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Species-specific identification of Pseudomonas based on 16S–23S rRNA gene internal transcribed spacer (ITS) and its combined application with next-generation sequencing



Pseudomonas species are widely distributed in the human body, animals, plants, soil, fresh water, seawater, etc. Pseudomonas aeruginosa is one of the main pathogens involved in nosocomial infections. It can cause endocarditis, empyema, meningitis, septicaemia and even death. However, the Pseudomonas classification system is currently inadequate and not well established.


In this study, the whole genomes of 103 Pseudomonas strains belonging to 62 species available in GenBank were collected and the specificity of the 16S–23S ribosomal RNA internal transcribed spacer (ITS) sequence was analysed. Secondary structures of ITS transcripts determining where the diversity bases were located were predicted. The alignment results using BLAST indicated that the ITS sequence is specific for most species in the genus. The remaining species were identified by additional frequency analyses based on BLAST results. A double-blind experiment where 200 ITS sequences were randomly selected indicated that this method could identify Pseudomonas species with 100% sensitivity and specificity. In addition, we applied a universal primer to amplify the Pseudomonas ITS of DNA extracts from fish samples with next-generation sequencing. The ITS analysis results were utilized to species-specifically identify the proportion of Pseudomonas species in the samples.


The present study developed a species-specific method identification and classification of Pseudomonas based on ITS sequences combined NGS. The method showed its potential application in other genera.

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The Pseudomonas was described in 1894 and is one of the most diverse and ubiquitous bacterial genera [1]. Detailed information about Pseudomonas is available at this link: Pseudomonas is a group of Gram-negative, and has both aerobic and anaerobic species [2], with a capsule and flagella. They are widely distributed in the human body, animals, plants, soil, fresh water, seawater, etc. [3,4,5,6]. The Pseudomonas genus is a metabolically versatile group of organisms, which are known to occupy numerous ecological niches. Their diversity influences the phylogenetic diversity and heterogeneity of these communities [7]. Pseudomonas consists of more than 200 effectively described species, most of which are closely related to clinical medicine. For example, P. aeruginosa is one of the most common pathogens causing infections in humans and warm-blooded animals, with the ability to migrate, evade host immune responses, damage host cells and be highly adaptable to a variety of environment [8]. It can lead to a wide range of disease manifestations, commonly chronic and difficult to eradicate. P. aeruginosa is considered as an opportunistic hospital-acquired Gram-negative pathogen, and is more likely to occur in immunocompromised populations. It can cause infection of airways, urinary tracts, skin and soft tissue associated with burns and surgery, eye and blood, especially for people with hypoimmunity [9]. It can also cause endocarditis, empyema, meningitis, septicaemia and even death. Infections of P.aeruginosa infections often manifest severe drug resistance, even causing death. Other strains of this genus are associated with fish pathogens that can cause haemorrhagic septicaemia and ulcer syndrome [10]. P. fluorescens can cause iatrogenic acute infection and has been reported in clinical samples from the oral cavity, stomach and lungs. Sepsis, septic shock and intravascular coagulation may occur after infection. As many antibiotics are not sensitive to P. fluorescens, the fatality rate is high [11]. Similarly, P. stutzeri has been shown to be associated with endocarditis [12]. In order to detect specific pathogens early and carry out clinical intervention as soon as possible, it is necessary to accurately and systematically identify Pseudomonas [13].

The traditional molecular detection of Pseudomonas usually focuses on specific genomic DNA sequences of one or several pathogens. These methods are not appropriate for systematic microecological analysis. Recently, next-generation sequencing (NGS) has been utilized as a powerful tool for pathogen detection and microecological research [14,15,16,17]. The target sequences for NGS bacterial analysis are variable regions in the 16S rRNA gene sequence [18]. Some new sequencing platforms can sequence approximate full-length 16S rRNA gene [19]. These methods can identify prokaryotes to the genus level, and have been widely used in the analysis of human and environmental microecology. However, a phylogenetic tree analysis of Pseudomonas demonstrated the low value of at least 30 species type strains to differentiate among species [20]. Nevertheless, genus identification usually cannot meet the microecological analysis needs. Although the physiological or biochemical characteristics of bacteria in the same genus are similar, their pathogenicity is quite different. For example, P. aeruginosa is an opportunistic pathogen causing a wide range of diseases in humans, whereas P. stutzeri, found in plants, rarely causes human diseases [21, 22].

