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Comparison of Treponema pallidum genomes for the prediction of resistance genes

  • Ronaldo Omizolo De Souza
  • Kesia Esther da Silva
  • Rodrigo Matheus Pereira
  • Simone SimionattoEmail author
Brief communication
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Abstract

Syphilis is a sexually transmitted infection caused by Treponema pallidum, which is highly prevalent in several countries, including Brazil. The use of bioinformatics’ tools for the identification of resistance genes is an important practice for the study of microorganisms, such as T. pallidum. In this study, the complete genomes of 43 strains of T. pallidum, isolated from different countries, were analyzed. A total of 41,514 sequences were obtained, and compared against prokaryote resistance gene databases using BLASTn, BLASTx and RGI for gene alignment and prediction. From the alignments, it was possible to identify antibiotic resistance genes for each strain. The genes identified in each comparison were grouped according to the antibiotic category in which they show resistance to. The antibiotic-resistant genes related to drugs used to treat syphilis were grouped separately. The in silico tools used have shown to be effective in identifying resistance genes in genomes of T. pallidum strains. Due to the lack of research and accurate information regarding the antibiotic resistance genes in T. pallidum, this study serves as a basis for studies in molecular biology whose aim is the identification of these genes, besides being a reference to help in the control and treatment of this infection.

Keywords

Bioinformatics genomes molecular biology resistance genes syphilis 

1 Introduction

According to the World Health Organization (WHO) sexually transmitted infections (STIs) are an important cause of infertility, sequelae and death. Each year, an estimated 357 million people acquire one of these four STIs: syphilis, gonorrhea, chlamydia and trichomoniasis. The ulcerative lesions characteristic of syphilis may increase the risk of human immunodeficiency virus (HIV) acquisition. Syphilis contributes approximately 5.6 million new cases annually, of which more than 90% in developing countries such as Brazil (Newman et al. 2015).

Syphilis is a chronic infectious disease, sexually transmitted and eventually transplacental, caused by the spirochete Treponema pallidum subspecies pallidum. This bacterium has no cell membrane, is not stained by the Gram technique, does not grow in culture media, multiplies by binary fission every 32–36 h and man is the only obligatory host (Kojima and Klausner 2018). Syphilis continues to be an important public health problem in Brazil. Adolescents and young people constitute a vulnerable group for syphilis and other STIs (Garcia et al. 2010; Gomes et al. 2017).

According to the guidelines of the Ministry of Health for the control of congenital syphilis, the disease is classified as recent or late and may present in the primary, secondary, tertiary and latent phases, the latter being asymptomatic, where the diagnosis is only possible by serological tests or molecular biology techniques (Aids 2005). Penicillin has been used as the main drug for controlling syphilis. However, other antibiotics, including tetracyclines, macrolides and cephalosporins, have also been used in patients who are allergic to penicillin, or because of a lack of antibiotics. However, during the last three decades, especially since 2004, many failures in the treatment and resistance to antibiotics associated with macrolides have been reported (Workgroup 2012; Control CD and Prevention 2015; Molini et al. 2016).

The use of resources applied to the selection of DNA sequences linked to bioinformatics techniques has been used to study genes associated with resistance (Marra et al. 2006; Houston et al. 2018). By analyzing molecular sequences and structural data, bioinformatics can provide a global perspective for the cell or organisms in question (Biswas et al. 2008; Kaloudas et al. 2018). The development of specific databases as well as in silico tools has contributed to advances in this area, especially in fastidious microorganisms such as T. pallidum (Kumar Jaiswal et al. 2017; Houston et al. 2018; Kaloudas et al. 2018). Bearing in mind the scarcity of data on the identification of antibiotic resistance genes in T. pallidum subsp. pallidum, this study aimed to use bioinformatics tools to evaluate the presence of these genes in sequenced genomes seeking to predict, in silico, genes related to mechanisms of resistance to antibiotics.

2 Materials and methods

To perform this study, FASTA (text-based format to represent both nucleotide and peptide sequences) sequences of T. pallidum subspecies pallidum with sequenced complete genome, from different countries, were searched. The genomic database of the National Center for Biotechnology Information (NCBI) was used as primary database. The Comprehensive Antibiotic Resistance Database (CARD) was also used to search for data on antibiotic resistance genes. This bank is built entirely using open source software and tools for performing analyses, being constantly updated (Jia et al. 2017).

