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Abstract

Molecular markers serve to assign individual samples to specific groups. Such markers should be easily identified and have a high discrimination power, being highly conserved within groups while showing sufficient variability between the groups that are to be distinguished. The availability of a large number of complete genomic sequences now enables the informed selection of genes as molecular markers based on the observed patterns of variability. We derived a new scoring system based on observed DNA polymorphic differences, and which uses the Bayes theorem as adapted by Wilcox. For validation, we applied this system to the problem of identifying individual species within a prokaryotic (Vibrio) and a eukaryotic (Diphyllobothrium) genus for validation. Top-scoring candidates genes Chromosome segregation ATPase and ATPase-subunit 6 showed better discrimination power in Vibrio and Diphyllobothrium, respectively, as compared to standard molecular markers (recA, dnaJ and atpA for Vibrio, and 18s rRNA, ITS and COX1 for Diphyllobothrium).

Keywords

molecular marker genome analysis Bayes's theorem DNA polymorphism

Background

Molecular methods to assign biological samples to specific groups (eg, taxonomic groups) have largely replaced morphological comparisons, allowing hundreds or even thousands of characters to be compared across samples.¹ Historically, numerous DNA-based approaches encompassing random whole-genomic analysis have been used to discriminate groups of organisms. These include methods like, among many others, restriction fragment length polymorphism (RFLP), or random amplification of polymorphic DNA (RAPD).^2,3 Alternatively, sequences from genes, usually selected by their conserved, housekeeping roles, can be used.² However, it is often the case that existing markers provide insufficient resolution or are confounded by homoplasy, homologous recombination and lateral gene transfer.^4,5 In recent years, thanks to great advances in sequencing technologies,^6,7 the number and diversity of completely sequenced genomes is growing exponentially. This provides the basis for optimizing the selection of marker genes based on the analysis of the whole genetic complement of a given set of organisms. Earlier attempts to use whole-genome information to select marker genes that could best serve as predictors of phylogenetic relatedness include the use of scores based on the level of sequence identities from whole-genome alignments,⁸ or the selection of unique sequence signatures present in a few species.⁹ These methods, however, do not exploit the information from sequence variability within a species. Here we propose and evaluate an alternative algorithm for the selection of optimal genetic markers, which is based on the comparison of complete genomes. In brief, the basis of our strategy is to rank different genes according to the level of DNA polymorphism within and between defined taxonomic groups. More specifically, DNA polymorphism is measured as the average number of nucleotide differences per site,¹⁰ and a conditional probabilistic statistic based on Bayes's Theorem as adapted by Willcox¹¹ is used to prioritize genes, so that genes presenting higher levels of polymorphism between groups but lower variation within a group receive higher scores. In order to validate the methodology, we apply it to the problem of selecting marker genes for the identification of individual species within a prokaryotic (Vibrio) and a Eukaryotic (Diphyllobothrium) genus. Publicly available genomic sequences were analyzed to select high-scoring marker genes, which were subsequently amplified and sequenced in a set of additional, non-sequenced strains of these groups. The discrimination power (DP) of these newly obtained sequences was compared to that of traditional marker genes.

Methods

Sequence data

Complete genome sequences were downloaded from the National Center of Bioinformatics Information (NCBI) in Genbank (.GBK) format. These were: (1) chromosome I from the following Vibrio species and strains: V. cholerae (NC_002505), V. vulnificus (NC_004459), V. parahaemolyticus (NC_004603), V. harveyi (NC_009783), V. fischeri (NC_006840), Alivibrio salmonicida (NC011312), V. splendidus (NC_011753), V. cholerae (NC_009457), V. cholerae (NC_012578), and V. cholerae (NC_012668); (ii) Whole mitochondrial genomes from Different Diphyllobothrium species and strains: D. latum (NC_008945), D. nihonkaiense (NC_009463), D. latum (AB269325) and D. latum (DQ985706).

