- Research article
- Open Access
Corynebacterium pseudotuberculosis may be under anagenesis and biovar Equi forms biovar Ovis: a phylogenic inference from sequence and structural analysis
- Alberto Oliveira1,
- Pammella Teixeira1,
- Marcela Azevedo1,
- Syed Babar Jamal1,
- Sandeep Tiwari1,
- Sintia Almeida1,
- Artur Silva3,
- Debmalya Barh4,
- Elaine Maria Seles Dorneles5,
- Dionei Joaquim Haas5,
- Marcos Bryan Heinemann6,
- Preetam Ghosh7,
- Andrey Pereira Lage5,
- Henrique Figueiredo8,
- Rafaela Salgado Ferreira2 and
- Vasco Azevedo1Email author
© The Author(s). 2016
- Received: 2 April 2015
- Accepted: 25 May 2016
- Published: 2 June 2016
Corynebacterium pseudotuberculosis can be classified into two biovars or biovars based on their nitrate-reducing ability. Strains isolated from sheep and goats show negative nitrate reduction and are termed biovar Ovis, while strains from horse and cattle exhibit positive nitrate reduction and are called biovar Equi. However, molecular evidence has not been established so far to understand this difference, specifically if these C. pseudotuberculosis strains are under an evolutionary process.
The ERIC 1 + 2 Minimum-spanning tree from 367 strains of C. pseudotuberculosis showed that the great majority of biovar Ovis strains clustered together, but separately from biovar Equi strains that also clustered amongst themselves. Using evolutionarily conserved genes (rpoB, gapA, fusA, and rsmE) and their corresponding amino acid sequences, we analyzed the phylogenetic relationship among eighteen strains of C. pseudotuberculosis belonging to both biovars Ovis and Equi. Additionally, conserved point mutation based on structural variation analysis was also carried out to elucidate the genotype-phenotype correlations and speciation. We observed that the biovars are different at the molecular phylogenetic level and a probable anagenesis is occurring slowly within the species C. pseudotuberculosis.
Taken together the results suggest that biovar Equi is forming the biovar Ovis. However, additional analyses using other genes and other bacterial strains are required to further support our anagenesis hypothesis in C. pseudotuberculosis.
- Corynebacterium pseudotuberculosis
- Molecular phylogeny
- Structural biology
Within the genus Corynebacterium, the species Corynebacterium pseudotuberculosis is reported to be a facultative intracellular pathogen in mammals [9, 10]. The pathologies associated with C. pseudotuberculosis are of great importance to veterinary medicine because this bacterium is considered the main etiologic agent of caseous lymphadenitis (CLA). CLA is characterized by abscess formation in the thorax and abdomen, or in major lymph nodes, causing dermonecrosis and finally resulting in hypertrophy in the affected region [9, 11]. CLA is mostly found in small ruminants (mainly sheep and goats), but other mammals, such as cattle, pigs, deer, sheep, horses, camels and even human beings, although with very rare incidences, can be affected [12, 13]. Curiously, infection by C. pseudotuberculosis may cause other diseases,such as ulcerative lymphangitis (UL), a pathology of lymphatic vessels of the lower extremities, particularly hind legs, which is most frequent in horses [14, 15].
In order to understand differences in clinical presentation by infection of C. pseudotuberculosis, some studies have proposed to classify this microorganism from genetic, morphological and biochemical points of view [16–18]. Especially based on its ability to breakdown nitrate [19, 20], C. pseudotuberculosis was classified into two biovars, Ovis and Equi. Strains isolated from sheep and goats, which are usually negative in nitrate reductase activity, were classified as biovar Ovis; whereas the strains isolated from horse and cattle, which are usually positive in the nitrate reduction test, were classified as biovar Equi.
Additionally, studies have attempted to define these two biovars (Ovis and Equi) using restriction endonucleases (EcoRV and PstI) on chromosomal DNA or focusing on nitrate reduction determination methods . Discrimination of both isolates was also possible using other methodologies, such as restriction fragment length polymorphism analysis of 16S ribosomal DNA [19, 20, 22] and pulsed-field gel electrophoresis (PFGE) in combination with biochemical analysis . Other studies have investigated the possible evolutionary divergences of the genus Corynebacterium using 16S rRNA sequences [24–26], the preferred genetic tool used to characterize organisms taxonomically . Although 16S rRNA (rmsE) gene sequencing is highly useful with regards to bacterial classification, published data has proven analysis of the partial nucleotide sequences of the RNA polymerase β-subunit gene (rpoB) is more accurate for Corynebacterium species [18, 28].
