Abstract
Bacterial identification on the basis of the highly conserved 16S rRNA (rrs) gene is limited by its presence in multiple copies and a very high level of similarity among them. The need is to look for other genes with unique characteristics to be used as biomarkers. Fifty-one sequenced genomes belonging to 10 different Yersinia species were used for searching genes common to all the genomes. Out of 304 common genes, 34 genes of sizes varying from 0.11 to 4.42 kb, were selected and subjected to in silico digestion with 10 different Restriction endonucleases (RE) (4–6 base cutters). Yersinia species have 6–7 copies of rrs per genome, which are difficult to distinguish by multiple sequence alignments or their RE digestion patterns. However, certain unique combinations of other common gene sequences—carB, fadJ, gluM, gltX, ileS, malE, nusA, ribD, and rlmL and their RE digestion patterns can be used as markers for identifying 21 strains belonging to 10 Yersinia species: Y. aldovae, Y. enterocolitica, Y. frederiksenii, Y. intermedia, Y. kristensenii, Y. pestis, Y. pseudotuberculosis, Y. rohdei, Y. ruckeri, and Y. similis. This approach can be applied for rapid diagnostic applications.
Electronic supplementary material
The online version of this article (doi:10.1007/s12088-015-0552-6) contains supplementary material, which is available to authorized users.
Keywords: Biomarkers, Diagnosis, Genome, In silico, Restriction endonuclease, Yersinia
Introduction
Yersinia as a bacterial pathogen spreads through contaminated food or water and blood transfusion [1–3]. In spite of the availability of a large number of phenotypic and genotypic methods, reliable detection of this pathogenic organism continues to be a challenge [4]. The major hurdle crops up due to their high similarities to many enteric bacteria and their slow-growing nature. Genomes of different Yersinia spp. show high level heterogeneity and possess genes responsible for virulence and pathogenesis, which may be located on the plasmids and chromosomes. The need is to develop a sensitive, rapid, and economical method to detect this bacterium. Review of literature reveals that cultural, immunological, and molecular methods are available, but each has its own limitations.
Cultural Methods
The microbiological culturing technique continues to be the gold standard for the detecting pathogens. The culture-based methods, though effective in detecting Yersinia, however, are time consuming. Commercially available kits like Biolog and API 20E systems involve expensive strips and equipments, which thus limits their usage on a routine basis [2].
Immunological Methods
Immunoassays permit detection and identification of microbes without culturing them. Latex agglutination is the simplest immunoassay, where latex beads coated with pathogen-specific antibodies agglutinate antigens, and the precipitate is easy to visualize. In Immunomagnetic separation (IMS) assay, magnetic beads coated with antibodies separate the target organism, which is then confirmed by polymerase chain reaction (PCR) or enzyme-linked immunosorbent assay (ELISA) [5, 6]. A few variants of immunological assays used for detecting Yersinia are: ELISA using swine antibodies against lipopolysaccharide, multiplexed sandwich chemiluminescent ELISA and commercial kits to detect the O-antigens [7]. A more sensitive method being employed is the surface plasmon resonance (SPR)-based immunosensor method for assessing antigen–antibody interactions [8]. Due to its ability to detect very low cell density, this method is likely to prove effective for detecting Yersinia enterocolitica in food products. This method is highly specific, needs antibodies for each strain, before it can be exploited. As the assay is extremely dependent upon in vitro testing conditions, there is a high likely hood of misinterpretation and false identification.