Alignments, polymorphism analysis, and molecular marker score calculation

Genome sequences mentioned above were divided into four different groups: (1) VibrioDS, containing only one representative genome for each Vibrio species, using the Vibrio cholerae strain (NC_002505); (2) VibrioSS, comprising the four different Vibrio cholerae strains; (3) DiphyllobothriumDS containing one genome per Diphyllobothrium species using NC_008945 as D. latum representative; (4) DiphyllobothriumSS containing all D. latum strains. Each group was aligned using MAUVE v2.3.1 using the progressiveAligner option.¹² Output files were re-formatted to Variscan–-extended multi-FASTA (XMFA) format with a custom PERL Script (XMFA.pl) and analyzed using Variscan v2.0.¹³ The resulting files were used as an input for the molecular marker score calculation implemented in a custom PERL script (SCORE.pl), and using two different window sizes of 300pb and 500 pb, for Vibrio and Diphyllobothrium, respectively. The final output consists of a plain text file listing the potential marker genes, sorted in a descending order of their scores.

Algorithm

The Bohle-Gabaldón (BG) score calculation is based on the level of DNA polymorphism in the Distinct Species (DS) group and Same Species (SS) groups, as inferred from the average of nucleotide differences per site ( $π^$ ). Not more than one SS group may be considered. The Bayes's theorem as adapted by Willcox¹¹ is used as follows. If the number of genome sequences in DS group is lower than 4 and there is no length constraint for the marker, formula (1) is used.

If molecular marker with specific size is required (S_ref) formula (2) is used, S_i is the nucleotides length of gene i. Also, if the amount of whole-genomes for DS group is 4 or more, is possible include Tajima's D (D_i) without specific size requirement (3) or with (4), which better account for the possibility of rare haplotypes. Based on Willcox conditions, higher $\hat{π}$ in Different Species (π_i(DS)) and lower in Same Species (π_i(SS)) is better. For (D_i(SS))) in DS group more negative values are preferred and, finally, the size of molecular marker (S_ref) is arbitrary. In order to reduce sequencing costs we selected rather small sizes (300 pb-500 pb).

BG score using DNA polymorphism (less than 4 genomes):

S c o r e_{i} = {\hat{π}}_{i (D S)} (1 - {\hat{π}}_{i (S S)})

Scoring using DNA polymorphism and Size (less than 4 genomes)

S c o r e_{i}^{+ S i z e} = {\hat{π}}_{i (D S)} (1 - {\hat{π}}_{i (S S)}) (\frac{S_{i}}{S_{i} + | S_{r e f} - S_{i} |})

Scoring using DNA polymorphism and Tajima's D¹⁴ (4 genomes and more):

S c o r e_{i}^{_{- S i z e}^{+ T a j i m a}} = {\hat{π}}_{i (D S)} (1 - {\hat{π}}_{i (S S)}) (- \frac{{\hat{D}}_{i (D S)}}{2})

Scoring using DNA polymorphism, Tajima's D and Size (4 genomes and more):

\begin{array}{l} S c o r e_{i}^{_{- S i z e}^{+ T a j i m a}} = {\hat{π}}_{i (D S)} (1 - {\hat{π}}_{i (S S)}) \\ \times (- \frac{{\hat{D}}_{i (D S)}}{2}) (\frac{S_{i}}{S_{i} + | S_{r e f} - S_{i} |}) \end{array}

The maximum value for Score is 1 using π_i(DS) = π_i(sS) = 0, Tajima's D = -2 and S_i = S_ref. The minimum value for Score is 0 considering π_i(DS) =0, π_i(sS) = 1, Tajima's D = +2 and S_i ≠ S_ref.