There is phenotypic evidence (nitrate test) and genotypic evidence (Enterobacterial repetitive intergenic consensus sequence-based - ERIC-PCR and Single-nucleotide polymorphism (SNP) analysis [29, 30]) showing differences between the two biovars. However, our goal in this paper is to investigate the evolutionary differences between biovars Equi and Ovis of C. pseudotuberculosis. Thus, in this study, we performed ERIC 1 + 2 PCR and evolutionary analysis, using maximum likelihood method in combination with gene and protein structural analysis, of genes rsmE, a vital component of the ribosome, and rpoB, a region of strong influence of RNA polymerase activity, to find a kinship and phylogenetic distances. To study the molecular divergences between biovar Ovis and Equi of C. pseudotuberculosis, we also considered other genes like gapA, which has been used as a target in taxonomic comparisons of bacteria, taking into account the functions that this gene infers in possible differences in the metabolism of carbohydrates, glycolysis and cell survival [31, 32], and fusA, that has been used for phylogenetic analysis and taxonomic classification of bacterial species of the genus Pantoea, a pathogen to humans and plants .
ERIC 1 + 2 PCR Minimum-spanning tree
The minimum-spanning tree (MST) was generated using ERIC1 + 2-PCR data from 367 C. pseudotuberculosis strains, including 226 biovar Ovis field strains [17, 29, 34], 139 biovar Equi field strains (34 strains with published data  and 105 strains isolated from equines in USA – unpublished data), type strain ATCC 19410T and vaccine strain1002. The C. pseudotuberculosis ATCC 19410T type strain and 1002 vaccine strain were genotyped by ERIC1 + 2-PCR one time in each of the four different assays gathered in the present study ([17, 29, 34] and unpublished data). The MST was built using UPGMA, to calculate the distance matrix, and Prim’s algorithm associated with the priority rule and permutation resampling [35, 36]. The MST presented is the top scoring tree, i.e., the tree with the highest overall reliability score.
Eighteen C. pseudotuberculosis strains with available genome sequences (Additional file 1) had their sequences of genes/proteins rpoB, gapA, fusA, and rsmE (Additional file 2) submitted to phylogenetic and structural analyses. ERIC1 + 2-PCR results were available for 13 of those 18 strains. All genome sequences were available from the NCBI database [37, 38]. Protein functional information was from UniProt database annotation .
Alignment and phylogenetic analysis
We have used Clustal-X  for multiple sequence alignment (MSA) and Jalview  to visualize and edit the MSA, create phylogenetic trees, explore molecular structures and annotation. The analyses on the transition and transversion mutations were done using the software MEGA 6 . Average Nucleotide Identity (ANI)  was employed to evaluate relatedness among strains in substitution of the labour-intensive DNA-DNA hybridization (DDH) technique.
In order to create the phylogenetic trees, we first obtained an evolutionary model adapted to the MSA. Therefore, we used Adaptive Server Evolution (http://www.datamonkey.org/) portal to define one evolutionary model. The outcome of these tests indicate the TN93 model . Seaview  was then used to construct the tree based on the model previously presented by the Adaptive Server Evolution portal. The tree was created using the maximum likelihood method performed by PHYML , which is available in Seaview. Branch support consistencies were evaluated using the nonparametric bootstrap test  with 250 replicates and the approximate likelihood ratio test (ALRT) . The viewing and editing of the tree was carried out using the Figtree tool [49, 50] that enabled us to either characterize different gene groups based on the bootstrap values calculation or to represent the evolutionary time scale. The multiple sequence alignments contain all four genes used in this work.
The alignment with all 4 genes from two C. pesudotuberculosis strains, vaccine strain 1002 from biovar Ovis and strain E19 from biovar Equi, and one Arcanobacterium haemolyticum strain, AH 20595 employed as an external group, were used for this statistical analysis. P-value less than 0.05 were used to reject the null hypothesis of equal rates between lineages.
Estimation of the pattern of nucleotide substitution
In order to observe the probability of transition (G↔A) and transversion (A↔C) substitutions, the MSA, of all 4 genes from biovars Equi and Ovis, was taken into account, with the aim to determine the types of molecular changes that were occurring. The Maximum Composite Likelihood Estimate of the Pattern of Nucleotide Substitution method was used with the gamma model that corrects for multiple hits, taking into account the rate substitution differences between nucleotides and the inequality of nucleotide frequencies . The analysis involved 19 (external group inside) nucleotide sequences. The transition/tranversion ratio (R), that is the number of these replacement, was calculated by R = [A*G*k 1 + T*C*k 2 ]/[(A + G)*(T + C)] with rate ratios of k1 evaluating purines and rate ratios of k2 evaluating pyrimidines. Codon positions included were 1st + 2nd + 3rd + Noncoding. All positions containing gaps and missing data were eliminated. There were a total of 6542 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 .
