Data in Brief

Data Brief. 7: 551-557

Data on phylogenetic analyses of gazelles (genus Gazella) based on mitochondrial and nuclear intron markers

Hannes Lerpab⁎, Sebastian Klausb, Stefanie Allgöwerb, Torsten Wronskicd, Markus Pfenningere, Martin Plathf

a. Natural History Collections, Museum Wiesbaden, Friedrich-Ebert-Allee 2, 65185 Wiesbaden, Germany

b. Department of Ecology and Evolution, J.W. Goethe-University Frankfurt, Max-von-Laue-Str. 13, 60438 Frankfurt am Main, Germany

c. Zoological Society of London, Conservation Programs, Regent’s Park, London NW1 4RY, United Kingdom

d. King Khalid Wildlife Research Center, Saudi Wildlife Authority, P.O. Box 61681, Riyadh 11575, Kingdom of Saudi Arabia

e. Molecular Ecology Group, Senckenberg Biodiversity and Climate Research Centre (BiK-F), 60325 Frankfurt am Main, Germany

f. College of Animal Science and Technology, Northwest A&F University, Yangling 712100, PR China

⁎. Corresponding author. hannes.lerp@gmx.de

© 2016 The Authors

DOI: 10.1016/j.dib.2016.02.062

Published online: 02 March 2016

Abstract

The data provided is related to the article “Phylogenetic analyses of gazelles reveal repeated transitions of key ecological traits and provide novel insights into the origin of the genus Gazella” [1]. The data is based on 48 tissue samples of all nine extant species of the genus Gazella, namely Gazella gazella, Gazella arabica, Gazella bennettii, Gazella cuvieri, Gazella dorcas, Gazella leptoceros, Gazella marica, Gazella spekei, and Gazella subgutturosa and four related taxa (Saiga tatarica, Antidorcas marsupialis, Antilope cervicapra and Eudorcas rufifrons). It comprises alignments of sequences of a cytochrome b data set and of six nuclear intron markers. For the latter new primers were designed based on cattle and sheep genomes. Based on these alignments phylogenetic trees were inferred using Bayesian Inference and Maximum Likelihood methods. Furthermore, ancestral character states (inferred with BayesTraits 1.0) and ancestral ranges based on a Dispersal-Extinction-Cladogenesis model were estimated and results׳ files were stored within this article.

1. Specifications table

See full table

2. Value of the data

•. New nuclear intron primers for phylogenetic investigations of closely related bovid species.

•. Data provide phylogenetic insight into the genus Gazella.

•. Ancestral character state and ancestral range information for the genus Gazella were inferred with this data.

3. Data

Data provided with this article are newly established primer sequences of nuclear intron markers for bovids and sequence alignments of the respective markers and Cyt b including species from the genera Gazella, Eudorcas, Antilope, Saiga and Antidorcas. Furthermore, phylogenetic tree files and result files from analyses of ancestral character state estimation and ancestral ranges estimation for the genus Gazella are shared.

4. Experimental design, materials and methods

4.1. PCR primer design

We designed new nuclear primers for the amplification of introns of the nuclear encoded genes zinc finger protein 618 (ZNF618), epidermal growth factor receptor substrate 15-like 1 (EPS15L1), SPARC-related modular calcium-binding protein 1 (SMOC1), pantothenate kinase 4 (PANK4), NACHT, LRR and PYD domains-containing protein 2 (NLRP2) and chromodomain-helicase-DNA-binding protein 2 (CHD2; Table 1). We used the sheep (Ovis aries) genome, available on the website of the international sheep genomics consortium (http://www.livestockgenomics.csiro.au/sheep/oar1.0.php), and cattle (Bos taurus) genome, available from the Ensembl genome database (http://www.ensembl.org/Bos_taurus/Info/Index). We searched the sheep genome for annotated protein-coding genes and used the provided Swiss-Prot number to search for the corresponding gene sequences in the cattle genome. If those sequences contained introns of a length between 400 and 1000 bp, we assembled the exons of the respective gene with the complete gene sequence of sheep using Geneious Pro 5.4.2 (Biomatters Ltd., available from http://www.geneious.com). Primers were subsequently designed according to conserved regions of the exons of cattle and sheep in a way that the resulting sequences stretched across at least one intron. To avoid linkage disequilibrium we only used genes on different chromosomes. Primers were designed using the Oligonucleotide Properties Calculator [2] and the reverse complement converter (http://www.bioinformatics.org/sms/rev_comp.html). All primers were synthesized by Eurofins MWG Synthesis GmbH.