Generally, a candidate target for exact phylogenetic analysis must depend on other alternative housekeeping genes of bacteria, such as gyrB, rpoD, and rpoB [23,24,25]. Meanwhile, the 16S–23S rRNA gene internal transcribed spacer (ITS) is a non-coding sequence in the rrn operon. It has also been utilized in bacterial phylogenetic analysis and identification [26]. As a non-coding sequence, the ITS is more specific than the 16S rRNA gene [27,28,29,30]. In our previous study, further analysis of BLAST results based on ITS sequences alignment were utilized to identified Vibrio and Streptococcus species-specifically [31,32,33].

The taxonomy of the Pseudomonas genus is controversial. In this study, we applied a method based on ITS for species-specific identification of Pseudomonas. Then, we actually utilized the method combined NGS to analyze a sample from fish. The results showed that Pseudomonas species indeed can be accurately identified at the species level. It may be applied to the detection of other prokaryotes.


Primary and secondary structures

For this study, the complete genome sequences of 103 Pseudomonas strains from GenBank were collected and analyzed. These sequences belonged to 62 Pseudomonas species. The following is a summary of the ITS sequence characteristics of Pseudomonas. A total of 560 rrn operon sequences were collected and the number of ITSs in each strain was 3–8. In the Pseudomonas species selected, the number of operons was different (Supplemental Table 1). According to the tRNA gene (tDNA) contained in the ITS, all rrn operons can be divided into two types: (1) type N (ITS without tDNA), with a length of 310 ± 20 bp; and (2) type-IA (ITS contains tDNAIle and tDNAAla), with a length of 500 ± 50 bp. Type-IA appears in all Pseudomonas species, whereas type N appears much less often, we selected the type-IA ITS as the research sequences. In addition, according to statistics, type-IA accounts for 91.1% of the 560 ITS sequences collected. The Pseudomonas species containing N-type ITS sequences were P. putida, P. antarctica, P. entomophila, P. fulva, P. mandelii, P. plecoglossicida and P. psychrophila, and their proportions of ITS types were different.

Type-IA ITS sequences were arranged and aligned using the GeneTool Lite 1.0 software and it showed a mosaic structure. These sequences were divided into five parts: (1) the upstream sequence of tDNAIle (US), with a length of 90 ± 30 bp; (2) tDNAIle; (3) the linker sequence between tDNAIle and tDNAAla (LS), with a length of 20 ± 10 bp; (4) tDNAAla; and (5) downstream of the tDNAAla sequence (DS), with a length of 240 ± 20 bp. All the US, LS, DS and the whole ITS sequence (WS) contain C regions and V regions. The N-type sequences of 13 strains were aligned, and the C regions and V regions could also be identified from the ITS sequence.

After simulating the secondary structure of the type-IA rrn operon by RNA structure 4.2 software, the secondary structure of Pseudomonas species was found to share a common trunk. Taking P. aeruginosa, for example (Fig. 1), the secondary structure contains a variety of stem-loop structures. There are three hybridized stems respectively with the upstream of 16S rRNA gene or the downstream of 23S rRNA gene, constituting reverse complementary sequences called hybrid stems (h-stem), and two stems folded with the neighbouring sequences called inner items (i-stem). In addition, each ITS sequence contains three C regions (C1, C2, C3) and three V regions (V1, V2, V3). It corresponds to the mosaic structure obtained from the GeneTool Lite software. We determined that the diversity of the sequence was mostly in the inner stems. The ITS sequence participates in the folding of the 16S and 23S rRNA genes, indicating that the ITS is an important and indispensable structure.

Fig. 1
figure 1

Secondary structures of P. aeruginosa ITS. Green frame, C1,C2,C3 block; gray frame, upstream of 16S rRNA gene and downstream of 23S rRNA gene; red frame, tRNAIle and tRNAAla; blue frame, V block; orange frame, mutation region; I – stem, inner stem; H – stem, hybrid stem

Species-specific analysis of ITS sequences

The specificity of the four Pseudomonas substructures, which are US, LS, DS and WS, were evaluated by BLAST. The Gap values of 62 strains of Pseudomonas were obtained according to the RS values difference of the lowest target bacteria and the highest non-target bacteria in BLAST results (Fig. 2).