Molecular Methods
Detection of microbes based on their DNA has led to a range of molecular tools, which have made the methods to be rapid, economical, and precise. Methods used for identifying bacteria include colony hybridization, PCR, microarray, and loop-mediated isothermal amplification (LAMP) among other variants of these basic approaches, including Restriction Endonuclease digestion (RE) [1, 9–12]. Most studies employ 16S rRNA (rrs), the most conserved gene, for identifying bacteria. The Ribosomal Database Project (RDP) (https://rdp.cme.msu.edu/) has more than 3.0 million entries as of now. This gene (rrs) may not be sufficient to distinguish very closely related taxa or bacteria possessing multiple copies of the gene per genome [13]. In cases where rrs alone does not prove effective in distinguishing closely related species, one has to resort other housekeeping genes (HKGs): heat shock proteins, ATPase-ß-subunit, RNA polymerases or recombinase, etc. [14, 15]. For distinguishing members within a genus, a few specific genes have been identified: (1) gyrA gene for Bacillus subtilis, (ii) gyrB for Acinetobacter, Mycobacterium, Pseudomonas, and Shewanella, and (iii) rpoB for Mycobacterium; etc. [12]. The assays commonly used for identifying Yersinia, involve genes responsible for pathogenesis: (i) ail (attachment and invasion locus, 454 nts), (ii) inv (invasion, 570 bp), or (iii) yst (Yersinia stable toxin, 145 nts), (iv) myf (adhesin), and (v) yop (yersinia outer protein), (vi) vir (transcriptional regulator, 700 nts) genes [16–19]. The process is hindered by the presence of DNA from closely related competing microflora. It is, however, more sensitive than conventionally employed cultural methods. In comparison, to single gene based PCR methods, multiplex PCRs are being preferred. The detection of food-borne pathogens by PCR based methods are being monitored by European Committee for Standardization (CEN) [20]. A few major limitations of these PCR based protocols are (i) high rate of false-positive results, (ii) inability to differentiate viable and non-viable cells. It is thus imperative to include sufficient numbers of negative and internal positive controls [21]. Microarray is another method, which is quite sensitive and effective for identifying the target microbe [9, 22–24]. A more recent DNA based diagnostic tool for identifying food borne pathogens method is the Loop-mediated isothermal amplification (LAMP). It is relatively quite simple and does not involve reagents or any specialized equipment for visualization [10, 25, 26]. The genes used have been gyrB (gyrase B), phoP (transcriptional response regulator) [26, 27]. A few highly sensitive methods, which have been introduced for detecting Yersinia include: (i) a silicon-based optical thin-film biosensor chip [28], (ii) Fourier transform infrared spectroscopy and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry [29–31]. These are beneficial for high-throughput analysis.
In our endeavour to meet the challenge to provide biomarkers to rapidly identify bacteria, we have developed novel genomic tools to elucidate the latent features of rrs, such as: (i) a Phylogenetic Framework, (ii) unique signature sequences, and (iii) unique RE digestion patterns. This approach has enabled identification of organisms up to species level: (i) Bacillus, (ii) Clostridium, (iii) Pseudomonas, and (iv) Streptococcus [12, 32–36]. In an attempt to enhance the effectiveness, of this approach, especially, to identify the target bacteria from a mixed population, two sets of genes in Clostridium were segregated: (i) common to all species, and (ii) unique to a species. Based on the RE digestion patterns of these genes, unique combination of genes and REs, were suggested to rapidly identify Clostridium species [15]. For thorough and effectual surveillance of Yersinia, the need is to develop novel and innovative methods, which can prove as powerful tools in the hands of Health Departments to handle any outbreaks. These methods should be easy to use, can detect even very low population densities of the pathogen, culture-independent, in situ and more reliable. In the present work, we have extended the techniques used for distinguishing Clostridium strains [15] to develop biomarkers for rapid diagnosis of Yersinia.
Materials and Methods
Sequence Data and Comparative Genome Analysis
Completely sequenced genomes of the 51 strains of 10 species belonging to genus Yersinia were retrieved (http://www.ncbi.nlm.nih.gov/): Y. pestis (22); Y. pseudotuberculosis (12); Y. enterocolitica (3); Y. enterocolitica subsp. palearctica (2); Y. ruckeri (2); and one each of Y. aldovae; Y. enterocolitica (type O:5); Y. enterocolitica subsp. enterocolitica; Y. frederiksenii; Y. intermedia; Y. kristensenii; Y. pestisbiovar Medievalis; Y. pestisbiovar Microtus; Y. rohdei; Y. similis (Table S1). Information of the Yersinia genomes for the following parameters such as Accession number, GC percentage, size, and number of genes has been presented (Table S1). Pair-wise comparisons among the Yersinia genomes were done to identify common genes (Table S2). Out of all the genomes, 304 protein encoding common genes (including 6 with two copies each) could be distinguished. Out of these 304 common genes, 34 were selected on the basis of the size of the gene, at an increment of around 100 nucleotides in the range of 114 nucelotides (nts) to 4446 nts (Tables S2 and S3). The most commonly used non-protein coding gene, rrs was also taken into consideration, as it is used conventionally for bacterial identification.
