A Bioinformatics-Based Approach for Designing Primer Sets in Determination of Meat Specificity

Yıl 2021, , 1669 - 1675, 31.10.2021

Nursel Söylemez Milli , İsmail Hakkı Parlak Ercan Selçuk Ünlü , Mehmet Milli Omer Eren

https://doi.org/10.29130/dubited.898519

Öz

Polymerase chain reaction (PCR) and its derivatives are one of the most widely used DNA-based methods in species determination studies in meat and meat products. Chromosomal or mitochondrial genes of the species can be targeted in PCR-based analyzes used in species detection studies. Many researchers are able to realize oligonucleotide differences between species through online alignment programs on mitochondrial DNA. Using chromosomal DNA would provide more concise results in quantification studies. However, determining the marker regions for genomic DNA is challenging due to the large size of the chromosomes. Bioinformatics approaches are available for selected applications. However, using those approaches requires intensive knowledge of computer science, molecular biology, and bioinformatics in addition to high computational power. In this study, a pipeline is presented that will provide a user-friendly approach to be adopted by facilities where contamination analyzes are routinely performed.

Anahtar Kelimeler

Sequence alignment, Bioinformatics, Biocomputing, Food quality

Destekleyen Kurum

BAİBÜ-BAP

Proje Numarası

2017.09.04.1123

Teşekkür

This study was supported by Bolu Abant İzzet University Scientific Research Projects. In addition the authors would like to thank the Scientific, Industrial and Technological Application and Research Center (STARC) of Bolu Abant İzzet Baysal University for utilization of laboratories.

Et Özgüllüğünün Belirlenmesinde Primer Setlerinin Tasarımına Yönelik Biyoinformatik Tabanlı Bir Yaklaşım

Öz

Polimeraz zincir reaksiyonu (PCR) ve türevleri, et ve et ürünlerinde tür belirleme çalışmalarında en yaygın kullanılan DNA bazlı yöntemlerden biridir. Tür tespit çalışmalarında kullanılan PCR tabanlı analizlerde türlerin kromozomal veya mitokondriyal genleri hedeflenebilir. Birçok araştırmacı, mitokondriyal DNA üzerindeki çevrimiçi hizalama programları aracılığıyla türler arasındaki oligonükleotid farklılıklarını gerçekleştirebilmektedir. Kromozomal DNA kullanmak, kantifikasyon çalışmalarında daha kısa sonuçlar sağlayacaktır. Bununla birlikte, genomik DNA için işaretleyici bölgelerin belirlenmesi, kromozomların büyüklüğünden dolayı zordur. Biyoinformatik yaklaşımlar, seçilmiş uygulamalar için mevcuttur. Ancak, bu yaklaşımları kullanmak, yüksek hesaplama gücüne ek olarak yoğun bilgisayar bilimi, moleküler biyoloji ve biyoinformatik bilgisi gerektirir. Bu çalışmada, kontaminasyon analizlerinin rutin olarak yapıldığı tesisler tarafından benimsenmesi için kullanıcı dostu bir yaklaşım sağlayacak bir kod akışı sunulmuştur.