Fig. 2
figure 2

S-gap of Pseudomonas ITS. Vertical axis, S-gap value. Blue rhombic point, S-gap of US; red circular point, S-gap of LS; gray square point, S-gap of DS; green triangle point, S-gap of WS

Although five red dots representing LS reached the Gap value of 100%, the short sequence caused no difference between target bacteria and non-target bacteria in BLAST results, often resulting in the Gap value of zero. This was shown in the figure of 41 strains, including P. aeruginosa, P. denitrificans, P. entomophila, P. fluorescens, P. granadensis and P. knackmussii. LS is not suitable as a species-specific DNA marker. In addition to the red dots, it can be clearly seen from the figure that the grey dots representing DS and the green dots representing WS show a higher Gap, whereas the blue dots representing US show a lower Gap because their sequence length is only about one-third of the DS.

By analyzing the results of Gap value, the US, DS and WS of 27 Pseudomonas species showed positive results, such as P. alcaligenes, P. alcaliphila, P. asturiensis, P. balearica, P. corrugata, P. cremoricolorata, etc. The ITS sequence used as a genetic marker in these 27 species was efficient. However, the Gap value of the other 35 species was negative number, and the performance of US, DS and WS was consistent.

Therefore, 35 strains were further analyzed and their frequency graphs were generated by calculating the RS values of target bacteria and non-target bacteria at each stage. According to the frequency analysis results of these 35 species, they can be divided into four types.

The first type. P. aeruginosa is the species with the largest amount of data in NCBI; the BLAST results are more complex as well. Its specificity was discussed in three aspects: (1) a frequency diagram was made based on BLAST results (Fig. 3a–c); (2) a frequency diagram was made with BLAST results containing only genome-complete data (Fig. 3d–f); and (3) the three P. aeruginosa sequences with low RS values from BLAST results were used as target bacteria to conduct BLAST again (Fig. 3g–i). The coverage of target bacteria and non-target bacteria was crossed, but the RS value of target bacteria aggregated on the horizontal axis was significantly higher than that of non-target bacteria. Therefore, frequency analysis can reveal the species-specificity of the ITS in P. aeruginosa. In addition, this conclusion can be obtained by frequency analysis of similar types of P. putida, P. fluorescens, P. stutzeri, etc.

Fig. 3
figure 3

Frequency analysis of P. aeruginosa. Vertical axis, frequency value; horizontal axis, RS value. The red line, P. aeruginosa as the target species; the blue line, non-target species. a US of P. aeruginosa based on BLAST results. b DS of P. aeruginosa. c WS of P. aeruginosa. d US of P. aeruginosa based on genome-complete data. e DS of P. aeruginosa. f WS of P. aeruginosa. g WS of P. aeruginosa strain EPa3. h WS of P. aeruginosa strain G1. i WS of P. aeruginosa strain ATCC 10145 T

The second type. The sequence of P. parafulva is few in NCBI database. In BLAST results, one of the ITS sequences showed a low RS value (0.62), making the RS of some non-target bacteria exceed it and showing a negative Gap value. In terms of this situation, the low RS sequence and the BLAST result were targeted to obtain six frequency diagrams (Fig. 4a–f). We still get good results from frequency analysis. This conclusion can be obtained from the analysis of the same type of P. mordocina, P. monteilii and P. bassicacearum.

Fig. 4
figure 4

Frequency analysis of P. parafulva and P. amygdali. Vertical axis, frequency value; horizontal axis, RS value. a-f The red line, P. parafulva as the target species; the blue line, non-target species. a US of P. parafulva. b DS of P. parafulva. c WS of P. parafulva. d US of P. parafulva of lower RS values. e DS of P. parafulva of lower RS values. f WS of P. parafulva of lower RS values. g-i The red line, P. amygdali as the target species; the gray line: P. syringae; the blue line, other non-target species. g US of P. amygdali. h DS of P. amygdali. i WS of P. amygdali

The third type. From the BLAST results of P. amygdali, the GAP value of P. amygdali affected by P. syringae was shown. Instead of comparing two objects, P. amygdali, P. syringae and non-target bacteria were compared. The frequency diagram is shown in Fig. 4g–i. The RS value of P. amygdali represented by the red line aggregated on the horizontal axis was significantly higher than those of the other two groups, indicating the species-specificity of the ITS in P. amygdali.

