ABSTRACT
Providencia rettgeri is a clinically significant opportunistic pathogen that is involved in urinary tract infections. Due to the resolution limitations of identification, distinguishing P. rettgeri from closely related species is challenging by commercial biochemical test systems. Here, we first reported a novel species, Providencia hangzhouensis, which had been misidentified as P. rettgeri. Exhibiting ≤91.97% average nucleotide identity (ANI) and ≤46.10% in silico DNA-DNA hybridization values with all known Providencia species, P. hangzhouensis falls well beneath the established species-defining thresholds. We conducted a population genomics analysis of P. hangzhouensis isolates worldwide. Our study revealed that P. hangzhouensis has emerged in many countries and has formed several transmission clusters. We found that P. hangzhouensis shared the highest ANI values (91.54% and 91.97%) with P. rettgeri and P. huaxiensis, respectively. The pan-genome analysis revealed that these three species possessed a similar component of pan-genomes. Two genes associated with metabolism, folE2 and ccmM, were identified to be specific to P. hangzhouensis. Furthermore, we also observed that carbapenem-resistance genes frequently occur in P. hangzhouensis with the bla IMP-27 being the most prevalent (46.15%; 36/78). The emergence of P. hangzhouensis is often accompanied by extended-spectrum β-lactamase and carbapenem-resistance genes, and calls for tailored surveillance of this species as a clinically relevant species in the future.
IMPORTANCE
Our study has identified and characterized a novel species, Providencia hangzhouensis, which is associated with urinary tract infections and was previously misidentified as Providencia rettgeri. Through this study, we have identified specific genes unique to P. hangzhouensis, which could serve as marker genes for rapid PCR identification. Additionally, our findings suggest that the emergence of P. hangzhouensis is often accompanied by extended-spectrum β-lactamase and carbapenem-resistance genes, emphasizing the need for attention to clinical management and the importance of accurate species identification and proper drug use.
KEYWORDS: novel species, Providencia hangzhouensis, Providencia rettgeri, Providencia huaxiensis, carbapenem resistance, pan-genome analysis
INTRODUCTION
The Providencia genus is a member of the Enterobacterales order, which constitutes the tribe Proteeae along with the Proteus and Morganella genera. Providencia includes Gram-negative bacteria that produce urease and are accountable for causing diverse human infections (1). Among the species belonging to Providencia, P. rettgeri and P. stuartii are the most frequent etiologic agents of catheter-associated urinary tract infections, particularly among elderly patients with long-term indwelling urinary catheters (1). Moreover, there are several case reports of other infections associated with P. rettgeri, such as ocular diseases (2), peritonitis (3), and neonatal septicemia (4), indicating that this pathogen has the potential to cause serious infections beyond catheter-associated urinary tract infections.
Precise species and subspecies classification of bacterial isolates is fundamental for understanding their epidemiology, pathogenesis, and microbiological characteristics. The approaches for prokaryotic species delineation include DNA-DNA hybridization (DDH), 16S rRNA identification, and average nucleotide identity (ANI) analysis. Although DDH analysis holds the distinction of being the gold standard for species classification (5), it is known to be error-prone, with low reproducibility and time-consuming procedures. Furthermore, it is widely acknowledged that analyzing the 16S rRNA gene sequence alone is insufficient for accurate bacterial species assignment (6). As a result, with the significant reduction in the cost of sequencing, genome-based ANI and in silico digital DNA-DNA hybridization (dDDH) analyses are becoming increasingly popular in microbiology laboratories (7). Pairwise ANI analysis with a cutoff of ≥96% and dDDH with a cutoff of ≥70.0% have been widely used for precise species identification (8 – 11).
In this study, we performed whole-genome sequencing of a clinical isolate of Providencia rettgeri, PR-310, and found it to represent a novel species with 92.03% identity with P. rettgeri, which we name Providencia hangzhouensis, through ANI analysis. We then examined the isolates labeled as P. rettgeri in the NCBI database and discovered that the novel species had been mistakenly identified as P. rettgeri previously. Additionally, we investigated the population structure and genetic diversity of P. hangzhouensis using comparative genomic and population genomic analysis.
RESULTS
Providencia hangzhouensis represents a novel species distinct from Providencia rettgeri
Since strain PR-310, which was isolated from a patient with urinary tract infection and identified as Providencia rettgeri by Matrix-Assisted Laser Desorption/Ionization - Time of Flight mass spectrometry (MALDI-TOF MS) (Vitek MS system, bioMerieux, France), is an uncommon clinical opportunistic pathogen and displayed resistance to cephalosporins; this strain was then subjected to whole-genome sequencing. The complete genome of PR-310 is composed of a chromosome and two plasmids, named pPR-310_79 and pPR-310_2. The chromosome of PR-310 spans a length of 4,515,965 bp and exhibits a Guanine-Cytosine (GC) content of 40.26%. Additionally, the lengths of the two plasmids are 79,375 and 2,683 bp, respectively. Analysis of the 16S rRNA gene sequences, taken from the EzBioCloud database (12) and corroborated by a derived phylogenetic tree (Fig. S1A), confirmed that the PR-310 strain classifies within the Providencia genus. This strain exhibited the highest sequence identity of the 16S rRNA gene with P. rettgeri DSM 4542 (99.73%; AM040492) and P. huaxiensis WCHPr000369 (99.66%; CP031123). Nevertheless, the limitations of using the 16S rRNA sequence for taxonomic assignment are widely acknowledged (13). Hence, we performed a whole-genome-based phylogenetic analysis on PR-310 and type strains within the Providencia genus. Our results affirmed that PR-310 indeed belongs to the Providencia genus, and shares the closest relationship with P. rettgeri NCTC11801 (Fig. S1B). A comparative analysis of the ANI (77.20%–91.97%) and dDDH (21.20%–46.10%) values of strain PR-310 with other type strains (Table 1) revealed these to be significantly below the accepted thresholds for species classification (ANI > 96; dDDH > 70%). Thus, the collective results suggest that strain PR-310 should be regarded as a novel species, which we propose to name Providencia hangzhouensis, rather than P. rettgeri.
