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Article

Whole Genome Sequencing Based Taxonomic Classification, and Comparative Genomic Analysis of Potentially Human Pathogenic Enterobacter spp. Isolated from Chlorinated Wastewater in the North West Province, South Africa

by
Tawanda E. Maguvu
* and
Cornelius C. Bezuidenhout
Unit for Environmental Sciences and Management-Microbiology, North-West University, Private Bag X6001, Potchefstroom 2520, South Africa
*
Author to whom correspondence should be addressed.
Microorganisms 2021, 9(9), 1928; https://doi.org/10.3390/microorganisms9091928
Submission received: 17 August 2021 / Revised: 4 September 2021 / Accepted: 5 September 2021 / Published: 10 September 2021
(This article belongs to the Section Systems Microbiology)
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Abstract

:
Comparative genomics, in particular, pan-genome analysis, provides an in-depth understanding of the genetic variability and dynamics of a bacterial species. Coupled with whole-genome-based taxonomic analysis, these approaches can help to provide comprehensive, detailed insights into a bacterial species. Here, we report whole-genome-based taxonomic classification and comparative genomic analysis of potential human pathogenic Enterobacter hormaechei subsp. hoffmannii isolated from chlorinated wastewater. Genome Blast Distance Phylogeny (GBDP), digital DNA-DNA hybridization (dDDH), and average nucleotide identity (ANI) confirmed the identity of the isolates. The algorithm PathogenFinder predicted the isolates to be human pathogens with a probability of greater than 0.78. The potential pathogenic nature of the isolates was supported by the presence of biosynthetic gene clusters (BGCs), aerobactin, and aryl polyenes (APEs), which are known to be associated with pathogenic/virulent strains. Moreover, analysis of the genome sequences of the isolates reflected the presence of an arsenal of virulence factors and antibiotic resistance genes that augment the predictions of the algorithm PathogenFinder. The study comprehensively elucidated the genomic features of pathogenic Enterobacter isolates from wastewaters, highlighting the role of wastewaters in the dissemination of pathogenic microbes, and the need for monitoring the effectiveness of the wastewater treatment process.

1. Introduction

The genus Enterobacter comprises common human pathogens, with the Enterobacter cloacae complex (ECC) species (Enterobacter cloacae subsp. cloacae, Enterobacter asburiae, Enterobacter kobei, Enterobacter hormaechei, Enterobacter ludwigii among others) being notoriously found in hospitals, causing a wide range of infections such as lower respiratory tract infections, urinary tract infections, bacteremia, and meningitis [1,2,3]. The clinical significance of the ECC strains is aided by the fact that most of the strains carry multiple antibiotic resistance genes [4,5]. Although the ECC strains are of clinical significance, global surveillance and characterization of the virulence factors associated with these species are still limited [6].
Precise species and subspecies assignation of bacterial isolates lays a foundation for understanding the epidemiology, pathogenesis, and microbiological features of bacteria, allowing for better global surveillance and comparative genomics. Taxonomic classification of the genus Enterobacter is complicated; recently, Wu et al. (2020) updated the taxonomy of Enterobacter species and subspecies based on whole-genome taxonomic analysis [7]. The complexity resulted from the fact that classification in the genus Enterobacter had been defined based on low-resolution analytical methods, and is therefore in need of careful re-examination [8,9,10]. As shown with the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), proper identification of strains and variants is critical, as they have important implications for diagnosis, treatment, prognosis, and prevention. Whole-genome sequences have greatly improved the identification of species, as they elucidate functional profiles of taxonomic groups and easily resolve ambiguities in the phylogeny of higher taxa, which would have been difficult through traditional approaches [11,12].
In the current study, we present whole-genome-based taxonomic classification and comparative genomic analysis of potential human pathogenic E. hormaechei subsp. hoffmannii (designated as isolates S4 and S5) isolated from chlorinated wastewater. This study aids with the global baseline surveillance of ESKAPE pathogens and provides useful resources (genome sequences), which further helps understanding of the global dynamics of an important bacterial species, Enterobacter. More importantly, the taxonomic classification analysis from this study supports a recent report by Wu et al. (2020) that E. cloacae subsp. dissolvens and E. hormaechei subsp. hoffmannii are species rather than subspecies; Enterobacter xiangfangensis, E. hormaechei subsp. oharae, and E. hormaechei subsp. steigerwaltii are the same species. Thus, it is important to apply whole-genome-based taxonomic classification, particularly when dealing with isolates of clinical relevance, since their proper identification is critical for proper diagnosis, treatment, prognosis, and prevention.

2. Materials and Methods

2.1. Sample Isolation, Whole-Genome Sequencing, and Analysis of Genomic Features

Samples were isolated from tertiary wastewater effluent, collected from a wastewater treatment plant that uses (5 mg/L) chlorine for treatment in the North West Province, South Africa, as part of our laboratory routine ESKAPE pathogens monitoring program. An API 20E micro-organism identification kit (bioMerieux, Midrand, South Africa), and whole-genome sequencing were used to ascertain the identity of the isolates. Bacterial DNA was extracted from overnight broth cultures using a Nucleospin® Tissue extraction kit (Macherey-Nagel, Düren, Germany) following the manufacturer’s protocol. Agarose gel electrophoresis and NanoDrop spectrophotometry (ND-100, NanoDrop Technologies Inc, Wilmington, DE, USA) were used to determine the integrity and the purity of the resultant DNA, respectively. Paired-end libraries were prepared from 1 ng bacterial DNA using a Nextera XT DNA Sample Preparation Kit and a Nextera XT Index kit (Illumina Inc., San Diego, California, USA) following the manufacturer’s protocol. Sequencing was performed using a MiSeq 2000 Illumina platform (250 bp paired-end reads). Raw sequence reads were quality filtered using FastQC; trimming was performed using Trimmomatic [13]. De novo assembling of the quality reads was performed using SPAdes version 3.13.0 [14]. CheckM [15] was used to assess the quality of the SPAdes assembled genomes. SPAdes assembled genomes were annotated by RAST [16] with default settings.