The fourth type. P. syringae can be pathogenic to a variety of organisms, which can be divided into P. syringae pv. actinidiae, P. syringae pv. tomato, P. syringae pv. syringae and other pathogenic bacteria. According to the frequency analysis, the species-specificity of the ITS in P. syringae pv. actinidiae and P. syringae pv. tomato was demonstrated (Fig. 5a–f). However, during the analysis of P. syringae pv. syringae and P. syringae pv. maculicola, their specificity could not be accurately obtained because of interference by other pathogenic bacteria (Fig. 5g, h). In conclusion, the ITS has high specificity with P. syringae specie.

Fig. 5
figure 5

Frequency analysis of P.syringae. Vertical axis, frequency value; horizontal axis, RS value. a-f The red line, the target species; the blue line, non-target species. a US of P. syringae pv. actinidiae. b DS of P. syringae pv. actinidiae. c WS of P. syringae pv. actinidiae. d US of P. syringae pv. tomato. e DS of P. syringae pv. tomato. f WS of P. syringae pv. tomato. g WS of P. syringae pv. syringae. The red line, P. syringae pv. syringae as the target species; the gray line, P. syringae pv. porri; the blue line, other non-target species. h WS of P. syringae pv. maculicola. The Red line, P. syringae pv. maculicola as the target species; the gray line, P. syringae pv. actinidiae; the blue line, other non-target species

Therefore, through frequency analysis with different ways of 35 species without GAP value, all species researched can be accurately distinguished by frequency chart. These results suggest that ITS and its subdomain can be used as DNA markers expressing species-specificity. These results this method is highly specific inter species, but not intra species.

Identification of Pseudomonas by ITS

To verify this conclusion, 200 ITS sequences from the NCBI were selected, including ITS sequences of 160 Pseudomonas strains and ITS sequences of 40 non-Pseudomonas strains but belonging to Pseudomonadales, and then randomly scrambled for a double-blind experiment (Supplemental Table 2). In this experiment, neither the experimenter nor the analyst knew which strain the sequence belonged to. The results showed that 66 ITS sequences could be directly identified by Gap, and the remaining 134 ITS sequences were identified successfully by further frequency analysis, with a success rate of 100% (Table 1).

Table 1 Results of the double-blind experiment

Identification of Pseudomonas sp. in samples

ITS sequences of the bacteria in samples were amplified, and species-level identification was performed. The ITS sequence analysis revealed the species and proportion of Pseudomonas in the samples. Abundance values of Pseudomonas were obtained from 12 samples. The three bacteria with the highest abundance in each sample and their proportions are listed in Table 2. P. putida, P. monteilii, P. koreensis, P. aeruginosa and P. fluorescens are widely distributed in water. Among them, P. putida is the most widely distributed, with the highest abundance.