For structural analysis, the gene sequences were translated to amino acid sequences using the Transeq program: http://www.ebi.ac.uk/Tools/emboss/. After the amino acid alignment, we constructed 3-D models of the proteins and interpreted the molecular differences and consequences. Comparative molecular modeling of proteins was performed with the software Modeller . Additional file 3 presents the details of the template structures using information from NCBI  and PDB (Protein Data Bank) . Twenty models were built for each of the templates using MODELLER and one model for each template. PyMOL V.18.104.22.168 was used to visualize three-dimensional protein structures (Schrödinger, LLC.). All homology models were evaluated using several different model evaluation tools such as PROCHECK , evaluating the stereochemistry quality, Discrete Optimized Protein Energy (DOPE) score , a statistical potential able to provide a energetic validation and RMSD obtained from a structural alignment with a protein with similar function, to provide an functional validation. The latter was carried out in the software PyMOL. For all analysis we selected amino acids that are closer than 4 angstroms (Å) from the variant amino acid.
*We want to inform that this work does not use any participants, children, parent or guardian.
C. pseudotuberculosis biovar Ovis and biovar Equi strains clustered separately by ERIC 1 + 2 PCR
The clonal complex labelled 1 was almost totally composed of C. pseudotuberculosis strains from Minas Gerais State, Brazil, whereas clonal complex 2 contained strains from the states of Minas Gerais, Pernambuco and São Paulo, Brazil. The last clonal complex related to C. pseudotuberculosis biovar Ovis strains (3) showed the most diverse composition, including strains from Argentina, Australia, Brazil (Minas Gerais, Bahia, and São Paulo), Egypt, Israel, Scotland and USA. C. pseudotuberculosis biovar Equiclonal complexes were mainly composed of strains from Egypt (4) and USA (5).
The repeatability observed for the C. pseudotuberculosis ATCC 19410T and 1002 vaccine strain in ERIC1 + 2-PCR was 84 % considering the four different experiments conducted [17, 29, 34] and also the unpublished data from 105 strains.
Statistical analysis and molecular phylogeny from C. pseudotuberculosis Ovis and Equi biovars
Results from Tajima’s neutrality test
Statistical differences and transition and transversion point mutations detected in the multiple sequence alignment from biovares Ovis and Equi
Maximum composite likelihood estimate of the pattern of nucleotide substitution
Specific amino acid variation among biovar Ovis and Equi
Next, we evaluated the location of these point mutations on protein structure and whether the physicochemical characteristics of the mutated residues differ between the two biovars. We considered the protein sequences from the four genes used in this study, and amino acid differences were evaluated based on a sequence alignment with proteins (Additional file 9) possessing the same function and known. After the alignment, the position of each mutation in the structure was analyzed in comparison to the active site of each protein. In general, mutations were located in the surface and distant from the active site region (Additional file 10).
For fusA protein observed two residue modifications (Val177Ile and Asp371Glu) when comparing the two biovars. In both cases the amino acids within each pair have the same physicochemical characteristics: both valine and isoleucine are nonpolar and hydrophobic, and aspartate and glutamate are both polar and negatively charged. Therefore, both mutations are conservative and the mutations most likely don’t affect the function of this protein (Additional file 11).
For gapA protein, we observed that Asparagine (Asn) was changed to Aspartic acid (Asp) at position 97 when in both the biovars. Both are polar amino acids, however they differ in physicochemical terms, since Asn is neutral and contains a hydrogen bond donor, while Asp is usually negatively charged at neutral pH. Additionaly, at position 207 another amino acid change was observed, from threonine (Thr) to isoleucin (Ile). In this case, the amino acids differ in size (Ile has a bigger side chain) and polarity (while Ile is nonpolar, Thr is a neutral polar amino acid). Interestingly, C. pseudotuberculosis strain 162 that belongs to biovar Equi does not show the same changes when compared to the other strains from the same biovar.
Distribution of amino acids from proteins in biovars. The amino acid variants positions present physicochemical differences, and are observed to be specific to a biovar type. Some variations exhibit an increase or decrease in the number of interactions between amino acids that influence the stability of the protein
Aspartic acid (D)
Aspartic acid (D)
Distribution of amino acids from protein rpoB in biovars. The position 979 has variation in amino acids: Alanine for some strains as 1002, 106, 3/99, 31, 258, 52.97, 162 and FRC41; while the same position is Threonine for other biovars as 42/02, 267, 231, I19, P54B96 and PAT10
We earlier suggested from data of 102 C. pseudotuberculosis strains that C. pseudotuberculosis biovar Ovis and Equi exhibited different ERIC1 + 2-PCR clustering pattern (Dorneles et al., ). The present data from 373 strains also showed a clear difference in clustering pattern between C. pseudotuberculosis biovar Ovis and Equi, with a few exceptions (Fig. 1-a). However, as the present MST results were based on a representative number of genotyped strains (139 C. pseudotuberculosis biovars Equi field strains, 226 C. pseudotuberculosis biovar Ovis field strains, from twelve countries and isolated from eight different hosts), it allows us to make more reliable inferences. The five largest clonal complexes were observed in the MST, three were mainly related to C. pseudotuberculosis biovar Ovis strains (1, 2, and 3) and two with C. pseudotuberculosis biovar Equi strains (4 and 5), from which other clonally related isolated groups emerge. The distinct clustering pattern of C. pseudotuberculosis biovar Ovis and Equi strains might reflect the number of genes specific to each biovar .
