or moderate susceptibility. The resistance spectrum analysis of 102 Aeromonas isolates showed that they are resistant to at least one and up to seven antibiotics (Table 4). The range of multi antibiotic resistant (MAR) index was from 0.08 to 0.54. The two isolates with the highest MAR index are strain 201006 and 21502 which exhibited resistant to seven antibiotics. The MAR index of 18 strains was 0.08, indicating these strains were resistant to at least one type of antibiotic. 44 out of 102 Aeromonas isolates were resistant to at least three antibiotics, and the MAR index was higher than 0.23.
A total of 26 resistance spectrum (designated A-Z) were observed in the 102 Aeromonas isolates. The most prevalent resistance spectrum was Type B (CED/AMX) which accounted for 32.35% of the isolates, followed by type A (AMX), which accounted for 17.6%. All 102 Aeromonas isolates were resistant to amoxicillin, and 40/102 isolates (39.21%) were resistant to two antibiotics. Of 102 isolates, 44 isolates (43.14%) were phenotypically multidrug-resistant bacteria with resistant to three or more antibiotics: 24/102 (23.53%) for three antibiotics, 10/102 (9.80%) for four antibiotics, 5/102 (4.90%) for five antibiotics, 3/102 (2.94%) for six antibiotics, and 2/102 (1.96%) for seven antibiotics (Table 4). Eleven isolates (10.78%) were phenotypically resistant to the most common multidrug-resistant spectrums, type K (MEM/CED/AMX) spectrum, followed by five isolates (4.9%) each in the type G (TCY/CED/AMX) and M (MEM/TCY/CED/AMX) spectrums (Table 4).
Clustering tree analysis was performed on the antimicrobial resistance spectrum of each strain by integrating information on strain origin, bacterial species, antimicrobial resistance spectrum, and type of resistance spectrum. The clustering tree analysis divided into two main branches: the first branch, consisted of 23 resistance spectrums, and the second branch, consisted of 3 resistance spectrums (Fig 2). Branch I contained 82 isolates, which were subdivided into four sub-branches (A, B, C, and D). Ten of the 82 isolates belong to sub-branch A, with nine types of resistance spectrums: type V (FLR/ATM/TCY/CED/AMX), Z (FLR/MEM/CIP/SXT/TCY/CED/AMX), X (FLR/MEM/SXT/TCY/CED/AMX), P (FLR/TCY/CED/AMX), Q (FLR/SXT/CED/AMX), U (FLR/MEM/SXT/CED/AMX), R (FLR/MEM/CED/AMX), L (FLR/CED/AMX), and W (FLR/ATM/MEM/CED/AMX). The diversity of the resistance spectrum in this sub-branch A is relatively affluent, with type Q and type U clustering together and Type R and type L forming another cluster. Notably, Strain 21502 (Z) and Strain 21402 (V) cluster together due to their resistance spectrum containing four antibiotics (FLR/TCY/CED/AMX). Strain 21617 (W) is remarkable as it is the only isolate in sub-branch A simultaneously resistant to ampicillin and meropenem.
The cluster tree was generated by the BioNumerics software. Clustering tree analysis was performed on the antimicrobial resistance spectrums of each bacterial strain, and information on strain origin, bacterial species, antimicrobial resistance spectrums, and resistance spectrums (Type) were incorporated into the clustering tree.
Ten isolates belong to sub-branch B, which has four types of resistance spectrums, including CED/AMX antibiotics: type B (CED/AMX), H (POL/CED/AMX), K (MEM/CED/AMX), and T (MEM/SXT/POL/CED/AMX). Interestingly, Strain 20910 is phenotypically resistant to Type K spectrum, but differs from other type K strains belonging to sub-branch C in that due to moderately sensitive to meropenem and resistant to cefradine and amoxicillin.
In sub-branch C, 60 isolates are contained with 12 resistance spectrums: type M (MEM/TCY/CED/AMX), G (TCY/CED/AMX), F (MEM/AMX), K (MEM/CED/AMX), B (CED/AMX), J (SXT/TCY/AMX), C (TCY/AMX), N (MEM/SXT/TCY/AMX), I (SXT/CED/AMX), D (SXT/AMX), S (SXT/AMK/CXM/CED/AMX), and O (MEM/SXT/AMK/AMX). Sub-branch C is the most diverse regarding the presence of resistant spectrums compared to the other sub-branches. Strains with Type B, D, F, and G cluster together due to their identical resistance phenotypes, while strains with Type S, O, and N cluster separately because of their distinct resistance spectrums. Four strains share Type I but are separated into three clusters based on different resistance levels and sensitivity to the antibiotics in the resistance spectrum. Strain WX6 is distinguished from other Type M strains as it exhibits moderate sensitivity to tetracycline, whereas all other Type M strains are resistant. Strain 20801 and 21507 cluster together in Type K because they exhibit resistance to meropenem, while all other Type K strains show only moderate sensitivity.
Sub-branch D contained two isolates with two types of resistance spectrums: type X (FLR/MEM/SXT/TCY/CED/AMX) and Y (MEM/CIP/SXT/AMK/TCY/CED/AMX). The strain 20504 is resistant only to tetracycline and amoxicillin and moderately sensitive to other antibiotics, clearly distinguishing it from type K strains belonging to sub-branch A.