Table 2 Results of next generation sequencinga


The NCBI has collected 1582 pieces of complete Pseudomonas genomes, which are divided into 98 species in Berger’s manual release 5.0 May 2004. Recently, analysis of the Pseudomonas genus suggested that rearrangement within the genus is needed. However, for more understandable, we still used the Berger’s manual system of Pseudomonas in this paper [20, 34, 35]. To ensure the randomness of species selection and to cover all species labelled and annotated as much as possible, 103 strains of Pseudomonas were selected. According to the primary structure comparison and secondary structure prediction, the variable region of the ITS sequence was preliminarily obtained in this genus to determine ITS diversity. Twenty-seven strains could be distinguished simply by Gap analysis and 35 strains could be distinguished by frequency analysis. Therefore, the conclusions drawn in this paper are more representative. During the study, this method could identify Pseudomonas to the species level, but also classify its pathovar, including P. syringae pv. tomato, P. syringae pv. actinidiae, P. syringae pv. porri and P. coronafaciens pv. oryzae (Fig. 5c, f). We used various software for analysis the sequences and evaluation the identification method developed in present study. The most important one of them is RNAstructure version 4.2, which illustrated the relationship between species-specific sequence in ITS and the secondary structure of ITS. We also arranged and compared the sequences by GeneTool version 1.0 and MEGA version 7.0. The GeneTool is faster, and MEGA is ready for a phylogenetic analysis. The GView Server is also employed to illustrated the ITS arrangement in the complete genome sequences. The output graphic of GView Server is more understandable and comprehensible than that of NCBI website. Due to the high similarity of 16S rRNA gene among Pseudomonas species [20], it is impossible to identify strains by 16S rRNA sequencing, which means it need to be verified by combining multiple genes. This is the innovation of this article. Although we have no method to verify our sequencing analysis results, we have made a full theoretical analysis in this article, and more samples can be combined to verify this method in future research. Currently, this method is still carried out manually. In addition, compared with previous research results on the distribution of Pseudomonas in water [36, 37], the NGS results obtained are simpler and more accurate for classification. This suggests that the ITS identification method combined with NGS has a wide scope of application. This study can provide a new genetic basis for updating the classification of Pseudomonas, and the combination of ITS and NGS is an effective tool for conducting microecological research. We will systematize and standardize the application of this technology to hospital laboratories, to accurately identify pathogens using molecular biology sequencing.


The present study developed a species-specific method identification and classification of Pseudomonas based on ITS sequences combined NGS. The method showed its potential application in other genera.


ITS sequence alignment and secondary structure prediction.

To analyze the ITS of Pseudomonas accurately, whole-genome sequences of 103 Pseudomonas strains were collected, belonging to 62 species, from the nucleotide database of the National Center for Biotechnology Information (NCBI) website ( The accession numbers of these strains are listed in Supplemental Table 1. The complete genome sequences was analyzed and illustrated the ITS arrangement by the GView Server software ( [38]. All rrn operons in each genome were extracted and stored uniformly. The order of the rrn operon was arranged from 5′ to 3′ like the 16S rRNA gene, 23S rRNA gene and 5S rRNA gene; the 5′-end of 16S rRNA gene marked as (GAACTG); the 3′-end of 16S rRNA gene marked as (CCTTAA); the 5′-end and 3′-end of 23S rRNA gene marked as (GTTATA) and (ACAATT) respectively. Then, the ITS sequences were grouped based on their tDNA. The ITS sequences were analyzed using the GeneTool Lite 1.0 software. The consensus regions (C regions) and variable regions (V regions) were assessed by permutation. Then, the secondary structure of rrn transcript products was predicted using RNAstructure 4.2 software.

ITS sequence specificity analysis

The whole ITS sequence (WS) was divided into three substructures by tDNA. These were the upstream sequence of tDNAIle (US), the sequence linking tDNAIle and tDNAAla (LS), and the downstream of tDNAAla sequence (DS). All subsequences were aligned with the WSs of each genome using BLAST with the default setting on the NCBI ( Further analysis of BLAST results were performed [31, 39]. The Max Score was used as it covers the comprehensive results of the E value and other results. Briefly, the sequences in the BLAST result were divided into a target group (belonging to the same species as a query sequence) and a non-target group. The relative score (RS; defined as the ratio of the Max score of each sequence to the highest value of the Max score in every alignment) for each sequence. Then, the difference (Gap value) between the lowest RS value of target group and the highest RS value of non-target group was calculated. A positive Gap of any sequences(US, LS, DS and WS) would prove its species-specific. We analyze the results of the different sequences separately to determine which sequence is better at identifying the species.

For a negative Gap, further frequency analysis was performed. First, the RS values of target and non-target strains were grouped. Second, the RS values were divided from 0 to 100% into ten subgroups, with a spacing of 10%, and the RS = 100% sequence was separately divided into one subgroup, resulting in a total of 11 subgroups. Third, the number of sequences in each subgroup was calculated and the frequency of each subgroup sequence was determined. If the frequency of the target species is greater than that of non-target species in the RS = 100% subgroup, then we compared the frequency value in the next subgroup. If their frequencies are equal, the frequency values of the last subgroup can be compared. Then, these sequences are classified and a workflow can be developed for species-specific analysis of Pseudomonas (Fig. 6a).