Restriction Endonuclease Analysis of Common Genes
A total of 10 Type II REs were considered for digestion on the basis of our previous works [12, 33, 35, 37]. The following REs were used: (i) 4 base cutters AluI (AG′CT), BfaI (C′TA_G), BfuCI (_GATC′), CviAII (C_AT′G), HpyCH4V (TG′CA), RsaI (GT′AC), TaqI (T_CG′A), Tru9I (T_TA′A), and (ii) 6 base cutters HaeI (WGG′CCW), Hin1I (GR_CG′YC). All 34 common gene sequences (Table S3) were entered into Cleaver (http://cleaver.sourceforge.net/) to obtain RE digestion patterns. Subsequently, emphasis was laid on Data matrices of those REs, which produced 5–15 fragments. Consensus RE patterns, frequency of occurrence of RE sites and the pattern of nucleotide fragments were determined for each gene by employing the 10 REs listed above. Finally, the study was focused on those RE sites which generated digestion patterns unique to a strain.
Results
The 51 completely sequenced genomes of Yersinia spp.: Y. aldovae, Y. enterocolitica, Y. frederiksenii, Y. intermedia, Y. kristensenii, Y. pestis, Y. pseudotuberculosis, Y. rohdei, Y. ruckeri, and Y. similis (Table S1) showed high heterogeneity at the genetic level. The number of genes within each genome varies from 3219 to 5596 and the overall GC content ranges from 46.96 to 48.05 mol % (Table S1). The initial analysis was based on rrs gene, which is conventionally used for identifying bacteria. Equally, we could not trace unique features in rrs by which all the genomes could be identified, the focus was switched to other factors, which were common to all the genomes.
rrs Gene Analysis of Yersinsia Species
The frequency of occurrence of the rrs gene in Yersinia strains varied between 6 and 7. Within each genome, these rrs genes could be segregated into two groups: (1) 2 copies and (2) 4 or 5 copies. Within each group the copies showed high similarity. Multiple sequence alignments of 335 copies of rrs from all the Yersinia genomes revealed that these can be represented by 102 sequences, the rest being additional copies. RE digestion patterns of 102 rrs sequences representing 51 genomes showed that gene sequences belonging to different genomes clubbed together into groups.
With RE-AluI
102 rrs gene sequences from 51 Yersinia genomes could be segregated into 6 major groups. RE digestion patterns ·79·42·361·211·207·375· was recorded in both the copies of rrs in 35 genomes. This RE digestion pattern was exactly similar to that of one of the two copies of 10 other Yersinia genomes. In the latter instance, the second rrs copy in all the 10 genomes had a similar RE digestion pattern of ·42·361·211·207·375·. Another two groups of 2 genomes each had the following RE digestion patterns: (i) both the rrs copies had ·86·79· 42·328·33·211·207/209·375· (CP009539.1 and CP011078.1), (ii) the two rrs copies had the following patterns: (a) ·79·42·361·211·207·375· (CP009759.1) and (b) ·42·361·211·207·375· (CP009757.1). Only two genomes had unique RE-AluI digestion patterns in their rrs: (i) CP009787.1—·79·42·172·189·210·207·375·; and (ii) CP007448.1—·86·79·42·361·211·207·375·. Thus, out of 51 genomes, RE-AluI enabled us to distinguish only two genomes on the basis of their unique rrs digestion patterns.