Branch II contained 20 isolates with three resistance spectrums: type A (AMX), type E (CIP/AMX), and H (POL/CED/AMX). Type A was clustered into for amoxicillin moderately sensitive strains and amoxicillin-resistant, respectively. Type E (CIP/AMX) and type H (POL/CED/AMX) contains two rare antibiotics, ciprofloxacin and polymyxin B, respectively.
Drug-resistant phenotypes of the recovered isolates
The relationship between drug resistance and species of isolates was analyzed by using the WHONET software. The drug-resistant phenotype of A. sobria was type X according to the spectrum; The drug-resistant phenotype of A. caviae was type A; A. salmonicida contains four drug-resistant phenotypes type A, B, G, and I; A. hydrophila contained four phenotypes, type A, B, I and M; A. veronii contains all spectrum type A-Z. It can be seen that there is a large difference in the number of samples. Except for type A and B, the frequency of other drug-resistant phenotypes has no obvious correlation with strains.
Drug-resistance genotype and phenotype analysis
We used 11 drug resistance genes in 7 classes to analyze the relationship between genotypic and phenotypic drug resistance profiles in 102 isolates. Out of 11, eight genes were successfully detected but three (sul3, catA7, and oqxB) were not detected. The detection rates of different drug resistance genes were different: TEM (100%), tetA (75%), floR (72.7%), sul2 (50%), tetE (40%), tetB (30%), qacE△1 (27.2%), and Ant(3”)-Ⅰ (22.7%) (Table 5).
TEM gene was detected in all 102 isolates, consistent with the previously speculated natural resistance of Aeromonas to amoxicillin. Of the 18 isolates showing a resistance spectrum of resistance to CO trimoxazole (SXT), the sul2 gene was detected in nine (201001, 20471, 21501, 21607, 201006, 20809, 21406, 21412, 21622) isolates, demonstrating a 50% concordance between drug-resistance phenotype and genotype. floR gene was amplified in eight of 11 isolates, representing a spectrum of resistance to florfenicol (FLR), resulting in 72.7% concordance between drug-resistance phenotype and genotype. In addition, among the 20 isolates with a tetracycline resistance spectrum, 75% (15/20), 30% (6/20), and 40% (8/20) of the tetA, tetB, and tetE genes were consistent between phenotypes and genotypes, respectively. Of the 20 isolates, only the WX6 strain was undetectable for any of the tetracycline resistance genes (S3 Table).
Ten of 44 multidrug-resistant strains were detected in Ant(3”)-Ⅰ (20471, 20503, 201002, 201006, 21406, 21410, 21502, 21607, 21614 and WX6). Among them, three isolates (201006, 21607 and 21614) were resistant to amikacin (AMK) in the drug sensitivity test, and the Ant(3”)-Ⅰ gene was detected in these isolates, demonstrating a 100% concordance between drug-resistance phenotype and genotype. The drug resistance phenotype and genotype were not completely consistent. The cluster analysis of the drug resistance profile showed that the phenotype of the isolates was not significantly correlated with factors such as the sampling year, location, host, species and other factors. This suggests that the environmental selection pressure may cause bacterial drug resistance to change over time.
Multilocus sequence typing of multidrug-resistant Aeromonas
To investigate the genetic correlations, six housekeeping genes (gyrB, groL, gltA, metG, ppsA, recA) were analyzed in a total 47 strains, containing 33 multidrug-resistant Aeromonas isolates and 14 reference strains. Among the 33 isolates, the six housekeeping genes and the concatenated genes showed a remarkable abundance of polymorphic and parsimony informative sites (Table 6). Polymorphic sites ranged from a minimum of 26.83% for the gyrB gene to a maximum of 35.75% for the ppsA gene, while parsimony informative sites varied from a minimum of 16.56% for the gyrB gene to a maximum of 25.70% for the ppsA gene. The G+C content showed slight variations among the housekeeping genes, ranging from 57.7% (metG) to 63.5% (ppsA). Nucleotide diversity (π) values spanned from 0.0608 (gyrB) to 0.0810 (ppsA). Mean nucleotide differences (θ) per site were distributed across a range of 0.0440 (gyrB) to 0.0766 (ppsA). The high values observed for both π and θ indicate a substantial level of polymorphism in all gene sequences in this study. The genetic equilibrium of the alleles was analyzed using Tajima’s D neutrality test, which showed that all D values were less than 0, indicating population expansion and increased population variability. The distribution of Ka/Ks values ranged from 0.0202 (gyrB) to 0.0704 (metG), and all seven gene sequences had much less than 1, indicating purifying selection as the selective pressure and demonstrating that the concatenated genes are suitable for population studies.
The ratio of synonymous substitutions to nonsynonymous substitutions was much higher, with the groL gene having the highest ratio of 19.75-fold and the recA gene having the lowest ratio of 4-fold, suggesting that the six selected housekeeping genes are also suitable for population studies. The concatenated genes exhibited an average of 937 polymorphic sites, and other diversity parameters consistently displayed high values, consistent with the results of the six housekeeping genes.
To obtain MLST typing data, MLST method was used to by processing concatenated sequences of 33 multidrug-resistant Aeromonas and submitting them to the PubMLST website. A total 47 strains, including 14 reference strains were integrated and analyzed (Table 7). A total of 43 sequence types (ST) were identified in 47 strains, of which 30 new STs (ST1024-ST1053) for the gyrB, gltA, groL, metG, ppsA, and recA genes were found in 33 strains. The six strains were classified into three STs, two each. 20712 and 20705 were assigned to ST1042, 20503 and 20901 to ST1030, and 21409 and 20801 to ST1026 (Table 7).