The comparison of the sequenced genomes of T. pallidum with the CARD nucleotide database was performed using the Basic Local Alignment Search Tool (BLAST). For this search the BLASTn tool was used, which makes a comparison between FASTA nucleotide sequences. The genomes of the T. pallidum strains were individually aligned against nucleotide sequences in the FASTA format from CARD (version 1.1.4). The alignment parameters between the similar sequences were: e-value of at least 1e−5, with e-value being the probability that the alignment occurred at random.

The second analysis was an alignment of the FASTA sequences from the sequenced genomes of each of the T. pallidum strains against the CARD mutation database (version 1.1.4). This bank contains genes that have undergone mutations and then started conferring antibiotic resistance. For this, the BLASTn tool was used. The sequences of the T. pallidum strains were individually aligned against the CARD mutation database. To perform the alignment, the same cut-off parameter for e-value of at least 1e−5 was used.

As a way of increasing the possible prediction of antibiotic resistance genes in T. pallidum genomes, a BLAST-based alignment was performed. BLASTx was used to translate the incoming nucleotide sequence into protein. The nucleotide sequences of each of the T. pallidum strains were translated, in silico, into protein sequences, after which the program performed an alignment against the CARD protein database (version 1.1.4) containing protein sequences which confer resistance to antibiotics. To perform the alignment of similar sequences the e-value of at least 1e−5 was used.

The Resistance Gene Identifier (RGI) (https://card.mcmaster.ca/analyze/rgi) was used to detect antimicrobial resistance genes and mutations in genomes or proteins. The genomes originally obtained in NCBI were automatically submitted to the GeneMark tool (http://exon.gatech.edu/GeneMark/) for prediction of open-reading frames (ORFs). The ORFs found were analyzed by the RGI through the CARD portal, using the cut-off parameter for e-value of at least 1e−5 (Besemer and Borodovsky 2005; Jia et al. 2017).

A local database was created, using MySQL, a free database management tool. A table was added with annotated information about the alignments that were made. This information was obtained in the analysis of similarity and the prediction through the RGI and was used to analyze the aligned data. A new table was created in the database with resistance genes aligned from all T. pallidum genomes submitted to BLASTx. The information from the Annotation table (containing annotated information on the resistance genes of each strain) was added to the database table containing the genes predicted by BLASTx. The information obtained through the prediction of genes performed by the RGI was deposited with predictions of all the strains used in the study, for later analysis of the results. Each of the antibiotic resistance genes found were divided as to the type of antibiotic that they are resistant to. From the results obtained between the alignments performed of each of the strains and prediction by the RGI, the repeated information was filtered. For the identification and organization of the results, each of the antibiotic resistance genes found were divided according to the type of antibiotic that they offer resistance, the results of these analyses were tabulated.

3 Results and discussion

A total of 43 strains of T. pallidum subspecies pallidum were found in the NCBI genome database (https://www.ncbi.nlm.nih.gov/genome/) with the complete genome sequenced. These were from studies performed in three countries: China (n = 1), the United States (n = 17) and Portugal (n = 25). A division of the resistance genes found by means of the BLASTx and the prediction of the RGI was performed, based on the chemical classification of the antibiotics. A special division was made with the antibiotics that are used as therapy for the treatment of syphilis, according to the standards established by the Centers for Disease Control and Prevention (CDC) and the WHO (Control CD and Prevention 2015; Newman et al. 2015). A line was also included to represent a gene encoding an efflux pump.

The results of each gene were divided with respect to the antibiotics to which they confer resistance, as well as among those that show resistance to the classes of antibiotics used in the treatment of syphilis (tables 1 and 2). Mechanisms of action and nomenclature were also provided for each of the genes found in both analyses. The 43 sequenced strains showed 41,514 sequences, which were evaluated in this study. Of these, 14 (0.03%) presented the profile of a resistance gene (vanG gene) by comparison with the nucleotide database of CARD through the BLASTn analysis. No mutated genes were identified. Alignment through BLASTx identified 2280 (5.49%) antibiotic resistance genes, while prediction with the RGI identified 2305 (5.55%). Information on the strains evaluated is present in the supplementary tables (S1 and S2).
Table 1

Genes aligned by BLASTx and antibiotics that they confer resistance to

Antibiotics

Chemical aligned genes classification

Amino acid derivatives

adeJ, arnA, bcrA, clbA, CRP, mecC, vanHO, vanKI, vanRM, vanTG, vanXYC, vgaB, smeS;

Sugar derivatives

adeJ, clbA, cpxR, CRP, drrA, efrA, ErmH, kdpE, macB, mecC, mexI, mfd, smeS, vgaB,;

Acetate and propionate derivatives

adeJ, mexI, tetB(P), tetT, (ARO:3003359|Streptomyces);

Other

clbA, CRP, efrA, mdtK, mecC, mexH, QnrB27, tsnr;

Efflux pump

sav1866.

