african journal of biotechnology - 26 april, 2012 issue

African Journal of

BiotechnologyVolume 11 Number 34 26 April, 2012

ISSN 1684-5315

ABOUT AJB The African Journal of Biotechnology (AJB) is published bi-weekly (one volume per year) by Academic Journals. African Journal of Biotechnology (AJB) a new broad-based journal, is an open access journal that was founded on two key tenets: To publish the most exciting research in all areas of applied biochemistry, industrial microbiology, molecular biology, genomics and proteomics, food and agricultural technologies, and metabolic engineering. Secondly, to provide the most rapid turn-around time possible for reviewing and publishing, and to disseminate the articles freely for teaching and reference purposes. All articles published in AJB are peer-reviewed.

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Editors

George Nkem Ude, Ph.D Plant Breeder & Molecular Biologist Department of Natural Sciences Crawford Building, Rm 003A Bowie State University 14000 Jericho Park Road Bowie, MD 20715, USA N. John Tonukari, Ph.D Department of Biochemistry Delta State University PMB 1 Abraka, Nigeria Prof. Dr. AE Aboulata Plant Path. Res. Inst., ARC, POBox 12619, Giza, Egypt 30 D, El-Karama St., Alf Maskan, P.O. Box 1567, Ain Shams, Cairo, Egypt Dr. S.K Das Department of Applied Chemistry and Biotechnology, University of Fukui, Japan Prof. Okoh, A. I Applied and Environmental Microbiology Research Group (AEMREG), Department of Biochemistry and Microbiology, University of Fort Hare. P/Bag X1314 Alice 5700, South Africa Dr. Ismail TURKOGLU Department of Biology Education, Education Faculty, Frat University, Elaz, Turkey Prof T.K.Raja, PhD FRSC (UK) Department of Biotechnology PSG COLLEGE OF TECHNOLOGY (Autonomous) (Affiliated to Anna University) Coimbatore-641004, Tamilnadu, INDIA. Dr. George Edward Mamati Horticulture Department, Jomo Kenyatta University of Agriculture and Technology, P. O. Box 62000-00200, Nairobi, Kenya.

Dr Helal Ragab Moussa Bahnay, Al-bagour, Menoufia, Egypt. Dr VIPUL GOHEL Flat No. 403, Alankar Apartment, Sector 56, Gurgaon-122 002, India. Dr. Sang-Han Lee Department of Food Science & Biotechnology, Kyungpook National University Daegu 702-701, Korea. Dr. Bhaskar Dutta DoD Biotechnology High Performance Computing Software Applications Institute (BHSAI) U.S. Army Medical Research and Materiel Command 2405 Whittier Drive Frederick, MD 21702 Dr. Muhammad Akram Faculty of Eastern Medicine and Surgery, Hamdard Al-Majeed College of Eastern Medicine, Hamdard University, Karachi. Dr. M.MURUGANANDAM Departtment of Biotechnology St. Michael College of Engineering & Technology, Kalayarkoil, India. Dr. Gkhan Aydin Suleyman Demirel University, Atabey Vocational School, Isparta-Trkiye, Dr. Rajib Roychowdhury Centre for Biotechnology (CBT), Visva Bharati, West-Bengal, India. Dr.YU JUNG KIM Department of Chemistry and Biochemistry California State University, San Bernardino 5500 University Parkway San Bernardino, CA 92407

Editorial Board

Dr. Takuji Ohyama Faculty of Agriculture, Niigata University Dr. Mehdi Vasfi Marandi University of Tehran Dr. Fgen DURLU-ZKAYA Gazi niversity, Tourism Faculty, Dept. of Gastronomy and Culinary Art Dr. Reza Yari Islamic Azad University, Boroujerd Branch Dr. Zahra Tahmasebi Fard Roudehen branche, Islamic Azad University Dr. Tarnawski Sonia University of Neuchtel Laboratory of Microbiology Dr. Albert Magr Giro Technological Centre Dr. Ping ZHENG Zhejiang University, Hangzhou, China. Prof. Pilar Morata University of Malaga Dr. Greg Spear Rush University Medical Center Dr. Mousavi Khaneghah College of Applied Science and Technology-Applied Food Science, Tehran, Iran.

Prof. Pavel KALAC University of South Bohemia, Czech Republic. Dr. Krsat KORKMAZ Ordu University, Faculty of Agriculture, Department of Soil Science and Plant nutrition Dr. Tugay AYAAN ukurova Agricultural Research Institute, PK:01321, ADANA-TURKEY. Dr. Shuyang Yu Asistant research scientist, Department of Microbiology, University of Iowa

Address: 51 newton road, 3-730B BSB bldg.Tel+319-335-7982, Iowa City, IA, 52246, USA. Dr. Binxing Li E-mail: [email protected] Dr Hsiu-Chi Cheng National Cheng Kung University and Hospital. Dr. Kgomotso P. Sibeko University of Pretoria, South Africa. Dr. Jian Wu Harbin medical university , China.

Electronic submission of manuscripts is strongly encouraged, provided that the text, tables, and figures are included in a single Microsoft Word file (preferably in Arial font). The cover letter should include the corresponding author's full address and telephone/fax numbers and should be in an e-mail message sent to the Editor, with the file, whose name should begin with the first author's surname, as an attachment. Article Types Three types of manuscripts may be submitted: Regular articles: These should describe new and carefully confirmed findings, and experimental procedures should be given in sufficient detail for others to verify the work. The length of a full paper should be the minimum required to describe and interpret the work clearly. Short Communications: A Short Communication is suitable for recording the results of complete small investigations or giving details of new models or hypotheses, innovative methods, techniques or apparatus. The style of main sections need not conform to that of full-length papers. Short communications are 2 to 4 printed pages (about 6 to 12 manuscript pages) in length. Minireview: Submissions of mini-reviews and perspectives covering topics of current interest are welcome and encouraged. Mini-reviews should be concise and no longer than 4-6 printed pages (about 12 to 18 manuscript pages). Mini-reviews are also peer-reviewed. Review Process All manuscripts are reviewed by an editor and members of the Editorial Board or qualified outside reviewers. Authors cannot nominate reviewers. Only reviewers randomly selected from our database with specialization in the subject area will be contacted to evaluate the manuscripts. The process will be blind review. Decisions will be made as rapidly as possible, and the journal strives to return reviewers comments to authors as fast as possible. The editorial board will re-review manuscripts that are accepted pending revision. It is the goal of the AJB to publish manuscripts within weeks after submission.

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Instructions for Author

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Smith (2000), Blake et al. (2003), (Kelebeni, 1983), (Chandra and Singh,1992),(Chege, 1998; Steddy, 1987a,b;

Gold, 1993,1995), (Kumasi et al., 2001) References should be listed at the end of the paper in alphabetical order. Articles in preparation or articles submitted for publication, unpublished observations, personal communications, etc. should not be included in the reference list but should only be mentioned in the article text (e.g., A. Kingori, University of Nairobi, Kenya, personal communication). Journal names are abbreviated according to Chemical Abstracts. Authors are fully responsible for the accuracy of the references. Examples: Diaz E, Prieto MA (2000). Bacterial promoters triggering biodegradation of aromatic pollutants. Curr. Opin. Biotech. 11: 467-475. Dorn E, Knackmuss HJ (1978). Chemical structure and biodegradability of halogenated aromatic compounds. Two catechol 1, 2 dioxygenases from a 3-chlorobenzoate-grown Pseudomonad. Biochem. J. 174: 73-84. Pitter P, Chudoba J (1990). Biodegradability of Organic Substances in the Aquatic Environment. CRC press, Boca Raton, Florida, USA. Alexander M (1965). Biodegradation: Problems of Molecular Recalcitrance and Microbial Fallibility. Adv. Appl. Microbiol. 7: 35-80. Boder ET, Wittrup KD (1997). Yeast surface display for screening combinatorial polypeptide libraries. Nat. Biotechnol. 15: 537-553. Short Communications Short Communications are limited to a maximum of two figures and one table. They should present a complete study that is more limited in scope than is found in full-length papers. The items of manuscript preparation listed above apply to Short Communications with the following differences: (1) Abstracts are limited to 100 words; (2) instead of a separate Materials and Methods section, experimental procedures may be incorporated into Figure Legends and Table footnotes; (3) Results and Discussion should be combined into a single section. Proofs and Reprints: Electronic proofs will be sent (e-mail attachment) to the corresponding author as a PDF file. Page proofs are considered to be the final version of the manuscript. With the exception of typographical or minor clerical errors, no changes will be made in the manuscript at the proof stage.