Homologous recombination was a significant complicating factor when analyzing phylogenetic relationships among closely related genotypes, exerting a substantial influence on sequence analysis. The number of locus variants (nLV) at each genetic locus was determined utilizing the eBURST algorithm(http://eburst.mlst.net), facilitating the establishment of connections between sequence types within the constructed minimum spanning tree (Fig 3). Considering single locus variants (SLV), only two strains, ST1027 and ST1047, formed a clonal complex, when single locus variants (SLV) were considered (Fig 3A). Considering double-locus variants (DLVs) and triple-locus variants (TLVs), the number increased to four and five clonal complexes, respectively (Fig 3B and 3C). These observations suggest that 33 multidrug-resistant Aeromonas are likely to have distant genetic relationships with no obvious clonal connections.
GrapeTree of the minimum spanning tree was based on n = 0, 1, 2, 3. The numbers accompanying the sequence types in the legend denote their respective frequencies, while the sizes of the circles represent their kurtosis values. Furthermore, the numbers present on the connecting lines signify the locus differences. (A) This shows the original minimum spanning tree, where n = 0 and none of the 30 STs are clustered together, and the least different types are ST1027 and ST1047, which differ by only one locus. (B) This shows the SLV (single locus variant) minimum spanning tree, where ST1027 and ST1047 form a clonal complex. (C) This shows the DLV (double locus variant) minimum spanning tree, where ST1053 and ST1042, ST1030 and ST1033, ST1037 and ST1039 merge. (D) This shows the TLV (triple locus variant) minimum spanning tree, where ST1044 clusters with ST1052, ST1028 clusters into ST1027, ST1043 clusters into ST1053, and ST1038 clusters into ST1029.
To further investigate the genetic diversity, we integrated data from all isolates in China available into the PubMLST database. We analyzed 524 isolates and found that only 36 clonal complexes were present. Interestingly, only one central sequence type (ST94) was identified in this dataset. This expanded dataset included eight central sequence types (ST94, ST267, ST377, ST609, ST745, ST750, ST751, and ST755), and we Performed eBURST analysis on all STs uploaded to the database. Consistent with our analysis of the 33 multidrug-resistant Aeromonas in this experiment, we found that the clonal groups with highly prevalent did not clustered among the Aeromonas strains in the database.
Gene phylogenetic analysis based on concatenated and housekeeping gene
To estimate the phylogenetic relationships among the isolates, the concatenated six housekeeping gene sequences derived form 47 strains was constructed phylogenetic trees using two methods (NJ and ML). A phylogenetic tree using PhyML with ML method comprises three primary branches (Branch I, II, and III). Two primary branches (Branch I and II) contain only DSM 4881 and NCIMB 882 strains, respectively. In comparison, Branch III clusters 33 multidrug-resistant Aeromonas with the reference strain CECT 4257T of A. veronii, strain CECT 4245T of A. sobria, and CECT 4199T of A. allosaccharophila (Fig 4).
The phylogenetic tree generated by the PhyML 3.0 sofware. According to the SMS tool, the best model for the phylogenetic tree is GTR+G+I with Proportion of invariable sites = 0.583; Gamma shape parameter = 0.666; Number of substitution rate categories = 4.
A phylogenetic tree with NJ was constructed by MEGA11 using the concatenated nucleotide sequences (3084 bp) and amino acid sequences of the six housekeeping genes from 47 strains. Additionally, each nucleotide sequence of six housekeeping genes from the 33 multidrug-resistant Aeromonas was compared using BLAST, and those with >98% similarity and the highest scores were considered for identification (Fig 5).
Based on concatenated gene sequences, the phylogenetic tree shows the relationships among 47 strains. (A) A neighbor-joining phylogenetic tree based on concatenated gene nucleotide sequences shows the presence or absence of housekeeping genes for each strain. It shows identification results when each housekeeping gene is used alone. (B) Neighbor joining phylogenetic tree based on concatenated gene amino acid sequences.
The phylogenetic tree based on the concatenated nucleotide sequences comprise three primary branches. Branch I consisted of only two reference strains, A. schubertii CECT 4240T and A. enteropelogenes CECT 4255T (Fig 5A). Branch II contained eight reference strains, each of which had different branches in the phylogenetic tree, making them easily distinguishable. Branch III comprised four reference strains, including A. allosaccharophila CECT 4199T, A. sobria CECT 4245T, A. jandaei CECT 4228T, A. veronii CECT 4257T, and all 33 isolates. Interestingly, six isolates, 20712 and 20705, 20503 and 20901, and 21409 and 20801, were clustered into one isolate each (Fig 5). These three groups were also assigned ST1042, ST1030, and ST1026, respectively, in the MLST analysis, with consistent results. Based on the multilocus sequence analysis, all 33 multidrug-resistant Aeromonas were identified as belonging to the A. veronii, which is inconsistent with the identification results based on the 16S rRNA gene. Based on the 16S rRNA gene, strains 20503 and 20901 were identified as A. hydrophila, and strain 20801 was identified as A. media, and strains 20905 and 21622 were identified as A. salmonicida.