Experimental validation analysis

Additional Vibrio sequences for the candidate genes were obtained from biological samples stored in the Collection of Aquatic Important Microorganism (CAIM) at the Center of Research for Nutrition and Development (Mexico). Collected strains were: V. ordalii CAIM608, V. aestuarianus CAIM592, V. orientalis CAIM332, V. tubiashii CAIM313, V. splendidus CAIM319, V. cyclitrophicus CAIM 596, V. fortis CAIM629, V. parahaemolyticus CAIM320, V. harveyi CAIM513, V. rotiferianusCAIM577, V. mytiliCAIM528, V. navarrensis CAIM609, V. fluvialis CAIM593, V. agarivorans CAIM615, V. mimicus CAIM602, V. metschnikovii CAIM317, V. vulnificus CAIM610, V. aerogenes CAIM906 and V. neptunius CAIM532. Similarly, additional sequences for candidate Diphyllobothrium marker genes were obtained from samples fixed in ethanol at the Parasitology Institute of Biology Center of the Czech Republic. These included the strains D. latum TS-07/17, D. pacificum TS-06/30a.b., D. dendriticum TS-04/39, D. nihonkaiense TS-06/236, D. polyrugosum TS-05/58 and D. ditremum TS-02/32.

DNA purification and amplification

Genomic DNA from Vibrio species was purified using E.Z.N.A. Bacterial DNA Kit (Omega Biotek, USA). Diphyllobothrium samples were diluted (1) in nuclease-free water, macerated with mortar, to subsequently purify DNA using E.Z.N.A. Tissue DNA Kit (Omega Biotek, USA), following manufacturer's instructions. The final volume for PCR were 50 μL with 5 μL Buffer 10x (20 nM Tris-HCl pH 8.0, 40 nM NaCl, 2 mM Sodium phosphate, 0.1 mM EDTA, 1 mM DTT, stabilizers, 50% (v/v) glycerol), 1 μL dNTPs (10 mM), 6 μL MgCl₂ (50 mM), 1 μL primers (10 μM), 0.5 μL Platinum Taq DNA polymerase (2.5 U), 5 μL template DNA and 31.5 μL free nuclease water. Primers for target gene amplification were designed based on the level of observed sequence conservation. The primers used for Vibrio were forward 5′-ATG GTT TCA ATT AAN GGN TTR CCK CC-3′ and reverse 5′-TTA GAT GTA RAK ATC GAC MCC NA-3′ and for Diphyllobothrium target gene were forward 5′-ATG ATC TTT AGT GGT TAT TCA -3′ and reverse 5′-CTA ATG GTC CAC TGA AAA TGA TAA TAT-3′. The thermal profile used was the following: initial activation (2 min, 95 °C), followed by 35 cycles of denaturation (1 min, 95 °C), annealing (1 min, 55 °C) and extension (1 min, 72 °C), and a final extension (4 min, 72 °C). Electrophoresis agarose gel (1.5%) stained with Ethidium bromide was used to identify the PCR products from Vibrio (~300 pb) and Diphyllobothrium (~500 pb). PCR products were purified using Minielute gel extraction kit (QIAGEN, USA) and cloned using CloneJET PCR cloning kit (Fermentas, USA). This kit includes the positive selection cloning vector pJET1.2/blunt that contains a lethal gene which is disrupted by ligation of a DNA insert into the cloning site. As a result, only cells with recombinant plasmids are able to propagate. Finally, DNA from the E. coli top 10 colonies was purified using E.Z.N.A. bacterial DNA Kit (Omega Biotek, USA). Total DNA obtained from clones was amplified using primers pJET1.2 forward and reverse (CloneJET, Fermentas, USA) with BigDye Terminator v3.1 Cycle sequencing Kit (Applied Biosystem, USA) using manufacturer's instructions. The PCRs products were purified for Dyes using Dye Terminator Removal kit (Omega Biotek, USA) and sequenced using ABI PRISM 310 machine (Applied Biosystem, USA). The sequences obtained were edited, assembled, aligned and compared using CLC Genomics Workbench v3.5.5 (CLC Bio, Denmark).

Molecular marker discrimination power analysis

To prioritize the markers, we developed a simple Discrimination Power (DP) score (5) based in Bayes's Theorem adapted by Willcox¹¹ which evaluates the maximum identity (ΔI^max_i) for each species in each molecular marker gene (x) analyzed.

D P_{x} = \prod_{i = 1}^{n} (1 - Δ I_{i}^{\max})

The maximum value for DP is 1 (ie, perfect molecular marker), if maximum difference of identity for the closest species in each species for each molecular marker tends to 0. The minimum value for DP is 0 when the level of identity of that marker in the closest species tends to 1 for each species.