4.2. Sequence alignments

DNA was extracted using the Qiagen DNeasy blood and tissue kit according to the manufacturer’s protocol. Sequences were obtained by Sanger sequencing, and newly established sequences were deposited in GenBank (Table 2). We aligned sequences with MUSCLE ([3]; gapopen=−400; gapextend=−200). In total, the concatenated alignment consisted of 4,623 nucleotides. The Cyt b gene partition was translated into amino acid sequences and checked for stop codons that would indicate potential pseudogenes. The alignments for the six nuclear introns of the genes ZNF618, EPS15L1, SMOC1, PANK4, NLRP2, CHD2 and the mitochondrial Cytochrome b gene are supplemented to this article (Lerp_et_al_Gazella_{gene code}_alignment.nexus).

4.3. Phylogenetic analyses

Phylogeny and divergence times were estimated with a Bayesian approach in BEAST MC3 1.7.5 [4]. Additionally, we inferred a species tree using a coalescence approach on the multiple loci as implemented in the *BEAST algorithm [8] that we used for subsequent ancestral character (1000 trees) and range (maximum clade credibility tree) estimation. Molecular clock rates and substitution schemes were unlinked between partitions. We inferred the most likely substitution model for each marker using jModelTest 2.1.3 [9], considering models with equal/unequal base frequencies and with/without rate variation among sites (base tree for likelihood calculations=ML tree; tree topology search operation=NNI; the best model was inferred based on the Akaike Information Criterion). This resulted in a HKY+G model of sequence evolution for all genes except for PANK4 with a HKY model. We applied a Yule tree prior to account for independently evolving lineages. We chose an uncorrelated log-normal relaxed molecular clock using an external substitution rate for the Cytb gene (normally distributed rate with a mean of 1.50±0.15% per Ma; 5–95% interquantile range: 1.25–1.75% per Ma; [10]). This rate was estimated based on four different alignments of primate protein-coding mitochondrial sequences and fossil calibration points for six primate data sets using a Bayesian approach [10]. For the more conserved nuclear genes reliable external rates were not available, and so we assumed a very broad exponentially distributed prior with a mean of 0.01% per Ma (5–95% interquantile range: 0.01–0.30% per Ma).

We ran three chains for 50 M iterations, sampling every 10,000th iteration. Convergence of sampled parameters and potential autocorrelations (effective sampling size for all parameters>200) were investigated in Tracer 1.6 [11]. We discarded the first 10% of sampled trees as burn-in. The maximum clade credibility tree was chosen and parameter values annotated using TreeAnnotator (part of the BEAST package). The resulting substitution rates were 0.97% per Ma for Cyt b (95% credibility interval, CI: 0.05–1.45%), 0.12% per Ma for EPS15L1 (CI: 0.05–0.19%), 0.17% per Ma for NLRP2 (CI: 0.08–0.27%), 0.16% per Ma for SMOC1 (CI: 0.04–0.32%), 0.21% per Ma for ZNF618 (CI: 0.1–0.32%) and 0.11% per Ma for PANK4 (CI: 0.05–0.18%).

To confirm the tree topology calculated in BEAST we also analyzed the concatenated data set with a Maximum Likelihood (ML) approach. ML-analysis was performed with RAxML 8.0.14 [5] under a GTR+Γ model that was unlinked for all partitions. Support of nodes was assessed with 1,000 bootstrap replicates. Phylogenetic (Bayesian and ML) and species trees are Supplemented to this article (Lerp_et_al_Gazella_phylogeny_{program}.nwk and Lerp_et_al_Gazella_Species_Tree_starBEAST.nwk).