2.2. Whole Genome-Based Taxonomic Analysis

For whole-genome-based taxonomic analysis, the SPAdes-assembled genomes were uploaded to the Type Strain Genome Server (TYGS) [17]. Pairwise comparison of the user-uploaded genomes (isolates S4 and S5) and the phylogenetically related type strains were performed using Genome Blast Distance Phylogeny (GBDP). Inter-genomic distances were inferred using the trimming algorithm and distance formula d5, with 100 replicates [18]. Digital DNA to DNA hybridization (dDDH) values and confidence intervals were calculated using the recommended settings of the Genome-to-Genome Distance Calculator (GGDC) 2.1. The resulting inter-genomic distances were used to infer a balanced minimum evolution tree with branch support via FASTME 2.1.4, including SPR post-processing [19]. The trees were rooted at the midpoint and visualized with PhyD3 [20]. FastANI version 0.1.2 [21] was used to estimate the average nucleotide identity (ANI) of the isolates using closely related genomes of Enterobacter species as reference genomes.

2.3. Comparative Genomics

RAST annotated whole-genome sequences of species closely related to the isolates (S4 and S5) were uploaded onto the Kbase server for downstream analysis [22]. The used species were E. asburiae (CP011863), Enterobacter bugandensis (GCA_015137655), Enterobacter cancerogenus (GCA_000478345), Enterobacter chengduensis (CP043318), Enterobacter chuandaensis (GCA_003594915), E. cloacae subsp. dissolvens (NC_018079), E. cloacae subsp. cloacae (CP001918), E. hormaechei subsp. hormaechei (GCA_001875655), E. hormaechei subsp. steigerwaltii (CP017179), E. hormaechei subsp. hoffmannii (CP017186), Enterobacter huaxiensis (RWHU01000000), E. kobei (CP017181), E. ludwigii (CP017279.1), Enterobacter mori (GCA_000211415), Enterobacter oligotrophica (AP019007), Enterobacter quasihormaechei (GCA_004331385), Enterobacter roggenkampii (CP017184), Enterobacter sichuanensis (GCA_002939185), Enterobacter soli (GCA_001654845), Enterobacter timonensis (GCA_900021175), Enterobacter wuhouensis (GCA_004331265) and Enterobacter xiangfangensis (CP017183) Domains in the genome sets were annotated using HMMER version 3.1 b [23], Gapped BLAST and PSI-BLAST [24]. The annotated domains were used to view the difference in functional roles among the genomes based on The SEED and COG (https://www.ncbi.nlm.nih.gov/research/cog-project/, accessed on 25 January 2021) categories/roles.

3. Results and Discussions

3.1. Genomic Features and Assembling Metrics of the Genomes of the Isolates

The genomes assembled with SPAdes resulted in high-quality genomes, as shown in Table 1. CheckM quality assessment of the assembled genomes showed that the genomes of isolates S4 and S5 had an estimated 97.43% and 94.67% completeness, respectively, and had 0.08% predicted contamination. Isolate S4 had a genome size of 4,658,088 bp, which corresponds to an N50 of 89,616 bp, having 71 contigs. Isolate S5 had a genome size of 4,442,534 bp, which corresponds to an N50 of 39,615 bp, having 132 contigs. The GC content was homogeneous with S4 and S5 having a GC content of 55.26% and 55.35%, respectively. RAST annotation of the genomes predicted that isolate S4 had 4545 coding genes while isolate S5 had 4341 coding genes. The genes for S4 were predicted to have 3997 distinct functions, with 1991 of the genes having SEED annotation ontology across 1806 distinct SEED functions. For S5, there were 3651 predicted distinct functions, with 1930 of the genes having SEED annotation ontology across 1751 distinct SEED functions.

3.2. Whole-Genome-Based Taxonomic Analysis

Taxonomic classification in the genus Enterobacter has been problematic, since some species and most subspecies have been assigned based on low-resolution methods [8,9]. In addition, this complexity has been made worse by the fact that phenotype-based methods cause misidentification of Enterobacter species and are unreliable for precise species identification [6]. To correctly classify our isolates (S4 and S5), we applied a more robust whole-genome-based taxonomic classification through comparison of the whole-genome sequences of our isolates with the whole-genome sequences of established type strains; for this purpose, we used the Type Strain Genome Server (TYGS) [17]. The TYGS results showed that isolates S4 and S5 form a highly supported clade corresponding to a species cluster with Enterobacter hormaechei subsp. hoffmannii (Figure 1). The main criterion for species affiliation is the 70% DNA–DNA hybridization threshold [25,26]. Digital DNA-DNA hybridization (dDDH) computed by the Genome–Genome Distance Calculator (GGDC) between the whole genomes of our isolates (S4 and S5) and that of E. hormaechei subsp. hoffmannii was well above the species cut-off point of 70%: 92.8% and 92.9%, respectively (Table 2), indicating that isolates S4 and S5 are indeed E. hormaechei subsp. hoffmannii. The GGDC results showed low delta values coupled with high average branch support, which denotes a high phylogenetic accuracy [27,28]. Another overall genome relatedness index important for the delineation of species based upon genome similarity is average nucleotide identity (ANI). This is one of the most important indices, since sequence identity has clockwise-like properties and provides the likelihood of correlation with times of divergence [29]. ANI is widely acknowledged as a measure of genomic relatedness, with an ANI ≥ 96% being the proposed cut-off point for species delineation [30]. In the present study, the ANI of the isolates S4 and S5 were well above the cut-off point compared to the closely phylogenetically related species E. hormaechei subsp. hoffmannii (Supplementary Table S1), indicating that the isolates used in this study are E. hormaechei subsp. hoffmannii.
Based on ANI and dDDH, the taxonomic classification analysis from this study supports the recent report that E. cloacae subsp. dissolvens and E. hormaechei subsp. hoffmannii are species rather than subspecies; and E. xiangfangensis, E. hormaechei subsp. oharae, and E. hormaechei subsp. steigerwaltii are the same species (Supplementary Table S1). Based on the recently updated taxonomic classification of the genus Enterobacter by Wu et al. (2020), isolates S4 and S5 are E. hoffmannii [7]. Whole-genome sequences have greatly improved the identification of species as they elucidate functional profiles of taxonomic groups, and easily resolve ambiguities in the phylogeny of higher taxa, which would have been difficult through traditional approaches [11,12]. Low-resolution methods were applied in the earlier classification of Enterobacter species [8,9,10], which caused the above-mentioned inconsistency; more robust methods should be applied when classifying Enterobacter species.