With RE-BfuCI
A total of 7 RE digestion patterns were recorded—(i) 13 ·114·152·424·652·174·56 (in 24 genomes) and (ii) 105·152·424·652·174·9 (in 12 genomes), (iii) 18·266·424·652·125 (in 2 genomes), (iv) 105·152·424·652·156 (in 9 genomes), (v) 254·424·652·160 (in 2 genomes). Two genomes (CP010247 and CP011078) had RE-BfuCI digestion patterns: (i) 7/13·114·152·424·652/654·174·8/17 (common to both the genomes), and (ii) 10·114·152·424·50·602·174·3 in CP010247 and 17·114·152·424·575·77·174 ·21 in CP011078. In spite of such a variation in the digestion patterns obtained with RE-BfuCI, only 2 genomes (CP010247, CP011078), were found to have unique features in one of their rrs copies. Hence, unless information on both the rrs copies is available, it will be difficult to distinguish them without any ambiguity.
With RE-CviAII
RE digestion patterns of 51 genomes could be grouped into 9 categories (Table S4). In spite of the availability of a large variation in the RE-CviAII, digestion patterns-only 1 genome (CP009539), has certain unique features in all the copies of rrs. For identifying CP009935, CP001585 and CP001589, the information on digestion pattern can be exploited only if the nucleotide sequences of both the rrs copies are available. RE digestions of rrs copies of the rest of the genomes have similarities among themselves, which do not permit their distinction.
With other Restriction Endonucleases
RE digestion of rrs with TaqI could lead to provide some information, which can be used to distinguish only two Yersinia genomes: CP009759 and CP009364. Here, two patterns were recorded within each of the genomes (i) CP009759—(a) 55·760·139·361·216 and (b) 56·760·53·86·361·213, and (ii) CP009364—(a) 55·760·53·86·361·216, and (b) 56·123·636·53·86·361·213.
Even so, with one of the patterns being common, it is difficult to distinguish them in an unambiguous manner. TaqI digestion of the rest of the 49 Yersinia genomes had an exactly similar digestion pattern: (i) 61·760·53·86·361·264, which could thus not be distinguished by this RE. A slightly better scenario was recorded with RE-Tru9I here, it was possible to distinguish only 4 genomes of Yersinia: (i) CP007230: 462·9·79·42·26·251·34·52·134·355·98, (ii) CP011078: 2·457·7·88·42·26·251·34·52· 134·355·106, (iii) (a) HF571988: 24·464·88·42·26·251·34·52·134·355·38, and (b) 456·88·42·26 ·251·34·52·134·355·51, and (iv) CP009757: (a) 457·88·42·26·285·52·134·355·90, and (b) 456·88· 42·26·251·34·52·134·355·93. The rest of the 47 genomes had a similar RE Tru9I digestion pattern: 462·88·42·26·251·34·52·134·355·141.
The cases with none to few RE digestion sites were as follows: (1) with HaeI—no digestion, (ii) with Hin1I—single cut site led to two fragments (in 50 genomes) and 3 fragments in CP007448, (iii) with RsaI—two cut sites in 50 genomes and 3 cut sites in one copy of rrs in genome-CP009367. With RE-BfaI—all the copies of rrs in all the 51 genomes had a similar digestion pattern: 247·24·14·365·694·241. With RE-HpyCH4V—2 RE digestion patterns: (i) 111·110·264·87·333·89·153·92·310·50 (in 50 genomes), and (ii) (a) 109·110·351·333·89·152·22·312·59, and (b) 111·110·264·87·333·89·153·22·362 (in a single genome-CP010023).
Common Gene Analysis
In view of the observations made with the presence of multiple copies of rrs gene and high similarity among them in all the sequenced genomes of Yersinia, we shifted our focus on other genes. A comparative analysis of all the genes present in the 51 Yersinia genomes, we could trace 304 genes, which were common among them. Out of these 304 genes, we selected 34 genes, which varied in size from 114 to 4446 nts, in such a manner that genes of all sizes were represented (Tables S2 and S3).
In silico RE Digestion Patterns
In silico RE digestion patterns of 33genes (in addition to rrs), which were common to all the 51 genomes of Yersinia with 10 different REs revealed some very interesting features in them (Tables S5-S13 and Tables S14 to S22).