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International Journal of Medicine and Medical Sciences

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African Journal of Biotechnology

Table of Contents: Volume 11 Number 34 26 April, 2012, ences

ARTICLES Review BioMatriX: Sequence analysis, structure visualization, phylogenetics and linkage analysis workbench 8414 Shagufta Kanwal, Usman Ali, Muhammad Irfan Khan, Zainab noor, Farhat-ul-ain Mirza Research Articles GENETICS AND MOLECULAR BIOLOGY Identification of SNPs in chemerin gene and association with carcass and meat quality traits of Qinchuan Cattle 8417

Fubiao Song, Hongcheng Wang, Hong WangHongbao Wang, Yaping Xin and Linsen Zan Genetic diversity of Stipa bungeana populations in the Loess Plateau of China using inter-simple sequence repeat (ISSR) markers 8425 Yu Jing, Jing Zhao-Bin and Cheng Ji-Min PLANT AND AGRICULTURAL TECHNOLOGY The role of rhizospheric Aspergillus flavus in standing maize crop contamination in different ecological zones of Khyber Pakthunkhwa, Pakistan 8433 Saleem Ullah, Hamid Ullah Shah, Anwar Ali Shad and Sahib Alam Calcium enhances cadmium tolerance and decreases cadmium accumulation in lettuce (Lactuca sativa) 8441 Walid Zorrig, Zaigham Shahzad, Chedly Abdelly and Pierre Berthomieu

ARTICLES Impact of different moisture regimes and nitrogen rates on yield and yield attributes of maize (Zea mays L.) 8449 Muhammad Maqsood, Muhammad Asif Shehzad, Muhammad Aqeel Sarwar, Hafiz Tassawar Abbas and Salman Mushtaq Physico-chemical properties of indigenous micro organism-composts and humic acid prepared from selected agro-industrial residues 8456 A. Norida Hanim, A. M. Nik Muhamad, O. H. Ahmed, K. Susilawati and A. Khairulmazmi Biological control of Fusarium foot rot of wheat using fengycin-producing Bacillus subtilis isolated from salty soil 8464 REBIB Hanene, HEDI Abdeljabbar, ROUSSET Marc, BOUDABOUS Abdellatif, LIMAM Ferid and SADFI-ZOUAOUI Najla Degrading capability and activity of extracellular xylanase secreted by a composite microbial system XDC-2 8476 WANG Hui, GUO Peng, WANG Xiaofen, WANG Xiaojuan and CUI Zongjun Improving planting pattern for intercropping in the whole production span of rubber tree 8484 Zeng Xianhai, Cai Mingdao and Lin Weifu ENVIRONMENTAL BIOTECHNOLOGY Biocontrol properties of indigenous Trichoderma isolates from North-east India against Fusarium oxysporum and Rhizoctonia solani 8491 Th. Kamala and S. Indira Devi Enhanced accumulation of root hydrogen peroxide is associated with reduced antioxidant enzymes under isoosmotic NaCl and Na2SO4 salinities 8500 Mahmoudi Hela, Baatour Olfa, Ben Salah Imen, Nasri Nawel, Wissal Abidi, Huang Jun, Zargouni Hanen, Hannoufa Abdelali, Lachaal Mokhtar and Ouerghi Zeineb

Table of Contents: Volume 11 Number 34 26 April, 2012 ences

ARTICLES Crystal phases of calcium carbonate within otoliths of Cyprinus carpio. L. from Miyun Reservoir and Baiyangdian Lake, China 8510 Liang-Feng Yang, Sheng-Rong Li, Guo-Wu Li and Jun-Yan Luo INDUSTRIAL MICROBIOLOGY Synthesis of ZnO nanoparticles and their antibacterial effects 8520 Mohammad Reza Arefi, Saeed Rezaei-Zarchi and Saber Imani APPLIED BIOCHEMISTRY Characterization of polygalacturonases from fruit spoilage Fusarium oxysporum and Aspergillus tubingensis 8527 Ahmed R. Al-Najada, Rashad R. Al-Hindi and Saleh A. Mohamed Changes in photosynthesis and activities of enzymes involved in carbon metabolism during exposure to low light in cucumber (Cucumis sativus) seedlings 8537 Guoquan Mi, Liying Liu, Zhenxian Zhang and Huazhong Ren Optimization of lactic acid production with immobilized Rhizopus oryzae 8546 Muhammet aban Tanyldz, ule Bulut, Veyis Selen and Dursun zer


ARTICLES MEDICAL AND PHARMACEUTICAL BIOTECHNOLOGY Biofilm production and antibiotic susceptibility profile of Escherichia coli isolates from HIV and AIDS patients in the Limpopo Province 8560 Samie, A. and Nkgau, T. F ENTOMOLOGY Entomophaga maimaiga New entomopathogenic fungus in the Republic of Serbia 8571 Mara Tabakovi-Toi,Georgi Georgiev, Plamen Mirchev, Dragutin Toi and Vesna Golubovi-urguz FISHERY SCIENCE Toxicity bioassay and effects of sub-lethal exposure of malathion on biochemical composition and haematological parameters of Clarias gariepinus 8578 Zubair Ahmad BIOTECHNIQUES Influence of technological treatments on bacterial communities in tilapia (Oreochromis niloticus) as determined by 16S rDNA fingerprinting using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) 8586 Mawore J, Tatsadjieu Ngoune L, Goli T, Montet D and Mbofung C. M. F Development of rapid PCR-RFLP technique for identification of sheep, cattle and goats species and fraud detection in Iranian commercial meat products 8594 Hamideh Amjadi, Mohammad Javad Varidi, Seyed Hassan Marashi, Ali Javadmanesh and Shahrokh Ghovvati


ARTICLES A new screening method for discovering antibacterial agents from filamentous fungi 8600 Guohua Chen, Yehui Tan, Kaizhi Li, Fangqing Chen, Ruiping Li, Chaoyin Yue and Wei Shao ANIMAL SCIENCE Influence of turmeric rhizome and black pepper on blood constituents and performance of broiler chickens 8606 Abdollah Akbarian, Abolghasem Golian, Hassan Kermanshahi, Ali Gilani and Sajad Moradi Gastro-protective activity of aqueous Carica papaya seed extract on ethanol induced gastric ulcer in male rats 8612 OKEWUMI Tolunigba Abisola and OYEYEMI Adekunle Wahab


African Journal of Biotechnology Vol. 11(34), pp. 8414-8416, 26 April, 2012 Available online at http://www.academicjournals.org/AJB DOI: 10.5897/AJB12.427 ISSN 16845315 2012 Academic Journals

Review

BioMatriX: Sequence analysis, structure visualization, phylogenetics and linkage analysis workbench

Shagufta Kanwal1*, Usman Ali2, Muhammad Irfan Khan1, Zainab noor1, Farhat-ul-ain Mirza1

1International Islamic University, H-10, Islamabad, Pakistan.

2Lahore University of Management Sciences, DHA, Lahore Cantt, Pakistan.

Accepted 14 March, 2012

The BioMatriX (Build Mine Xplore) is a bioinformatics work bench (http://www.bmx-biomatrix.blogspot.com) developed for biological science community to augment scientific research regarding genomics, proteomics, phylogenetics and linkage analysis in one platform. BioMatriX offers multi-functional services to perform specific tasks like DNA/RNA/Protein sequence analysis with graphical representations, sequence editing, sequence alignment, restriction enzyme mapping, protein structure visualization, mutation and structure superimposition programs along with phylogenetics tree construction supporting dendrograms, neighbor joining and unweighted pair group method with arithmetic mean (UPGMA) programs. Genomic studies like linkage programs are also implemented. Special emphasis has been paid to integrate all the resources in one software so that the researcher does not have to install numerous pieces of software to analyze his data. Key words: Bioinformatics, linkage, visualizer, alignment, superimposition, phylogenetics.