4.4. Ancestral character state estimation

We estimated ancestral characters for ecological and behavioral traits using a Bayesian approach to character evolution in BayesTraits multistate 1.0 [6]. The analysis was conducted with 1000 randomly selected post-burn-in trees to account for uncertainty in phylogenetic reconstruction; outgroups were removed with exception of Antilope cervicapra (the sister group to Gazella, see [1]). We estimated ancestral character states for three key ecological/behavioral traits: habitat type (mountainous vs. plain-dwelling), group size (small groups<15 individuals vs. large herds), and movement patterns (sedentary vs. migratory; see input files). In addition, we reconstructed ancestral character states for presence or absence of horns in females, and the occurrence of twinning (see Table S2 in [1]). We ran the analysis for 20 M iterations, sampling every 10,000th iteration and discarding the first 10% as burn-in. To specify the range of values used to seed the prior distribution, we applied an exponential hyperprior with a mean ranging from 0.0 to 0.5 and a rate deviation of seven (twinning=2, female horns=6), resulting in mean acceptance rates between 20% and 40%. To further corroborate the ancestral state in the most recent common ancestor (MRCA) of the genus Gazella we additionally applied a model testing approach. In separate runs – with the general MCMC setting as described above – we constrained the ancestral condition of the MRCA of Gazella to each of the alternative states and compared the harmonic mean of likelihoods (as an estimator of marginal likelihoods) using the Bayes factor (BF). As harmonic means tend to be unstable, we repeated each run five times and calculated the BF from the arithmetic means. Result files of the ancestral character state estimation (ACSE) are supplemented to this article (Lerp_et_al_Gazella_ACSE_{trait}.txt).

4.5. Biogeography

To estimate ancestral ranges based on a Dispersal-Extinction-Cladogenesis (DEC) model as implemented in the software Lagrange v. 20130526 [7] the species tree (maximum clade credibility tree with median heights) obtained through Bayesian inference was used as phylogenetic input. Species were assigned to one of four discrete geographic areas: (a) Africa, (b) Middle East, (c) Central Asia, and (d) India (Figure 3 in [1]). We did not take into account the distribution data of the more distant outgroups, but included the genus Antilope as the nearest extant relative of the genus Gazella.

To test for the direction of dispersal we calculated three models of range evolution: without constrained dispersal (H₀); with dispersal only from Africa to Asia (i.e., Middle East, Central Asia, India) allowed (Afr→As), and a third model allowing only dispersal from Asia to Africa (As→Afr). We compared the resulting global maximum likelihood at the root nodes and the AIC between models (Table 1 in [1]). In all three models, Africa was assumed adjacent only to the Middle East, while adjacency between the three Asian ranges was not constrained. Model results can be found within this article (Lerp_et_al_Gazella_DEC_H0.txt, Lerp_et_al_Gazella_DEC_Afr→As.txt, Lerp_et_al_Gazella_DEC_As→Afr.txt).

See full tableTable 1. Newly designed intron primers for bovid species with chromosome number of sheep and cattle, Swiss-Prot number, melting (TM) and annealing temperatures, amplification lengths and GC contents.

See full tableTable 2. Accession numbers of sequences used in this study.

Supplementary Material

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Footnotes

Appendix A. Supplementary data associated with this article can be found in the online version at 10.1016/j.dib.2016.02.062.

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References

Lerp H.; Klaus S.; Allgöwer S.; Wronski T.; Pfenninger M.; Plath M., authors. Phylogenetic analyses of gazelles reveal repeated transitions of key ecological traits and provide novel insights into the origin of the genus Gazella. Mol. Phylogenet. Evol. 98:2016;1–10. [PubMed]

Kibbe W.A., author. OligoCalc: an online oligonucleotide properties calculator. Nucl. Acids Res. 35:2007;W43–W46. [PubMed]

Edgar R.C., author. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucl. Acids Res. 32:2004;1792–1797. [PubMed]

Drummond A.J.; Rambaut A., authors. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7:2007;214 [PubMed]

Stamatakis A., author. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 30:2014;1312–1313. [PubMed]

Pagel M.; Meade A.; Barker D., authors. Bayesian estimation of ancestral character states on phylogenies. Syst. Biol. 53:2004;673–684. [PubMed]

Ree R.H.; Smith S.A., authors. Maximum likelihood inference of geographic range evolution by dispersal, local extinction, and cladogenesis. Syst. Biol. 57:2008;4–14. [PubMed]

Heled J.; Drummond A.J., authors. Bayesian inference of species trees from multilocus data. Mol. Biol. Evol. 27:2010;570–580. [PubMed]

Darriba D.; Taboada G.L.; Doallo R.; Posada D., authors. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods. 9:2012;772 [PubMed]

Ho S.Y.W.; Phillips M.J.; Cooper A.; Drummond A.J., authors. Time dependency of molecular rate estimates and systematic overestimation of recent divergence times. Mol. Biol. Evol. 22:2005;1561–1568. [PubMed]