In order to understand the clustering formation, we have used algorithms and techniques to construct qualitative phylogenetic trees to explain the best possible evolutionary relationship between biovars Ovis and Equi from C. pseudotuberculosis strains. The difference in the biovars can be clearly observed from the ERIC 1 + 2-PCR MST and phylogenetic tree (Figs. 1 and 2). However, it was not possible to determine if the two biovars belongs to different species based on the results obtained after the phylogenetic tree and average nucleotide identity (ANI) analyses. Although there are some genetic and biochemical differences between them, it was not clear whether the branches, observed at phylogenetic tree, have distinct groups of organisms. Based on our data, we hypothesized that biological speciation may be occurring, for instance, anagenesis, which is a process of progressive evolution of species involving changes in the gene frequency of a population .
Based on the anagenesis mode of speciation, we have hypothesized that speciation is occurring within the C. pseudotuberculosis at a molecular level. The genes rmsE, rpoB, fusA, and gapA that we used are strong candidates for phylogenetic analysis because they are involved in many important cellular processes, such as maintenance of cellular integrity, cell survival and several metabolic reactions [31–33]. According to Dorela et al. , rpoB is a relevant gene used to explore evolutionary routes serving as a tool to search for new species, differently from 16S rRNA gene sequencing data, that presents resolution problems at genus and/or species level. In our study, it was observed that the 16S sequence may be better in characterizing the molecular differences (mainly in structural analysis) between the two biovars, when compared to the rpoB gene. Our multiple sequence alignments analysis showed that all the genes have transition and transversion point mutations, which is defining and separating C. pseudotuberculosis biovar Equi from Ovis. In addition, it was observed from the timetree analysis that differences between the two C. pseudotuberculosis biovars were due to time of acquired mutations. It is possible to observe that the strains of biovar Equi are more prone to have mutations while those of C. pseudotuberculosis biovar Ovis were more stable. It is not possible, even with these findings, to state that the two groups constitute different species. However, taking into account the time slice versus genetic differences, C. pseudotuberculosis strains belonging to biovar Equi are under a constant mutational process, regarding the lengths of the branches. Considering the analyses of C. pseudotuberculosis strain 162, that have phylogenetic features approaching biovar Ovis, we interpret that some changes are being stabilized giving rise to strains of biovar Ovis. This event may indicate that a biological speciation may be occurring slowly in C. pseudotuberculosis. Our hypothesis is that an anagenesis process is happening from C. pseudotuberculosis biovar Equi to C. pseudotuberculosis biovar Ovis. Such anagenesis is observed when a sufficient number of mutations are fixed in a population, which makes the emergence of a new phenotype possible in the future. Based on our data, we dismissed the possibility of a cladogenesis event because we did not observe the transformation of an organism into two others, but rather the possible genesis of one organism into another. Therefore, it is highly likely that the mutations described in this study strengthen the idea of anagenesis.
Also, the results from Tajima’s D statistical test give us the biological interpretation that the proportion of mutations that alter codons for amino acids is higher than expected in both biovars and the population is evolving as per mutation-drift equilibrium. Furthermore, also is possible be interpreted rare alleles present at low frequencies in both biovars and population expansion after a recent bottleneck. Our structural analysis showed that none of the described mutations occurred within these sites or in positions previously described as critical for activity. Therefore, probably they do not affect protein function, but they are useful to indicate phylogenetic distance. For example, it was observed that C. pseudotuberculosis strain 162, which belongs to biovar Equi, is phylogenetically closer to biovar Ovis strains than to biovar Equi strains, as observed in the phylogenetic tree (Fig. 2). Probably this phylogenetic rapprochement is due to C. pseudotuberculosis strain 162 present Ovis specific mutations, although it is included in Equi biovar based on the nitrate test. Regarding this observation we hypothesize that biovar Ovis is being originated from biovar Equi, as mentioned before, as C. pseudotuberculosis 162 strain shares point mutations from both biovars.