Antibiotics for the treatment of syphilis

Aligned genes

Beta-lactamases (penicillin and ceftriaxone)

adeJ, CRP, mecC, smeS;

Macrolides (azithromycin)

adeJ, clbA, CRP, efrA, ErmH, macB, mecC, mexI, vgaB;

Aromatic (doxycycline and tetracycline)

adeJ, mexI, tetB(P), tetT, (ARO:3003359|Streptomyces).

adeJ, efflux protein RND; arnA, polymyxin resistance protein; bcrA, ABC type carrier which confers resistance to bacitracin; clbA, ribosomal (23S) alteration conferring resistance to antibiotics; cpxR, a gene subunit that modulates the efflux of antibiotics; CRP, global regulator that represses the efflux factor MdtEF; drrA, efflux pump subunit that confers resistance to aminoglycosides; efrA, an efflux pump subunit that confers resistance to macrolides; ErmH, streptogramin resistance protein; kdpE, aminoglycoside resistance protein; macB, ABC type transporter which eliminates macrolides; mdtK, multidrug transporter and toxic compound extrusion (MATE); mecC, carries out the synthesis of peptidoglycan in the presence of beta-lactamases; mexH gene subunit that modulates the efflux of antibiotics; mexI, internal membrane conveyor; mfd, aminoglycoside resistance protein; QnrB27, quinolone resistance protein; sav1866, antibiotic efflux pump (ABC) with ATP binding terminal; smeS, protein kinase sensor; tetB (P) and tetT, tetracycline-resistant ribosomal protection proteins; tsnr, peptide resistance protein; vanHO, a group of glycopeptide resistance genes; vanKI, a group of glycopeptide resistance genes; vanRM, a group of glycopeptide resistance genes; vanTG, a group of glycopeptide resistance genes; vanXYC, a group of glycopeptide resistance genes; vgaB, confers resistance to streptogramin A; (ARO: 3003359 | Streptomyces), Streptomyces cinnamoneus elongation factor conferring resistance to elfamycin-type antibiotics.

Table 2

Genes predicted by RGI and antibiotics that they confer resistance to

Antibiotics (chemical classification)

Predicted genes

Amino acid derivatives

arnA, bcrA, CIPa, CRP, mecA, vanHO, vanKI, vanRM, vanTG, vanYM, vgaB;

Sugar derivatives

baeS, CIPa, CRP, drrA, Erm(33), hmrM, lmrD, macB, mexI, mexV, mfd, oleC, vgaB;

Acetate and propionate derivatives

adeR, EF-Tu, mexI, mexV, tetB(P), tetT;

Other

alaS, arlR, CIPa, CRP, hmrM, ileS, pncA, QnrB27, tsnr;

Efflux pump

sav1866.

Antibiotics for the treatment of syphilis

Predicted genes

Beta-lactamases (penicillin and ceftriaxone)

CRP e mecA;

Macrolides (azithromycin)

CIPa, CRP, Erm(33), macB, mexI, mexV, oleC, vgaB;

Aromatic (doxycycline and tetracycline)

adeR, EF-Tu, mexI, mexV, tetB(P), tetT.

adeR, efflux pump that confers resistance to aromatics; alaS, aminocoumarin resistance gene; arlR, efflux pump that confers resistance to quinolones; arnA, polymyxin resistance protein; baeS, efflux pump that confers resistance to aminoglycosides; CIPa, antibiotic target modifying enzyme; CRP, global regulator that represses the efflux factor MdtEF; drrA, efflux pump subunit that confers resistance to aminoglycosides; EF-Tu, a gene involved in self-resistance to antibiotics; Erm (33), antibiotic target modifying enzyme; hmrM, efflux pump that confers resistance to quinolones and aminoglycosides; ileS, mupirocin resistance gene; lmrD, efflux pump that confers resistance to lincosamides; macB, ABC type transporter which eliminates macrolides; mecA, a group of beta-lactamase resistance genes; mexI, internal membrane conveyor; mexV, efflux pump that confers resistance to aromatic antibiotics, macrolides and quinolones; mfd, aminoglycoside resistance protein; oleC, efflux pump that confers macrolide resistance; pncA, fosfomycin resistance gene; QnrB27, quinolone resistance protein; sav1866, antibiotic efflux pump (ABC) with ATP binding terminal; tetB (P) and tetT, tetracycline-resistant ribosomal protection proteins; vanHO, a group of glycopeptide resistance genes; tsnr, peptide resistance protein; vanKI, a group of glycopeptide resistance genes; vanRM, a group of glycopeptide resistance genes; vanTG, a group of glycopeptide resistance genes; vanYM, group of glycopeptide resistance genes; vgaB, confers resistance to streptogramin A.