TABLE 1.
Average nucleotide identity and in silico DNA-DNA hybridization values between strain PR-310 and the type strains of Providencia species
| Species and strain | Assembly accession | ANI (%) | dDDH (%) |
|---|---|---|---|
| P. alcalifaciens ATCC 51902 | CP023536.1 | 77.76 | 21.20 |
| P. manganoxydans LLDRA6 | CP067099.1 | 77.25 | 21.70 |
| P. rustigianii NCTC12026 | NZ_UGUA01000002.1 | 77.99 | 21.30 |
| P. burhodogranariea DSM 19968 | NZ_KB233222.1 | 77.15 | 21.50 |
| P. heimbachae NCTC12003 | LS483422.1 | 79.16 | 22.20 |
| P. sneebia DSM 19967 | CM001773.1 | 77.08 | 21.70 |
| P. huaxiensis WCHPr000369 | CP031123.2 https://www.ncbi.nlm.nih.gov/nuccore/CP031123.2 | 91.97 | 46.10 |
| P. rettgeri NCTC11801 | GCA_900455085.1 | 91.54 | 45.40 |
| P. stuartii ATCC 25827 | GCA_000154865.1 | 77.20 | 21.50 |
| P. thailandensis KCTC 23281 | NZ_BMYH01000001.1 | 77.25 | 21.30 |
| P. vermicola DSM 17385 | NZ_CP048796.1 | 81.49 | 23.90 |
To delve deeper into this novel species, represented by strain PR-310, we included in our study the genome sequences of P. rettgeri and P. huaxiensis, obtained from NCBI database. These species, with an ANI value>90%, provide a relevant genomic context for a more comprehensive analysis due to their close genetic relationship with P. hangzhouensis. Interestingly, of the 336 P. rettgeri sequences retrieved, 201 were classified as Providencia genus, and 11 belonged to P. huaxiensis. Only 52 isolates were identified as true P. rettgeri through ANI analysis. Additionally, we identified 99 isolates with ANI values >96% to PR-310, indicating that P. hangzhouensis had appeared previously but had been misidentified as P. rettgeri.
General characteristics of the P. hangzhouensis isolates
Biochemical characteristics of strain PR-310 and type strains of other Providencia species are shown in Table 2. The strain PR-310 is negative for oxidase activity and is Gram-negative, motile, facultatively anaerobic, and rod-shaped. Colonies appear circular, raised, yellow, opaque, and smooth after 24 h of incubation at 37°C on nutrient agar. The strain is able to utilize acid from D-glucose, D-mannitol, inositol, L-rhamnose, and amygdalin, but not from D-sorbitol, sucrose or melibiose, and D-arabinose. It tests positive for deaminase activity but negative for β-galactosidase, arginine dihydrolase, ornithine decarboxylase, lysine decarboxylase, and gelatinase. Additionally, the strain is positive for urease activity, indole production, and Voges-Proskauer reaction, and can utilize citrate, but does not produce H2S. The strain PR-310 can be distinguished from all other Providencia species by its positive response to acetoin production and citrate utilization tests, while it is unable to metabolize melibiose.
TABLE 2.
Biochemical characteristics of strains PR-310 and type strains of other Providencia species a
| Characteristic | PR-310 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| β-Galactosidase | − | − | − | − | − | − | − | − | − | − | + | − |
| Arginine dihydrolase | − | − | − | − | − | − | − | − | − | − | + | − |
| Lysine decarboxylase | − | − | − | − | − | − | − | − | − | − | − | − |
| Ornithine decarboxylase | − | − | − | − | − | − | − | − | − | − | + | − |
| Citrate utilization | + | + | − | − | + | + | − | − | − | + | + | − |
| H2S production | − | − | − | − | − | − | − | − | − | − | − | − |
| Urea hydrolysis | + | + | − | − | − | + | + | − | + | − | − | − |
| Deaminase | + | + | + | + | + | + | + | + | + | + | + | + |
| Indole production | + | + | + | + | − | + | + | + | + | + | − | + |
| Acetoin production | + | − | − | − | − | − | + | − | − | − | + | − |
| Gelatinase | − | − | − | − | − | − | − | − | − | − | + | − |
| D-glucose | + | + | + | + | + | + | + | + | + | + | + | + |
| D-mannitol | + | + | − | + | − | + | − | − | + | − | + | + |
| Inositol | + | + | − | + | − | + | + | − | − | + | + | + |
| D-sorbitol | − | − | − | − | − | − | − | − | + | − | + | − |
| L-rhamnose | + | − | − | − | − | − | + | − | − | − | + | − |
| Sucrose | − | + | − | − | − | − | − | − | − | + | + | − |
| Melibiose | − | − | − | − | − | − | − | − | − | − | + | − |
| Amygdalin | + | − | − | − | − | + | + | − | + | − | − | − |
| Arabinose | − | − | − | + | − | − | − | − | − | − | + | + |
Strains: 1, P. manganoxydans LLDRA6; 2, P. alcalifaciens DSM 30120; 3, P. burhodogranariea DSM 19968; 4, P. heimbachae DSM 3591; 5, P. huaxiensis KCTC 62577; 6, P. rettgeri DSM4542; 7, P. rustigianii DSM 4541; 8, P. sneebia DSM 19967; 9, P. stuartii DSM 4539; 10, P. thailandensis KCTC 23281; 11, P. vermicola DSM 17385. Data for species other than P. hangzhouensis PR-310 are from reference (14). + represents 90 to 100% positive reaction and − represents 0 to 10% positive reaction.