3.3. Predictions of Potential Human Pathogenicity, Resistome, Virulome, and Biosynthetic Gene Clusters

Identifying pathogenic bacterial strains and understanding the biological mechanisms of pathogenicity is important for timely intervention programs, designing control strategies, as well as the development of targeted vaccines. To predict the pathogenicity of our isolates, we used the PathogenFinder algorithm [31]; both S4 and S5 were predicted to be human pathogens with a probability of greater than 0.78. The genome sequence of isolate S4 matched a total of 63 pathogenic families from a broad range of pathogens, including Yersinia pestis, Salmonella enterica subsp. arizonae, several pathogenic Escherichia coli strains, Klebsiella pneumonia strains, Citrobacter koseri, Shigella boydii, and Enterobacter sakazakii. Supplementary Materials Supporting Information 1a provides a detailed description of the matched families, which includes location (chromosomal or plasmid), completeness, and identity percentage for S4. Isolate S5 matched a total of 58 pathogenic families; Supplementary Materials Supporting Information 1b gives a detailed description of the matched families.
For effective colonization of the host, pathogens require an arsenal of strategies to enable adherence, persistence, aggression, and evasion of innate and adaptive immunity [32,33,34]. We used the Virulence Factors of Pathogenic bacteria database to determine the presence of genes encoding the above-described arsenal. Both isolates S4 and S5 have the necessary virulence factors for successful host colonization and perpetuation of the pathogenesis process, supporting their potential pathogenicity (Table 3). In addition to the virulence factors, the isolates have several antibiotic resistance genes, which included genes encoding for beta-lactamase resistance. The mechanisms for antibiotic resistance were predicted to be antibiotics inactivation, antibiotic target alteration, reduced permeability to antibiotic, and antibiotic efflux (Supplementary Table S2). The presence of all the arsenal required for pathogenesis and the ability of the isolates to survive the chlorination process emphasize the importance of continuously monitoring wastewaters and evaluating the wastewater treatment process. A number of biosynthetic gene clusters have been shown to offer competitive fitness advantages and aid virulence. For example, aryl polyenes (APEs) were shown to function as fitness factors that increase protection from oxidative stress and contribute to biofilm formation [35]. In E. coli, APEs are present in most pathogenic strains, whereas they are typically absent from commensals and laboratory strains [36]. We used antiSMASH to identify the secondary metabolite biosynthetic gene clusters in our genome sequences [37]. Both S4 and S5 consist of aryl polyenes (Figure 2A,B); this could possibly explain their ability to survive the chlorination treatment process of the wastewater. We also identified aerobactin (Figure 2C,D), augmenting the predicted human pathogenic nature of the isolates. Lack of available iron is one of the first lines of defense to avoid bacterial infection [38,39,40]. Aerobactin, a hydroxymate-type siderophore, has been shown to offer a selective advantage under iron starvation and increases the virulence of E. coli strains [41,42].

3.4. Comparative Genomics

The development of next-generation sequencing technologies (NGS) has allowed for comparative genomics of multiple genomes. Comparative genomics, in particular pan-genome analysis, more accurately reflects the notion of bacterial species [11,43]. Pan-genome refers to the whole gene repertoire of a study group [44,45]. The pan-genome is divided into three components: (1) the core genome, which is a set of all genes common to all strains of the study; (2) non-core/accessory genome, which is a set of genes present in more than one, but not in all of the strains used in a study; and (3) singletons, which are genes unique to individual strains used in the study.
From the 24 Enterobacter genomes used in this study, there were a total of 6032 functions detected, of which 40.4% (2438) were core functions. The remaining 59.6% were part of the accessory genome. The core function genes covered the essential basics for survival; the clusters of orthologous genes (COGs) for the core functions are shown in Figure 3. They are homogeneously distributed, covering cellular processes and signaling, metabolism, and information storage and processing. Isolates S4 and S5 are more identical; thus, including all isolates in the pan-genome analysis masked how distinct the isolates are from other species (Figure 4A). However, removing one of the isolates (S4) revealed that the isolates are greatly distinct from other samples used in this study (Figure 4B), having a total of 234 singletons. Supplementary Materials Supporting Information 2 provides details of all the singletons for isolate S5 compared to all the other samples, excluding isolate S4. A large fraction of the accessory genomes is mobile genetic elements [45]. These accessory genomes place a host cell in an advantageous position to be viable under specific conditions [46,47]. The singletons comprise many virulence factors, antibiotic resistance genes, phage related proteins, with hypothetical proteins and genes of unknown function constituting 54% of the singletons. Of the singletons, some of the genes to note are (phage-encoded virulence determinant Bor, several phage related proteins/genes, type IV secretion complex proteins (VirB5, VirB6, VirB9, VirB1, VirD4, VirB2, VirB8, VirB10, VirB11), T6SS secretion lipoprotein TssJ (VasD), T1SS secreted agglutinin RTX, nitrilotriacetate monooxygenase component A (EC 1.14.13.-), multidrug resistance protein MdtH, SOS-response repressor and protease LexA (EC 3.4.21.88), inner-membrane proton/drug antiporter (MSF type) of tripartite multidrug efflux system, arsenite/antimonite pump-driving ATPase ArsA (EC 3.6.3.16), ferric hydroxamate outer membrane receptor FhuA, diguanylate cyclase/phosphodiesterase (GGDEF and EAL domains) with PAS/PAC sensor(s), and COG3121: P pilus assembly protein, chaperone PapD), a clear indication of the virulence nature of the isolates (Supplementary Materials Supporting Information 2). Expression of bor significantly increases the survival of the Escherichia coli host cell in animal serum. This property is a well-known bacterial virulence determinant [48,49]. Prophages are known to increase the virulence of pathogenic strains [50,51], and there is a positive correlation between the phage-related DNA content of a given Enterobacterium and its pathogenicity [52]. In this study, the singletons for S5 constitute a higher proportion of phage DNA, and the isolates were predicted to be human pathogens. Type IV secretion systems mediate the translocation of virulence factors (proteins and/or DNA) from Gram-negative bacteria into eukaryotic cells [53,54].
Comparison of protein domain content allows for the construction of bacteria phylogeny independent of gene sequence [55], which is a better indicator of shared physiology and ecology [56]. This also takes into account the impacts of mutations, gene loss, and horizontal gene transfer. The domain architecture is preserved at large phylogenetic distances [57,58]. Protein domains are distinct, functional units responsible for specific functions and interactions; thus, we also applied the SEED protein domain-class content to study the species diversity. Under the SEED category virulence, subcategory Type_4_secretion_and_conjugative_transfer, isolates S4 and S5 have a higher proportion of domains related to this function (Figure 5). These are also relatively higher in E. soli, E. quasihormoachei, E. huaxiansis, E. chuandensis, and E. cancerogenus compared to other genomes, albeit lower than in the isolates S4 and S5. Moreover, S4 and S5 have a considerably higher level of phage_capsid_protein, with S5 being the only genome harboring Tn552. In addition, S5 shows a relatively higher level of domains related to copper homeostasis (Figure 5).