Using the BLASTn tool to compare the genomes of T. pallidum against the resistance gene database from CARD, it was possible to identify a specific gene (vanG gene) in 14 (32.5%) strains, which was presented in a single copy. This gene, previously described (Abadia Patino et al. 2002), is frequent in Enterococcus faecalis and produces a type of homologous ligase that can synthesize an alternative substrate for the peptidoglycan synthesis and thus reduces the binding affinity of antibiotics conferring resistance to glycopeptides. The strains that presented genes similar to vanG were: Nichols, Sea 81-4, SS14 JUL 2015, Chicago, DAL-1, Mexico A, CDC-A, Nichols Houston E, Nichols Houston J, PT_SIF1063, PT_SIF1135, PT_SIF1196, PT_SIF1299 and Seattle Nichols. All other strains did not return any results during the alignment of their genomes against the nucleotide database.

During alignment, through the BLASTn against the mutated gene bank of the CARD, it was not possible to identify sequences with a similar profile. This result suggests that among the 43 strains analyzed, none of them have mutated genes that confer resistance to antibiotics. However, previous studies identified a point mutation with substitution of A by G at position A2058 in the 23S (rRNA) gene conferring resistance to antibiotics. This mutation was identified as conferring macrolide resistance in T. pallidum and associated with resistance in bacteria containing one or two copies of these genes (Grimes et al. 2012; Workgroup 2012).

The prediction of genes using the BLASTx tool returned a total of 2280 resistance genes aligned. With this alignment it was possible to observe a similarity of the gene distribution among all the strains. However, in some of them the number of copies of one or more genes is reduced. The strains with the largest differences between the number distribution of genes were Sea 81-4 (USA), CDC-A (USA) and PT_SIF1140 (Portugal). The genes were divided according to the chemical classification of antibiotics. A subdivision was also created for sav1866 gene that encodes a transport system by means of an efflux pump, found in Staphylococcus aureus, as well as another division for the subclasses of antibiotics that are used for the treatment of syphilis, so then the genes conferring resistance to the antibiotics used as a way of treatment for syphilis could be identified.

BLASTx analysis identified resistance genes (adeJ, mexI, tetB (P), smeS, mecC, tetT, vgaB and clbA genes) that are resistant to more than one class of antibiotics including the beta-lactams, macrolides and aromatics used in the treatment of syphilis (table 1). The adeJ gene encodes an efflux protein and was first identified in Acinetobacter baumannii (Damier-Piolle et al. 2008). Until the present study, no cases about the presence of this gene in T. pallidum have been reported and future research can be carried out in an attempt to identify this resistance profile in this spirochete.

Through the prediction of resistance genes performed by the RGI, a total of 2304 resistance genes was identified. The similarity of the 43 strains was observed; however, Nichols, Sea 81-4, SS14 JUL 2015, CDC-A, PT_SIF1127 and PT_SIF1140 demonstrated differences in number of gene copies within each of the classes of antibiotics. Strain PT_SIF1127 did not present any copies of the sav1866 gene. The prediction of genes by the RGI proved to be effective in identifying possible resistance genes in the 43 strains studied. The resistance genes to the antibiotics used to treat syphilis identified by RGI are mecA, CRP, macB, vgaB, CIPa, oleC, Erm (33), mexV, methoxy, tetB (P), tetT, EF-Tu and adeR. None of these genes identified by the RGI confer resistance to all classes used in the treatment: macrolides, beta-lactams and aromatics. However, the CRP gene also identified by BLASTx confers resistance to beta-lactams and macrolides (table 2).