Taking into account the reports discussed in the literature, many differences within the genomes of some C. pseudotuberculosis strains were reported [8, 29, 57]. Recently, Almeida et al.  (unpublished data) found a 99 % concordance in detection by PCR of gene narG, responsible for nitrate reduction, and nitrate reduction test, suggesting that C. pseudotuberculosis biovars Equi could be identified by the presence of gene narG, whereas biovar Ovis does not present it. Guimarães et al.  showed a typing method based on PCR (ERIC-PCR), which proved to be a good method to discriminate genetic differences among C. pseudotuberculosis strains. Using this method it was possible to observe that C. pseudotuberculosis biovar Ovis and biovar Equi strains clustered separately . The differences in the clustering pattern of C. pseudotuberculosis biovar Ovis and biovar Equi strains could reflect the number of specific genes in each biovar . This fact is evident from the complete genome analyses of 18 C. pseudotuberculosis strains; among the total 1504 genes, it was shown that C. pseudotuberculosis biovar Ovis contains 314 orthologous genes that are shared by all strains from this biovar but are absent from one or more strains of C. pseudotuberculosis biovar Equi . Furthermore, C. pseudotuberculosis biovar Equi strains have 95 core genes that are absent from one or more strains of C. pseudotuberculosis biovar Ovis . Soares et al. , working with pathogenicity islands (PAI), found differences in some genes related to pilus formation in C. pseudotuberculosis biovar Equi and biovar Ovis strains. Pilus gene clusters are acquired normally in block through horizontal gene transfer and are composed of a specific sortase gene and the major, base and tip pilin genes. Genetic variation was found in genome analyses between the two C. pseudotuberculosis biovars (Almeida et al., ). C. pseudotuberculosis biovar Ovis strain VD57 had its genome analysed for the presence of SNP’s, and the average variation found when it was compared to C. pseudotuberculosis biovar Ovis strains was 823 nucleotides, whereas when compared to the C. pseudotuberculosis biovar Equi strains that number increased to 25285.3 SNP’s.
Regarding the anagenesis theory, we suggest that C. pseudotuberculosis biovar Equi is forming, after some genomic changes, C. pseudotuberculosis biovar Ovis. Hence, we postulated that C. pseudotuberculosis biovar Equi strains could be evolutionarily older compared to C. pseudotuberculosis biovar Ovis strains.
C. pseudotuberculosis biovar Equi and biovar Ovis contain different molecular characteristics, although they belong to the same species. The formation of a new species is an event that happens very slowly in nature. It is possible to interpret that a speciation process is occurring from the anagenesis event. With regard to the statistical data, analysis of sequence and structure of proteins addressed in this study, we conclude that C. pseudotuberculosis biovar Ovis is being formed from C. pseudotuberculosis biovar Equi through anagenesis.
CLA, caseous lymphadenitis; DOPE, discrete optimized protein energy; ERIC-PCR, enterobacterial repetitive intergenic consensus sequence-based; MSA, multiple sequence alignment; MST, minimum-spanning tree; NCBI, National Center Biotechnology Institute; PDB, Protein Data Bank; PFGE, pulsed-field gel electrophoresis; UL, ulcerative lymphangitis; Uniprot, Universal Protein Resource.
The authors would like to acknowledge the research institution UFMG and Professor Raphael Hirata from University from State of Rio de Janeiro.
This work was supported by grants from Conselho nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes), Brazil.
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article and its additional files. The phylogenetic tree was deposited in TreeBASE under the URL http://purl.org/phylo/treebase/phylows/study/TB2:S19338.
AO: Analysis and interpretation from this work, PT: support in modeling and phylogenetic processes, MA, SJ, ST, DB, PG: support with writing the paper, SA: support in obtaining sequences, DB, HF and RF: supports analysis, EMSD, DJH, MBH, APL: discussion on the ERIC PCR method, AS, HF and VA: Research support. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Hard GC. Corynebacterium ovis Electron Microscopic Examination of Corynebacterium ovis. J Bacteriol. 1969;97:1480–5.PubMedPubMed CentralGoogle Scholar