In addition, the BLAST analysis of the six housekeeping genes displayed distinct patterns for each gene. For gyrB gene, seven of the eight strains were identified as A. sobria, unlike the other five genes, and strain 201006 was identified as Aeromonas sp. that could not be identified at the species level. The strains 20801 in the gltA group, 21409 in the metG group, and 21502 in the recA group were identified as A. hydrophila, and the strains 20910 and 21510 in the recA group were identified as A. sobria. The phylogenetic tree based on amino acid sequences shows branching patterns similar to the concatenated nucleotide sequences (Fig 5B). A. allosaccharophila CECT 4199T and A. veronii CECT 4257T are clustered together with the 33 multidrug-resistant Aeromonas in Branch III. However, CECT 4257T is positioned in the middle and is closest to strain 21607. A. sobria CECT 4245T is separated from its original branch and clustered with six reference strains, including A. molluscorum CECT 5864T. A. schubertii CECT 4240T and A. trota CECT 4255T are separately classified into Branch I and II. Due to the relatively conserved nature of amino acid sequences, 14 strains of A. veronii, including WX6, are clustered into one branch, suggesting that nucleotide-based phylogenetic analysis may be more discriminating for sequences with high similarity or short length.
The gltA gene-based phylogenetic tree shows a topology similar to the concatenated sequence-based phylogenetic tree (Fig 6A). Strain CECT 4199T (A. allosaccharophila) clusters uniquely with A. veronii isolates in this study and reference strain CECT 4257T. In contrast, Branch I consists solely of strain NCIMB 882 (A. caviae), indicating distinct gltA gene characteristics for this bacterium. The recA gene-based phylogenetic tree presents a similar pattern, with strain 20910 and 21502 clustering with CECT 4228T (A. jandaei), consistent with strain 21502 matching as A. jandaei in Fig 6B. However, identification of strain 20910 as A. hydrophila in the BLAST result could be attributed to the similarity of the top three BLAST match scores (Fig 6B). These findings demonstrate the lower discriminatory power of BLAST comparisons compared to phylogenetic analyses and the lower discriminatory power of single-locus analyses compared to concatenated genes. The phylogenetic trees based on the single groL and gyrB genes are generally consistent with the concatenated gene phylogenetic tree, with some differences in reference strains (Fig 6C, 6D). For example, in the gyrB-based tree, strain CECT 4240T belongs to Branch I, whereas in the groL-based tree, it belongs to Branch II. Branch III topologies in both trees remain essentially consistent, indicating similar groL and gyrB gene sequences for strains CECT 4255T, CECT 4245T, CECT 4228T, and CECT 4199T, suggesting relative stability in these two genes during evolution. However, as shown in Fig 6A, we suggest that the phylogenetic analysis provides greater discriminatory power than gyrB gene-based BLAST comparisons. In the phylogenetic tree based on the metG gene, A. jandaei was included in Branch III, which includes 33 multidrug-resistant Aeromonas, in contrast to the concatenated tree. Unlike the groL based-gene tree, strain CECT 4240T occupies a distinctive position in the phylogenetic tree (Fig 6E). The ppsA tree displays a unique topology, with strain CECT 4257T clustered on in Branch II with A. trota, and A. allosaccharophila forming a separate branch with the tested strains (Fig 6F). Given the diverse topologies observed in the phylogenetic trees constructed based on individual loci, it is suggested that single housekeeping genes may have a limited impact on Aeromonas classification, with changes in concatenated genes or the entire genome serving as the primary driving factor.
Each phylogenetic tree represents a phylogenetic tree based on a single locus gene. (A) Neighbor joining phylogenetic tree based on the single locus of gltA. (B) Neighbor joining phylogenetic tree based on the single locus of recA. (C) Neighbor joining phylogenetic tree based on the single locus of groL. (D) Neighbor joining phylogenetic tree based on the single locus of gyrB. (E) Neighbor joining phylogenetic tree based on the single locus of metG. (F) Neighbor joining phylogenetic tree based on the single locus of ppsA.
Recombination analysis
To explore recombination events in the concatenated sequences of six housekeeping genes of 47 strains, we analyzed recombination events using RDP v5.5 software with seven algorithms (RDP, GENCONV, BootScan, MaxChi, Chimaera, SiScan, and 3Seq). A total of 42 unique recombination events were detected, and 22 events were verified by three or more algorithms (S4 Table).
In addition, the maximum likelihood method of the ClonalFrameML software was used to simultaneously detect the positions of recombination regions in the isolates and clones and to reconstruct phylogenetic trees. Only ten of the 47 strains (20503, 20705, 20712, 20801, 20901, 21409, 21410, 21614, 2010021 and 201010) showed no recombination events in the concatenated genes. 49 recombination events were observed out of the 37 Aeromonas expected to have recombination events (Fig 7).
Grey areas represent non-core regions of the 47 strains concatenated gene sequences. The concatenated gene sequences of the 47 strains were analyzed using ClonalFrameML software to detect recombination events. Recombination events were denoted by dark blue horizontal lines. Light blue positions on each branch indicated the absence of substitutions. Vertical lines of various colors from white to red indicated substitutions, where white indicated non-homoplastic substitutions and increasing shades of red indicated an increase in the degree of homoplastic substitutions.