INTRODUCTION BioMatriX is developed in Biojava language and major help and support is taken from Biojava Cook book (http://www.biojava.org/wiki/BioJava: CookBook3.0). BioMatriX is an interactive, multi-functional and user friendly bioinformatics tool kit. It represents similarities with most of the famous scientific research work benches like ExPAsy Proteomics Server (http://expasy.org/) and CLC Bio (http://www.clcbio.com/). BioMatriX is a desktop application just like CLC Bio and composite of various modules and functionalities implemented with graphical outputs in order to facilitate research analysis from various aspects. Although it has adopted many features of ExPAsy and CLC Bio (http://www.clcbio.com/ index.php?id=30), an effort has been made to represent sequences with proper scaling and color scheme. The module of genetic linkage is a distinguishing feature of BioMatriX which is not found integrated in any workbench yet. There are many standalone protein visualizers Like RASMOL (http://www.umass.edu/microbio/rasmol/) *Corresponding author. E-mail: [email protected].

(Rodger and James, 1995) that are freely available, but BioMatriX itself has a visualizer and other structural manipulation functions implemented in it. It is freely available for download for academic use for the scientific community (http://www.bmx-biomatrix.blogspot.com). An overview of the software main interface is shown in Figure 1. SOFTWARE PROGRAMS BioMatriX supports multiple file format reader, format converter and other file writing manipulation functions. This extensive tool kit comprises the following various modules implementing several programs. DNA sequence analysis This module is especially designed for DNA/RNA sequence analysis, which include basic functions like calculating nucleotide composition, DNA complement, DNA reverse complement, DNA transcription, RNA com-

plement, RNA reverse complement, protein translation, open reading frames (ORF) finding and alternate protein translation. This module also supports graphical repre-sentations like nucleotide concentration plot and molecular weight plot and nucleotide composition plot as shown in Figure 2.

Another useful feature of this module is sequence editing, which provides various sequence manipulation functions like sequence search by selecting sequence location and range, sequence insertion at the any selected location, sequence deletion by selecting location and number of nucleotides to be deleted like frameshift, point mutation, missense/non-synonymous and nonsense mutations. Sequence alignment provides pair-wise align-ment by two methods: the local alignment by imple-menting Smith-Waterman Algorithm

(Smith and

Waterman, 1981), and the global alignment by Needleman-Wunsch Algorithm (Zhihua and Lin, 2004). Multiple sequence alignment integrates ClustalW program (Thompson et al., 1994) which is a well known frequently used alignment tool with reliable results. Dot plot has been implemented for quick comparative visualization of two sequences with possible optimal matches in diagonal direction. Protein sequence analysis This module is designed for protein sequence analysis such as amino acid composition, amino acid to nucleotide conversion, molecular weight, charge density (PI) and protein nature (acidic/basic) along with graphical repre-sentations. The protein polarity plot, molecular weight plot, protein flexibility plot, accessibility plot, antigenic plot, exposed plot, turn plot, hydrophilicity plot and hydrophobicity plotting on several scales like Engleman-Steitz, Hopp-Woods, Kyte-Doolittle, Janin, Chothia & Eisenberg-Weiss, as evaluated by Kallol et al. (2003) are also implemented in this module (http://www.clcbio.com/ sciencearticles/BE-hydrophobicity.pdf). This module also supports sequence editing functions like sequence search, insertion, deletion and mutation. Likewise, sequence alignment is also implemented as earlier mentioned in the DNA sequence analysis. Phylogenetic analysis This module constructs phylogenetic trees. Outputs of multiple sequence alignment are taken as input to this program to display dendrograms. One of the examples of alignment tree is mentioned in Figure 3. Apart from dis-playing dendrograms, two important methods of constructing phylogenetics trees are implemented: the neighbor joining (Saitou and Nei, 1987) and UPGMA (Backeljau et al., 1996).

These programs take distance

matrix as input to display neighbor joining and the

Kanwal et al. 8415 unweighted pair group method with arithmetic mean (UPGMA) trees. Structure analysis This module provides protein structure analysis- extracts PDB information like structure ID, chains, length and residues information. Another program, Mutate a residue, is also implemented in order to mutate an amino acid and to show the change in protein structure. As an example, one of proteins, 1TNF has been displayed in Figure 4 with its original structure in one window and its structure after mutation is displayed in another window for com-parative structural analysis.

One of the useful features of this module is structure visualizer which is named as BIOMOL by integrating Jmol (http://www.jmol.org). This tool acquires all basic structure manipulations and visualization functions as that of JMOL. Another important feature of this module is structure superimposition. This program displays two different protein structures in two separate windows and superimposed structure in the third window along with alignment in the output tab. Genetic linkage analysis This module is subdivided into calculating linkage at single point locus and multipoint locus calculation. Single point locus helps in creating the pedigree file which is used as an input to the linkage program in order to find recombinants and non recombinants of family data of any genetic disorder and LOD score calculation on it. This module also displays graphical representations of LOD score plot and recombinant/non-recombinant ratio plot. An example of pedigree data and LOD score calculation are shown in Figure 5. Second is multipoint locus calculation which includes preparation of PRE, PED and DAT files required by MLINK (http://hg.wustl.edu/info/ linkage/mlink.html) which is a free ware linkage program (Goldgar and Oniki, 1992) and integrated in it. This module is integrated with a database of genetic markers in order to facilitate a research scientist or geneticist to keep the records of the genetic markers and corres-ponding reports. This database helps in adding new records, updating the old ones, deleting records and searching for genetic markers via markers identifier number (ID). Apart from analysis modules, various other miscellaneous programs have been implemented like restriction enzyme cutter and file format converter. CONCLUSION BioMatriX offers services with multiple functions on the researchers desk and freely available for the scientific

8416 Afr. J. Biotechnol. community. It is designed in an easy to use and environment friendly manner with production of precise results in minimum time. The use of this application will prove to be helpful in major analysis like sequence and structure of biomolecules (DNA/RNA/Protein), com-parative, evolutionary and genetic studies. Therefore, it will play an important role to build, mine and explore the scientific knowledge and can be a vital entity of every researcher at his desktop. REFERENCES Backeljau T, Bruyn LD, Jordaens LDWK, Dongen SV, Winnepenninckx

TB (1996). Multiple UPGMA and Neighbor-joining Trees and the Performance of Some Computer Packages. Mol. Biol. Evol. 13(2): 309-313.

Goldgar DE, Oniki RS (1992). Comparison of a multipoint identity-by-descent method with parametric multipoint linkage analysis for mapping quantitative traits. Am. J. Hum. Gene. 50(3): 598-606.

Kallol MB, Daniel RD, John GD (2003). Evaluation of methods for measuring amino acid hydrophobicities and interactions. J. Chromatogr. A. 1000: 637-655

Rodger S, James EM (1995). RasMol: Biomolecular graphics for

all, Trends in Biochem. Sci. (TIBS). 20: p. 374.

Saitou N, Nei M (1987). The neighbor-joining method: a new method for

reconstructing phylogenetic trees. Mol. Biol. Evol. 4(4): 406-425. Smith TF, Waterman MS (1981). Identification of Common Molecular

Subsequences. J. Mol. Biol. 47: 195-197.

Zhihua DU, Lin F (2004). Improvement of the Needleman-Wunsch Algorithm. SpringerLink, 3066: 792-797.

Thompson JD, Higgins DG, Gibson TJ (1994). CLUSTAL W: improving

the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22): 4673-4680.


Full Length Research Paper

Identification of SNPs in chemerin gene and association with carcass and meat quality traits of Qinchuan Cattle

Fubiao Song1, Hongcheng Wang1, Hong Wang1Hongbao Wang1,2,

Yaping Xin1,2 and Linsen Zan1,2*

1College of Animal Science and Technology, Northwest A and F University, Yangling, Shaanxi, 712100, P. R. China.

2National Beef Cattle Improvement Centre, Yangling, Shaanxi, 712100, P. R. China.