Fig. 6
figure 6

a Workflow for specific analysis of Pseudomonas. b Workflow for species identification of Pseudomonas by ITS sequence in the samples

Verification ITS sequence specificity in Pseudomonas

To verify the Gap analysis and frequency analysis methods of identifying Pseudomonas to the species level, 160 sequences of Pseudomonas were selected as samples, labeled and scrambled; then, BLAST comparison, Gap analysis and frequency analysis were performed. The results were then compared with the original data to determine the specificity and sensitivity of this method. Specificity refers to the probability that the actual test result is undetected and correctly judged as undetected, denoted as SP; then, 1-SP = α is the probability of a false positive diagnosis. Sensitivity refers to the probability that the actual test result is correctly judged to be detected, denoted as Se; then, 1-Se = β is the probability of a false negative diagnosis. After that, 40 sequences of non-Pseudomonas genera but under Pseudomonadales were selected to arrange this method mentioned above, further demonstrating its effectiveness in higher classification elements.

Application of the method for Pseudomonas in samples

A protocol for verifying the method was developed (Fig. 6b). To amplify the ITS sequences of Pseudomonas, a common primer set was designed (Pf, 5′-GAA GTC GTA ACA AGG TAG CCG TAG-3′ and Pr, 5′-AAC CGT CAG TCT CCG CTA CTT-3′). The primers can amplified the ITS sequences of Pseudomonas in the DNA samples. The species and proportion of ITS sequences in the DNA samples could be analysed based on the common primer combined with NGS using an Illumina NovaSeq 6000. For microecological analysis, we selected 12 DNA samples extracted from wild fish and identified Pseudomonas using the method mentioned in section 2.2.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the NCBI repository (


  1. Peix A, Ramírez-Bahena M-H, Velázquez E. Historical evolution and current status of the taxonomy of genus Pseudomonas. Infect Genet Evol. 2009;9:1132–47.

    Article  Google Scholar 

  2. Hiroyuki A. Regulation and Function of Versatile Aerobic and Anaerobic Respiratory Metabolism in Pseudomonas aeruginosa. Front Microbiol. 2011;2:103.

  3. Remold SK, Brown CK, Farris JE, Hundley TC, Perpich JA, Purdy ME. Differential habitat use and niche partitioning by Pseudomonas species in human homes. Microb Ecol. 2011;62:505–17.

    Article  Google Scholar 

  4. Silby MW, Winstanley C, Godfrey SA, Levy SB, Jackson RW. Pseudomonas genomes: diverse and adaptable. FEMS Microbiol Rev. 2011;35(4):652–80. Epub 2011 Mar 25. PMID: 21361996.

    CAS  Article  PubMed  Google Scholar 

  5. Peix A, Ramírez-Bahena MH, Velázquez E. The current status on the taxonomy of Pseudomonas revisited: an update. Infect Genet Evol. 2018;57:106–16.

    Article  Google Scholar 

  6. Saati-Santamaría Z, Rivas R, Kolarik M, García-Fraile P. A new perspective of Pseudomonas—host interactions: distribution and potential ecological functions of the genus Pseudomonas within the bark beetle Holobiont. Biology. 2021;10:164.

    Article  Google Scholar 

  7. Jun SR, Wassenaar TM, Nookaew I, Hauser L, Wanchai V, Land M, et al. Diversity of Pseudomonas genomes, including Populus-associated isolates, as revealed by comparative genome analysis. Appl Environ Microbiol. 2016;82:375–83.

    CAS  Article  Google Scholar 

  8. de Lorenzo V. Pseudomonas aeruginosa: the making of a pathogen. Environ Microbiol. 2015;17:1–3.

    Article  Google Scholar 

  9. Mielko KA, Jabłoński SJ, Milczewska J, Sands D, Łukaszewicz M, Młynarz P. Metabolomic studies of Pseudomonas aeruginosa. World J Microbiol Biotechnol. 2019;35:178.

    Article  Google Scholar 

  10. Behzadi P, Baráth Z, Gajdács M. It's not easy being green: a narrative review on the microbiology, virulence and therapeutic prospects of multidrug-resistant Pseudomonas aeruginosa. Antibiotics (Basel). 2021;10(1):42.

    CAS  Article  Google Scholar 

  11. Scales BS, Dickson RP, LiPuma JJ, Huffnagle GB. Microbiology, genomics, and clinical significance of the Pseudomonas fluorescens species complex, an unappreciated colonizer of humans. Clin Microbiol Rev. 2014;27:927–48.