The average recombinant fragment length (δ) among all recombination events was 621.89. The ratio of recombination rate to mutation rate (R/θ) was 5.44, indicating that the difference in the probability of recombination and mutation occurring in the concatenated sequences of six housekeeping genes was insignificant. The mean divergence of imported DNA (nu) was 0.025, and the effect of recombination on mutation (r/m) was 85.69, indicating that the number of substitutions due to recombination was 85 times higher than that of mutations. Therefore, although the probability of recombination and mutation occurring in the concatenated sequences of six housekeeping genes of 47 strains was similar, this suggests that recombination played a more important role in the phylogenetic process than mutation.
Discussion
This study identified 130 Aeromonas isolates collected from freshwater fish in Hebei Province, China, and confirmed the difficulty of accurate classification using existing 16S rRNA and gyrB gene. Sequence analysis revealed that the genetic diversity of the 16S rRNA gene was significantly lower than that of the gyrB gene, as evidenced by the Tajima’s D value (D value: -1.55 < 0, gyrB), indicating increased population differentiation within the gyrB group. Furthermore, the ratio of non-synonymous to synonymous substitution rates (Ka/Ks) suggested that the variability of the 16S rRNA gene was lower compared to the gyrB gene [61]. This indicates that the gyrB gene, which encodes a conserved protein of the DNA gyrase B subunit, is relatively more prone to mutations and has higher genetic diversity than the 16S rRNA gene. Martinez et al. (2011) reported showed that the gyrB sequence had about six times higher the average substitution rate of the 16S rRNA in the phylogenetic analysis of Aeromonas [62]. The results of our study are consistent with previous studies, showing that gyrB is more effective in distinguishing at the molecular biological level in Aeromonas spp. than 16s rRNA [27]. Therefore, it was anticipated that the identification results using the gyrB gene would exhibit a significant increase in detection rate and accuracy compared to the 16S rRNA gene, but this expectation was not matched. This discrepancy may be attributed to factors such as the high sequence similarity between the two genes within the Aeromonas genus or intragenomic heterogeneity [63, 64]. In other words, the gyrB gene may be more suitable for the identification of Aeromonas spp. than the 16S rRNA gene, but there are limitations to the method of using a single gene for a more accurate classification of Aeromonas.
In general, Maximum Likelihood method perform better than Neighbor-Joining method when utilized by appropriate nucleotide substitution models [65]. However, the actual construction shows that the difference between the tree construction based on the two different algorithms is relatively tiny for single gene. In the phylogenetic trees constructed for each of the two genes, 16S rRNA gene sequences are too similar due to their conserved characteristics to allow accurate classification of branches, whereas the phylogenetic tree of the gyrB gene is more precise and represents a relatively accurate branching situation. Therefore, we showed that identification of Aeromonas using the gyrB gene is more reliable.
Multilocus sequence typing (MLST) analysis using six housekeeping genes provides a deeper understanding of Aeromonas populations and clonal complexes, improving their host preferences, habitats, and taxonomic limits [31, 66]. We identified 44 multidrug-resistant Aeromonas isolates from the 102 Aeromonas isolates and subsequently performed MLST analysis on 33 of 44 multidrug-resistant Aeromonas. As a result, we discovered a wealth of 30 new high abundance (ST1024-ST1053) sequence types (STs) among the 33 multidrug-resistant Aeromonas, which we have uploaded to the PubMLST website. eBURST analysis revealed the formation of one clonal complex (ST1027, ST1047), and the observed high nucleotide diversity suggested that most strains possessed unique sequence types, indicating a high level of genetic diversity. It is important to note that although MLST can provide valuable insights into the clonal relationships among bacterial strains, it may only capture part of the complexity of their evolutionary history [13, 67]. Finally, recombination event analysis using RDP5 and ClonalFrameML software detected 42 and 49 recombination events, respectively, with 22 results validated by four or more algorithms. Both software applications detected substantial recombination events in the nucleotide sequences of the 47 strains, with an average recombination on mutation (r/m) value of 85.689, underscoring the significant role of recombination events in the evolution of strains in this experiment. This indicates that the discriminatory power of a single housekeeping gene sequence alignment is insufficient, and that clone diversity increases over time due to mutation and recombination events, which can lead to incorrect typing results when only a single gene locus sequence is compared. Therefore, the use of multiple concatenated gene loci is of great importance and has higher discriminatory power.
The 102 Aeromonas isolates identified in this study were resistant to amoxicillin. They exhibited varying levels of resistance to several antibiotics used in the experiments, including tetracycline, cefradine, meropenem, florfenicol, and co-trimoxazole. The presence of 44 multidrug-resistant Aeromonas, representing for 43.14% of the 102 Aeromonas isolates, provides theoretical evidence for the likelihood of resistance acquisition due to irrational antibiotic use on farms rather than environmental pressures or natural selection factors [62, 68]. Analysis of the correlation between drug-resistance genotypes and phenotypes revealed that strains with specific resistance genotypes do not necessarily have resistance to all antibiotics within their respective classes. In addition, due to the limited selection of resistance genes, it is challenging to accurately match strains with specific resistance phenotypes to their corresponding genotypes. Furthermore, we also investigated the possibility that other factors, such as the sampling time, sampling location, and Aeromonas species could influence the resistance spectrums, but found no significant differences between the 102 Aeromonas isolates under different conditions. Huys et al. showed that the resistance of Aeromonas isolates seems to be species-independent, which is consistent with the results of this experiment [69].