Following quality control, we amassed a total of 169 genomes, composed of 99 from P. hangzhouensis, our primary focus and the species with dominance in both quantity and geographical distribution, as well as 52 and 18 from P. rettgeri and P. huaxiensis, respectively. These latter species were included due to their high ANI values >90, indicating a close genetic relationship with P. hangzhouensis. These genomes span 18 countries, with P. hangzhouensis being most prevalent in the United States (n = 48), followed by China (n = 11), and Nigeria (n = 9) (Fig. 1A). The average genome size of P. hangzhouensis is 4.69 Mb, with a range of 4.34–5.25 Mb and an average GC content of 40.40%. To identify core genes in the three species, we employed a 90% amino acid identity threshold. A total of 2,170 genes, covering a length of 2.08 Mb, were concatenated to generate a single core-genome alignment for the construction of the maximum-likelihood (ML) phylogeny. The 169 genomes were divided into three main branches in the phylogenetic tree according to their species correlation, which is consistent with the results of ANI and dDDH (Fig. 1B). Furthermore, P. hangzhouensis has the highest clinical isolation rate (90.91%), which is much higher than 68.38% of P. rettgeri and 72.22% of P. huaxiensis, indicating that P. hangzhouensis might be more likely to appear in the clinical settings.
Fig 1.
Global diversity of the P. hangzhouensis. (A) Geographical distribution of the 169 isolates in the study. (B) Core-genome-based maximum-likelihood phylogeny of the 169 isolates. The hues of the terminal nodes are indicative of the geographic source of each individual isolate.
The 99 P. hangzhouensis genomes were further divided into five main clusters based on 37,561 core non-recombinant single nucleotide polymorphisms (SNPs) (Fig. S2). To determine the potential transmission pairs among P. hangzhouensis isolates, we analyzed the number of SNPs that differentiate these isolates. As shown in Fig. S3, there were large variations in the number of SNPs among the strains, ranging from 0 to 24,075. However, we identified a large transmission cluster comprising 45 isolates spanning four countries (the United States, Japan, Ghana, and Nigeria) with SNPs ≤ 58, indicating high relatedness among these clones.
Pan-genome analysis within species
To explore the genetic composition of individual species and the differences between species, we investigated the pan-genome features within three species. As shown in Fig. 2A, the pan-genome of P. hangzhouensis is composed of 18,912 gene families, of which 2,430 genes were core genes, 10,684 were accessory genes (genes present in two or more genomes), and 5,798 unique genes (a gene specific to a single genome). Similarly, there were 2,766 core genes, 5,119 accessory genes, and 5,384 unique genes in the P. rettgeri as well as 3,239 core genes, 2,484 accessory genes, and 2,650 unique genes in P. huaxiensis.
Fig 2.
Pan-genome analysis and cluster of orthologous groups (COG) functional annotation of P. hangzhouensis, P. rettgeri, and P. huaxiensis. (A) Pie charts showing the distribution of core, accessory, and unique genes of three species. (B) Accumulation curves for pan-genome (blue) and core genomes (red). (C) Distribution of COG functional categories for core, accessory, and unique genomes.
Analyzing the size of the pan-genome and its expansion or contraction upon the addition of new isolates can serve as a means for predicting the future rate of novel gene discovery within a given species (15, 16). As depicted in Fig. 2B, it is obvious that with the addition of genomes, the pan-genome sizes of the three species are increasing rapidly, while the core-genome size decreases and tends to be a plateau. In addition, we calculated the decay parameter α, where a value of α < 1.0 indicates that the size of the pan-genome is increasing and not limited by the number of genomes considered (15, 16). We obtained α values of 0.66, 0.67, and 0.75 by reusing 100 permutations for P. hangzhouensis, P. rettgeri, and P. huaxiensis, respectively. The results suggested that the pan-genomes of the three species are open, with a greater likelihood of discovering new genes as more genomes are sequenced in the future.
To gain a thorough understanding of the functional traits of each component in the pan-genome, we conducted a cluster of orthologous groups (COG) functional classification for core genes, accessory genes, and unique genes. The results revealed that the three species shared a similar pan-genomic composition in general (Fig. 2C). Specifically, both the accessory genes and unique genes were mainly abundant in replication, recombination, and repair, indicating that recombination was important for evolution.