- Paule BJ A, Meyer R, Moura Costa LF, Bahia RC, Carminati R, Regis LF, Vale VLC, Freire SM, Nascimento I, Schaer R, Azevedo V. Three-phase partitioning as an efficient method for extraction/concentration of immunoreactive excreted-secreted proteins of Corynebacterium pseudotuberculosis. Protein Expr Purif. 2004;34:311–6.View ArticlePubMedGoogle Scholar
- Songer JG. Bacterial phospholipases and their role in virulence. Trends Microbiol. 1997;5:156–61.View ArticlePubMedGoogle Scholar
- Bayan N, Houssin C, Chami M, Leblon G. Mycomembrane and S-layer: two important structures of Corynebacterium glutamicum cell envelope with promising biotechnology applications. J Biotechnol. 2003;104:55–67.View ArticlePubMedGoogle Scholar
- Funke G, Lawson PA, Collins MD. Heterogeneity within human-derived centers for disease control and prevention (CDC) coryneform group ANF-1-like bacteria and description of Corynebacterium auris sp. nov. Int J Syst Bacteriol. 1995;45:735–9.View ArticlePubMedGoogle Scholar
- Hall V. Corynebacterium atypicum sp. nov., from a human clinical source, does not contain corynomycolic acids. Int J Syst Evol Microbiol. 2003;53:1065–8.View ArticlePubMedGoogle Scholar
- Hard GC. Comparative toxic effect of the surface lipid of Corynebacterium ovis on peritoneal macrophages. Infect Immun. 1975;12:1439–49.PubMedPubMed CentralGoogle Scholar
- Dorella FAD, Gustavo L, Achecoa CP, Liveirab SCO, Iyoshia AM, Zevedoa VA. Corynebacterium pseudotuberculosis: microbiology, biochemical properties, pathogenesis and molecular studies of virulence. Vet Res. 2006;37:201–218Google Scholar
- Williamson LH. Caseous lymphadenitis in small ruminants. Vet Clin North Am Food Anim Pract. 2001;17:359–71. vii.View ArticlePubMedGoogle Scholar
- Trost E, Ott L, Schneider J, Schröder J, Jaenicke S, Goesmann A, Husemann P, Stoye J, Dorella FA, Rocha FS, Soares SDC, D’Afonseca V, Miyoshi A, Ruiz J, Silva A, Azevedo V, Burkovski A, Guiso N, Join-Lambert OF, Kayal S, Tauch A. The complete genome sequence of Corynebacterium pseudotuberculosis FRC41 isolated from a 12-year-old girl with necrotizing lymphadenitis reveals insights into gene-regulatory networks contributing to virulence. BMC Genomics. 2010;11:728.View ArticlePubMedPubMed CentralGoogle Scholar
- Dorella FA, Pacheco LG, Seyffert N, Portela RW, Meyer R, Miyoshi A, Azevedo V. Antigens of Corynebacterium pseudotuberculosis and prospects for vaccine development. Expert Rev Vaccines. 2009;8:205–13.View ArticlePubMedGoogle Scholar
- Marchand CH, Salmeron C, Bou Raad R, Méniche X, Chami M, Masi M, Blanot D, Daffé M, Tropis M, Huc E, Maréchal P, Decottignies P, Bayan N. Biochemical disclosure of the mycolate outer membrane of Corynebacterium glutamicum. J Bacteriol. 2012;194:587–97.Google Scholar
- Brown CC, Olander HJ, Alves SF. Synergistic hemolysis-inhibition titers associated with caseous lymphadenitis in a slaughterhouse survey of goats and sheep in Northeastern Brazil. Can J Vet Res. 1987;51:46–9.PubMedPubMed CentralGoogle Scholar
- Doherr MG, Carpenter TE, Wilson WD, Gardner IA. Application and evaluation of a mailed questionnaire for an epidemiologic study of Corynebacterium pseudotuberculosis infection in horses. Prev Vet Med. 1998;35:241–53.View ArticlePubMedGoogle Scholar
- Britz E, Spier SJ, Kass PH, Edman JM, Foley JE. The relationship between Corynebacterium pseudotuberculosis biovar equi phenotype with location and extent of lesions in horses. Vet J. 2014;200:282–6.View ArticlePubMedGoogle Scholar
- Judson R, Songer JG. Corynebacterium pseudotuberculosis: in vitro susceptibility to 39 antimicrobial agents. Vet Microbiol. 1991;27:145–50.View ArticlePubMedGoogle Scholar
- Dorneles EMS, Santana JA, Andrade GI, Santos ELS, Guimaraes AS, Mota RA, Santos AS, Miyoshi A, Azevedo V, Gouveia AMG, Lage AP, Heinemann MB. Molecular characterization of Corynebacterium pseudotuberculosis isolated from goats using ERIC-PCR. Genet Mol Res. 2012;11:2051–9.View ArticlePubMedGoogle Scholar
- Khamis A, Raoult D, Scola BLA. Comparison between rpoB and 16S rRNA Gene Sequencing for Molecular Identification of 168 Clinical Isolates of Corynebacterium Comparison between rpoB and 16S rRNA Gene Sequencing for Molecular Identification of 168 Clinical Isolates of Corynebacterium. J Clin Microbiol. 2005;43:1934–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Costa LR, Spier SJ, Hirsh DC. Comparative molecular characterization of Corynebacterium pseudotuberculosis of different origin. Vet Microbiol. 1998;62:135–43.View ArticlePubMedGoogle Scholar