Comparison of the BLASTx alignment and prediction with the RGI showed the identification of different resistance genes between the genomes (tables 1 and 2). This result is explained due to the type of analysis performed. While the alignment performed with the BLASTx makes a direct comparison of the sequences, the prediction with the RGI takes into account the ORFs. This suggests that the results obtained through the RGI can lead to the discovery of resistance genes active in T. pallidum, which needs to be further explored by studies of molecular biology. None of the genes found in this study were previously described in T. pallidum and the CARD database itself does not provide any information or research that points to the presence of these genes in this spirochete, including the mutation at position A2058 in the 23S gene (rRNA) of T. pallidum.

The bioinformatics tools used in this study allowed the in silico identification of antibiotic resistance genes in T. pallidum genomes, as well as the frequency in which they appear in each genome. It was possible to classify each gene for a class of antibiotics, including those used for the treatment of syphilis. Direct comparison of T. pallidum genomes against CARD antibiotic resistance gene banks demonstrated the identification of different genes in each of the genomes. The data obtained through the prediction demonstrated the diversity of genes in this spirochete. The RGI tool was able to predict genes using ORFs and therefore indicates what may be expressed in each genome. On the other hand, the BLASTx does the direct translation in six possibilities of both strands of the sequence, without the identification of the ORFs what diminishes the chance of identification of genes that can be active in a genome. Although we have not evaluated gene expression, considering that T. pallidum is not easily cultivable in the laboratory, our results are in agreement with previous studies performed with other microorganisms (Chesneau et al. 2005; Sun et al. 2014; Shaskolskiy et al. 2016; Xiao et al. 2016).

The strains could be identified though molecular analysis, such as a PCR method, that will aim to identify the resistant genes. These procedures are of utmost importance when it comes to help and avoid prescription of antibiotics for which the strain is known to be resistant. Finally, due to this study it was possible to conclude that the BLASTn, BLASTx and RGI tools have shown to be effective in the identification of resistance genes in T. pallidum. This study can be used as a basis for molecular biology research that aims at identifying the existence of resistance genes in the etiologic agent of syphilis.

Notes

Acknowledgements

This work was partially supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq grant 440245/2018-4), Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT grants 092/2015 and 041/2017), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, grant 001) and Universidade Federal da Grande Dourados (UFGD). KES received a scholarship from CAPES.

Supplementary material

12038_2019_9855_MOESM1_ESM.docx (25 kb)
Supplementary material 1 (DOCX 25 kb)