Accepted 11 April, 2011

Chemerin is a novel adipokine that regulates adipogenesis and adipocyte metabolism via its own receptor. In this study, two novel SNPs (868A>G in exon 2 and 2692C>T in exon 5) of chemerin gene were identified by PCR-SSCP and DNA sequencing technology. The allele frequencies of the novel SNPs were determined in the genetically diverse bovine breeds including six Chinese indigenous cattle breeds (Caoyuan red, Jiaxian red, Luxi, Nanyang, Qinchuan and Xianan cattle). We evaluated the potential association of the SNPs with traits measured by ultrasound measurement in 214 Qinchuan individuals. Furthermore, meat quality traits data gotten from carcass measurement in another 69 Qinchuan individuals were analyzed by the comparison between the genotypes and their phenotypic data. Results showed that SNP 868A>G had a significant association with the ultrasound loin-muscle area (P < 0.05), loin-eye area and water holding capability (P < 0.05). And also revealed significant effects of genotype on the ultrasound backfat thickness (P < 0.05), backfat thickness and water holding capability (P < 0.05) of SNP 2692C>T. It was shown that associations do exist between chemerin gene and carcass and meat quality traits. As a result of the small sample size of this study, it is proposed that further effort is required to validate these findings in larger populations. It could be concluded that ultrasound measurements are similar in accuracy to carcass measurements for predicting carcass and meat quality traits in cattle, and could be a useful predictor of retail yield in live animals. Key words: Bos bovine, chemerin gene, PCR-SSCP, SNP, meat quality traits.

INTRODUCTION Chemerin is a new adipokine associated with obesity and the metabolic syndrome in human (Bozaoglu et al., 2007) and mouse (Ernst et al., 2010). Chemerin gene is also known as retinoic acid receptor responder 2 (RARRES2) and tazarotene-induced gene 2 (TIG2) (Nagpal et al., 1997), has been isolated as a novel chemoattractive agonistic protein binding to the G-protein-coupled receptor ChemR23 (Gantz et al., 1996; Meder et al., 2003), also known as chemokine-like receptor-1 (CMKLR1) (Wittamer et al., 2003; Zabel et al., 2005). The chemerin gene of Bos bovine is located on chromosome 4 (GenBank: NC_007302) and consists of six exons, with exon 2, 3, 4 and exon 5 coding a protein with 162 amino

*Corresponding author. E-mail: [email protected] Tel: + 86-29-87091247. Fax: +86-29-87092614

acid (Song et al., 2010). Through its binding to chemerinR, chemerin is involved in regulating adipogenesis and adipocyte metabolism (Goralski et al., 2007), innate and adaptive immunity (Meder et al., 2003; Wittamer et al., 2005; Zabel et al., 2005), bone development (Methner et al., 1997) and immunodeficiency virus infections (Martensson et al., 2006). It potentiates insulin-stimulated glucose uptake and insulin signaling in 3T3-L1 adipocytes, which identifies chemerin as a novel adipokine (Takahashi et al., 2008). It was reported that chemerin gene was expressed in many tissues, such as liver, lung, pituitary glands, ovaries, kidney and so on (Bozaoglu et al., 2007; Roh et al., 2007), but white adipose tissue was the only histiocyte that express high level chemerin and ChemerinR (Goralski et al., 2007; Meder et al., 2003).

Recently, Song et al. (2010) cloned chemerin gene and acquired its receptor gene from the adipose tissues of

8418 Afr. J. Biotechnol. Japanese Black cattle, and found that their DNA sequences and amino acid sequence were highly homologous to those of humans, mice and pigs, and the bovine chemerin mRNA was highly expressed in the adipose and liver tissues than the other histiocyte. In addition, they found that the transcripts of chemerin and expression of its receptor were up-regulated during adipocyte differentiation. So, chemerin primarily acts on adipogenesis and adipocyte metabolism through its own receptor. To our knowledge, few polymorphisms of bovine chemerin gene have been reported. Based on the important roles in organisms, it could be a potential gene for carcass and meat quality traits in bovine. Therefore, the objective of this study was to detect SNPs of chemerin gene in bovine and explore their possible association with carcass and meat quality traits in Qinchuan cattle breed.

MATERIALS AND METHODS

Sample collection and DNA extraction

Individuals were randomly selected from six cattle breeds of 627 adult animals as follows: Qinchuan (QC, no = 214, Shaanxi province of China), Caoyuan red (CYR, no = 112, Jilin province of China), Jiaxian red (JR, no = 71, Henan province of China), Luxi (LX, no = 69, Shandong province of China), Nan yang (NY, no = 81, Henan province of China) and Xianan cattle (XN, no = 76, Henan province of China). Ultrasound measurements were available for 214 Qinchuan cattle (Brethour et al., 1994; Hamlin et al., 1995) including ultrasound backfat thickness (UBF), ultrasound loin-muscle area (ULMA) and ultrasound marbling score (UMAR). As we know, ultrasound technology has been used extensively used in beef cattle and swine enterprises over the past decade, and its use is continuing to gain popularity (Wall et al., 2004). Apart from that 69

individual Qinchuan heifers 2.5 to 3.0 years old were selected randomly and slaughtered in Shaanxi Kingbull Livestock Development Co., Ltd, and data with seven meat quality traits were collected, including live weight (LW), carcass weight (CW), backfat thickness (BFT), loin-eye area (LEA), marble score (MAR), water holding capacity (WHC) and tenderness (TD). DNA preparation

The animals blood samples were obtained and treated with 2% heparin and stored at -80C. And the genomic DNA was extracted from blood leucocytes by a standard phenol-chloroform protocol method (Mullenbach et al., 1989).

PCR conditions

In order to amplify DNA region, primer A (F: 5'-CAGGAGACGGAGGTGAAGC-3', R:5'-CACCGTGTCTGCCGCATT-3';) and primer B (F:5'-GTGGTAGGCGCTGGCAGGAA-3'; R:5'-CGTGAGGGAGGCGGTCTTT-3') were designed to amplify 196 and 288 bp fragments from exon 2 and exon 5 of the bovine chemerin gene (GenBank: NC_007302) by Primer 5.0 software, respectively. Each PCR was performed in a 20-l reaction volume containing 50 ng genomic DNA, 10 mM of each primer, 2.5 mM Mgcl2, 0.20 mM dNTP and 0.5U Taq DNA polymerase (TaKaLa, Dalian, China). The cycling protocol was 5 min at 95C, 32 cycles of

94C for 30 s, 58.3C (or 64.5C) annealing for 30 s, 72C for 35 s, with a final extension at 72C for 1 0 min. PCR products were electrophoresis on 1.5% agarose gels. Then the products for

sequencing were purified with Axygen kits (MBI Fermentas, Canada) and sequenced in both directions in an ABI PRIZM 377 DNA sequencer (Perkin-Elmer, USA). The sequences were analyzed with the SeqMan software. SSCP polymorphism and sequencing

Aliquots of 6 l of the PCR products were mixed with 10 l denaturing solution (95% formamide, 25 mM EDTA, 0.025% xylene-cyanole and 0.025% bromophenol blue), heated for 10 min at 98C and chilled on ice immediately after heated. Then 16 l of this mixture was applied to a 12% polyacrylamide gel (29:1 acrylamide:bis), 14% (V/V) glycerol and 10 TBE buffer, Electrophoresis was carried out with 1TBE buffer at 250 V for 30 min and 115 V for 14 h at room temperature. The gel was stained with 0.1% silver nitrate (Lan et al., 2007) and visualized with 2% NaOH solution (containing 0.1% formaldehyde) according to Zhang et al. (2007). After the polymorphism was detected, the PCR products of different electrophoresis patterns were sequenced in both directions in an ABI PRIZM 377 DNA sequencer. The sequences were analyzed by DNASTAR 5.0 package. Statistical analysis

Based on the genotype number in analyzed breeds, genotypic frequencies and allelic frequencies of chemerin locus were calculated directly; Hardy-Weinberg equilibriums and differences in genotypic frequencies were analyzed using 2 test, which were performed by SPSS software (version 17.0). Population genetic indexes: He (gene heterozygosity), Ne (effective allele numbers) and PIC (polymorphism information content) were calculated according to Nei and Roychoudhury (1974) and Nei and Li (1979),

respectively. The software SPSS (version 17.0) was used to analyze the

relationship between the genotypes and records of traits (UBF, ULMA and UMAR) on 214 Qinchuan individuals, which were measured by ultrasound, according to the following statistical linear model: Yijkl = + Ai+Gj +Sk + BFl +ijkl (1)

Meat quality traits (BFT, EMA, MAR, WHC, MC and TD) were also evaluated by the comparison between the genotypes of 69 Qinchuan individuals and their phenotypic data by the least-squares method according to the following statistical linear model:

Yijkl = + Ai+Gj +Sk +ijkl (2)

Where, Yijkl is the observation for the traits; is the overall population mean; Ai is the fixed effect of the ith age; Gj is the fixed effect of jth genotype (AA, AG and GG genotype); Sk is the fixed effect of sex; BFl is the fixed effects of breed and farm and Eijk is the random error.