    Article  Google Scholar 

  12. Halabi Z, Mocadie M, El Zein S, Kanj SS. Pseudomonas stutzeri prosthetic valve endocarditis: a case report and review of the literature. J Infect Public Health. 2019;12:434–7.

    Article  Google Scholar 

  13. Gomila M, Busquets A, Mulet M, García-Valdés E, Lalucat J. Clarification of taxonomic status within the Pseudomonas syringae species group based on a Phylogenomic analysis. Front Microbiol. 2017;8:2422.

    Article  Google Scholar 

  14. Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed. 2013;98:236–8.

    Article  Google Scholar 

  15. Le Gallo M, Lozy F, Bell DW. Next-generation sequencing. Adv Exp Med Biol. 2017;943:119–48.

    Article  Google Scholar 

  16. Gu W, Miller S, Chiu CY. Clinical metagenomic next-generation sequencing for pathogen detection. Annu Rev Pathol. 2019;14:319–38.

    CAS  Article  Google Scholar 

  17. Besser J, Carleton HA, Gerner-Smidt P, Lindsey RL, Trees E. Next-generation sequencing technologies and their application to the study and control of bacterial infections. Clin Microbiol Infect. 2018;24:335–41.

    CAS  Article  Google Scholar 

  18. Slatko BE, Gardner AF, Ausubel FM. Overview of next-generation sequencing technologies. Curr Protoc Mol Biol. 2018;122:e59.

    Article  Google Scholar 

  19. Levy SE, Myers RM. Advancements in next-generation sequencing. Annu Rev Genomics Hum Genet. 2016;17:95–115.

    CAS  Article  Google Scholar 

  20. Lalucat J, et al. Past, present and future of the boundaries of the Pseudomonas genus: Proposal of Stutzerimonas gen. Nov. Syst Appl Microbiol. 2022;45(1):126289 ISSN 0723–2020.

    CAS  Article  Google Scholar 

  21. Matar GM. Editorial: Pseudomonas and Acinetobacter: from drug resistance to pathogenesis. Front Cell Infect Microbiol. 2018;8:68.

    Article  Google Scholar 

  22. Nikel PI, de Lorenzo V. Pseudomonas putida as a functional chassis for industrial biocatalysis: from native biochemistry to trans-metabolism. Metab Eng. 2018;50:142–55.

    CAS  Article  Google Scholar 

  23. Yamamoto S, Kasai H, Arnold DL, Jackson RW, Vivian A, Harayama S. Phylogeny of the genus Pseudomonas: intrageneric structure reconstructed from the nucleotide sequences of gyrB and rpoD genes. Microbiol. 2000;146(10):2385–94.

    CAS  Article  Google Scholar 

  24. Mulet M, Bennasar A, Lalucat J, García-Valdés E. An rpoD-based PCR procedure for the identifification of Pseudomonas species and for their detection in environmental samples. Mol Cell Probes. 2009;23(3–4):140–7.

    CAS  Article  Google Scholar 

  25. Tayeb AL, Ageron E, Grimont F, Grimont PA. Molecular phylogeny of the genus Pseudomonas based on rpoB sequences and application for the identifification of isolates. Res Microbiol. 2005;156(5–6):763–73.

    Article  Google Scholar 

  26. Gürtler V, Subrahmanyam G, Shekar M, Maiti B, Karunasagar I. Chapter 12 - bacterial typing and identification by genomic analysis of 16S–23S rRNA intergenic transcribed spacer (ITS) sequences, p 253-274. In: Goodfellow M, Sutcliffe I, Chun J, editors. Methods in microbiology, vol. 41: Academic Press; 2014.

    Google Scholar 

  27. Gürtler V, Grando D. New opportunities for improved ribotyping of C. difficile clinical isolates by exploring their genomes. J Microbiol Methods. 2013;93:257–72.

    Article  Google Scholar 

  28. Gurtler V, Grando D, Krishna K, Maiti B, Karunasagar I, Karunasagar I. The use of recombined ribosomal RNA operon (rrn) type-specific flanking genes to investigate rrn differences between Vibrio parahaemolyticus environmental and clinical strains. Gene Rep. 2016;4:16–25.