Genes potentially contributing to the success of P. hangzhouensis
Combined with the occupancy of the database, the clinical isolation rate, and the only strain of P. hangzhouensis we have received in the past year, this suggests that P. hangzhouensis has evolved more successfully and might have higher fitness under hospital settings than the other two species. Only four genes specific to P. hangzhouensis were identified, two of which were non-adjacent hypothetical proteins and the remaining two adjacent genes, folE2 and ccmM, were associated with the metabolic adaptation (Table S1). Comparative genomic analysis revealed that these two consecutive genes, folE2 and ccmM, are located in a relatively conserved region of P. hangzhouensis, while they are absent in the other two species (Fig. 3). The presence of these two genes related to glucose metabolism might promote energy metabolism and contribute to their expansion and survival in certain settings. Notably, a BLAST search revealed that the folE2-like and ccmM-like genes are not present in the other Providencia species, nor in other genera. We next conducted an amino acid-level search and found that the folE2 and ccmM proteins exhibited some similarities to those in other genera, particularly Obesumbacterium proteus, with the highest identity of 61.74% and 82.8% and coverage of 96% and 100%, respectively.
Fig 3.
Comparative genetic environment upstream and downstream of folE2 and ccmM among three species. Genes are shown as arrows and colored based on gene function classification. Those genes without direct gene names are marked with gray.
Recombination in P. hangzhouensis
To investigate the effect of recombination on P. hangzhouensis, we first analyzed the recombination events in three closely related species. We obtained a complex evolutionary network based on the core-genome alignment of three species (Fig. 4A), indicating that recombination has occurred over a considerable part of the genome. We next used fastGEAR to explore the recombined events among species and the results showed that there were few ancestral recombination (occurring during the speciation) and extensive recent recombination (occurring after the speciation) between species, contributing to the higher diversity of P. hangzhouensis genomes (Fig. S4).
Fig 4.
Genomic recombination of the P. hangzhouensis. (A) Phylogenetic network of the P. hangzhouensis, P. rettgeri, and P. huaxiensis based on the core-genome alignment. (B) Number of recent and ancestral recombination events in P. hangzhouensis. (C) Frequency histogram of the size of recombination events in the pan-genome of P. hangzhouensis.
To further investigate the recombination hotspot genes in P. hangzhouensis genomes, we performed analysis of each core gene and accessory gene alignment using the fastGEAR algorithm. A total of 2,840 genes were found to have experienced genetic recombination, accounting for 15.01% of the pan-genome, among which two gene families without specific gene name (group 567 and group 885) and a gene named rsxC showed the highest recombination frequency (Fig. 4B; Table S2). Group 567 represents an insulinase family protein related to proteolysis; and group 885 represents a sel1 repeat family protein involved in signal transduction (17). RsxC is a component of a membrane-bound complex that couples electron transfer with the movement of ions across the membrane.
Most of the recombination events were shorter in length (<300 bp), and the larger recombination events (>1,000 bp) occurred less frequently (Fig. 4C), which indicates that recombination genes in P. hangzhouensis are more inclined to frequent replacement of short DNA fragments.
Distribution of ESBL and carbapenem-resistance genes
The presence of extended-spectrum β-lactamase (ESBL) and carbapenem-resistance genes frequently results in beta-lactam drug resistance in clinic; therefore, we investigated the distribution of these genes in P. hangzhouensis. A total of 84 of 99 strains (84.85%) were detected to carry these genes, and 38 of them (45.24%; 38/84) carry both types (Fig. 5). More precisely, ESBL genes were detected in 44 isolates, with bla OXA-10 the most prevalent (n = 22), followed by bla OXA-1 (n = 14). Carbapenem-resistance genes were detected in 78 isolates, among which bla IMP-27 (n = 36) and bla NDM-1 (n = 26) were the most predominant. Moreover, we analyzed the distribution of plasmids in bacteria harboring these genes. The most prevalent plasmids belonged to the col3M group (n = 52), followed by, IncC type (n = 10), IncT plasmids (n = 8), and IncQ1 plasmids (n = 7).
Fig 5.
Distributions of ESBL and carbapenem-resistance genes, and plasmids’ replicon types in P. hangzhouensis. The presence of the trait category is represented by the colored squares.
Genetic context of bla IMP-27
To obtain a better insight into the most prevalent carbapenem-resistance gene bla IMP-27, we analyzed all contigs (length > 10 kb) carrying this gene and observed three different types of genetic environments of bla IMP-27 (Fig. 6). In all these genetic contexts, bla IMP-27 was found to be located in an Intl2 Integron. The contexts of type 1 and type 2 imipenem-hydrolyzing metallo-β-lactamase (IMP)-carrying sequences were similar to that of plasmid pPM187 (NOWA01000087.1) isolated from Proteus mirabilis (18), with the exception of an insertion sequence (IS2) fragment inserted downstream of the integrase in type 2. The composition of the downstream gene of bla IMP-27 in type 3 has changed considerably when compared to type 1 and type 2, which is consistent with the previously reported structure of pPR1 (NOWC01000095.1) from P. rettgeri (18).
Fig 6.
Schematic diagram of the genetic context surrounding bla IMP-27. Genes are illustrated as arrows and colored according to their function classification. The resistance genes are depicted in red, mobile elements are represented in shades of purple and green, other functional genes are illustrated in yellow, and genes without direct annotation are expressed in gray.