RESULTS Genetic polymorphism of Bos bovine chemerin gene The 196 and 288 bp fragments of bovine chemerin gene

Figure 1. The electrophoresis patterns of PCR-SSCP exon 2 of bovine chemerin gene.

Figure 2. The electrophoresis patterns of PCR-SSCP exon 5 of

bovine chemerin gene.

of exon 2 and exon 5 were amplified by PCR. Then, by sequencing, two SNPs were revealed. They were synonymous mutation of leucine and aspartic acid, respectively. An adenine (A)-to-guanine (G) transition and an adenine (A) cytosine (C)-to-thymine (T) transition (868A>G and 2692C>T) were shown in two SNPs. The genetic polymorphisms of the six bovine breeds were detected by SSCP in the locus of 868A>G (Figure 1) and 2692C>T (Figure 2). The polymorphism of 868A>G locus was induced by A-G SNP at nucleotide 868 bp (Figure 3), and the polymorphism of 2692C>T locus was induced by C-T SNP at nucleotide 2962 bp of chemerin gene (Figure 4). Genotypic, allelic frequencies and genetic characters in the six bovine breeds The allele and genotype frequencies of the 868A>G and 2692C>T polymorphisms obtained for the different genetic groups are shown in Tables 1 and 2, respectively. The two alleles of the 868A>G polymorphism were observed in all genetic groups analyzed. Frequency of allele G was the predominant allele of locus 868A>G,

Song et al. 8419

Figure 3

AA

AG

GG

The sequencing maps of the novel SNP for locus 868A>G

Figure 3. The sequencing maps of the novel SNP for locus

868A>G of the chemerin gene. Sample chromatograms of heterozygous (AG) and homozygous (AA and AG) genotypes are shown. The arrow denotes the location of the

polymorphism.

expect for CYR and NY breeds. And three genotypes (named AA, AG and GG) were detected in all genetic groups; with respect to the 2692C>T polymorphism, both alleles were detected in the sample of animals studied. Allele A had signicant lower frequent in the CYR group as compared to the other detected groups.

The 2-test showed that the genotype distributions in

the detected breeds were in agreement with Hardy-Weinberg equilibrium (PHW value > 0.05) of locus 868A>G, except CYR breed, while XN breed was not at locus 2692C>T (PHW value < 0.05). The

2-test showed

that the genotype distributions in the detected breeds were in agreement with Hardy-Weinberg equilibrium (P > 0.05), except CYR (P < 0.01) and XN (P < 0.05). This observation may be as a result of the occurrence of strict choice made by the people for forming the CYR and XN breed. According to the classification of PIC, all Bos taurus population belongs to the median polymorphism

8420 Afr. J. Biotechnol.

Figure 4

AA

AB

BB

The sequencing maps of the novel SNP

Figure 4. The sequencing maps of the novel SNP for locus 2692C>T of the chemerin

gene. This figure was the reverse sequencing map of the locus 2692C>T; Sample chromatograms of heterozygous (AB) and homozygous (AA and BB) genotypes are

shown. The arrow denotes the location of the polymorphism.

level and there was no significant difference of PIC value in the six breeds (Table 3). Effect of the polymorphism locus on carcass traits Association studies between each SNP genotypes and studied traits in Qinchuan cattle were given in Tables 4 and 5. In polymorphism locus 868A>G, animals with the

genotype AA have higher mean values of UBF and ULMA than those with genotype AG (P < 0.05). While the association between genotypes and carcass and meat quality traits were analyzed, from which we can see significant differences on the LEA (P < 0.05) and WHC (P < 0.05) among different genotypes. Animals of AA genotype have greater mean values for BFT, LEA and WHC than those with AG genotypes. For locus 2692C>T SNP genotypes, the cattle with the AA genotype showed

Song et al. 8421

Table 1. Genotype frequencies (%) of the locus 868A>G of chemerin gene in Bos bovine populations.

Breed Observed genotype(number)

Total Allelic frequency

2(HW)

PHW value

AA AG GG A G

QC 0.2150(46) 0.5047(108) 0.2804(60) 214 0.4673 0.5327 0.0401 0.9801

CYR 0.4911(55) 0.2679(30) 0.2411(27) 112 0.6250 0.3750 20.5714** < 0.01

JR 0.1690(12) 0.4507(32) 0.3803(27) 71 0.3944 0.6056 0.2265 0.8927

LX 0.0870(6) 0.5942(41) 0.3188(22) 69 0.3841 0.6159 4.5198 0.1044

NY 0.3333(27) 0.4321(35) 0.2346 (19) 81 0.5494 0.4506 1.3124 0.5188

XN 0.1579(12) 0.5000(38) 0.3421(26) 76 0.4079 0.5921 0.0938 0.9542

HW, Hardy-Weinberg equilibrium; QC, Qinchuan cattle breed; CYR, Caoyuan red cattle breed; JR, Jiaxian red cattle breed; LX, Luxi

cattle breed; NY, Nan yang cattle breed; XN, Xianan cattle breed. Generally, PHW value is classified into the following three types: in Hardy-Weinberg equilibrium (PHW value > 0.05), not in Hardy-Weinberg equilibrium (0.01 < PHW value < 0.05) and highly not in Hardy-Weinberg equilibrium (PHW value < 0.01).

Table 2. Genotype frequencies (%) of the locus 2692C>T of chemerin gene.

Breed Observed genotype (number)

Total Allelic frequency

2(HW)

PHW value

AA AG GG A G

QC 0.4626(99) 0.3972(85) 0.1402(30) 214 0.6612 0.3388 2.7539 0.2523

CYR 0.2679(30) 0.4018(45) 0.3304(37) 112 0.4688 0.5313 4.1839 0.1234

JR 0.4507(32) 0.4085(29) 0.1408(10) 71 0.6549 0.3451 0.6589 0.7193

LX 0.3913(27) 0.4348(30) 0.1739(12) 69 0.6087 0.3913 0.5259 0.7687

NY 0.3457(28) 0.3951(32) 0.2593 (21) 81 0.5432 0.4568 3.3686 0.1855

XN 0.3158(24) 0.3553(27) 0.3289(25) 76 0.4934 0.5066 6.3630* 0.0415

HW, Hardy-Weinberg equilibrium; QC, Qinchuan cattle breed; CYR, Caoyuan red cattle breed; JR, Jiaxian red cattle breed; LX, Luxi cattle

breed; NY, Nan yang cattle breed; XN, Xianan cattle breed. Generally, PHW value is classified into the following three types: In Hardy-Weinberg equilibrium (PHW value > 0.05), not in Hardy-Weinberg equilibrium (0.01 < PHW value < 0.05) and highly not in Hardy-Weinberg equilibrium (PHW value < 0.01).

Table 3. Allele and genotype frequencies of the locus 868A>G and 2692C>T polymorphism of

chemerin gene in the different genetic groups.