  29. Trček J, Barja F. Updates on quick identification of acetic acid bacteria with a focus on the 16S-23S rRNA gene internal transcribed spacer and the analysis of cell proteins by MALDI-TOF mass spectrometry. Int J Food Microbiol. 2015;196:137–44.

    Article  Google Scholar 

  30. Pourahmad F, Adams A, Thompson KD, Richards RH. Identification of aquatic mycobacteria based on sequence analysis of the 16S-23S rRNA internal transcribed spacer region. J Med Microbiol. 2019;68:221–9.

    CAS  Article  Google Scholar 

  31. Yu J, Zhu B, Zhou T, Wei Y, Li X, Liu Y. Species-specific identification of Vibrio sp. based on 16S-23S rRNA gene internal transcribed spacer. J Appl Microbiol. 2020;129:738–52.

    CAS  Article  Google Scholar 

  32. Yu J, Peng X, Wei Y, Mi Y, Zhu B, Zhou T, et al. Relationship of diversity and the secondary structure in 16S–23S rDNA internal transcribed spacer: a case in Vibrio parahaemolyticus. FEMS Microbiol Lett. 2018;365:fny177.

  33. Yu J, Wang F, Zhan X, Wang X, Zuo F, Wei Y, et al. Improvement and evaluation of loop-mediated isothermal amplification combined with a chromatographic flow dipstick assay and utilization in detection of Vibrio cholerae. Anal Bioanal Chem. 2019;411:647–58.

    CAS  Article  Google Scholar 

  34. Saati-Santamaría Z, et al. Phylogenomic analyses of the genus Pseudomonas Lead to the rearrangement of several species and the definition of new genera. Biology. 2021;10:782.

    Article  Google Scholar 

  35. Passarelli-Araujo H, et al. Network analysis of ten thousand genomes shed light on Pseudomonas diversity and classification. Microbiol Res. 2022;254:126919 ISSN 0944–5013.

    CAS  Article  Google Scholar 

  36. Anzai Y, Kim H, Park JY, Wakabayashi H, Oyaizu H. Phylogenetic affiliation of the pseudomonads based on 16S rRNA sequence. Int J Syst Evol Microbiol. 2000;50(Pt 4):1563–89.

    CAS  Article  Google Scholar 

  37. Mulet M, Montaner M, Román D, Gomila M, Kittinger C, Zarfel G, et al. Pseudomonas species diversity along the Danube River assessed by rpoD gene sequence and MALDI-TOF MS analyses of cultivated strains. Front Microbiol. 2020;11:2114.

    Article  Google Scholar 

  38. Ranjbar R, Behzadi P, Mammina C. Respiratory tularemia: Francisella Tularensis and microarray probe designing. Open Microbiol J. 2016;10:176–82.

    Article  Google Scholar 

  39. Yu J, Zhou T, Zhu B, Wei Y, Li X, Liu Y. Species-specific identification of Streptococcus based on DNA marker in 16S-23S rDNA internal transcribed spacer. Curr Microbiol. 2020;77:1569–79.

    CAS  Article  Google Scholar 

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This work was supported by a project from National College Students Innovation and Entrepreneurship Training Program (no.202010055411 and no.201910055108).


This work was supported by a project from National College Students Innovation and Entrepreneurship Training Program (no.202010055411 and no.201910055108). The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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SH and XL were major contributors in writing the manuscript and analysing the data. SH prepared the figures and tables. XY helped with the double-blind experiment. All authors read and approved the final manuscript.

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Correspondence to Yin Liu.

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Supplementary Information

Additional file 1: Table. S1.

Groups and the number of ITS in complete genome of Pseudomonas.

Additional file 2: Table. S2.

Strains in the double-blind experiment.

Additional file 3: Table. S3.

Identification process of Gap value for P. balearica by BLAST.

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Hu, S., Li, X., Yin, X. et al. Species-specific identification of Pseudomonas based on 16S–23S rRNA gene internal transcribed spacer (ITS) and its combined application with next-generation sequencing. BMC Microbiol 22, 188 (2022).

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  • Bacterial identification
  • Pseudomonas
  • Internal transcribed spacer
  • ITS
  • Next-generation sequencing