4. Conclusions

This study provides comprehensive insights into the genomic structure of E. hormaechi subsp. hoffmmanii from South Africa, and aids with the baseline of global surveillance and monitoring of Enterobacter species, a member of the clinically significant ESKAPE pathogens. From the genome sequences, several important features including virulence factors, antibiotic resistance genes, and biosynthetic gene clusters (BGCs) were explored. The genome sequences provide an important resource for future comparative genomics. Moreover, the study demonstrates the risks associated with wastewater and the need for continuous monitoring of the treated wastewaters to ensure efficacy in the removal of pathogens. It also helps local authorities to understand the potential dangers associated with the wastewater, and encourage them to reconsider their treatment processes. The results from whole-genome taxonomic classification support recent updates in the taxonomy of the genus Enterobacter, emphasizing the importance of using high-resolution analytic methods for species identification.

Supplementary Materials

The supplementary materials are available online at https://www.mdpi.com/article/10.3390/microorganisms9091928/s1.

Author Contributions

Conceptualization, T.E.M. and C.C.B.; data curation, T.E.M.; formal analysis, T.E.M.; funding acquisition, C.C.B.; investigation, C.C.B.; methodology, T.E.M.; project administration, C.C.B.; resources, C.C.B.; software, T.E.M.; supervision, C.C.B.; validation, T.E.M.; visualization, T.E.M.; writing–original draft, T.E.M.; writing–review and editing, T.E.M. and C.C.B. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge with thanks the financial support from the North West University, South Africa; and the Water Research Commission (WRC), South Africa (project 2019/2020-00224). The views expressed are those of the authors and not of the funders.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw sequences generated for this study were deposited into the National Centre for Biotechnology Information (NCBI) database under the BioProject ID: PRJNA735620, with BioSample Accessions SAMN19590289, SAMN19590290. All the other data supporting the findings of this study are available in this published article and its supplementary information files.

Conflicts of Interest

The authors declare that they have no competing interest.