Locus Breed Gene heterozygosity Effective allele number PIC

868A>G

QC 0.4979 1.9915 0.3739

CYR 0.4688 1.8824 0.3589

JR 0.4777 1.9145 0.3636

LX 0.4731 1.8979 0.3612

NY 0.4951 1.9807 0.3725

XN 0.4830 1.9344 0.3664

2692C>T

QC 0.4480 1.8117 0.3477

CYR 0.4980 1.9922 0.3740

JR 0.4520 1.8248 0.3498

LX 0.4764 1.9097 0.3629

NY 0.4963 1.9852 0.3731

XN 0.4999 1.9997 0.3750

PIC, Polymorphism information content; QC, Qinchuan cattle breed; CYR, Caoyuan red cattle breed; JR,

Jiaxian red cattle breed; LX, Luxi cattle breed; NY, Nan yang cattle breed; XN, Xianan cattle breed. Generally, PIC is classified into the following three types: Low polymorphism (PIC value < 0.25), median polymophism (0.25 < PIC value < 0.5) and high polymorphism (PIC value > 0.5).

greater UBF, BFT and WHC in comparison with the cattle with the AB and BB genotypes (P < 0.05).


Table 4. Association between locus 868A>G and 2692C>T genotypes of chemerin gene and UBF, ULMA and UMAR traits in Qinchuan cattle.

Polymorphism Genotype Trait (Mean SE)

UBF (mm) ULMA (cm2) UMAR

868A>G

AA 0.9800.3a 74.7411.483

a 2.7180.055

AG 0.9000.22b 70.5161.068

b 2.5850.040

GG 0.9250.2ab

73.4601.170ab

2.6460.043

P value 0.104 0.042 0.145

2692C>T

AA 0.9820.023a 73.0441.240 2.6530.042

AB 0.8980.025b 74.2501.337 2.5500.046

BB 0.8950.042ab 72.9992.242 2.6950.076

P value 0.031 0.779 0.144 ab

Means with different superscripts are significantly different (P < 0.05).

Moreover, the A>G synonymous mutation of leucine results in the increase of the part of the phenotypic variation, especially on the BFT and LEA phenotypes in animals studied, and the C>T synonymous mutation of aspartic acid results in the higher BFT phenotypes. DISCUSSION Although, initially chemerin was reported to play an important role in the innate and adaptive immunity (Parolini et al., 2007), recent researches gave a new point that chemerin play a crucial role in adipocyte metabolism, differentiation, obesity and diabetes in human and mice (Sell et al., 2009) and is related to the pathogenesis of metabolic syndrome (Michiko et al., 2008). Chemerin and its receptor ChemR23 are abundantly expressed in mouse and human adipose tissue (Thamer et al., 2008). Furthermore, chemerin is a well-known target gene of the retinoic acid receptor (Mssig-K et al., 2009), and a growing body of evidence supports a link between retinoic acid signalling and adipocyte differentiation (Safonova et al., 1994). A recent study showed that the human chemerin gene have three SNPs, in which rs10278590 is associated with increased visceral fat mass in non-obese subjects of human, and in generalized obesity, this genetic effect may be masked by the close association between whole-body obesity and visceral fat mass (Mssig et al., 2009). The cloning and expression analysis of chemerin and chemerin receptor in Japanese Black cattle showed that bovine chemerin mRNA was highly expressed in the adipose and liver tissues, and is the TNF--up-regulated gene with a role in adipogenesis (Song et al., 2010). Above all, the available association studies on bovine and other livestock have never been reported. Therefore, we detected the SNPs of this gene in cattle, and found two SNPs (868A>G and 2692C>T) in exon 2 and exon 5, respectively.

Statistical analysis revealed that the chemerin gene

polymorphisms of 868A>G had a signicant effect on BFT and LEA in the Qinchuan cattle population studied, and the additive genetic effects were signicant for WHC as well. Similarly, the polymorphisms of 2692C>T were signicant on BFT and WHC. Therefore, we assumed that the mutation for 868A>G and 2692C>T might influence the carcass and meat quality traits; it could be a candidate molecular marker for the quality improvement of Qinchuan cattle. The meat quality traits of bovine are affected by many factors, such as genotype, breed, herd, location, sex and other random environmental factors. We got a new statistical model in which the three factors (breed, herd and location) were involved and then, we employed the least-squares method in GLM procedure of SPSS software to do the related analysis and we did not find any significant difference (P > 0.05) (data not shown).

Ultrasound technology has been popularly used in recent years, for example, Trejo et al. (2010) used the ultrasound technology to measure the backfat and marbling deposition in feedlot cattle to evaluate the different effect on the ultrasound backfat and marbling deposition. Previously, Hamlin et al. (1995) indicated that ultrasonic predictors showed about 10% less variation in retail product percentage than did carcass measurements. Greiner et al. (2003) found that the ultrasound measurements were useful predictors of retail yield in live animal, such as 12th-rib fat thickness and longissimus muscle area. Our result showed that the relevance in this study was the same as the traits measured by ultrasound with the carcass traits (Jiao et al., 2010). Implications Our data showed that the chemerin gene might have potential influence on carcass and meat quality traits in Qinchuan cattle. And the impact of SNPs on these traits variability represents a vast area for further research. It is also significant to investigate whether the chemerin gene

Song et al. 8423

Table 5. Association between locus 868A>G and 2692C>T genotypes of chemerin gene and carcass and meat quality traits in Qinchuan cat tle.

Polymorphism Genotype

Trait (Mean SE)

Live weight (LW)/kg

Carcass weight (CW)/kg

Backfat thickness (BFT) (cm)

Loin eye area (LEA)/cm

2

Marbling score (MS)/1-5

Meat tenderness

(MT)/kg

Water holding capability (WHC)/%

868A>G

AA 400.7659.511 198.0358.956 1.2140.086a 81.2514.344

a 2.6470.127 2.1540.136 79.7881.539

a

AG 388.3337.160 185.5876.741 1.0130.065b 69.0043.270

b 2.3330.095 2.0620.102 74.5231.159

b

GG 397.5008.005 189.7337.537 1.0870.072ab

78.5553.656ab

2.5000.107 2.2300.115 75.0831.295b

P 0.521 0.542 0.064 0.048 0.138 0.548 0.021

2692C>T

AA 392.1036.892 193.7386.239 1.2150.058a 73.8963.452 2.5520.100 2.0130.105 78.3481.260a

AB 396.2087.576 185.9676.859 1.0270.064b 77.4043.795 2.3750.110 2.2930.115 74.2881.385b

BB 389.44412.371 183.04411.20 1.0140.104ab 74.1756.197 2.5560.179 2.2890.188 73.4332.261ab

P 0.872 0.593 0.061 0.777 0.452 0.160 0.050 ab

Means with different superscripts are significantly different (P < 0.05).

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Genetic diversity of Stipa bungeana populations in the Loess Plateau of China using inter-simple sequence

repeat (ISSR) markers

Yu Jing1, Jing Zhao-Bin2 and Cheng Ji-Min1, 2, 3*

1College of Resources and Environment, Northwest A and F University, Yangling 712100, Shaanxi, P. R. China.

2College of Animal Science and Technology, Northwest A and F University, Yangling 712100, Shaanxi, P. R. China.

3Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling

712100, Shaanxi, P. R. China.

Accepted 22 March, 2012

Inter-simple sequence repeat (ISSR) markers were used to determine the genetic diversity and genetic differentiation for eight natural populations of Stipa bungeana in the Loess Plateau of China. 15 primers produced stable and reproducible amplification bands which were selected from the screened 96 primers. Among a total of 390 amplified bands, 335 (85.90%) were polymorphic loci. At species level, Nei's gene diversity index and Shannon's information index were 0.1633 and 0.2703, respectively. Based on the cluster analysis, the eight populations were divided into three groups. Analysis of molecular variance (AMOVA) demonstrated that the genetic variation was found mainly among populations, which accounted for 68.71% of the total variation, but the within-population accounted for 31.29%. Mantel test revealed no significant correlation between genetic distance and geographic distance (r= -0.1345, P= 0.71). In this study, the lower genetic diversity of S. bungeana may be related with human activities and habit destroyed. The conservation strategies further were proposed for this plant. Key words: Stipa bungeana, inter simple sequence repeats, genetic diversity, the Loess Plateau.