- Sutherland SS, Hart RA, Buller NB. Genetic differences between nitrate-negative and nitrate-positive C. pseudotuberculosis strains using restriction fragment length polymorphisms. Vet Microbiol. 1996;49:1–9.View ArticlePubMedGoogle Scholar
- Songer JG, Beckenbach K, Marshall MM, Olson GB, Kelley L. Biochemical and genetic characterization of Corynebacterium pseudotuberculosis. Am J Vet Res. 1988;49:223–6.PubMedGoogle Scholar
- Vaneechoutte M, Riegel P, de Briel D, Monteil H, Verschraegen G, De Rouck A, Claeys G. Evaluation of the applicability of amplified rDNA-restriction analysis (ARDRA) to identification of species of the genus Corynebacterium. Res Microbiol. 1995;146:633–41.View ArticlePubMedGoogle Scholar
- Connor KM, Quirie MM, Baird G, Donachie W. Characterization of United Kingdom Isolates of Corynebacterium pseudotuberculosis Using Pulsed-Field Gel Electrophoresis Characterization of United Kingdom Isolates of Corynebacterium pseudotuberculosis Using Pulsed-Field Gel Electrophoresis. J Clin Microbiol. 2000;38:2633–7.PubMedPubMed CentralGoogle Scholar
- Khamis A, Raoult D, La Scola B. Comparison between rpoB and 16S rRNA gene sequencing for molecular identification of 168 clinical isolates of Corynebacterium. J Clin Microbiol. 2005;43:1934–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Bujnicki JM. Phylogenomic analysis of 16S rRNA:(guanine-N2) methyltransferases suggests new family members and reveals highly conserved motifs and a domain structure similar to other nucleic acid amino-methyltransferases. FASEB J. 2000;14:2365–8.View ArticlePubMedGoogle Scholar
- Pascual C, Lawson PA, Farrow JA, Gimenez MN, Collins MD. Phylogenetic analysis of the genus Corynebacterium based on 16S rRNA gene sequences. Int J Syst Bacteriol. 1995;45:724–8.View ArticlePubMedGoogle Scholar
- Balch WE, Fox GE, Magrum LJ, Woese CR, Wolfe RS. Methanogens: reevaluation of a unique biological group. Microbiol Rev. 1979;43:260–96.PubMedPubMed CentralGoogle Scholar
- Khamis A, Raoult D, La Scola B. rpoB gene sequencing for identification of Corynebacterium species. J Clin Microbiol. 2004;42:3925–31.View ArticlePubMedPubMed CentralGoogle Scholar
- Dorneles EMS, Santana JA, Ribeiro D, Dorella FA, Guimaraes AS, Moawad MS, Selim SA, Garaldi ALM, Miyoshi A, Ribeiro MG, Gouveia AMG, Azevedo V, Heinemann MB, Lage AP. Evaluation of ERIC-PCR as Genotyping Method for Corynebacterium pseudotuberculosis Isolates. PLoS One. 2014;9:e98758.View ArticlePubMedPubMed CentralGoogle Scholar
- Almeida, s., Sandeep Tiwari, Mariano, d., rocha, f. s., Jamal, Syed Babar, Coimbra, n. a. r., Raittz, r. t., Dorella, f. a., Carvalho, a. f., Pereira, f. l., Leal, c. a. g., Debmalya Barh, Ghosh, p., Figueiredo, h. c. p., Moura-Costa, l. f., Portela, r. w V: The Genome Anatomy of Corynebacterium pseudotuberculosis VD57 a Highly Virulent Strain Causing Caseous lymphadenitis. Stand Genomic Sci 2015;57:1-8.Google Scholar
- Toyoda K, Teramoto H, Inui M, Yukawa H. Involvement of the LuxR-type transcriptional regulator RamA in regulation of expression of the gapA gene, encoding glyceraldehyde-3-phosphate dehydrogenase of Corynebacterium glutamicum. J Bacteriol. 2009;191:968–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Toyoda K, Teramoto H, Inui M, Yukawa H. Expression of the gapA gene encoding glyceraldehyde-3-phosphate dehydrogenase of Corynebacterium glutamicum is regulated by the global regulator SugR. Appl Microbiol Biotechnol. 2008;81:291–301.View ArticlePubMedGoogle Scholar
- Delétoile A, Decré D, Courant S, Passet V, Audo J, Grimont P, Arlet G, Brisse S. Phylogeny and identification of Pantoea species and typing of Pantoea agglomerans strains by multilocus gene sequencing. J Clin Microbiol. 2009;47:300–10.View ArticlePubMedPubMed CentralGoogle Scholar
- Guimarães ADS, Dorneles EMS, Andrade GI, Lage AP, Miyoshi A, Azevedo V, Gouveia AMG, Heinemann MB. Molecular characterization of Corynebacterium pseudotuberculosis isolates using ERIC-PCR. Vet Microbiol. 2011;153:299–306.View ArticleGoogle Scholar
- Feil EJ, Li BC, Aanensen DM, William P, Spratt BG, Hanage WP. eBURST : Inferring Patterns of Evolutionary Descent among Clusters of Related Bacterial Genotypes from Multilocus Sequence Typing Data eBURST : Inferring Patterns of Evolutionary Descent among Clusters of Related Bacterial Genotypes from Multilocus Sequen. J Bacteriol. 2004;186:1518–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Salipante SJ, Hall BG. Inadequacies of minimum spanning trees in molecular epidemiology. J Clin Microbiol. 2011;49:3568–75.View ArticlePubMedPubMed CentralGoogle Scholar