In conclusion, further investigation of resistance factors at the species level, including strains, habitats, and temperature, is required to reveal the drug resistance and infection mechanisms of Aeromonas [70]. A deeper mechanism study requires an accurate and efficient classification method for multidrug-resistant Aeromonas species. Therefore, we wanted to confirm that the MLST method (multiple concatenated gene loci) was more helpful for accurate classification than the previously used single gene (16S rRNA, gyrB gene) method. Therefore, multidrug-resistant Aeromonas obtained from fish living in Hebei Province, China, were classified using two methods. The MLST method is suitable to contribute to the classification of multidrug-resistant Aeromonas species and provides theoretical information for multidrug-resistant Aeromonas infection prevention and antibiotic selection in the aquaculture industry in Hebei Province. The findings of this study will serve as a foundation for genetic research on the clear infection mechanism and antibiotic resistance mechanism of Aeromonas according to environmental conditions.
Supporting information
S2 Table. Primer information for drug resistance genes.
https://doi.org/10.1371/journal.pone.0298745.s002
(XLSX)
S3 Table. Detection rate of tetracycline antibiotic resistance genes.
https://doi.org/10.1371/journal.pone.0298745.s003
(XLSX)
S4 Table. Recombination events detected by RDP5.
https://doi.org/10.1371/journal.pone.0298745.s004
(XLSX)
Acknowledgments
The authors gratefully acknowledge the Animal Ethics Committee of Hebei Normal University (Protocol Number: 198012) and Center for Bio-Medical Engineering Core Facility at Dankook University.
References
- 1. Didugu H, Thirtham M, Nelapati K, Reddy KK, Kumbhar BS, Poluru A, et al. A study on the prevalence of Aeromonas spp. and its enterotoxin genes in samples of well water, tap water, and bottled water. 2015;8(10):1237.
- 2. Carnahan A, Joseph SJE. Aeromonas update: new species and global distribution. 1991;47(5):402–3. pmid:2044682
- 3. Tomás JJISRN. The main Aeromonas pathogenic factors. 2012;2012.
- 4. Rasmussen-Ivey CR, Figueras MJ, McGarey D, Liles MRJFim. Virulence factors of Aeromonas hydrophila: in the wake of reclassification. 2016:1337.
- 5. Altwegg M, Geiss HK, Freij BJJCCRiM. Aeromonas as a human pathogen. 1989;16(4):253–86.
- 6. Igbinosa IH, Igumbor EU, Aghdasi F, Tom M, Okoh AIJTSWJ. Emerging Aeromonas species infections and their significance in public health. 2012;2012.
- 7. Chen J, Liu N, Zhang H, Zhao Y, Cao XJD, Immunology C. The effects of Aeromonas hydrophila infection on oxidative stress, nonspecific immunity, autophagy, and apoptosis in the common carp. 2020;105:103587. pmid:31875516
- 8. Harikrishnan R, Balasundaram CJRiFS. Modern trends in Aeromonas hydrophila disease management with fish. 2005;13(4):281–320.
- 9. Dubey S, Ager-Wick E, Kumar J, Karunasagar I, Karunasagar I, Peng B, et al. Aeromonas species isolated from aquatic organisms, insects, chicken, and humans in India show similar antimicrobial resistance profiles. 2022;13:1008870. pmid:36532495
- 10. Dien LT, Ngo TPH, Nguyen TV, Kayansamruaj P, Salin KR, Mohan CV, et al. Non‐antibiotic approaches to combat motile Aeromonas infections in aquaculture: Current state of knowledge and future perspectives. 2023;15(1):333–66.
- 11. Beaz‐Hidalgo R, Figueras MJJofd. A eromonas spp. whole genomes and virulence factors implicated in fish disease. 2013;36(4):371–88.
- 12. Cai S-H, Wu Z-H, Jian J-C, Lu Y-S, Tang J-FJBJoM. Characterization of pathogenic Aeromonas veronii bv. veronii associated with ulcerative syndrome from Chinese longsnout catfish (Leiocassis longirostris Günther). 2012;43:382–8.
- 13. Zhu Z, Jiang X, Deng S, Wang B, Li H, Xu TJAS, et al. Isolation, identification and in vitro antimicrobial susceptibility of pathogenic aeromonas veronii from soft-shelled turtles. 2016;17(4):804.
- 14. Janda JM, Duffey PSJRoid. Mesophilic aeromonads in human disease: current taxonomy, laboratory identification, and infectious disease spectrum. 1988;10(5):980–97. pmid:3055195
- 15. Algammal A, Hetta HF, Mabrok M, Behzadi PJFiM. Emerging multidrug-resistant bacterial pathogens “superbugs”: a rising public health threat. 2023;14:1135614.