DISCUSSION
In this study, we first reported a novel species, P. hangzhouensis, characterized via comprehensive genome and phenotype analyses. The results from whole-genome ANI and dDDH analyses clearly indicated a significant divergence between P. hangzhouensis and other Providencia species with similarities falling well beneath the established species or subspecies threshold. Furthermore, differential biochemical properties effectively distinguished this species. The collective data strongly affirmed its standalone species status. Historically, P. hangzhouensis has been erroneously classified as P. rettgeri (19, 20), an error that has significantly obscured our understanding of its accurate epidemiology and clinical implications. Therefore, distinguishing P. hangzhouensis from P. rettgeri with precision, through phenotypic or genotypic methodologies, is of paramount importance. Currently, whole-genome-based ANI analysis is a reliable method for distinguishing different species (21), and the phylogeny based on the core genome also could provide a high resolution (Fig. 1B). Interestingly, our study identified two genes associated with metabolism specific to P. hangzhouensis, which hold potential for future rapid PCR identification and differentiation from P. rettgeri. Additionally, it is worth noting that a substantial number of genome sequences labeled as P. rettgeri in GenBank will need a correction to account for the newly identified species P. hangzhouensis.
Although P. hangzhouensis possessed a high degree of genetic relatedness with P. rettgeri and P. huaxiensis at the ANI level (91.54%–91.97%), it exhibited a higher global distribution and larger quantity. The phylogeny based on the core genome of the three species suggested that P. hangzhouensis might be the most recently diverged species (Fig. 1B). The timing of the sample records also indirectly supports this, which can be traced back to 2012 at the earliest (Table S3). Furthermore, it is important to note that P. hangzhouensis genomes had a higher proportion of clinical isolates (Fig. 1B). The minimum spanning tree (MST) based on the core SNPs (Fig. S3) indicated that this species has been widely spread within and between countries, highlighting the need for increased attention and surveillance.
The pan-genome analysis of P. hangzhouensis showed that its genome has a tendency to be open, indicating a high potential for the acquisition of new genes. COG functional classification analysis demonstrated a strong association between the pan-genomes of P. hangzhouensis and recombination. Additionally, the results obtained from fastGEAR indicated that recent recombination events were widespread in three species. Furthermore, a significant proportion (15.01%) of pan-genomes of P. hangzhouensis had a recombination history. These results suggest that the occurrence of recombination in P. hangzhouensis has contributed to the formation and diversity of this species.
It is worth noting that ESBL and carbapenem-resistance genes occurred at a high frequency in P. hangzhouensis, which could often lead to treatment failures in clinical settings. The most frequent carbapenem-resistance gene identified in this study was bla IMP-27, with a frequency of 46.15% (36/78). This gene was first reported in Proteus mirabilis in 2016 (22) and was subsequently found in P. rettgeri (18, 23), while Robert et al. (18) reported that the bla IMP-27 was located on transferable plasmids, which undoubtedly aggravated the risk of its transmission. Furthermore, other IMP-type metallo-β-lactamase-producing P. hangzhouensis isolates have also been reported in Japan (19). Given the high frequency of these carbapenem-resistance genes, it is crucial to pay close attention to their occurrence not only in nosocomial infections but also in other types of infections attributable to P. hangzhouensis.
Our study has limitations. Since nearly half of the isolates analyzed in this study were from the United States, and consequently, our findings may not fully represent the global molecular epidemiology of P. hangzhouensis. In addition, the strains we collected were primarily obtained from databases where the accuracy of collection time may be uncertain. We attempted to perform a Bayesian evolutionary analysis to estimate the date of origin for P. hangzhouensis, but unfortunately, no significant temporal signal was detected. Moreover, due to the previous misidentification of P. hangzhouensis as P. rettgeri, some uploaded plasmids that were labeled or characterized by the authors could not be accurately classified into the correct category, which posed great difficulties for us to conduct a detailed study of the plasmids.
In conclusion, our study identified a novel species, P. hangzhouensis, which was previously misidentified as P. rettgeri. We characterized the novel species through population genomics and analyzed its differences from P. rettgeri and P. huaxiensis. P. hangzhouensis frequently emerges with the emergence of ESBL and carbapenem-resistance genes, highlighting the importance of customized surveillance measures for this species in the future.
MATERIALS AND METHODS
Isolate collection and whole-genome sequencing
In August 2021, a strain of Providencia rettgeri, which is not commonly encountered in clinical settings but exhibited high resistance to cephalosporins, was isolated from a urine specimen of a patient. Genomic DNA of this isolate was extracted using an AxyPrep bacterial genomic DNA miniprep kit (Axygen Scientific, Union City, CA, USA). Whole-genome sequencing was performed on an Illumina HiSeq 2500 platform (paired-end run; 2 × 150 bp) and an Oxford Nanopore MinION platform. The publicly available genome assemblies and short-read data for P. rettgeri and P. huaxiensis were retrieved from the Assembly and SRA databases of NCBI using Kingfisher v7.6.1 (https://github.com/onevcat/Kingfisher) and ncbi-genome-download v0.3.1 (https://github.com/kblin/ncbi-genome-download), respectively. Assemblies with the label of poor by PATRIC (https://www.patricbrc.org/) and those derived from metagenomic isolates were excluded from the analysis.
Genome assembly and species identification
Sequencing reads were subjected to adaptor trimming and quality filtering using fastp v0.23.2 (24). Trimmed reads were assembled using shovill v1.1.0 (https://github.com/tseemann/shovill) in the careful mode, and contigs with a length below 200 bp were filtered. The complete PR-310 genome was generated by synergizing the short-read data from Illumina sequencing and long-read data from Oxford Nanopore MinION sequencing through the Unicycler hybrid assembly pipeline (25). The quality of all assemblies was assessed using QUAST v5.2.0 (26), and assemblies with a contig count >300 were classified as low-quality genomes, and thus, excluded from subsequent analysis. The 16S rRNA gene sequences of PR-310 were procured through PCR, employing universal primers 27F and 1492R. Corresponding gene sequences from other species were acquired from the EzBioCloud database (12). These 16S rRNA gene sequences were compared using MAFFT v7.471 (27). Genomic ANIs were computed using OrthoANI (28) and in silico digital DNA-DNA hybridization index was assessed using genome-to-genome distance calculator (formula 2) (11). Table 1 enumerates the type strains of Providencia that are utilized for the purpose of species identification.