Results

Automated prioritization of marker genes

Publicly available genomes from Vibrio and Diphyllobothrium were downloaded and subjected to the selection of marker genes approach aforementioned. For each genus, a list of potential marker genes sorted in descending order of their BG scores was produced. For Vibrio species (Table 1), the best molecular marker is a protein-coding gene with locus tag VC1988 in chromosome 1 of the reference genome V. cholerae NC_002505. This gene encodes a chromosome segregation ATPase, a protein essential for cell division that forms part of a chromosomal segregation complex. In the case of Diphyllobothrium, the analysis of completely sequenced mito-chondrial genomes revealed the gene encoding the subunit 6 of the ATPase complex as the best potential marker gene (Table 2). This enzyme is part of the mitochondrial oxidative phosphorylation and is essential for the generation of ATP.¹⁵

Table 1

10 top-scoring marker genes for Vibrio species discrimination using S_i = 300 pb.

Score_i	Locus tag	Size (pb)	π _i(DS)	Tarima's D_(DS)
0.00308	VC1988	0.98387	0.03469	-0.09022
0.00252	VC1954	0.33667	0.05809	-0.12885
0.00238	VC2163	0.78667	0.03703	-0.08185
0.00237	VC2354	0.47667	0.04847	-0.10258
0.00233	VC2665	0.96667	0.03374	-0.07132
0.00222	VC2189	0.59667	0.04396	-0.08477
0.00212	VC1986	0.60653	0.04145	-0.08437
0.00208	VC2658	0.82189	0.03318	-0.07621
0.00207	VC2652	0.56667	0.03689	-0.09897
0.00207	VC1534	0.59817	0.04150	-0.08352

Table 2

10 top-scoring marker genes o Diphyllobothrium species discrimination using S_i = 500 pb.

Score	Gen	Size (pb)	π _i(DS)	π _i(SS)
0.01175	ATP6	509	0.01196	0.00013
0.01066	ND6	458	0.01156	0.00015
0.00733	ND3	356	0.00944	0.00019
0.00563	ND4L	260	0.00833	0.00028
0.00524	COX2	569	0.00596	0.00023
0.00479	ND2	878	0.00841	0.00015
0.00433	ND4	1250	0.01083	0.00017
0.00404	ND1	890	0.00719	0.00022
0.00355	ND5	1568	0.01115	0.00047
0.00230	COX1	1565	0.00720	0.00004

Experimental Validation

In order to validate the effectiveness of our approach we amplified these marker genes from additional strains of known taxonomic assignment but with no current genomic sequences available. The effectiveness of the markers, as measured by the Discrimination Power score (DP) described above, was compared to that of common markers used previously for these species. These were atpA,¹⁶ dnaJ¹⁷ and recA,¹⁸ for Vibrio and 18S rRNA, COX1 and 18s rRNA + ITS + 5.8s rRNA^19,20 for Diphyllobothrium.

Twenty new sequences were obtained from the chromosome segregation ATPase gene in different Vibrio species. Remarkably, this gene showed the best Discrimination Power value (Table 3) with a DP score of 6.3 × 10⁻¹⁴. Standard markers showed lower discrimination powers: dnaJ (DP_dnaJ = 3.5 × 10⁻¹⁹), atpA (DP_atpA = 11. × 10⁻²⁶) finally recA (DP_recA = 7.9 × 10⁻²⁷). In the case of Diphyllobothrium, seven new sequences were obtained from ATPase-subunit 6 (ATP6) gene in different species. Again, the marker gene selected by our approach presented the highest Discrimination power (DP_ATP6 = 7.9 × 10⁻⁶), followed by COX1 (D_PCOX1 = 5.8 × 10⁻⁶), ITS rRNA(DP_ITS = 4.4 × 10⁻¹¹) and 18s rRNA(DP_18sRNA = 1.4 × 10⁻¹³) (Table 4).

Table 3

Prokaryotic molecular markers genes comparison using Discrimination power scoring.