- 16. Shafiq M, Zeng M, Permana B, Bilal H, Huang J, Yao F, et al. Coexistence of bla NDM–5 and tet (X4) in international high-risk Escherichia coli clone ST648 of human origin in China. 2022;13:1031688. pmid:36439817
- 17. Elbehiry A, Marzouk E, Aldubaib M, Moussa I, Abalkhail A, Ibrahem M, et al. Pseudomonas species prevalence, protein analysis, and antibiotic resistance: An evolving public health challenge. 2022;12(1):1–14. pmid:35532863
- 18. Algammal AM, Mohamed MF, Tawfiek BA, Hozzein WN, El Kazzaz WM, Mabrok MJP. Molecular typing, antibiogram and PCR-RFLP based detection of Aeromonas hydrophila complex isolated from Oreochromis niloticus. 2020;9(3):238. pmid:32235800
- 19. Algammal AM, Abo Hashem ME, Alfifi KJ, Al-Otaibi AS, Alatawy M, ElTarabili RM, et al. Sequence analysis, antibiogram profile, virulence and antibiotic resistance genes of XDR and MDR Gallibacterium anatis isolated from layer chickens in Egypt. 2022:4321–34. pmid:35971557
- 20. Algammal AM, Wahdan A, Elhaig MMJMp. Potential efficiency of conventional and advanced approaches used to detect Mycobacterium bovis in cattle. 2019;134:103574. pmid:31170450
- 21. Algammal AM, Ibrahim RA, Alfifi KJ, Ghabban H, Alghamdi S, Kabrah A, et al. A first report of molecular typing, virulence traits, and phenotypic and genotypic resistance patterns of newly emerging XDR and MDR Aeromonas veronii in Mugil seheli. 2022;11(11):1262. pmid:36365013
- 22. Janda JM, Abbott SLJCID. Evolving concepts regarding the genus Aeromonas: an expanding panorama of species, disease presentations, and unanswered questions. 1998;27(2):332–44. pmid:9709884
- 23. Carnahan A, Chakraborty T, Fanning G, Verma D, Ali A, Janda J, et al. Aeromonas trota sp. nov., an ampicillin-susceptible species isolated from clinical specimens. 1991;29(6):1206–10.
- 24. Overman TL, Janda JMJJocm. Antimicrobial susceptibility patterns of Aeromonas jandaei, A. schubertii, A. trota, and A. veronii biotype veronii. 1999;37(3):706–8.
- 25. Ndi O, Barton MJJoFD. Incidence of class 1 integron and other antibiotic resistance determinants in Aeromonas spp. from rainbow trout farms in Australia. 2011;34(8):589–99.
- 26. Martinez-Murcia AJJIJoS, Microbiology E. Phylogenetic positions of Aeromonas encheleia, Aeromonas popoffii, Aeromonas DNA hybridization group 11 and Aeromonas group 501. 1999;49(4):1403–8.
- 27. Yamamoto S, Harayama SJA, microbiology e. PCR amplification and direct sequencing of gyrB genes with universal primers and their application to the detection and taxonomic analysis of Pseudomonas putida strains. 1995;61(3):1104–9.
- 28. Maiden MC, Bygraves JA, Feil E, Morelli G, Russell JE, Urwin R, et al. Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms. 1998;95(6):3140–5.
- 29. Urwin R, Maiden MCJTim. Multi-locus sequence typing: a tool for global epidemiology. 2003;11(10):479–87. pmid:14557031
- 30. Maiden MCJARM. Multilocus sequence typing of bacteria. 2006;60:561–88.
- 31. Litvintseva AP, Thakur R, Vilgalys R, Mitchell TGJG. Multilocus sequence typing reveals three genetic subpopulations of Cryptococcus neoformans var. grubii (serotype A), including a unique population in Botswana. 2006;172(4):2223–38.
- 32. Weinstein MP, Lewis JSJJocm. The clinical and laboratory standards institute subcommittee on antimicrobial susceptibility testing: background, organization, functions, and processes. 2020;58(3): 01864–19. pmid:31915289
- 33. Rennie R, Turnbull L, Brosnikoff C, Cloke JJJocm. First comprehensive evaluation of the MIC evaluator device compared to Etest and CLSI reference dilution methods for antimicrobial susceptibility testing of clinical strains of anaerobes and other fastidious bacterial species. 2012;50(4):1153–7.
- 34. Saraceni PR, Romero A, Figueras A, Novoa BJFim. Establishment of infection models in zebrafish larvae (Danio rerio) to study the pathogenesis of Aeromonas hydrophila. 2016;7:1219.
- 35. Kooli WM, Junier T, Shakya M, Monachon M, Davenport KW, Vaideeswaran K, et al. Remedial treatment of corroded iron objects by environmental Aeromonas isolates. 2019;85(3):e02042–18. pmid:30478230
- 36. Ip M, Liyanapathirana V, Ang I, Fung KS, Ng T, Zhou H, et al. Direct detection and prediction of all pneumococcal serogroups by target enrichment-based next-generation sequencing. 2014;52(12):4244–52. pmid:25274995
- 37. Frank JA, Reich CI, Sharma S, Weisbaum JS, Wilson BA, Olsen GJJA, et al. Critical evaluation of two primers commonly used for amplification of bacterial 16S rRNA genes. 2008;74(8):2461–70. pmid:18296538
- 38. Martino ME, Fasolato L, Montemurro F, Rosteghin M, Manfrin A, Patarnello T, et al. Determination of microbial diversity of Aeromonas strains on the basis of multilocus sequence typing, phenotype, and presence of putative virulence genes. 2011;77(14):4986–5000. pmid:21642403
- 39. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJJJomb. Basic local alignment search tool. 1990;215(3):403–10.
- 40. Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, He Y, et al. TBtools: an integrative toolkit developed for interactive analyses of big biological data. 2020;13(8):1194–202. pmid:32585190
- 41. Librado P, Rozas JJB. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. 2009;25(11):1451–2. pmid:19346325
- 42. Tamura K, Stecher G, Kumar SJMb, evolution. MEGA11: molecular evolutionary genetics analysis version 11. 2021;38(7):3022–7.