Phenotypic characterization for the strain of novel species Providencia hangzhouensis
As previously described (29), the Gram-stain was performed and the biochemical characteristics of strain PR-310 were determined using the bioMérieux API 20E kits according to the manufacturer’s instructions. Oxidase activity was determined using an oxidase reagent (bioMérieux).
Pan-genome analysis
Assemblies were annotated using Prokka v1.14.6 (30) with default settings. Pan-genome analysis for three species (Providencia rettgeri, Providencia huaxiensis, and Providencia hangzhouensis) was performed using Roary v3.13 (31) with a parameter of 100% coverage (i.e., only genes present in all isolates were thought to be core genes), respectively. The pan-genome’s decay parameter (α) was calculated using pagoo (32) and the accumulation curves of pan-genomes and core-genomes were visualized using ggplot2 (33). COG functional categories of the core, accessory, and unique gene sets of each species were functionally characterized using eggNOG-mapper v2.1.9 (34).
Core-genome phylogeny
Core genes for all isolates of three species were determined by Roary with the parameters of a minimum of 90% blastp identity and 100% coverage. Nucleotide alignments of core genes were first generated by MAFFT v7.471 (27) and then concatenated to give a single core-genome alignment. TrimAl v1.4.1 (35) was used for further trimming and refinement of the alignment. Those clusters containing paralogous genes were filtered out of the final core gene alignment. Based on the refined core-genome alignment, an ML phylogeny tree was reconstructed using RaxML v8.2.12 (36), with a General Time Reversible (GTR) nucleotide substitution model selected as the optimal model by ModelTest-NG (37), and applying gamma correction with 1,000 bootstrap replications. The phylogenetic tree was visualized in ggtree (38) and phylogenetic network was detected using SplitsTree v4.14.5 (39) with NeighborNet algorithm base on the refined core-genome alignment. In order to search for genes specific to P. hangzhouensis, Roary was utilized to establish a minimum threshold value of 80% for the blastp threshold, as well as parameters that permitted paralogous division. Only those genes that were present in all isolates of P. hangzhouensis and absent in all isolates from other species were deemed to be unique genes (40).
Recombination analysis
Genomic recombination events between three species were detected using fastGEAR (41) with default parameters. For further detecting the recombinations in P. hangzhouensis, fastGEAR was used to determine genes that undergo high-frequency recombination in the core genes and accessory genes. Recombined regions within the core genome were identified using Gubbins v3.3 (42) with the whole-genome alignment generated by Snippy v4.6.0 (https://github.com/tseemann/snippy) (CP029736.1 genome as a reference) as input file. A phylogenetic tree was constructed using RAxML based on the recombination-free core SNPs alignment with a GTR model and gamma correction (1,000 bootstrap replications). The population structure based on non-recombinant SNPs was estimated using RhierBAPS (43). The SNP-based MSTs were generated using PHYLOViZ v2.0 (https://online.phyloviz.net/index).
Resistance genes and plasmid replicon type detection
Antimicrobial resistance genes and plasmid replicon types were identified using ABRicate v1.0.0 (https://github.com/tseemann/abricate) based on ResFinder, CARD, and PlasmidFinder database. Mobile genetic elements were identified using ISFinder (44) and INTEGRALL (45) with default parameters. ESBL and carbapenem-resistance genes were defined according to the BLDB database (46), and the presence or absence of these genes was visualized using the ComplexHeatmap (47). Comparisons of the nucleotide sequences were conducted using BLASTN and visualized in genoplotR (48).
ACKNOWLEDGMENTS
The authors would like to acknowledge all study participants who contributed to this study.
The authors also acknowledge the support of National Infectious Disease Medical Center startup fund (Y.Z.) (B2022011-1), Jinan Microecological Biomedicine Shandong Laboratory project (JNL-2022050B), and Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (no. 2021R01012).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Contributor Information
Ying Zhang, Email: yzhang207@zju.edu.cn.
Bonnie Chase Prokesch, University of Texas Southwestern Medical Center, Dallas, Texas, USA .
DATA AVAILABILITY
The information of strains used in this study is listed in Table S3. The complete genome sequences of PR-310 have been deposited in the GenBank database under BioProject accession no. PRJNA940527.
SUPPLEMENTAL MATERIAL
The following material is available online at https://doi.org/10.1128/spectrum.01227-23.
Fig. S1-S4, Table of Contents of all Supplemental figures and tables.
Table S1-S3.
ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse.