INTRODUCTION Understanding the genetic diversity of a species and a population with the relation to ecological factors is a prerequisite for effective conservation and management of a species and a population. The genetic diversity and genetic differentiation of a species was considered important for species diversity and protection (Zhao et al., 2006). Genetic diversity within populations was con-sidered highly important for possible adaptation to environmental changes for long-term survival of plant species (Bauert et al., 1998). To preserve the genetic diversity of population was considered important to stabilize population and ecosystem dynamics (Hughes et al., 2008). The genetic structure of population were affected by various environment and human activity

*Corresponding author. E-mail: [email protected]. Tel: 86-29-87012272. Fax: 86-29-87012210.

factors, including life history, mecological traits, repro-ductive mode, over grazing and mowing and fire distur-bance (Nybom, 2004; Mohamed et al., 2010) .

The Loess Plateau of China is located in the upper and middle of Yellow River. The total area is about 52 million hectares. At present, the Loess Plateau is a typical region of ecological fragile that is suffering from water and soil erosion and drought for a long time. The amount of annual soil erosion is estimated to be over 2200 million tons (Zhang et al., 2010). Under this weak condition, Stipa bungeana is the dominant and constructive species of the typical steppe in the Loess Plateau (Cheng et al., 2011). Its distribution covers some major provinces in China including Tibet, Gansu, Ningxia, Xinjiang, Qinghai, Shaanxi, Shanxi, and Inner Mongolia. In addition, S. bungeana is one of the major grassland types in the temperate zone of Asia. S. bungeana is a perennial grass which clonally grows by tiller. It mainly depends on vegetative propagation by repeatedly producing tiller


Figure 1. Geographic distribution of the eight sampled populations of S. bungeana in the Loess Plateau.

ramets from shoot base. It have large root systems and big crown width to promote few seed survival depending on the water and nutrients offered by parents. S. bungeana contains high level of nutrients and palatable for livestock. It tolerates drought and grazing and can be grazed by sheep in different growth stages. Therefore, it is important for ecological restoration in the Loess Plateau.

Gustafson et al. (1999) showed that the genetic variation of dominant (constructive) species could provide the information for the utilization and protection of these species and the evaluation of the ecosystems genetic health. At present, the genetic diversity of a few Stipa genus plants have been reported including Stipa grandis, Stipa purpurea and Stipa tenacissima (Zhao et al., 2006, 2008; Wu et al., 2010; Mohamed et al., 2010; Liu et al., 2009). A number of studies have reported on S. bungeana ecology (Cheng et al., 2011; Huang et al., 2001a, 2001b). However, up till now, there is no report on the genetic diversity and the genetic structure of the S. bungeana populations from the Loess Plateau of China. Inter-simple sequence repeat (ISSR) is now established as a powerful approach for detecting species and population genetic diversity and differen-tiation

(Zietkiewicz et al., 1994; Zhao et al., 2008). In this study, ISSR molecular markers were used to

analyze the genetic diversity and genetic differentiation of S. bungeana populations in the Loess Plateau of China. The objectives of this study were to (1) investigate the level of genetic diversity of S. bungeana natural populations; (2) reveal the level of genetic variations within and among populations; (3) analyze the relation-ship between and among genetic diversity and environmental factors; (4) provide scientific conservation strategies for S. bungeana. MATERIALS AND METHODS

Sample collection

A total of 160 individuals of S. bungeana were collected from eight populations in Loess Plateau in September 2010 (Figure 1). Habitat conditions of the populations are shown in Table 1. 20 individuals from each population were sampled randomly, and the distance between individual plants were kept at least 10 cm within the same population. Young and fresh leaves were collected from each

sampled individuals and immediately stored in zip lock bags with silica gel and taken back to the laboratory and stored at -80C in ultra-freezer for DNA extracted.

Jing et al. 8427

Table 1. Locations and the habitat characters.

Location ID Altitude

(m) Longitude

(E) Latitude

(N) Annual mean

precipitation (mm) Annual mean

temperature (C) Habitat

Chengchuan, Inner-Mongolia A1 1364 10723 3806 294.1 7.4 Desert steppe

Machangjie, Inner-Mongolia A2 1354 10743 3802 294.1 7.4 Desert steppe

Huining, Gansu A3 1726 10501 3558 340.0 6.4 Typical steppe

Jingchuan, Gansu A4 1305 10731 3519 555.0 10.0 Forest steppe

Pucheng, Shaanxi A5 430 10945 3453 533.2 13.3 Forest steppe

Binxian,Shaanxi A6 1161 10850 3515 602.3 9.2 Forest steppe

Nanshan, Yunwu Mountain,Ningxia A7 2049 10637 3625 445.0 5.8 Typical steppe

Deteriorated grassland, Yunwu Mountain, Ningxia A8 2047 10637 3627 423.5 6.1 Typical steppe

DNA extraction

Genomic deoxyribonucleic acid (DNA) was extracted using

the modified cetyltrimethyl ammonium bromide (CTAB) method (Zhao et al., 2006). DNA concentration and purity were determined with ultraviolet-visible spectroscopy (UVVIS) spectrophotometer and the DNA quantity was detected by using 0.8% (w/v) agarose gels. The solution was diluted to 20 ng L-1and then stored at -20C for polymerase chain reaction (PCR) analysis.

Inter-simple sequence repeat (ISSR) -PCR amplification

According to the primer sequence published by University of British Columbia (UBC), a total of 96 inter-simple sequence repeat (ISSR) primer sequence were synthe-sized by Beijing Aoke Biological Technology Co., Ltd. Four individual plants from different populations were used for the primer screening. Finally, 15 primers (Table 2) were selected for the study based on clarity and reproducibility, and high polymorphism of amplified product bands.

PCR amplification reaction system with had total volume of 20 l, containing 20 ng of template DNA, 10 PCR buffer (100 Mm Tris-HC, pH 8.3; 500 mM KCl), 0.18 mM of each dNTPs, 0.75 mM of each primer, 1.87 mM of MgCl2, and 1 units of Taq DNA polymerase (TaKaRa Biotech-nology Dalian Co., Ltd., China). PCR amplification was

carried out on Eppendorf PCR instrument and the PCR reaction programs as follows: initial 5 min at 94C, followed by 35 cycles of 45 s at 94C , 45 s annealing at 50C

(varies according to different primers), and 90 s extension at 72C, ending with a final extension of 5 min at 72C. PCR products were separated on 6% denatured poly-

acrylamide gel and detected by silver staining. Then, clear and reproducible bands were recorded and used in the analysis. Data analysis

Clear and reproducible bands were selected for statistical analysis. Amplified bands were scored for each individual as presence (1) or absence (0). DCFA1.1 program was used to build the original document of data analysis (Zhang et al., 2002). Assuming Hardy-Weinberg equilibrium, POPGENE1.32 (Yeh et al., 1997) was used to calculate the following genetic diversity parameters: the percentage of polymorphic bands (PPB), Shannons information index (I), Neis gene diversity (H), the effective number of alleles (ne), observed number of alleles (na), gene differentiation (Gst) and Neis genetic distance (Nei, 1973). The average level of gene flow (Nm) among populations was indirectly calculated using the formula: Nm = 0.5 (1-Gst)/Gst. (McDermott and McDonald, 1993). An unweighted pair-group method using arithmetic mean (UPGMA) dendrogram was constructed based on the matrix of Neis genetic distance using NTSYS-pc 2.1e program (Rohlf, 2000).

In addition, analysis of molecular variance (AMOVA)

procedure was used to estimate the coefficient of genetic variation among and within populations (Excofier et al., 1992). The variance components were tested statistically

by nonparametric randomization tests using 1000 permutations. Meanwhile, Pearson correlation analysis was used to detect the correlation between genetic

diversity parameters and environment factors, including altitude, longitude, latitude, annual mean temperature and annual mean precipitation. All these data analyses were calculated by SPSS11.0 (SPSS, 2001). Finally, the relationship between genetic distance and corresponding geographical distance among all populations was tested by Mantels Test (Mantel, 1967).