- 43. Kimura MJJome. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. 1980;16:111–20. pmid:7463489
- 44. de Kaspar HM, Koss MJ, He L, Blumenkranz MS, Ta CNJAjoo. Antibiotic susceptibility of preoperative normal conjunctival bacteria. 2005;139(4):730–3. pmid:15808182
- 45. Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas M, Giske C, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. 2012;18(3):268–81. pmid:21793988
- 46. O’Brien TF, Stelling JMJDm, disease i. WHONET: removing obstacles to the full use of information about antimicrobial resistance. 1996;25(4):163–8.
- 47. Nawaz M, Sung K, Khan SA, Khan AA, Steele RJA, microbiology e. Biochemical and molecular characterization of tetracycline-resistant Aeromonas veronii isolates from catfish. 2006;72(10):6461–6.
- 48. Chen X, Liu Y, Li W, Liu J, Li JJCJoZ. Characterization phenotypes and mechanisms of phenicols resistance among Enterococcus species of swine origin from Shandong Province. 2013;29(10):959–64.
- 49. Wang R, Geng Y, Wang J, Feng J, Li GJMES. Antibiotic resistant genes in aquacultural bacteria. 2012;31(3):323–8.
- 50. Weng X, MI Z, Jin HJCJoCID. Acquired resistance-related genes and index cluster analysis in multidrug-resistant Escherichia coli. 2011:154–8,81.
- 51. Albornoz E, Lucero C, Romero G, Quiroga MP, Rapoport M, Guerriero L, et al. Prevalence of plasmid-mediated quinolone resistance genes in clinical enterobacteria from Argentina. 2017;23(2):177–87. pmid:27728774
- 52. Shuang M, Wang YL, LIU CG, Jing Y, Min Y, Bai XN, et al. Genetic diversity, antimicrobial resistance, and virulence genes of Aeromonas isolates from clinical patients, tap water systems, and food. 2020;33(6):385–95. pmid:32641201
- 53. Feil EJ, Li BC, Aanensen DM, Hanage WP, Spratt BGJJob. eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data. 2004;186(5):1518–30. pmid:14973027
- 54. Zhou Z, Alikhan N-F, Sergeant MJ, Luhmann N, Vaz C, Francisco AP, et al. GrapeTree: visualization of core genomic relationships among 100,000 bacterial pathogens. 2018;28(9):1395–404.
- 55. Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel OJSb. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. 2010;59(3):307–21. pmid:20525638
- 56. Letunic I, Bork PJB. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. 2007;23(1):127–8. pmid:17050570
- 57. Martin DP, Murrell B, Golden M, Khoosal A, Muhire BJVe. RDP4: Detection and analysis of recombination patterns in virus genomes. 2015;1(1):vev003.
- 58. Didelot X, Wilson DJJPcb. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. 2015;11(2):e1004041. pmid:25675341
- 59. Behzadi P, Ranjbar RJAmeiH. DNA microarray technology and bioinformatic web services. 2019;66(1):19–30.
- 60. Krumperman PHJA, microbiology e. Multiple antibiotic resistance indexing of Escherichia coli to identify high-risk sources of fecal contamination of foods. 1983;46(1):165–70.
- 61. Hurst LDJTiG. The Ka/Ks ratio: diagnosing the form of sequence evolution. 2002;18(9):486–7.
- 62. Martinez-Murcia AJ, Monera A, Saavedra MJ, Oncina R, Lopez-Alvarez M, Lara E, et al. Multilocus phylogenetic analysis of the genus Aeromonas. 2011;34(3):189–99. pmid:21353754
- 63. Küpfer M, Kuhnert P, Korczak BM, Peduzzi R, Demarta AJIjos, microbiology e. Genetic relationships of Aeromonas strains inferred from 16S rRNA, gyrB and rpoB gene sequences. 2006;56(12):2743–51.
- 64. Morandi A, Zhaxybayeva O, Gogarten JP, Graf JJJob. Evolutionary and diagnostic implications of intragenomic heterogeneity in the 16S rRNA gene in Aeromonas strains. 2005;187(18):6561–4. pmid:16159790
- 65. Yoshida R, Nei MJMb, evolution. Efficiencies of the NJp, maximum likelihood, and Bayesian methods of phylogenetic construction for compositional and noncompositional genes. 2016;33(6):1618–24.
- 66. Platt S, Pichon B, George R, Green JJCm, infection. A bioinformatics pipeline for high-throughput microbial multilocus sequence typing (MLST) analyses. 2006;12(11):1144–6.
- 67. Monette S, Dallaire A, Mingelbier M, Groman D, Uhland C, Richard J-P, et al. Massive mortality of common carp (Cyprinus carpio carpio) in the St. Lawrence River in 2001: diagnostic investigation and experimental induction of lymphocytic encephalitis. 2006;43(3):302–10.
- 68. Bricio-Moreno L, Sheridan VH, Goodhead I, Armstrong S, Wong JK, Waters EM, et al. Evolutionary trade-offs associated with loss of PmrB function in host-adapted Pseudomonas aeruginosa. 2018;9(1):2635. pmid:29980663
- 69. Huys G, Cnockaert M, Swings JJS, microbiology a. Aeromonas culicicola Pidiyar et al. 2002 is a later subjective synonym of Aeromonas veronii Hickman-Brenner et al. 1987. 2005;28(7):604–9.
- 70.
Janda JM. Aeromonas and Plesiomonas. Molecular medical microbiology: Elsevier; 2002. p. 1237–70.