Anahtar Kelimeler

Sekans hizalama, Biyoinformatik, Biyo-hesaplama, Gıda kalitesi

Proje Numarası

2017.09.04.1123

Kaynakça

• [1] Q. Zia, M. Alawami, N. F. K. Mokhtar, R. M. H. R. Nhari and I. Hanish, “Current analyticalmethods for porcine identification in meat and meat products,” Food Chem., vol. 324, no. April 2019, pp. 126664, 2020.
• [2] M. A. M. Hossain, S. M. K. Uddin, S. Sultana, S.Q. Bonny, M. F. Khan and Z. Z. Chowdhury, “Heptaplex polymerase chain reaction assay for the simultaneousdetection of beef, buffalo, chicken, cat, dog, pork, and fish in raw and heat-treated food products,” J. Agric. Food Chem., vol. 67, no. 29, pp. 8268–8278, 2019.
• [3] A. Lopez-Oceja, C. Nuñez, M. Baeta, D. Gamarra, and M. M. de Pancorbo, “Speciesidentification in meat products: A new screening method based on high resolution melting analysis ofcyt b gene,” Food Chem., vol. 237, pp. 701–706, 2017.
• [4] M. E. Ali, M.A. Razzak, S. B. A. Hamid, M. M. Rahman, M. Al Amin, N. R. A. Rashid, and Asign, “Multiplex PCR assay for the detection of five meat species forbidden inIslamic foods,” Food Chem., vol. 177, pp. 214–224, 2015.
• [5] M. E. Ali, U. Hashim, S. Mustafa, Y. B. Che Man, Th S. Dhani, M. Kashif, M. K. Uddin, and S. B. A. Hamid, “Analysis of pork adulteration in commercial meatballs targeting porcinespecific mitochondrial cytochrome b gene by TaqMan probe real-time polymerase chain reaction,” Meat Sci., vol. 91, no. 4, pp. 454–459, 2012.
• [6] R. Grujić and D. Savanović, “Analysis of myofibrillar and sarcoplasmic proteins in pork meatby capillary gel electrophoresis,” Foods Raw Mater., vol. 6, no. 2, pp. 421–428, 2018.
• [7] M. Montowska and E. Pospiech, “Differences in two-dimensional gel electrophoresis patternsof skeletal muscle myosin light chain isoforms between Bos taurus, Sus scrofa and selected poultryspecies,” J. Sci. Food Agric., vol. 91, no. 13, pp. 2449–2456, 2011.
• [8] M. Alikord, H. Momtaz, J. Keramat, M. R. Kadivar, and A. Homayouni, “Species identification and animal authentication in meat products : a review,” J. Food Meas. Charact., vol. 12, no. 1, pp. 145–155, 2018.
• [9] J. M. N. Marikkar, M. E. Mirghani, and I. Jaswir, “Application of chromatographic andinfra-red spectroscopic techniques for detection of adulteration in food lipids: a review,” J. Food Chem. Nanotechnol., vol. 2, no. 1, pp. 32–41, 2016.
• [10] J. Mandli, I. EL Fatimi, N. Seddaoui and A. Amine, “Enzyme immuno assay (ELISA/immuno sensor) for a sensitive detection of pork adulteration in meat,” Food Chem., vol. 255, no. January, pp. 380–389, 2018.
• [11] P. K. Singh, G. Jairath, S. S. Ahlawat, A. Pathera and P. Singh, “Biosensor: an emerging safety tool for meat industry,” J. Food Sci. Technol., vol. 53, no. 4, pp. 1759–1765, 2016.
• [12] S. Roy, I. A. Rahman, J. H. Santos, and M. U. Ahmed, “Meat species identification usingDNA-redox electrostatic interactions and non-specific adsorption on graphene biochips,” FoodControl, vol. 61, pp. 70–78, 2016.
• [13] S. Roy, N. F. Mohd-Naim, M. Safavieh and M. U. Ahmed, “Colorimetric nucleic aciddetection on paper microchip using loop mediated isothermal amplification and crystal violetdye,” ACS Sensors, vol. 2, no. 11, pp. 1713–1720, 2017.
• [14] X. Tian, J. Wang, and S. Cui, “Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors,” J. Food Eng., vol. 119, no. 4, pp. 744–749, 2013.
• [15] X. Tian, J. Wang, Z. Ma, M. Li, Z. Wei, and J. M. Díaz-Cruz, “Combination of an E-Nose andan E-Tongue for adulteration detection of minced mutton mixed with pork,” J. Food Qual., vol. 2019, 2019.