References

  1. Keller, R.; Pedroso, M.Z.; Ritchmann, R.; Silva, R.M. Occurrence of virulence-associated properties in Enterobacter cloacae. Infect. Immun. 1998, 66, 645–649. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Streit, J.M.; Jones, R.N.; Sader, H.S.; Fritsche, T.R. Assessment of pathogen occurrences and resistance profiles among infected patients in the intensive care unit: Report from the sentry Antimicrobial Surveillance Program (North America, 2001). Int. J. Antimicrob. Agents 2004, 24, 111–118. [Google Scholar] [CrossRef] [PubMed]
  3. Pauuw, A.; Caspers, M.P.M.; Schuren, F.H.J.; Leverstein-van Hall, M.A.; Deletoile, A.; Montijin, R.; Verhoef, J.; Fluit, A.C. Genomic diversity within the Enterobacter cloacae complex. PLoS ONE 2008, 3, e3018. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Leverstein-van Hall, M.A.; Blok, H.E.; Paauw, A.; Fluit, A.C.; Troelstra, A.; Mascini, E.M.; Bonten, M.J.M.; Verhoef, J. Extensive hospital-wide spread of a multidrug-resistant Enterobacter cloacae clone, with late detection due to a variable antibiogram and frequent patient transfer. J. Clin. Microbiol. 2006, 44, 518–524. [Google Scholar] [CrossRef] [Green Version]
  5. Paauw, A.; Verhoef, J.; Fluit, A.C.; Blok, H.E.; Hopmans, T.E.; Troelstra, A.; Leverstein-van Hall, M.A. Failure to control an outbreak of qnrA1-positive multidrug-resistant Enterobacter cloacae infection despite adequate implementation of recommended infection control measures. J. Clin. Microbiol. 2007, 45, 1420–1425. [Google Scholar] [CrossRef] [Green Version]
  6. Mezzatesta, M.L.; Gona, F.; Stefani, S. Enterobacter cloacae complex: Clinical impact and emerging antibiotic resistance. Future Microbiol. 2012, 7, 887–902. [Google Scholar] [CrossRef]
  7. Wu, W.; Feng, Y.; Zong, Z. Precise species identification for Enterobacter: A genome sequence-based study with reporting of two novel species Enterobacter quasiroggenkampii sp. nov. and Enterobacter quasimori sp. nov. mSystems 2020, 5, e00527-20. [Google Scholar] [CrossRef]
  8. Hoffmann, H.; Roggenkamp, A. Population genetics of the nomen-species Enterobacter cloacae. Appl. Environ. Microbiol. 2003, 69, 5306–5318. [Google Scholar] [CrossRef] [Green Version]
  9. Hoffmann, H.; Stindl, S.; Ludwig, W.; Stumpf, A.; Mehlen, A.; Monget, D.; Piérard, D.; Ziesing, S.; Heesemann, J.; Roggenkamp, A.; et al. Enterobacter hormaechei subsp. oharae subsp. nov., E. hormaechei subsp. hormaechei comb. nov., and E. hormaechei subsp. steigerwaltii subsp. nov., three new subspecies of clinical importance. J. Clin. Microbiol. 2005, 43, 3297–3303. [Google Scholar] [CrossRef] [Green Version]
  10. Sutton, G.G.; Brinkac, L.M.; Clarke, T.H.; Fouts, D.E. Enterobacter hormaechei subsp. hoffmannii subsp. nov., Enterobacter hormaechei subsp. xiangfangensis comb. nov., Enterobacter roggenkampii sp. nov., and Enterobacter muelleri is a later heterotypic synonym of Enterobacter asburiae based on computational analysis of sequenced Enterobacter genomes. F1000Research 2018, 7, 521. [Google Scholar]
  11. Whitman, W.B. Genome sequences as the type material for taxonomic descriptions of prokaryotes. Syst. Appl. Microbiol. 2015, 38, 217–222. [Google Scholar] [CrossRef] [Green Version]
  12. Ramasamy, D.; Mishra, A.K.; Lagier, J.C.; Padhmanabhan, R.; Rossi, M.; Sentausa, E.; Raoult, D.; Fournier, P.-E. A polyphasic strategy incorporating genomic data for the taxonomic description of novel bacterial species. Int. J. Syst. Evol. Microbiol. 2014, 64, 384–391. [Google Scholar] [CrossRef] [PubMed]
  13. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Nurk, S.; Bankevich, A.; Antipov, D.; Gurevich, A.A.; Korobeynikov, A.; Lapidus, A.; Prjibelski, A.D.; Pyshkin, A.; Sirotkin, A.; Sirotkin, Y.; et al. Assembling single-cell genomes and mini-metagenomes from chimeric MDA products. J. Comput. Biol. 2013, 20, 714–737. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Parks, D.H.; Imelfort, M.; Skennerton, C.T.; Hugenholtz, P.; Tyson, G.W. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015, 25, 1043–1055. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Aziz, R.K.; Bartels, D.; Best, A.; DeJongh, M.; Disz, T.; Edwards, R.; Formsma, K.; Gerdes, S.; Glass, E.M.; Kubal, M.; et al. The RAST Server: Rapid annotations using subsystems technology. BMC Genom. 2008, 9, 75. [Google Scholar] [CrossRef] [Green Version]
  17. Meier-Kolthoff, J.P.; Göker, M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat. Commun. 2019, 10, 2182. [Google Scholar] [CrossRef]
  18. Meier-Kolthoff, J.P.; Auch, A.F.; Klenk, H.-P.; Göker, M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinform. 2013, 14, 60. [Google Scholar] [CrossRef] [Green Version]
  19. Lefort, V.; Desper, R.; Gascuel, O. FastME 2.0: A comprehensive, accurate, and fast distance-based phylogeny inference program. Mol. Biol. Evol. 2015, 32, 2798–2800. [Google Scholar] [CrossRef] [Green Version]
  20. Kreft, L.; Botzki, A.; Coppens, F.; Vandepoele, K.; Van Bel, M. PhyD3: A phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization. Bioinformatics 2017, 33, 2946–2947. [Google Scholar] [CrossRef] [PubMed]
  21. Jain, C.; Rodriguez, R.L.M.; Phillipy, A.M.; Konstantinidis, K.T.; Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 2017, 9, 5114. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Arkin, A.P.; Cottingham, R.; Henry, C.S.; Harris, N.L.; Stevens, R.L.; Maslov, S.; Dehal, P.; Ware, D.; Perez, F.; Canon, S.; et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 2018, 36, 566–569. [Google Scholar] [CrossRef] [Green Version]
  23. Eddy, S.R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 2011, 7, e1002195. [Google Scholar] [CrossRef] [Green Version]
  24. Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 1997, 25, 3389–3402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Wayne, L.G.; Moore, W.E.C.; Stackebrandt, E.; Kandler, O.; Colwell, R.R.; Krichevsky, M.I.; Truper, H.G.; Murray, R.G.E.; Grimont, P.A.D.; Brenner, D.J.; et al. Report of the ad hoc committee on reconciliation of approaches to bacterial systematics. Int. J. Syst. Bacteriol. 1987, 37, 463–464. [Google Scholar] [CrossRef] [Green Version]