Species	Accession number	SC	recA			dnaJ			atpA			Chromosome segregation ATPase
Species	Accession number	SC	CSC	Id	(1-Id)	CSC	Id	(1-Id)	CSC	Id	(1-Id)	CSC	Id	(1-Id)
V. aestuarianus	JN040521	1	2	0.999	0.001	5	0.801	0.199	8	0.899	0.101	18	0.708	0.292
V. alginolyticus	NZ_AAPS01000071	2	1	0.999	0.001	15	0.883	0.117	14	0.967	0.033	14	0.809	0.191
V. cholerae	NC_002505	3	11	0.921	0.079	11	0.932	0.068	11	0.958	0.042	11	0.876	0.124
V. coralliilyticus	NZ_ACZN01000015	4	12	0.971	0.029	12	0.904	0.096	20, 12	0.957	0.043	13	0.779	0.221
V. cyclitrophicus	JN040526	5	18	0.924	0.076	18	0.904	0.096	18	0.973	0.027	18	0.844	0.156
V. fischeri	NC_006840	6	16	0.876	0.124	16	0.852	0.148	16	0.918	0.082	16	0.761	0.239
V. fluvialis	JN040529	7	3, 11	0.861	0.139	2	0.848	0.152	4	0.889	0.111	1	0.673	0.327
V. fortis	JN040527	8	5	0.882	0.118	19	0.853	0.147	15	0.942	0.058	18	0.802	0.198
V. harveyi	JN040517	9	15	0.979	0.021	15	0.925	0.075	15	0.979	0.021	14	0.868	0.132
V. metschnikovii	JN040531	10	15	0.845	0.155	7	0.825	0.175	20	0.835	0.165	7	0.646	0.354
V. mimicus	JN040530	11	3	0.921	0.079	3	0.932	0.068	3	0.958	0.042	3	0.876	0.124
V. neptunius	JN040535	12	4	0.971	0.029	4	0.904	0.096	4	0.957	0.043	19	0.577	0.423
V. orientalis	JN040523	13	17	0.893	0.107	13	0.851	0.149	19	0.974	0.026	19	0.787	0.213
V. parahaemolyticus	JN040516	14	9	0.917	0.083	2	0.867	0.133	15	0.970	0.030	9	0.868	0.132
V. rotiferianus	JN040518	15	9	0.979	0.021	9	0.925	0.075	9	0.979	0.021	14	0.774	0.226
V. salmonicida	NC_011312	16	6	0.876	0.124	6	0.852	0.148	6	0.918	0.082	6	0.761	0.239
V. shilonii	NC_ABCH01000040	17	13	0.893	0.107	13	0.826	0.174	15	0.912	0.088	1	0.539	0.461
V. splendidus	JN040524	18	5	0.924	0.076	5	0.904	0.096	5	0.973	0.027	5	0.844	0.156
V. tubiashii	JN040522	19	9	0.886	0.114	14, 8	0.853	0.147	13	0.935	0.065	13	0.787	0.213
V. vulnificus	JN040533	20	9, 11	0.859	0.141	9	0.842	0.158	17	0.904	0.096	9	0.700	0.3
Discrimination power score			7.980 × 10⁻²⁷			3.530x10⁻¹⁹			1.070 × 10⁻²⁶			6.310 × 10⁻¹⁴

Notes: Underline Score is highest. JN040516-JN040535: In this work.

Abbreviations: SC, Specie code; CSC, Closest specie code; Id, Identity (Match nucleotides/total nucleotides).

Table 4

Eukaryotic molecular markers genes comparison using Discrimination power scoring.

Species	Accession number	SC	18 rRNA			COX1			18 + ITS + 5.8s rRNA			ATPase6
Species	Accession number	SC	CSC	Id	(1-Id)	CSC	Id	(1-Id)	CSC	Id	(1-Id)	CSC	Id	(1-Id)
D. dendriticum	JN040538	1	2	0.999	0.001	4	0.936	0.064	2	0.999	0.001	3	0.935	0.065
D. ditremum	JN040539	2	1	0.999	0.001	3	0.902	0.098	1	0.999	0.001	1,3	0.892	0.108
D. latum	JN040536	3	1,2	0.999	0.001	4	0.905	0.095	1,2	0.989	0.011	1	0.935	0.065
D. nihonkaiense	JN040540	4	1,2,3	0.996	0.004	3	0.935	0.065	1,2	0.973	0.027	1	0.902	0.098
D. pacificum	JN040541	5	1,2,3,4	0.964	0.036	2	0.849	0.151	1,2	0.853	0.147	1,3	0.823	0.177
Discrimination power value			1.440 × 10⁻¹³			5.848 × 10⁻⁶			4.365 × 10⁻¹¹			7.914 × 10⁻⁶