- Sayers EW, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, Church DM, DiCuccio M, Edgar R, Federhen S, Feolo M, Geer LY, Helmberg W, Kapustin Y, Landsman D, Lipman DJ, Madden TL, Maglott DR, Miller V, Mizrachi I, Ostell J, Pruitt KD, Schuler JD, Sequeira E, Sherry ST, Shumway M, Sirotkin K, Souvorov A, Starchenko G, Tatusova TA. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2009;37(Database issue):D5–D15.Google Scholar
- Benson DA, Karsch Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2009;37(Database issue):D26–31.View ArticlePubMedPubMed CentralGoogle Scholar
- Apweiler R, Bateman A, Martin MJ, O’Donovan C, Magrane M, Alam-Faruque Y, Alpi E, Antunes R, Arganiska J, Casanova EB, Bely B, Bingley M, Bonilla C, Britto R, Bursteinas B, Chan WM, Chavali G, Cibrian-Uhalte E, Silva A, Giorgi M, Fazzini F, Gane P, Castro LG, Garmiri P, Hatton-Ellis E, Hieta R, Huntley R, Legge D, Liu W, Luo J. Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2014;42:D191–8.Google Scholar
- Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG. Clustal W and Clustal X version 2.0. Bioinformatics. 2007;23:2947–8.View ArticlePubMedGoogle Scholar
- Waterhouse AM, Procter JB, Martin DM A, Clamp M, Barton GJ. Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009;25:1189–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme PTJ. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol. 2007;57:1.View ArticleGoogle Scholar
- Tamura K, Nei M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol. 1993;10:512–26.PubMedGoogle Scholar
- Gouy M, Guindon S, Gascuel O. SeaView version 4: A multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol. 2010;27:221–4.View ArticlePubMedGoogle Scholar
- Guindon S, Gascuel O. A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood. Syst Biol. 2003;52:696–704.View ArticlePubMedGoogle Scholar
- Felsenstein J. Confidence Limits on Phylogenies: An Approach Using the Bootstrap. Evolution (N Y). 1985;39:783.Google Scholar
- Anisimova M, Gascuel O. Approximate likelihood-ratio test for branches: A fast, accurate, and powerful alternative. Syst Biol. 2006;55:539–52.View ArticlePubMedGoogle Scholar
- Suchard MA, Rambaut A. Many-Core Algorithms for Statistical Phylogenetics. Bioinformatics. 2009;25:1370–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Pybus OG, Rambaut A, Harvey PH. An integrated framework for the inference of viral population history from reconstructed genealogies. Genetics. 2000;155:1429–37.PubMedPubMed CentralGoogle Scholar
- Tamura K, Battistuzzi FU, Billing-Ross P, Murillo O, Filipski A, Kumar S. Estimating divergence times in large molecular phylogenies. Proc Natl Acad Sci U S A. 2012;109:19333–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Nei M, Kumar S, Nei M, Kumar S. Molecular Evolution and Phylogenetics. New York: Oxford University Press; 2000. p. 333. 2000(August).Google Scholar
- Tajima F. Statistical Method for Testing the Neutral Mutation Hypothesis by DNA Polymorphism. Genetics. 1989;123:585–95.PubMedPubMed CentralGoogle Scholar
- Tamura K, Nei M, Kumar S. Prospects for inferring very large phylogenies by using the neighbor-joining method. Proc Natl Acad Sci U S A. 2004;101:11030–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Eswar N, Webb B, Marti-Renom MA, et al. Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci. 2007;Chapter 2:Unit 2.9. doi:10.1002/0471140864.ps0209s50.
- Bernstein FC, Koetzle TF, Williams GJ, Meyer EE Jr., Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M. The Protein Data Bank: A Computer-based Archival File For Macromolecular Structures. J of Mol Biol. 1977;112(535).Google Scholar
- Soares SC, Silva A, Trost E, Blom J, Ramos R, Carneiro A, Ali A, Santos AR, Pinto AC, Diniz C, Barbosa EG V, Dorella FA, Aburjaile S, Rocha FS, Nascimento KKF, Guimaraes LC, Almeida S, Hassan SS, Bakhtiar SM, Pereira UP, Abreu VAC, Schneider MPC, Miyoshi A, Tauch A, Azevedo V. The pan-genome of the animal pathogen Corynebacterium pseudotuberculosis reveals differences in genome plasticity between the biovar ovis and equi strains. PLoS One. 2013;8:e53818.Google Scholar
- Sons JW. The hypercycle: A principle of natural self-organization. Am J Vet Res. 1978;65:23.Google Scholar