A. Rambaut, A.J. Drummond, Tracer 1.6, 2013.

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Subject area | Biology, genetics and genomics
More specific subject area | Phylogenetics and phylogenomics
Type of data | Tables, primer sequences, sequence alignments, phylogenetic trees, ancestral character state estimation and ancestral ranges estimation.
How data was acquired | Primers were designed using the Oligonucleotide Properties Calculator [2]. Sequences were aligned with MUSCLE [3].
Phylogenetic trees were inferred with BEAST MC3 1.7.5 [4] and RAxML 8.0.14 [5].
Ancestral character state estimation was conducted with BayesTraits multistate 1.0 [6].
Ancestral ranges were estimated based on a Dispersal-Extinction-Cladogenesis (DEC)-model implemented in Lagrange v. 20130526 [7].
Data format | Analyzed
Experimental factors | Sample types used for DNA extraction were tissue, skin, blood and hairs and were extracted using Qiagen DNeasy blood and tissue kit according to the manufacturer’s protocol.
Experimental features | We sampled gazelle species from a wide geographic range to cover as much of the extant diversity as possible.
Data source location | Samples were collected in Israel, Saudi Arabia, Oman, Chad, Algeria, Sudan, Tunisia, Mongolia, Pakistan, and from captive breeding stocks
Data accessibility | Data is available within the article.

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Table 1.

Newly designed intron primers for bovid species with chromosome number of sheep and cattle, Swiss-Prot number, melting (TM) and annealing temperatures, amplification lengths and GC contents.

Primer name | Protein | Chromosome number sheep | Chromosome number cattle | Swiss-Prot number | Primer forward | Reverse
ZNF618 | Zinc finger protein 618 | Chr 2 | Chr 8 | Q5T7W0 | TCC TAT GAG TGT GGA ATC TGT GG | TCT CCT GAG GTG GCT TCA GTG
EPS15L1 | Epidermal growth factor receptor substrate 15-like 1 | Chr 5 | Chr 7 | A7MB30 | CAA AGA CCA GTT CGC GTT AGC TA | TCC CCC GAT CCA AGA GTG CT
Smoc1 | SPARC-related modular calcium-binding protein 1 | Chr 7 | Chr 10 | Q9H4F8 | TGG CTA CTG CTG GTG TGT GC | CCTGTCCTTGAAGGGGTCCT
PANK4 | Pantothenate kinase 4 | Chr 12 | Chr 16 | Q4R4U1 | ACT GGG GGT GGG GCA TAC AA | GGT CAT CAC ATC CTC CTT GTC AA
NLRP2 | NACHT, LRR and PYD domains-containing protein 2 | Chr 14 | Chr 18 | Q9NX02 | CAG TCC CTC ACA TGC TTG AAC | CAG TTT CAC CCC ACG ATC TC

Primer name
TM (salt adjusted) [°C]

Annealing-temp. [°C] ( TM − 5 °C)
Amplification length [bp]
GC-content [%]

Forward
Reverse

Forward
Reverse

ZNF618
60.6
61.8
61→56
679
48
57

EPS15L1
60.6
61.4
61→56
365
48
60

Smoc1
61.4
61.4
61→56
659
60
60

PANK4
61.4
60.6
61→56
443
60
48

NLRP2
59.8
59.4
59→54
532
52
55

CHD2
61.0
61.8
61→56
733
46
57

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Table 2.

Accession numbers of sequences used in this study.