These 33 genes could be categorized into three distinct classes: (i) used for distinguishing most of the genomes—carB, fadJ, gluM, gltX, ileS, malE, nusA, ribD, and rlmL could be (ii) used in combination with certain REs generating supplementary information for identifying those genomes which could not be distinguished on the basis of genes of the category (i)—aceE, aceK, cpxP, cysJ, glpQ, gltB, gyrB, lacZ, leuD, ligA, lolD, metH, mukB, pheT, rpoB, and secA, and (iii) genes which could not be used as candidates for distinguishing Yersinia species—rnpA (114 nts), mltA (210 nts), rplW (303 nts), flgC (405 nts), nagB (801 nts), hisG (900 nts), thiP (1608 nts), and dnaK (1902 nts).
In silico Digestion Pattern of Genes as Potential Biomarkers
Out of the 9 genes [category (i)], which could be used for distinguishing genomes, digestion of fadJ with different REs proved to be instrumental in distinguishing 11 Yersinia genomes: Y. aldovae 670-83 (CP009781), Y. enterocolitica subsp. enterocolitica 8081 (AM286415), Y. enterocolitica LC20 (CP007448), Y. enterocolitica WA (CP009367), Y. enterocolitica (type O:5) str. YE53/03 (HF571988), Y. enterocolitica subsp. palearctica Y11 (FR729477), Y. intermedia Y228 (CP009801), Y. rohdei YRA (CP009787), Y. ruckeri Big Creek 74 (CP011078), Y. ruckeri YRB (CP009539), Y. similis 228 (CP007230) (Tables 1 and 2) The gene (fadJ) gave distinct digestion patterns with REs—HpyCH4V, BfuCI, CviAII, AluI, Hin1I, BfaI, RsaI, TaqI and Tru9I. RE-HpyCH4V was the only one which proved effective in distinguishing 11 genomes. In certain combinations Yersinia genomes—CP009367, CP009539, CP009787, CP011078, and HF571988, the following REs could not provide distinct digestion patterns: Hin1I, BfaI, RsaI, TaqI and Tru9I. FR729477 could be distinguished from all others on the basis of only one RE-HpyCH4V.
Table 1.
Unique in silico Restriction Endonuclease digestion pattern (5′–3′) of fadJ gene of Yersinia genomes
| Genome | fadJ gene digestion with restriction endonucleases | |||
|---|---|---|---|---|
| HpyCH4V | BfuCI | CviAII | AluI | |
| AM286415 | 48·294·448·464·52·24·469·301 | 839·531·13·717 | 135·149·1019·797 | 16·763·1321 |
| CP007230 | 52·179·294·912·608·181·117 | 210·56·54·556·353·337·569·208 | 318·109·313·746·257·306·294 | 37·69·350·358·113·691·565·156·4 |
| CP007448 | 163·15·8·439·303·464·545·295 | 831·926·475 | 273·149·133·886·500·126·165 | 154·329·388·492·117·447·54·94·157 |
| CP009367 | 186·294·448·464·52·24·469·301 | 38·939·531·13·717 | 422·1019·797 | – |
| CP009539 | 168·294·603·227·82·343·357·131 | 92·234·487·452·238·80·381·88·153 | 112·143·154·1014·782 | 393·10·1298·184·320 |
| CP009781 | 186·27·267·313·507·92·343·210·290 | 275·556·257·433·236·478 | 273·562·606·518·276 | 483·376·660·219·240·221·36 |
| CP009787 | 186·294·484·315·165·111·180·183·19·304 | 110·721·690·720 | 67·20·186·25·84·506·553·500·126·96·78 | 199·666·52·683·360·277·4 |
| CP009801 | 163·23·27·267·139·345·428·52·768·23 | 530·27·18·256·48·185·457·131·583 | 273·109·506·261·292·500·222·72 | 917·323·240·93·662 |
| CP011078 | 168·294·830·82·831 | 92·234·487·452·238·80·381·88·60·93 | 112·143·109·45·792·222·782 | 393·1308·184·320 |
| HF571988 | 186·294·559·353·52·24·469·80·221 | 977·544·104·613 | 273·149·1019·797 | 52·102·711·6·11·35·167·1154 |
| FR729477 | 480·912·52·24·22·447·80·221 | – | – | – |
(·): RE site in the gene sequence; (–): no digestion observed
Table 2.