  26. Tindall, B.J.; Rossello-Mora, R.; Busse, H.J.; Ludwig, W.; Kampfer, P. Notes on the characterization of prokaryote strains for taxonomic purposes. Int. J. Syst. Evol. Microbiol. 2010, 60, 249–266. [Google Scholar] [CrossRef] [Green Version]
  27. Auch, A.F.; Henz, S.R.; Holland, B.R.; Göker, M. Genome BLAST distance phylogenies inferred from whole plastid and whole mitochondrion genome sequences. BMC Bioinform. 2006, 7, 350. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Holland, B.R.; Huber, K.T.; Dress, A.; Moulton, V. δ Plots: A Tool for Analyzing Phylogenetic Distance Data. Mol. Biol. Evol. 2002, 19, 2051–2059. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Kuo, C.H.; Ochman, H. Inferring clocks when lacking rocks: The variable rates of molecular evolution in bacteria. Biol. Direct 2009, 4, 35. [Google Scholar] [CrossRef] [Green Version]
  30. Konstantinidis, K.T.; Tiedje, J.M. Genomic insights that advance the species definition for prokaryotes. Proc. Natl. Acad. Sci. USA 2005, 102, 2567–2572. [Google Scholar] [CrossRef] [Green Version]
  31. Cosentino, S.; Voldby Larsen, M.; Møller Aarestrup, F.; Lund, O. PathogenFinder—Distinguishing Friend from Foe Using Bacterial Whole Genome Sequence Data. PLoS ONE 2013, 8, e77302. [Google Scholar] [CrossRef]
  32. Falkow, S. What is a pathogen. ASM News 1997, 63, 359–365. [Google Scholar]
  33. Galán, J.E.; Collmer, A. Type III secretion machines: Bacterial devices for protein delivery into host cells. Science 1999, 284, 1322–1328. [Google Scholar] [PubMed] [Green Version]
  34. Hacker, J.; Kaper, J.B. Pathogenicity islands and the evolution of microbes. Ann. Rev. Microbiol. 2000, 54, 641–679. [Google Scholar] [CrossRef] [Green Version]
  35. Johnston, I.; Osborn, L.J.; Markley, R.L.; McManus, E.A.; Kadam, A.; Schultz, K.B.; Nagajothi, N.; Ahern, P.P.; Brown, J.M.; Claesen, J. Identification of essential genes for Escherichia coli aryl polyene biosynthesis and function in biofilm formation. bioRxiv 2020. [Google Scholar] [CrossRef] [Green Version]
  36. Cimermancic, P.; Medema, M.H.; Claesen, J.; Kurita, K.; Brown, L.C.W.; Mavrommatis, K.; Pati, A.; Godfrey, P.A.; Koehrsen, M.; Clardy, J.; et al. Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters. Cell 2014, 158, 412–421. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Medema, M.H.; Blin, K.; Cimermancic, P.; De Jager, V.; Zakrzewski, P.; Fischbach, M.A.; Weber, T.; Takano, E.; Breitling, R. antiSMASH: Rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 2011, 39, W339–W346. [Google Scholar] [CrossRef]
  38. Lawrence, R.M.; Hoeprieh, P.D. Resistance to infection. In Infectious Diseases; Hoeprich, P.D., Ed.; Harper and Row Publishers: Philadelphia, PA, USA, 1983; pp. 76–84. [Google Scholar]
  39. Bullen, J.J. The significance of iron in infection. Rev. Infect. Dis. 1981, 3, 1123–1138. [Google Scholar] [CrossRef] [PubMed]
  40. Weinberg, E.D. Iron withholding: A defense against infection and neoplasia. Physiol. Rev. 1984, 64, 65–102. [Google Scholar] [CrossRef] [PubMed]
  41. Williams, P.H. Novel iron uptake system specified by ColV plasmids; An important component in the virulence of invasive strains of Escherichia coli. Infect. Immun. 1979, 26, 925–932. [Google Scholar] [CrossRef] [Green Version]
  42. Williams, P.H.; Warner, P.J. ColV plasmid-mcdiatcd colicin V-independent iron uptake system of invasive strains of Escherichia coll. Infect. Immun. 1980, 29, 411–416. [Google Scholar] [CrossRef]
  43. Caputo, A.; Merhej, V.; Georgiades, K.; Fournier, P.-E.; Croce, O.; Robert, C.; Raoult, D. Pan-genomic analysis to redefine species and subspecies based on quantum discontinuous variation: The Klebsiella paradigm. Biol. Direct 2015, 10, 55. [Google Scholar] [CrossRef] [Green Version]
  44. Medini, D.; Donati, C.; Tettelin, H.; Masignani, V.; Rappuoli, R. The microbial pangenome. Curr. Opin. Genet. Dev. 2005, 15, 589–594. [Google Scholar] [CrossRef]
  45. Frost, L.S.; Leplae, R.; Summers, O.A.; Tossaint, A. Mobile genetic elements: The agents of open source evolution. Nat. Rev. Microbiol. 2005, 3, 722–732. [Google Scholar] [CrossRef]
  46. Read, T.D.; Ussery, D.W. Opening the pan-genomics box. Curr. Opin. Microbiol. 2006, 9, 496–498. [Google Scholar] [CrossRef]
  47. Mira, M.; Martin-Cuadrado, A.B.; Auria, G.D.; Rodriguez-Valera, F. The bacterial pan-genome: A new paradigm in microbiology. Int. Microbiol. 2010, 13, 45–57. [Google Scholar]
  48. Taylor, P. Bactericidal and bacteriolytic activity of serum against gram-negative bacteria. Microbiol. Rev. 1983, 47, 46–83. [Google Scholar] [CrossRef] [PubMed]
  49. Joiner, K.A.A. Complement evasion by bacteria and parasites. Rev. Microbiol. 1988, 42, 201–230. [Google Scholar] [CrossRef] [PubMed]
  50. Boyd, E.F. Bacteriophage-encoded bacterial virulence factors and phage-pathogenecity islands interactions. Adv. Virus Res. 2012, 82, 91–118. [Google Scholar] [PubMed]
  51. Varani, A.M.; Monteiro-Vitorello, C.B.; Nakaya, H.I.; Van Sluys, M.A. The role of prophage in plant pathogenic bacteria. Annu. Rev. Phytopathol. 2013, 51, 429–451. [Google Scholar] [CrossRef] [PubMed]
  52. Bobay, L.M.; Rocha, E.P.C.; Touchon, M. The adaptation of temperate bacteriophages to their host genomes. Mol. Biol. Evol. 2013, 4, 737–751. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Cascales, E.; Christie, P.J. The versatile bacteria type IV secretion systems. Nat. Rev. Microbiol. 2003, 2, 137–149. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Llosa, M.; O’Callaghan, D. Euro conference on the Biology of Type IV Secretion Processes: Bacterial gates into the outer world. Mol. Microbiol. 2004, 53, 1–8. [Google Scholar] [CrossRef] [PubMed]