• [16] E. Novianty, L. R. Kartikasari, J. H. Lee, and M. Cahyadi, “Identification of pork contamination in meatball using genetic marker mitochondrial DNA cytochrome b gene by duplex PCR,” IOP Conf. Ser. Mater. Sci. Eng., vol. 193, no. 1, 2017.
• [17] Z. Dai, J. Qiao, S. Yang and S. Hu, “Species authentication of common meat based on PCR analysis of the mitochondrial COI gene,” Appl Biochem Biotechnol., no. 461, pp. 1770–1780, 2015.
• [18] A. Doosti and P. G. Dehkordi, “Molecular assay to fraud identification of meat products,” JFood Sci Technol., vol. 51, no. January, pp. 148–152, 2014.
• [19] A. Di Pinto, M. Bottaro, E. Bonerba, G. Bozzo, E. Ceci, and P. Marchetti, “Occurrence ofmislabeling in meat products using DNA-based assay,” J Food Sci Technol., vol. 52, no. April, pp. 2479–2484, 2015.
• [20] R. Köppel, A. Ganeshan, S. Weber, K. Pietsch, C. Graf, R. Hochegger, K. Griffiths, and S. Burkhardt, “Duplex digital PCR for the determination of meat proportions of sausagescontaining meat from chicken, turkey, horse, cow, pig and sheep,” Eur. Food Res. Technol., vol. 245,
• [21] J. Ha, S. Kim, J. Lee, S. Lee and H. Lee, “Identification of pork adulteration in processed meat products using the developedmitochondrial DNA-based primers,” Korean J. Food Sci. Anim. Resour., vol. 37, no. 3, pp. 464–468,2017.
• [22] F. Guan, Y. Jin, J. Zhao, A. Xu and Y. Luo, “A PCR Method That Can Be Further Developedinto PCR-RFLP Assay for Eight Animal Species Identification,” J. Anal. Methods Chem., vol. 2018, 2018.
• [23] B. G. Mane and C. S. K. Hpkv, “PCR-RFLP assay for identification of species origin of meatand meat products 1,” vol. 2, no. 2, pp. 31–36, 2014.
• [24] M. Huang, Y. Horng, H. Huang, Y. Sin and M. Chen, “RAPD fingerprinting for the speciesidentification of animals,” Asian-Aust. J. Anim. Sci., vol. 16, pp. 1406–1410, 2003.
• [25] M. Baker, “Digital PCR hits its stride,” Nat. Methods, vol. 9, no. 6, pp. 541–544, 2012.
• [26] C. Floren, I. Wiedemann, B. Brenig, E. Schütz and J. Beck, “Species identification and quantification in meat and meat products using droplet digital PCR (ddPCR),” Food Chem., vol. 173,pp. 1054–1058, 2015.
• [27] H. R. Shehata, J. Li, S. Chen, H. Redda, S. Cheng, N. Tabujera, H. Li, K. Warriner, and R. Hanner, “Droplet digital polymerase chain reaction (ddPCR) assays integrated withan internal control for quantification of bovine, porcine, chicken and turkey species in food and feed,”
• [28] R. Köppel, F. Zimmerli and A. Breitenmoser, “Heptaplex real-time PCR for the identification and quanti W cation of DNA from beef , pork , chicken , turkey , horse meat , sheep (mutton)and goat,” Eur. Food Res. Technol., pp. 125–133, 2009.
• [29] G. Barcaccia, M. Lucchin and M. Cassandro, “DNA barcoding as a molecular tool to trackdown mislabeling and food piracy,” Diversity, vol. 8, no. 1, 2016.
• [30] K. Nakyinsige, Y. B. C. Man and A. Q. Sazili, “Halal authenticity issues in meat and meatproducts,” Meat Sci., vol. 91, no. 3, pp. 207–214, 2012.
• [31] N. Z. Ballin, F. K. Vogensen and A. H. Karlsson, “Species determination - Can we detect andquantify meat adulteration?,” Meat Sci., vol. 83, no. 2, pp. 165–174, 2009.
• [32] LAST, “No Title,” Genome-Scale Sequence Comparison. (2020, September 22). [Online]. Available:http://last.cbrc.jp/doc/last.html.
• [33] SBPD, “SBPD,” Software Based Primer Design. (2020, October 06) [Online]. Available: https://github.com/ihpar/FnaSrch.