Unique in silico Restriction Endonuclease digestion pattern (5′–3′) of fadJ gene of Yersinia genomes
| Genome | fadJ gene digestion with restriction endonucleases | ||||
|---|---|---|---|---|---|
| Hin1I | BfaI | RsaI | TaqI | Tru9I | |
| AM286415 | 2100 | 586·1144·370 | 323·162·195·243·1177 | 1490·610 | 10·414·34·147·324·24·741·403·3 |
| CP007230 | 347·632·1364 | 451·1017·875 | 336·332·574·830·132·139 | 726·303·239·63·303·15·108·213·276·97 | 722·888·330·403 |
| CP007448 | 617·1615 | 997·1235 | 291·188·516·66·628·135·408 | 119·1791·322 | 20·128·595·324·24·1014·127 |
| CP009367 | – | 724·1144·370 | – | 1628·610 | 148·414·34·147·324·24·741·403·3 |
| CP009539 | – | 400·306·1499 | – | – | – |
| CP009781 | 67·469·456·871·372 | 1588·647 | 1194·762·279 | 1286·202·116·9·315·307 | 148·448·147·324·1168 |
| CP009787 | – | 1123·1118 | 623·195·481·390·552 | 1235·369·9·15·613 | 20·128·414·34·495·741·409 |
| CP009801 | 1894·341 | 220·2015 | 182·279·600·75·61·731·10·297 | 331·883·195·195·258·119·254 | 20·128·448·147·333·15·671·473 |
| CP011078 | – | 706·1499 | – | – | – |
| HF571988 | – | – | 362·148·1728 | 1604·24·282·328 | – |
| FR729477 | – | – | – | – | – |
(·): RE site in the gene sequence; (–): no digestion observed
In order to distinguish genomes in addition to those 11 listed with fadJ, all gene-RE combinations were searched. It provided unique digestion patterns by which another 10 Yersinia genomes can be easily identified: Y. frederiksenii Y225 (CP009364.1), Y. kristensenii Y231 (CP009997.1), Y. pestis Harbin35 (CP009704.1), Y. pestis biovar Medievalis str. Harbin 35 (CP001608.1), Y. pestis D106004 (CP001585.1), Y. pestis D182038 (CP001589.1),Y. pestis str. Pestoides B (CP010023.1), Y. pseudotuberculosis ATCC 6904 (CP008943.1), Y. pseudotuberculosis IP 31758 (CP000720.1), and Y. pseudotuberculosis str. PA3606 (CP010067.1) (Table 3). Here, the following genes—aceE, aceK, cpxP, cysJ, glpQ, gltB, gyrB, lacZ, leuD, ligA, lolD, metH, mukB, pheT, rpoB, and secA, on digestion with specific REs—AluI, BfaI, HpyCH4V, CviAII, TaqI, BfuCI, RsaI, Hin1I, Tru9I (Table 3) provided information on unique digestion patterns. The patterns obtained with specific RE-gene combination can be used as biomarkers for demarcating Yersinia genomes with high specificity.
Table 3.