  55. Yang, S.; Doolittle, R.F.; Bourne, P.E. Phylogeny determined by protein domain content. Proc. Natl. Acad. Sci. USA 2005, 102, 373–378. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Koehorst, J.J.; Saccenti, E.; Schaap, P.J.; dos Santos, V.A.M.; Suarez-Diez, M. Protein domain architectures provide a fast, efficient and scalable alternative to sequence-based methods for comparative functional genomics. F1000Research 2016, 5, 1987. [Google Scholar] [CrossRef] [PubMed]
  57. Basu, M.K.; Poliakov, E.; Rogozin, I.B. Domain mobility in proteins: Functional and evolutionary implications. Brief Bioinform. 2009, 10, 205–216. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Zmasek, C.M.; Godzik, A. This Déjà Vu Feeling-Analysis of Multidomain Protein Evolution in Eukaryotic Genomes. PLoS Comput. Biol. 2012, 8, e1002701. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Microorganisms 09 01928 g001a 550Microorganisms 09 01928 g001b 550
Figure 1. Trees inferred with FastME 2.1.6.1 from the Genome Blast Distance Phylogeny (GBDP) distances calculated from whole-genome sequences. Isolates S4 (A) and S5 (B) form a species cluster with Enterobacter hormaechei subsp. hoffmannii, having a dDDH of >70%, a clear indication that the isolates belong to the same species as E. hormaechei subsp. hoffmannii.
Figure 1. Trees inferred with FastME 2.1.6.1 from the Genome Blast Distance Phylogeny (GBDP) distances calculated from whole-genome sequences. Isolates S4 (A) and S5 (B) form a species cluster with Enterobacter hormaechei subsp. hoffmannii, having a dDDH of >70%, a clear indication that the isolates belong to the same species as E. hormaechei subsp. hoffmannii.
Microorganisms 09 01928 g001aMicroorganisms 09 01928 g001b
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Figure 2. Biosynthetic gene clusters (BGCs) detected in the genome sequences of isolates S4 and S5. (A,B) Aryl polynes (APEs) for genomes S4 and S5, respectively. (C,D) Aerobactin for genomes S4 and S5, respectively. Query sequences are the genome sequences of the isolates S4 and S5.
Figure 2. Biosynthetic gene clusters (BGCs) detected in the genome sequences of isolates S4 and S5. (A,B) Aryl polynes (APEs) for genomes S4 and S5, respectively. (C,D) Aerobactin for genomes S4 and S5, respectively. Query sequences are the genome sequences of the isolates S4 and S5.
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Microorganisms 09 01928 g003 550
Figure 3. A heatmap showing clusters of orthologous genes (COGs) for isolates S4, S5, and a representative of all validly published Enterobacter species (see Materials and Methods for full species list and accession numbers). The COGs covered cellular processing and signaling (D: cell cycle control, cell division, chromosome partitioning; M: cell wall/membrane/envelope biogenesis; N: cell motility; O: post-translational modification, protein turnover, and chaperones; T: signal transduction mechanism; U: intracellular trafficking, secretion, and vesicular transportation; V: defense mechanisms; W: extracellular structures; Z: cytoskeleton), information storage and processing (A: RNA processing and modification; J: translation, ribosomal structure, and biogenesis; K: transportation; L: replication, recombination, and repair), metabolism (C: energy production and conversion; E: amino acid transport and metabolism; F: nucleotide transport and metabolism; G: carbohydrate transport and metabolism; H: coenzyme transport and metabolism; I: lipid transport and metabolism; P: inorganic ion transport and metabolism; Q: secondary metabolites biosynthesis, transport, and catabolism), as well as the poorly characterized (R and S).
Figure 3. A heatmap showing clusters of orthologous genes (COGs) for isolates S4, S5, and a representative of all validly published Enterobacter species (see Materials and Methods for full species list and accession numbers). The COGs covered cellular processing and signaling (D: cell cycle control, cell division, chromosome partitioning; M: cell wall/membrane/envelope biogenesis; N: cell motility; O: post-translational modification, protein turnover, and chaperones; T: signal transduction mechanism; U: intracellular trafficking, secretion, and vesicular transportation; V: defense mechanisms; W: extracellular structures; Z: cytoskeleton), information storage and processing (A: RNA processing and modification; J: translation, ribosomal structure, and biogenesis; K: transportation; L: replication, recombination, and repair), metabolism (C: energy production and conversion; E: amino acid transport and metabolism; F: nucleotide transport and metabolism; G: carbohydrate transport and metabolism; H: coenzyme transport and metabolism; I: lipid transport and metabolism; P: inorganic ion transport and metabolism; Q: secondary metabolites biosynthesis, transport, and catabolism), as well as the poorly characterized (R and S).
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Microorganisms 09 01928 g004 550
Figure 4. Pan-genome circular visualization output. (A) Isolates S4 and S5 compared to representatives of all validly published Enterobacter species (see Materials and Methods for full species list and accession numbers); each red strip indicates a gene that is unique to the isolate S5, which was used as a base genome. (B) Isolate S5 compared to validly published Enterobacter species; each red strip indicates a gene that is unique to the isolate S5. (B) Isolate S4 is not included in the analysis as it is highly identical to isolate S5 and would mask the true variation in the isolates compared to other Enterobacter species.
Figure 4. Pan-genome circular visualization output. (A) Isolates S4 and S5 compared to representatives of all validly published Enterobacter species (see Materials and Methods for full species list and accession numbers); each red strip indicates a gene that is unique to the isolate S5, which was used as a base genome. (B) Isolate S5 compared to validly published Enterobacter species; each red strip indicates a gene that is unique to the isolate S5. (B) Isolate S4 is not included in the analysis as it is highly identical to isolate S5 and would mask the true variation in the isolates compared to other Enterobacter species.