RESULTS Genetic diversity of S. bungeana

A total of 390 bands were generated using 15 primers for 160 individuals of eight populations, corresponding to an average of 26 bands per primer. Of these bands, 335 bands were poly-morphic, and the percentage of polymorphic bands was 85.90% (Table 3). Amplified bands ranged in size from 100 ~ 2000 bp. Assuming Hardy-Weinberg equilibrium, Neis gene diversity (H) was 0.1663, and Shannons information index (I) was 0.2703 at the species level. Among the eight populations, the highest genetic diversity occurred in population A8 (PPB = 34.36%, H = 0.0981,I = 0.1517) and the lowest was in


Table 2. Primer sequences used in inter-simple sequence repeat (ISSR) analyses of S. bungeana.

Primer Sequence (5-3) Tm (C)

UBC806 (TA)8G 41.5

UBC813 (CT)8T 48.4

UBC814 (CT)8A 50.0

UBC820 (GT)8C 57.7

UBC822 (TC)8A 55.7

UBC823 (TC)8C 57.7

UBC827 (AC)8G 56.4

UBC834 (AG)8YT 56.0

UBC840 (GA)8YT 54.1

UBC864 ( ATG)5 46.7

UBC868 (GAA)5 42.6

UBC880 (GGAGA)3 51.0

UBC886 VDV(CT)7 57.7

UBC887 VDV(TC)7 54.5

UBC891 HVH(TG)7 47.1

Y = (C,G); D = (A,G,T); V = (A,C,G) ; H = (A,C,T).

population A1 (PPB =10.51%, H = 0.0306, I = 0.0541) (Table 3). Genetic differentiation of S. bungeana Analysis of molecular variance (AMOVA) demonstrated that the genetic variation of S. bungeana existed mainly among populations, which accounted for 68.71% of the total variation, but the within-population accounted for 31.29%. The coefficient of genetic differentiation (ST) was highly significant (P

Jing et al. 8429

Table 3. Genetic diversity indexes of eight populations of Stipa bungeana.

Population Polymorphic loci PPB na ne H I

A1 41 10.51 1.1051 1.0604 0.0360 0.0541

A2 84 21.54 1.2154 1.1007 0.0608 0.0933

A3 93 23.85 1.2385 1.1352 0.0787 0.1184

A4 73 18.72 1.1872 1.0967 0.0583 0.0885

A5 48 12.31 1.1231 1.0757 0.0434 0.0642

A6 93 23.85 1.2385 1.1328 0.0751 0.1124

A7 109 27.95 1.2795 1.1298 0.0794 0.1231

A8 134 34.36 1.3436 1.1613 0.0981 0.1517

Mean 84.4 21.64 1.2164 1.1116 0.0662 0.1007

Species level 335 85.90 1.8590 1.2601 0.1663 0.2703

PPB = percentage of polymorphic bands; na = observed number of alleles; ne = effective number of alleles; H = Neis gene diversity; I = Shannons information index.

Table 4. Analysis of molecular variance (AMOVA) for eight populations of S. bungeana.

Source of variation

d.f. Sum of squares Variance component Percentage of variation (%) P value

Among groups 7 579.587 28.3343 68.71


Figure 2. Dendrogram generated by UPGMA based on Neis unbiased genetic distances for populations of S. bungeana.

Figure 3. The correlation between genetic distance and geographic distance for populations of S.

bungeana.

Table 6. Pearson correlation analyses for the relationships between genetic diversity index and ecological factors.

Diversity index

Altitude (m) Longitude Latitude Annual mean

temperature (C) Annual mean precipitation

(mm)

PPB 0.768 -0.583 -0.066 0.059 -0.629

H 0.730 -0.596 -0.187 0.110 -0.588

I 0.749 -0.605 -0.159 0.091 -0.606

PPB =percentage of polymorphic bands; H = Neis gene diversity; I = Shannons information index. All data significant level P > 0.05.

with human activities and habit destroyed.

Genetic differentiation among populations

Population genetic structure was defined as the non-random distribution pattern of genetic variation of one species or population in the spatial and temporal pattern. To a large extent, it represents the evolutionary potential of a species or population (Sun, 1996). The genetic differentiation is an important parameter to evaluate the population genetic structure (Hamrick and loveless, 1989). In the present study, genetic differentiation coefficient (ST) among eight populations was 0.687 by the AMOVA. Population differentiation of S. bungeana was higher than that of long-lived perennial species (ST = 0.25, n = 60), out-crossing species (ST = 0.25, n = 73), mixed breeding species (ST = 0.40, n = 18) and the widespread species (ST = 0.34, n = 32) (Nybom, 2004). Wright (1951) pointed that the genetic differentiation was strong when coefficient was greater than 0.25. Therefore, S. bungeana population appeared the greater genetic differentiation. Hamrick and Godt (1990) considered that the mean value of genetic differentiation coefficient is 0.20 for cross-pollination plant, and for the selfing species is 0.51. Compared to cross-pollination plant, the selfing plant has higher genetic variation among populations and lower genetic variation within population. S. bungeana is a selfing species, thus the genetic variation mainly existed among the populations and the genetic variation within populations was relatively lower. First, the significant population differentiation in S. bungeana can be explained by the geographic environment factors. In this study, the habitat of field survey and sampling are in the Loess Plateau. There is strong wind in autumn in this area, but the terrain is complicated, and the isolation of High Mountain leads to the big geographic distances among populations, this further hinder the long-distance spread of seed and pollen and affecting the genetic information exchange among different populations in different historical periods.

Second, S. bungeana is an excellent grass that is favored by the cattle, sheep or goats. Under the great pressure of animals graze, it is difficult to form a strong seed bank among populations. In addition, it is hard to achieve long-distance spread depend on wind or animal carrying. Meanwhile, human over-grazing and excessive deforestation also lead to the living environment of S. bungeana continue to shrank and deteriorate, which result in the discontinuous distribution of populations.

Third, our field survey also found that the germination rate of S. bungeana was low. It grow mainly by clonal reproduction to expand population, this special reproduction increased the mating opportunity among similar individuals and lacked effective gene flow for among different populations. Therefore, we assumed that selfing breeding system, lower gene flow, seed and pollen close range spread and the limited population size

Jing et al. 8431 may result in the greater genetic differentiation among populations of S. bungeana.

Hamrick and Godt (1990) concluded that plant breeding system, gene flow and seed dispersal mecha-nisms, reproduction mode, natural selection and other factors had a significant impact on the genetic structure of plants. Gene flow was considered as one of the main factors for homogenization of population genetic structure. The species with limited gene flow had a greater genetic differentiation than the species with wide gene flow (Hamrick and Loveless, 1989). In this study, the gene flow (Nm = 0.1139) was far less than 1, which indicated that the gene flow of S. bungeana populations was very limited. Wright (1951) pointed that if the estimated value of gene flow (Nm) was greater than 1, there would be certain gene flow among populations; when Nm is smaller than 1, the genetic drift was an important factor to lead to significant genetic differentiation among populations.

Implications for conservation of S. bungeana

The environmental degradation caused by natural and human factors exceeds the maximum limit, and then it must result in the loss of genetic diversity for a species or population (Yan et al., 2010). S. bungeana is widely distributed on the Loess Plateau, but with the development and utilization of grassland, the water loss and soil erosion becoming increasingly serious, which is causing the continuously destroy of natural growing environment for S. bungeana and further leads to the genetic diversity decreased. Therefore, it is necessary to take effective protection strategies and methods to protect the S. bungeana in the Loess Plateau. From this study, we known the lower genetic diversity of S. bungeana were related to the fragmented distribution range and the number of population continued reduction. Therefore, we should enhance in situ conservation of S. bungeana to ensure its normal growth and reproduction, and to restrain the decline trend of genetic diversity by prevent overgrazing and excessive deforestation. In addition, it is important to carry out in-depth research for seed reproduction characteristics of S. bungeana to improve its self-reproductive capacity by seeds in natural habitat.

ACKNOWLEDGEMENTS

We are grateful to Wang Xi-Ping and Yang Yong for the laboratory work and guide. This work was financially supported by "Strategic Priority Research Program-Climate Change: Carbon Budget and Related Issues" of the Chinese Academy of Sciences (XDA05050202), and the earmarked fund for Modern Agro-industry Technology Research System (CARS-35-40).

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