Yersinia genomes distinguished based on unique Gene: Restriction Endonuclease approach
| Genome | Gene | RE | Restriction endonuclease digestion Pattern |
|---|---|---|---|
| CP000720 | glpQ | AluI | 419·271·222·22·182 |
| CP001608 | ligA | AluI | 345·204·153·533·179·570·29 |
| CP010067 | gyrB | AluI | 884·266·12·775·216·262 |
| gltB | BfaI | 71·3224·483·538·142 | |
| cysJ | HpyCH4V | 63·330·222·469·26·150·210·303·84 | |
| mukB | AluI | 81·661·335·70·155·78·192·111·75·15·66·712·152·122·193·46·13·34·12·149·22·402·412·155·78·117 | |
| CviAII | 477·54·298·96·104·1231·303·180·63·125·168·748·290·229·6·6·80 | ||
| HpyCH4V | 112·62·57·87·171·531·204·36·117·168·72·211·68·276·252·393·58·401·123·747·138·118·41·15 | ||
| TaqI | 68·279·507·195·648·270·12·231·42·1383·15·59·304·30·83·332 | ||
| FR729477 | gltB | AluI | 533·331·321·159·57·226·25·40·340·914·320·103·90·220·151·400·205·5·21 |
| BfuCI | 131·55·365·28·30·714·81·150·180·600·143·16·42·843·168·243·356·106·38·172 | ||
| RsaI | 7·1804·24·39·257·289·667·192·216·321·233·81·10·321 | ||
| metH | Hin1I | 1400·6·2129·161 | |
| mukB | BfuCI | 69·83·249·367·204·1317··207·140·157·732·921 | |
| pheT | AluI | 634·528·174·13·569·98·48·79·245 | |
| secA | AluI | 111·150·85·141·6·230·181·33·268·348·516·473·173 | |
| BfuCI | 42·33··113·456·192·417·328·12·380·418·74·190·60 | ||
| TaqI | 104·147·372·24·549·120·56·328·216·192·33·574 | ||
| CP008943 | lacZ | BfaI | 919·1899·103·280 |
| CP009364 | aceE | BfuCI | 27·5·27·52·189·105·320·207·135·28·171·425·15·61·11·49·44·106·687 |
| cpxP | CviAII | 159·108·231 | |
| CP009997 | aceE | BfuCI | 27·5·27·52·189·105·320·207·135·28·171·425·15·61·11·49·44·106·687 |
| CP001589 | aceK | CviAII | 121·48·918·17·79·219·264·62 |
| CP009367 | cpxP | Hin1I | 37·461 |
| CP010023 | leuD | HpyCH4V | 46·44·234 |
| Tru9I | 128·162··34 | ||
| CP001585 | lolD | Tru9I | 221·218·118·148 |
| CP009704 | rpoB | Tru9I | 293·120·1011·384·18·375·6·531·429·69·793 |
Symbol filled circle (·) indicates RE site in the gene sequence
Discussion
The urgency to identify the disease causing organisms is always there, especially in case of an epidemic outbreak. Morphological and biochemical characteristics prove effective to a certain extent in identifying the organism. However, modern biological methods have narrowed down the search to the use of highly conserved genes such as rrs and a few HKGs for bacterial identification. The use of rrs has been the most extensive and adapted even by not so well equipped laboratories. In order to extend the usage of rrs for distinguishing very closely related strains, and provide biomarkers for rapidly identifying bacteria, certain latent features of rrs have been elucidated using molecular approaches. The use of REs to deduce unique rrs digestion patterns have proved to be quite effective in the cases of: (i) Bacillus, (ii) Clostridium, (iii) Pseudomonas, and (iv) Streptococcus [12, 32–36]. In spite of a roaring success observed in the usage of rrs, the phylogenetic analysis of rrs gene sequences has not proved very useful in an unambiguous manner in the case of Yersinia. This limitation in employing rrs in distinguishing Yersinia species has been assigned to the presence of its multiple copies per genome and high sequence similarity among themselves [38]. A similar scenario was observed in Clostridium spp., which also possesses multiple copies of rrs. These rrs copies also show high similarity [33, 35]. In order to circumvent this problem, we have employed a genome wide analysis for searching novel biomarkers in Clostridium [15]. Compared to only 22 genes being common to all the 27 genomes of Clostridium spp., we could observe 304 genes to be common to 51 sequenced genomes of Yersinia.
In silico digestion of nucleotide sequences 34 genes with different type II REs has allowed us to deduce unique combinations of genes and REs. A thorough analysis of RE digestion patterns revealed that 21 out of 51 strains representing 10 species of Yersinia. These differences in the RE digestion patterns arise because of changes in the nucleotide within the RE recognition motif [37]. By this approach we can use novel biomarkers—carB, fadJ, gluM, gltX, ileS, malE, nusA, ribD, and rlmL and their RE digestion patterns for rapidly identifying Yersinia species (Table 4). It may be remarked that these genes have not been reported earlier as biomarkers for identifying Yersinia. Previously, the genes involved in pathogenesis: (i) ail, inv, yst, myf, vir, and yop have been used for identifying Yersinia [16–19]. Incidentally, these 5 genes are not present in all the strains of Yersinia, making it difficult to use them as universal biomarkers [39, 40]. Even gyrB gene, which is commonly used for bacterial identification, did not prove very effective in distinguishing Yersinia strains being studied here. On the other hand, the biomarkers identified in this study are common to all the Yersinia strains, are highly specific and have a unique RE digestion pattern. Thus by using specific primer sets, we can amplify these genes through polymerase chain reaction. The amplicon can be digested with the specific RE, through which we can detect this organism even among a large group of unrelated bacterial populations e.g., those present in a soil sample, contaminated food or water sample or wounds and even on medical equipments, etc. Our data provides information to detect even non-pathogenic Yersinia, which may be present by chance. In spite of the fact that these genes, including fadJ are present in genus such as Pseudomonas, their RE digestion patterns were remarkably different (Data not shown). These biomarkers can thus be used for developing diagnostic kits specifically for Yersinia.