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Microorganisms 09 01928 g005 550
Figure 5. Distribution of a selected SEED domain categories between the isolates (S4, S5) and the representatives of all validly published Enterobacter species (see Materials and Methods for full species list and accession numbers). Interestingly, isolates S4 and S5 have the largest proportion of Type 4 Secretion System in addition to being the only genomes carrying Tn522.
Figure 5. Distribution of a selected SEED domain categories between the isolates (S4, S5) and the representatives of all validly published Enterobacter species (see Materials and Methods for full species list and accession numbers). Interestingly, isolates S4 and S5 have the largest proportion of Type 4 Secretion System in addition to being the only genomes carrying Tn522.
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Table
Table 1. Quality metrics of the isolates assembled genomes.
Table 1. Quality metrics of the isolates assembled genomes.
IsolateS4S5
Total Contigs71132
Total Length (bp)4,658,0884,442,534
Largest Contig (bp)329,202135,452
N50 (bp)89,61639,615
GC%55.2655.35
Coding Genes45454341
Non Coding Repeats1610
Non Coding RNA1656
Table
Table 2. Type Strain Genome Server calculation of digital DNA–DNA hybridizations of isolates (S4, S5), and some of their closely related type strains.
Table 2. Type Strain Genome Server calculation of digital DNA–DNA hybridizations of isolates (S4, S5), and some of their closely related type strains.
Query StrainSubject StraindDDH (d4, in %)C.I. (d4, in %)G + C Content Difference (in %)
S4SPAdes.contigs.faEnterobacter hormaechei subsp. hoffmannii DSM 14563T92.9[90.9–94.4]0.07
S5SPAdes.contigs.faEnterobacter hormaechei subsp. hoffmannii DSM 14563T92.8[90.8–94.4]0.01
S4SPAdes.contigs.faEnterobacter hormaechei subsp. oharae DSM 16687T66.[63.4–69.2]0.32
S5SPAdes.contigs.faEnterobacter hormaechei subsp. oharae DSM 16687T66.4[63.5–69.3]0.24
S4SPAdes.contigs.faEnterobacter hormaechei subsp. xiangfangensis LMG 27195T66.1[63.2–69.0]0.02
S5SPAdes.contigs.faEnterobacter hormaechei subsp. xiangfangensis LMG 27195T66.2[63.2–69.0]0.07
S4SPAdes.contigs.faEnterobacter hormaechei subsp. steigerwaltii DSM 16691T66[63.0–68.8]0.29
S5SPAdes.contigs.faEnterobacter hormaechei subsp. steigerwaltii DSM 16691T65.9[62.9–68.7]0.21
S4SPAdes.contigs.faEnterobacter hormaechei ATCC 49162T57.3[54.5–60.0]0.02
S5SPAdes.contigs.faEnterobacter hormaechei ATCC 49162T57.3[54.5–60.1]0.1
S4SPAdes.contigs.faEnterobacter bugandensis EB-247T35.1[32.7–37.6]0.74
S5SPAdes.contigs.faEnterobacter bugandensis EB-247T35.3[32.8–37.8]0.65
S4SPAdes.contigs.faEnterobacter mori LMG 25706T34.2[31.7–36.7]0.04
S5SPAdes.contigs.faEnterobacter mori LMG 25706T34.3[31.9–36.8]0.05
S4SPAdes.contigs.faEnterobacter roggenkampii DSM 16690T33.7[31.3–36.3]0.78
S5SPAdes.contigs.faEnterobacter roggenkampii DSM 16690T34[31.5–36.5]0.7
S4SPAdes.contigs.faEnterobacter oligotrophicus HUT 8142T33.4[31.0–35.9]0.96
S5SPAdes.contigs.faEnterobacter oligotrophicus HUT 8142T33.5[31.1–36.0]1.04
S4SPAdes.contigs.faEnterobacter cloacae subsp. dissolvens ATCC 23373T32[29.6–34.5]0.1
S5SPAdes.contigs.faEnterobacter cloacae subsp. dissolvens ATCC 23373T32.1[29.7–34.6]0.18
S4SPAdes.contigs.faEnterobacter cancerogenus ATCC 33241T31.3[28.9–33.8]0.42
S5SPAdes.contigs.faEnterobacter cancerogenus ATCC 33241T31.4[29.0–34.0]0.34
Table
Table 3. Virulence factors detected in the genome sequences of isolates S4 and S5.
Table 3. Virulence factors detected in the genome sequences of isolates S4 and S5.
Virulence Factor ClassVirulence FactorsS4S5
Type 3 fimbriae11
Type I fimbriae86
AdherenceCurli fibers(Escherichia)32
Hemorrhagic E. coli pilus (HCP) (Escherichia)22
Type IV (pili) (Yersinia)11
AntiphagocytosisCapsule1515
Efflux pumpAcrAB33
Aerobactin55
Ent Siderophore1212
Salmochelin11
Iron uptakeHeme transport (Shigella)11
Heme uptake (Escherichia)22
Iron/Manganese transport (Escherichia)11
RegulationRcsAB21
T6SS-I109
T6SS-II44
T6SS-III02
EPS type secretion system (Vibrio)11
Flagella (cluster I) (Yersinia)3232
Secretion systemHcp secretion island-1 encoded type VI secretion system (H-T6SS) (Pseudomonas)33
SCI-I T6SS (Escherichia)10
T2SS (Yst1) (Yersinia)11
T2SS (Aeromonas)11
TTSS (SPI-encode) (Salmonela)10
Serum resistanceLPS rfb locus11
ToxinHeat-stable cytotonic toxin11
AutotransporterEhaB1
EndotoxinLOS (Haemophilus)11
Agf/Csg (Salmonella)44
Fim (Salmonella)55
Fimbrial adherence determinantsSti (Salmonella)11
Stj (Salmonella)10
Stk (Salmonella)44
InvasionFlagella (Burkholderia)66
MotilityFlagella (Bordetella)11
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Maguvu, T.E.; Bezuidenhout, C.C. Whole Genome Sequencing Based Taxonomic Classification, and Comparative Genomic Analysis of Potentially Human Pathogenic Enterobacter spp. Isolated from Chlorinated Wastewater in the North West Province, South Africa. Microorganisms 2021, 9, 1928. https://doi.org/10.3390/microorganisms9091928

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Maguvu TE, Bezuidenhout CC. Whole Genome Sequencing Based Taxonomic Classification, and Comparative Genomic Analysis of Potentially Human Pathogenic Enterobacter spp. Isolated from Chlorinated Wastewater in the North West Province, South Africa. Microorganisms. 2021; 9(9):1928. https://doi.org/10.3390/microorganisms9091928

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Maguvu, Tawanda E., and Cornelius C. Bezuidenhout. 2021. "Whole Genome Sequencing Based Taxonomic Classification, and Comparative Genomic Analysis of Potentially Human Pathogenic Enterobacter spp. Isolated from Chlorinated Wastewater in the North West Province, South Africa" Microorganisms 9, no. 9: 1928. https://doi.org/10.3390/microorganisms9091928

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