Notes: Underline Score is higher. JN040536-JN040541: In this work.

Abbreviations: SC, Specie code; CSC, Closest specie code; Id, Identity (Match nucleotides/total nucleotides).

Discussion

We have proposed and validated a novel approach for the informed selection of marker genes based on the observed levels of DNA polymorphism¹⁰ among whole genomic sequences. Our results indicate that our approach effectively selects marker genes for species differentiation. Besides having greater discrimination powers than traditional markers, our markers also reduced the number of species that showed identical sequences for the marker. Nevertheless, in both genera studies, there are still some species that are too closely related to be differentiated with a single marker. The use of a combination of markers, or the selection of specific markers for that group of species within the genus would be required. Our approach has some minimal requirements. For instance, if the goal is to obtain marker genes for species differentiation in a given genus, a minimum of three different strain genomes belonging to two different species within the genus is required. Moreover, the design of primers may present problems if the sequences are too divergent, although this problem is shared with other approaches.

Our approach and scoring system method provides a new, powerful tool for the exploitation of available genome sequences to assist in the selection of marker genes. In both the eukaryotic and prokaryotic genera tested, the theoretical analyses showed excellent correlation with empirical results and showed a better performance than molecular markers previously proposed by different authors for the same species. The adaptation of Bayes theorem permitted the use of a conditioned statistic that prioritizes genes showing low DNA polymorphism inside the same species (different strains), while displaying high DNA polymorphism between different species.

Author Contributions

Conceived and designed the experiments: HB, TG. Analysed the data: HB, TG. Wrote the first draft of the manuscript: HB, TG. Contributed to the writing of the manuscript: HB, TG. Agree with manuscript results and conclusions: HB, TG. Jointly developed the structure and arguments for the paper: HB, TG. Made critical revisions and approved final version: HB, TG. All authors reviewed and approved of the final manuscript.

Footnotes

Supplementary Data

The scoring system and the necessary re-formatting scripts have been implemented in PERL. The PERL scripts (SCORE. pl and XMFA.pl) and a user manual for Windows, Linux and Mac are available at http://www.bioinformatics.cl.

Acknowledgements

We would like to thank Dr. Bruno Gomez-Gil for the donation of fixed biological material from different Vibrio species and Professor Dr. Tomas Scholz for the donation of fixed biological material from different Diphyllobothrium species. We would like to thank for Dr. Patricio Bustos from ADL Diagnostic Chile Ltda for economic support in empirical analysis. TG research is supported in part by a grant from the Spanish Ministry of Science (BFU2009-09168).

As a requirement of publication author(s) have provided to the publisher signed confirmation of compliance with legal and ethical obligations including but not limited to the following: authorship and contributorship, conflicts of interest, privacy and confidentiality and (where applicable) protection of human and animal research subjects. The authors have read and confirmed their agreement with the ICMJE authorship and conflict of interest criteria. The authors have also confirmed that this article is unique and not under consideration or published in any other publication, and that they have permission from rights holders to reproduce any copyrighted material. Any disclosures are made in this section. The external blind peer reviewers report no conflicts of interest.

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Selection of Marker Genes Using Whole-Genome DNA Polymorphism Analysis

Abstract

Keywords

Background

Methods

Sequence data

Alignments, polymorphism analysis, and molecular marker score calculation

Algorithm

Experimental validation analysis

DNA purification and amplification

Molecular marker discrimination power analysis

Results

Automated prioritization of marker genes

Experimental Validation

Discussion

Author Contributions

Footnotes

Supplementary Data

Acknowledgements

References