REFERENCES
- 1. Wie S-H. 2015. Clinical significance of Providencia bacteremia or bacteriuria. Korean J Intern Med 30:167–169. doi: 10.3904/kjim.2015.30.2.167 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Koreishi AF, Schechter BA, Karp CL. 2006. Ocular infections caused by Providencia rettgeri. Ophthalmology 113:1463–1466. doi: 10.1016/j.ophtha.2006.03.047 [DOI] [PubMed] [Google Scholar]
- 3. Wang TKM, Ahn Y, Dunlop J. 2014. Providencia rettgeri peritonitis in a patient on peritoneal dialysis with perforated appendicitis. Perit Dial Int 34:569–570. doi: 10.1177/089686081403400501 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Sharma D, Sharma P, Soni P. 2017. First case report of Providencia rettgeri neonatal sepsis. BMC Res Notes 10:536. doi: 10.1186/s13104-017-2866-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Stackebrandt E, Goebel BM. 1994. Taxonomic note: a place for DNA-DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology. Int J Syst Evol Microbiol 44:846–849. doi: 10.1099/00207713-44-4-846 [DOI] [Google Scholar]
- 6. Mulet M, Lalucat J, García-Valdés E. 2010. DNA sequence-based analysis of the Pseudomonas species. Environ Microbiol 12:1513–1530. doi: 10.1111/j.1462-2920.2010.02181.x [DOI] [PubMed] [Google Scholar]
- 7. Besser J, Carleton HA, Gerner-Smidt P, Lindsey RL, Trees E. 2018. Next-generation sequencing technologies and their application to the study and control of bacterial infections. Clin Microbiol Infect 24:335–341. doi: 10.1016/j.cmi.2017.10.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Ciufo S, Kannan S, Sharma S, Badretdin A, Clark K, Turner S, Brover S, Schoch CL, Kimchi A, DiCuccio M. 2018. Using average nucleotide identity to improve taxonomic assignments in prokaryotic genomes at the NCBI. Int J Syst Evol Microbiol 68:2386–2392. doi: 10.1099/ijsem.0.002809 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. 2007. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol 57:81–91. doi: 10.1099/ijs.0.64483-0 [DOI] [PubMed] [Google Scholar]
- 10. Richter M, Rosselló-Móra R. 2009. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci U S A 106:19126–19131. doi: 10.1073/pnas.0906412106 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. 2013. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics 14:60. doi: 10.1186/1471-2105-14-60 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Yoon S-H, Ha S-M, Kwon S, Lim J, Kim Y, Seo H, Chun J. 2017. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol 67:1613–1617. doi: 10.1099/ijsem.0.001755 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Chun J, Oren A, Ventosa A, Christensen H, Arahal DR, da Costa MS, Rooney AP, Yi H, Xu X-W, De Meyer S, Trujillo ME. 2018. Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. Int J Syst Evol Microbiol 68:461–466. doi: 10.1099/ijsem.0.002516 [DOI] [PubMed] [Google Scholar]
- 14. Li Z, Liao F, Ding Z, Chen S, Li D. 2022. Providencia manganoxydans sp. nov., a Mn(II)-oxidizing bacterium isolated from heavy metal contaminated soils in Hunan Province, China. Int J Syst Evol Microbiol 72. doi: 10.1099/ijsem.0.005474 [DOI] [PubMed] [Google Scholar]
- 15. Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. 2005. The microbial pan-genome. Curr Opin Genet Dev 15:589–594. doi: 10.1016/j.gde.2005.09.006 [DOI] [PubMed] [Google Scholar]
- 16. Tettelin H, Riley D, Cattuto C, Medini D. 2008. Comparative genomics: the bacterial pan-genome. Curr Opin Microbiol 11:472–477. doi: 10.1016/j.mib.2008.09.006 [DOI] [PubMed] [Google Scholar]
- 17. Mittl PRE, Schneider-Brachert W. 2007. Sel1-like repeat proteins in signal transduction. Cell Signal 19:20–31. doi: 10.1016/j.cellsig.2006.05.034 [DOI] [PubMed] [Google Scholar]
- 18. Potter RF, Wallace MA, McMullen AR, Prusa J, Stallings CL, Burnham CAD, Dantas G. 2018. blaIMP-27 on transferable plasmids in Proteus mirabilis and Providencia rettgeri. Clin Microbiol Infect 24:1019. doi: 10.1016/j.cmi.2018.02.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Iwata S, Tada T, Hishinuma T, Tohya M, Oshiro S, Kuwahara-Arai K, Ogawa M, Shimojima M, Kirikae T. 2020. Emergence of carbapenem-resistant Providencia rettgeri and Providencia stuartii producing IMP-type metallo-β-lactamase in Japan. Antimicrob Agents Chemother 64:e00382-20. doi: 10.1128/AAC.00382-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Tchuinte PLS, Rabenandrasana MAN, Ramparany L, Ratsima E, Enouf V, Randrianirina F, Collard J-M. 2020. Genome-based insights into the resistomes and mobilomes of two Providencia rettgeri strains isolated from wound infections in Madagascar. J Glob Antimicrob Resist 20:178–182. doi: 10.1016/j.jgar.2019.07.013 [DOI] [PubMed] [Google Scholar]
- 21. Wu W, Feng Y, Zong Z. 2020. 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 5:e00527-20. doi: 10.1128/mSystems.00527-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Dixon N, Fowler RC, Yoshizumi A, Horiyama T, Ishii Y, Harrison L, Geyer CN, Moland ES, Thomson K, Hanson ND. 2016. IMP-27, a unique metallo-β-lactamase identified in geographically distinct isolates of Proteus mirabilis. Antimicrob Agents Chemother 60:6418–6421. doi: 10.1128/AAC.02945-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Saavedra SY, Montilla-Escudero E, Wiesner M, González MN, Hidalgo AM, Ovalle MV, Chala MDS, Duarte C. 2022. First identification of the blaIMP-27 gene in a clinical isolate of Providencia rettgeri in Colombia. J Glob Antimicrob Resist 30:428–430. doi: 10.1016/j.jgar.2022.05.005 [DOI] [PubMed] [Google Scholar]