References

  1. Abadia Patino L, Courvalin P and Perichon B 2002 Vane gene cluster of vancomycin-resistant Enterococcus faecalis BM4405. J. Bacteriol. 184 6457–6464CrossRefGoogle Scholar
  2. Aids MdSSdVeSPNdDe 2005 Diretrizes para o controle da sífilis congênita: Ministério da Saúde BrasíliaGoogle Scholar
  3. Besemer J and Borodovsky M 2005 Genemark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res. 33 W451–W454CrossRefGoogle Scholar
  4. Biswas S, Raoult D and Rolain JM 2008 A bioinformatic approach to understanding antibiotic resistance in intracellular bacteria through whole genome analysis. Int. J. Antimicrob. Agents 32 207–220CrossRefGoogle Scholar
  5. Chesneau O, Ligeret H, Hosan-Aghaie N, Morvan A and Dassa E 2005 Molecular analysis of resistance to streptogramin A compounds conferred by the Vga proteins of staphylococci. Antimicrob. Agents Chemother. 49 973–980CrossRefGoogle Scholar
  6. Control CfD, Prevention 2015 Sexually transmitted diseases treatment guidelines, 2015. Ann. Emerg. Med. 66 526–528CrossRefGoogle Scholar
  7. Damier-Piolle L, Magnet S, Bremont S, Lambert T and Courvalin P 2008 AdeIJK, a resistance-nodulation-cell division pump effluxing multiple antibiotics in Acinetobacter baumannii. Antimicrob. Agents Chemother. 52 557–562CrossRefGoogle Scholar
  8. Garcia FLB, Turchi M, Guimaraes EMB, Carvalho N, Ribeiro CT, Reis MNG and Alves MFC 2010 Syphilis among young women: A population based survey in central Brazil. Int. J. Infect. Dis. 14 e210CrossRefGoogle Scholar
  9. Gomes NC, Meier DA, Pieri FM, Alves E, Albanese SP, Lentine EC, Arcencio RA and Dessunti EM 2017 Prevalence and factors associated with syphilis in a reference center. Rev. Soc. Bras. Med. Trop. 50 27–34CrossRefGoogle Scholar
  10. Grimes M, Sahi SK, Godornes BC, Tantalo LC, Roberts N, Bostick D, Marra CM and Lukehart SA 2012 Two mutations associated with macrolide resistance in Treponema pallidum: Increasing prevalence and correlation with molecular strain type in Seattle, Washington. Sex. Transm. Dis. 39 954–958CrossRefGoogle Scholar
  11. Houston S, Lithgow KV, Osbak KK, Kenyon CR and Cameron CE 2018 Functional insights from proteome-wide structural modeling of Treponema pallidum subspecies pallidum, the causative agent of syphilis. BMC Struct. Biol. 18 7CrossRefGoogle Scholar
  12. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, Lago BA, Dave BM, et al. 2017 CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45 D566–D573CrossRefGoogle Scholar
  13. Kaloudas D, Pavlova N and Penchovsky R 2018 EBWS: Essential bioinformatics Web services for sequence analyses. IEEE/ACM Trans Comput Biol Bioinform. 15 1–13CrossRefGoogle Scholar
  14. Kojima N and Klausner JD 2018 An update on the global epidemiology of syphilis. Curr. Epidemiol. Rep. 5 24–38CrossRefGoogle Scholar
  15. Kumar Jaiswal A, Tiwari S, Jamal SB, Barh D, Azevedo V and Soares SC 2017 An in silico identification of common putative vaccine candidates against Treponema pallidum: A reverse vaccinology and subtractive genomics based approach. Int. J. Mol. Sci. 18 1–15CrossRefGoogle Scholar
  16. Marra CM, Colina AP, Godornes C, Tantalo LC, Puray M, Centurion-Lara A and Lukehart SA 2006 Antibiotic selection may contribute to increases in macrolide-resistant Treponema pallidum. J. Infect. Dis. 194 1771–1773CrossRefGoogle Scholar
  17. Molini BJ, Tantalo LC, Sahi SK, Rodriguez VI, Brandt SL, Fernandez MC, Godornes CB, Marra CM and Lukehart SA 2016 Macrolide resistance in Treponema pallidum correlates with 23S rDNA mutations in recently isolated clinical strains. Sex. Transm. Dis. 43 579–583CrossRefGoogle Scholar
  18. Newman L, Rowley J, Vander Hoorn S, Wijesooriya NS, Unemo M, Low N, Stevens G, Gottlieb S, Kiarie J and Temmerman M 2015 Global estimates of the prevalence and incidence of four curable sexually transmitted infections in 2012 based on systematic review and global reporting. PLoS One 10 e0143304CrossRefGoogle Scholar
  19. Shaskolskiy B, Dementieva E, Leinsoo A, Runina A, Vorobyev D, Plakhova X, Kubanov A, Deryabin D and Gryadunov D 2016 Drug resistance mechanisms in bacteria causing sexually transmitted diseases and associated with vaginosis. Front. Microbiol. 7 747CrossRefGoogle Scholar
  20. Sun J, Deng Z and Yan A 2014 Bacterial multidrug efflux pumps: Mechanisms, physiology and pharmacological exploitations. Biochem. Biophys. Res. Commun. 453 254–267CrossRefGoogle Scholar
  21. Workgroup AGP 2012 Prevalence of the 23S rRNA A2058G point mutation and molecular subtypes in Treponema pallidum in the United States, 2007 to 2009. Sex. Transm. Dis. 39 794–798Google Scholar
  22. Xiao Y, Liu S, Liu Z, Xie Y, Jiang C, Xu M, Zhao F, Zeng T, Yu J and Wu Y 2016 Molecular subtyping and surveillance of resistance genes in Treponema pallidum DNA from patients with secondary and latent syphilis in Hunan, China. Sex. Transm. Dis. 43 310–316CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  • Ronaldo Omizolo De Souza
    • 1
  • Kesia Esther da Silva
    • 1
  • Rodrigo Matheus Pereira
    • 2
  • Simone Simionatto
    • 1
    Email author
  1. 1.Laboratório de Pesquisa em Ciências da Saúde, Universidade Federal da Grande Dourados - UFGDDouradosBrazil
  2. 2.Faculdade de Ciências Biológicas e Ambientais, Universidade Federal da Grande Dourados - UFGDDouradosBrazil

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