Sample ID | CHD2 | EPS15L1 | NLRP2 | PANK4 | SMOC1 | ZNF618 | Cyt b
GH1 | KU560704 | KU560659 | KU560746 | KU560790 | KU560880 | KU560837 | KU560629
TAUM 11861 | KU560705 | KU560660 | KU560747 | KU560791 | KU560881 | KU560838 | KC188775
TAUM 12479 | KU560706 | KU560661 | KU560748 | KU560792 | KU560882 | KU560839 | KC188774
TAUM 10170 | KU560707 | KU560662 | KU560749 | KU560793 | KU560883 | KU560840 | KC188740
TAUM 11048 | KU560708 | KU560663 | KU560750 | KU560794 | KU560884 | KU560841 | KC188759
GGF41 | KU560709 | KU560664 | KU560751 | KU560795 | KU560885 | KU560842 | KU560630
OmanI | KU560710 | KU560665 | KU560752 | KU560796 | KU560886 | KU560843 | KU560648
3455 | KU560711 | KU560666 | KU560753 | KU560797 | – | KU560844 | KU560649
182 | – | – | – | KU560798 | KU560887 | – | JN410348
3463 | KU560712 | KU560667 | KU560754 | KU560799 | KU560888 | – | KU560631
3466 | KU560713 | KU560668 | KU560755 | KU560800 | – | KU560845 | KU560650
3467 | KU560714 | KU560669 | KU560756 | KU560801 | – | KU560846 | KU560632
3469 | KU560715 | KU560670 | KU560757 | KU560802 | – | KU560847 | KU560651
Chad19 | KU560716 | KU560671 | KU560758 | KU560803 | KU560889 | – | JN410237
Chad7 | KU560717 | KU560672 | – | KU560804 | KU560890 | – | JN410235
2866 | KU560718 | KU560673 | KU560759 | KU560805 | KU560891 | KU560848 | JN410252
3564 | KU560719 | KU560674 | KU560760 | KU560806 | KU560892 | KU560849 | JN410230
AWWP 9159 | KU560720 | KU560675 | KU560761 | KU560807 | KU560893 | KU560850 | JN410319
PCGD59 | KU560721 | KU560676 | KU560762 | KU560808 | KU560894 | KU560851 | JN410251
PCGD1 | – | KU560677 | – | KU560809 | KU560895 | KU560852 | JN410257
3261 | KU560722 | KU560678 | KU560763 | KU560810 | KU560896 | KU560853 | JN410255
Mongo | KU560723 | KU560679 | KU560764 | KU560811 | KU560897 | – | KU560652
AWWP 9053 | KU560724 | KU560680 | KU560765 | KU560812 | KU560898 | KU560854 | KU560653
583 | KU560725 | KU560681 | KU560766 | KU560813 | KU560899 | KU560855 | KU560633
7 | KU560726 | KU560682 | KU560767 | KU560814 | KU560900 | KU560856 | JN410357
9 | KU560727 | KU560683 | KU560768 | KU560815 | KU560901 | KU560857 | JN410341
6 | KU560728 | KU560684 | KU560769 | KU560816 | KU560902 | KU560858 | JN410340
10 | – | – | KU560770 | KU560817 | KU560903 | KU560859 | KU560634
2887 | KU560729 | KU560685 | KU560771 | KU560818 | KU560904 | KU560860 | KU560635
2885 | KU560730 | KU560686 | KU560772 | KU560819 | KU560905 | KU560861 | KU560636
781 | KU560731 | KU560687 | KU560773 | KU560820 | KU560906 | KU560862 | JN410345
782 | KU560732 | KU560688 | KU560774 | KU560821 | KU560907 | KU560863 | JN410344
75 | KU560733 | – | KU560775 | KU560822 | KU560908 | KU560864 | KU560654
90 | KU560734 | KU560689 | – | KU560823 | KU560909 | KU560865 | KU560655
271 | – | KU560690 | KU560776 | KU560824 | KU560910 | KU560866 | KU560656
AWWP 7895 | – | KU560691 | KU560777 | KU560825 | – | KU560867 | KU560657
AWWP 9055 | KU560735 | KU560692 | KU560778 | KU560826 | – | KU560868 | KU560637
AWWP 8397 | – | KU560693 | KU560779 | KU560827 | – | KU560869 | KU560638
AWWP 7238 | KU560736 | KU560694 | KU560780 | – | – | KU560870 | KU560658
OZ1 | KU560737 | KU560695 | KU560781 | KU560828 | KU560911 | KU560871 | KU560639
OZ2 | KU560738 | KU560696 | KU560782 | KU560829 | KU560912 | KU560872 | KU560640
OZ3 | KU560739 | KU560697 | KU560783 | KU560830 | KU560913 | KU560873 | KU560641
OZ4 | KU560740 | KU560698 | KU560784 | KU560831 | – | KU560874 | KU560642
S06 | KU560741 | KU560699 | KU560785 | KU560832 | – | KU560875 | KU560643
S08 | KU560742 | KU560700 | KU560786 | KU560833 | – | KU560876 | KU560644
S10 | KU560743 | KU560701 | KU560787 | KU560834 | – | KU560877 | KU560645
S12 | KU560744 | KU560702 | KU560788 | KU560835 |  | KU560878 | KU560646
SB | KU560745 | KU560703 | KU560789 | KU560836 | – | KU560879 | KU560647

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