Table 4.
Segregation of Yersinia species with different genes
| Segregated by fadJ | Segregated by genes other than fadJ a |
|---|---|
| Y. aldovae 670-83 CP009781.1 | Y. frederiksenii Y225 CP009364.1 |
| Y. enterocolitica WA CP009367.1 | Y. kristensenii Y231 CP009997.1 |
| Y. enterocolitica (type O:5) str. YE53/03 HF571988.1 | Y. pestis Harbin35 CP009704.1 |
| Y. enterocolitica LC20 CP007448.1 | Y. pestis biovarMedievalis str. Harbin 35 CP001608.1 |
| Y. enterocolitica subsp. enterocolitica 8081 AM286415.1 | Y. pestis D106004 CP001585.1 |
| Y. enterocolitica subsp. palearctica Y11 FR729477.2 | Y. pestis D182038 CP001589.1 |
| Y. intermedia Y228 CP009801.1 | Y. pestis str. Pestoides B CP010023.1 |
| Y. rohdei YRA CP009787.1 | Y. pseudotuberculosis ATCC 6904 CP008943.1 |
| Y. ruckeri Big Creek 74 CP011078.1 | Y. pseudotuberculosis IP 31758 CP000720.1 |
| Y. ruckeri YRB CP009539.1 | Y. pseudotuberculosis str. Pa3606 CP010067.1 |
| Y. similis228 CP007230.1 |
| Could not be segregated | |
|---|---|
| Y. enterocolitica 2516-87 CP009838.1 | Y. pestis KIM10 + AE009952.1 |
| Y. enterocolitica subsp. palearctica 105.5R(r) CP002246.1 | Y. pestis Nepal516 CP000305.1 |
| Y. pestis Dodson CP009844.1 | Y. pestis Pestoides F CP000668.1 |
| Y. pestis El Dorado CP009785.1 | Y. pestis Pestoides F CP009715.1 |
| Y. pestis Nicholisk 41 CP009991.1 | Y. pestis Pestoides G CP010247.1 |
| Y. pestis PBM19 CP009492.1 | Y. pestis Z176003 CP001593.1 |
| Y. pestis Shasta CP009723.1 | Y. pseudotuberculosis1 CP009786.1 |
| Y. pestis A1122 CP002956.1 | Y. pseudotuberculosis EP2/+ CP009759.1 |
| Y. pestis A1122 CP009840.1 | Y. pseudotuberculosis MD67 CP009757.1 |
| Y. pestis Angola CP000901.1 | Y. pseudotuberculosis IP 32953 B X936398.1 |
| Y. pestis Angola CP009935.1 | Y. pseudotuberculosis IP32953 CP009712.1 |
| Y. pestis Antiqua CP000308.1 | Y. pseudotuberculosis PB1/+ CP001048.1 |
| Y. pestis Antiqua CP009906.1 | Y. pseudotuberculosis PB1/+ CP009780.1 |
| Y. pestisbiovarMicrotus str. 91001 AE017042.1 | Y. pseudotuberculosis YPIII CP000950.1 |
| Y. pestis CO92-AL590842.1 | Y. pseudotuberculosis YPIII CP009792.1 |
aSee Table 3 for list of genes
Electronic supplementary material
Acknowledgments
We are thankful to the Director of CSIR-Institute of Genomics and Integrative Biology (IGIB), and CSIR project GENESIS (BSC0121) for providing the necessary funds, facilities and moral support.
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