- 24. Chen S, Zhou Y, Chen Y, Gu J. 2018. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884–i890. doi: 10.1093/bioinformatics/bty560 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Wick RR, Judd LM, Gorrie CL, Holt KE. 2017. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 13:e1005595. doi: 10.1371/journal.pcbi.1005595 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Gurevich A, Saveliev V, Vyahhi N, Tesler G. 2013. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29:1072–1075. doi: 10.1093/bioinformatics/btt086 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Katoh K, Misawa K, Kuma K, Miyata T. 2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30:3059–3066. doi: 10.1093/nar/gkf436 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Lee I, Ouk Kim Y, Park S-C, Chun J. 2016. OrthoANI: an improved algorithm and software for calculating average nucleotide identity. Int J Syst Evol Microbiol 66:1100–1103. doi: 10.1099/ijsem.0.000760 [DOI] [PubMed] [Google Scholar]
- 29. Kamlage B. 1996. Methods for general and molecular Bacteriology. edited by P. Gerhardt, R. G. E. Murray, W. A. wood and N. R. Krieg. 791 pages, numerous figures and tables. American society for Microbiology, Washington, D.C., 1994. price: 55.00 £, Vol. 40, p 103. [Google Scholar]
- 30. Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068–2069. doi: 10.1093/bioinformatics/btu153 [DOI] [PubMed] [Google Scholar]
- 31. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, Fookes M, Falush D, Keane JA, Parkhill J. 2015. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31:3691–3693. doi: 10.1093/bioinformatics/btv421 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Ferrés I, Iraola G. 2021. An object-oriented framework for evolutionary pangenome analysis. Cell Rep Methods 1:100085. doi: 10.1016/j.crmeth.2021.100085 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. H W. 2016. ggplot2: Elegant Graphics for data analysis. Springer-Verlag New York. [Google Scholar]
- 34. Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. 2021. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol Biol Evol 38:5825–5829. doi: 10.1093/molbev/msab293 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. 2009. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25:1972–1973. doi: 10.1093/bioinformatics/btp348 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Stamatakis A. 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313. doi: 10.1093/bioinformatics/btu033 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Darriba D, Posada D, Kozlov AM, Stamatakis A, Morel B, Flouri T. 2020. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol Biol Evol 37:291–294. doi: 10.1093/molbev/msz189 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Yu G, Smith DK, Zhu H, Guan Y, Lam TT-Yuk. 2017. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 8. doi: 10.1111/2041-210X.12628 [DOI] [Google Scholar]
- 39. Huson DH, Bryant D. 2006. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol 23:254–267. doi: 10.1093/molbev/msj030 [DOI] [PubMed] [Google Scholar]
- 40. Luo T, Xu P, Zhang Y, Porter JL, Ghanem M, Liu Q, Jiang Y, Li J, Miao Q, Hu B, Howden BP, Fyfe JAM, Globan M, He W, He P, Wang Y, Liu H, Takiff HE, Zhao Y, Chen X, Pan Q, Behr MA, Stinear TP, Gao Q. 2021. Population genomics provides insights into the evolution and adaptation to humans of the waterborne pathogen Mycobacterium kansasii. Nat Commun 12:2491. doi: 10.1038/s41467-021-22760-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Mostowy R, Croucher NJ, Andam CP, Corander J, Hanage WP, Marttinen P. 2017. Efficient inference of recent and ancestral recombination within bacterial populations. Mol Biol Evol 34:1167–1182. doi: 10.1093/molbev/msx066 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Croucher NJ, Page AJ, Connor TR, Delaney AJ, Keane JA, Bentley SD, Parkhill J, Harris SR. 2015. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res 43:e15. doi: 10.1093/nar/gku1196 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Tonkin-Hill G, Lees JA, Bentley SD, Frost SDW, Corander J. 2018. RhierBAPS: an R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res 3:93. doi: 10.12688/wellcomeopenres.14694.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. 2006. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res 34:D32–D36. doi: 10.1093/nar/gkj014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Moura A, Soares M, Pereira C, Leitão N, Henriques I, Correia A. 2009. INTEGRALL: a database and search engine for integrons, integrases and gene cassettes. Bioinformatics 25:1096–1098. doi: 10.1093/bioinformatics/btp105 [DOI] [PubMed] [Google Scholar]
- 46. Naas T, Oueslati S, Bonnin RA, Dabos ML, Zavala A, Dortet L, Retailleau P, Iorga BI. 2017. Beta-lactamase database (BLDB) – structure and function. J Enzyme Inhib Med Chem 32:917–919. doi: 10.1080/14756366.2017.1344235 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Gu Z, Eils R, Schlesner M. 2016. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32:2847–2849. doi: 10.1093/bioinformatics/btw313 [DOI] [PubMed] [Google Scholar]
- 48. Guy L, Kultima JR, Andersson SGE. 2010. genoPlotR: comparative gene and genome visualization in R. Bioinformatics 26:2334–2335. doi: 10.1093/bioinformatics/btq413 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Fig. S1-S4, Table of Contents of all Supplemental figures and tables.
Table S1-S3.
Data Availability Statement
The information of strains used in this study is listed in Table S3. The complete genome sequences of PR-310 have been deposited in the GenBank database under BioProject accession no. PRJNA940527.