IDENTIFICATION OF LENS CULTIVARS IN MARKET BY MOLECULAR TOOLS: DNA BARCODING AND SSRs

Abstract

Substitution of plant cultivars of high commercial value with a cheaper, lower quality one is a common fraud committed against consumers and producers. Since it is one of the most widely grown legumes, lentil (Lens culinaris Medik.) is suitable for such frauds. This study aimed to identify lentil cultivars which are registered and authorized in the market in Türkiye by using current molecular methods. For this purpose, 26 lentil cultivars were analyzed for 15 SSR markers and two DNA barcode regions (trnH-psbA and matK). A high allele diversity was observed by 12 scorable SSR markers, and the average number of alleles was determined to be 16. One of the important findings was the presence of “cultivar-specific alleles” that can be used to identify each cultivar in the lentil market in Türkiye. At least one “cultivar-specific allele” was obtained for each cultivar. The lentil cultivars were also analyzed by two DNA barcode regions as trnH-psbA and matK. While it was observed that the rate of the intra-species variation for the trnH-psbA region was low and 26 varieties were divided into 7 groups, higher rate was found for matK and samples were distributed into 14 groups. Nevertheless, it was observed that intra-species discrimination can be made more effective when both loci are used together and 26 species were distributed into 18 different groups. We expect that the results of this study, especially the cultivar-specific SSR alleles and DNA barcoding sequence data may be used routinely to identify production and packaged products that are commercially available in markets.

Keywords

• food traceability
• DNA barcoding
• SSR
• Lens culinaris

Öz

Ticari değeri yüksek bitki çeşitlerinin daha ucuz ve düşük kaliteli olanlarla değiştirilmesi, tüketicilere ve üreticilere karşı yaygın bir hiledir. Mercimek (Lens culinaris Medik.) en yaygın yetiştirilen baklagillerden biri olduğu için bu tür hileler için uygun bir üründür. Bu çalışmada, güncel moleküler yöntemler kullanılarak Türkiye'de tescilli ve piyasada izinli mercimek çeşitlerinin tanımlanması amaçlanmıştır. Bu amaçla, 26 mercimek çeşidi 15 SSR markırı ve 2 DNA barkod lokusu (trnH-psbA ve matK) ile analiz edilmiştir. Değerlendirilen 12 SSR markırı ile yüksek bir allel çeşitliliği gözlenmiş ve ortalama allel sayısı 16 olarak belirlenmiştir. Türkiye'deki mercimek pazarında her bir çeşidi tanımlamak için kullanılabilecek "çeşide özgü allellerin" varlığı önemli bulgulardan biridir. Her bir çeşit için en az bir "çeşide özgü allel" elde edilmiştir. Mercimek çeşitleri ayrıca trnH-psbA ve matK olmak üzere iki DNA barkod bölgesi açısından da analiz edilmiştir. trnH-psbA bölgesi için tür içi varyasyon oranının düşük olduğu ve 26 çeşidin sadece 7 gruba ayrıldığı gözlenirken, matK için bu oran daha yüksek bulunmuş ve örnekler 14 grupta dağılım göstermiştir. Bununla birlikte, her iki lokus birlikte kullanıldığında tür içi ayrımın daha etkili hale getirilebileceği görülmüş ve 26 çeşit 18 farklı gruba dağılmıştır. Bu çalışmanın sonuçlarının, özellikle de çeşitlere özgü SSR allelleri ve DNA barkod dizisi verilerinin, piyasada ticari olarak bulunan üretim ve ambalajlı ürünlerin tanımlanmasında rutin olarak kullanılabileceğini düşünüyoruz.

References

1. 1. Andeden, E.E., Baloch, F.S., Çakır, E., Toklu, F. & Özkan, H. 2015. Development, characterization and mapping of microsatellite markers for lentil (Lens culinaris Medik.). Plant Breeding, 134(5): 589-598. https://doi.org/10.1111/pbr.12296
2. 2. Beser, N. & Mutafcilar, Z.C. 2020. Identification of SSR markers for differentiating rice (Oryza sativa L.) varieties marketed in Turkey. Journal of Agricultural Sciences, 26(3): 357-362. https://doi.org/10.15832/ankutbd.518276
3. 3. BOLD Systems: Taxonomy Browser: Magnoliophyta https://www.boldsystems.org/index.php/Public_SearchTerms?query=%22Lens%20culinaris%22[tax] (Date accessed: 11.10.2023)
4. 4. Bosmali, I., Ganopoulos, I., Madesis, P. & Tsaftaris, A. 2012. Microsatellite and DNA-barcode regions typing combined with High Resolution Melting (HRM) analysis for food forensic uses: A case study on lentils (Lens culinaris). Food Research International, 46(1): 141-147. https://doi.org/10.1016/j.foodres.2011.12.013
5. 5. Böhme, K., Calo-Mata, P., Barros-Velázquez, J. & Ortea, I. 2019. Review of recent DNA-based methods for main food-authentication topics. Journal of Agricultural and Food Chemistry, 67(14): 3854-3864. https://doi.org/10.1021/acs.jafc.8b07016.
6. 6. Bruno, A., Sandionigi, A., Agostinetto, G., Bernabovi, L., Frigerio, J., Casiraghi, M. & Labra, M. 2019. Food tracking perspective: DNA metabarcoding to identify plant composition in complex and processed food products. Genes, 10(3): 248. https://doi.org/10.3390/genes10030248
7. 7. Chase, M.W., Cowan, R.S., Hollingsworth, P.M., van den Berg, C., Madriñán, S., Petersen, G., Seberg, O., Jørgsensen, T., Cameron, K.M., Carine, M., Pedersen, N., Hedderson, T.A.J., Conrad, F., Salazar, G.A., Richardson, J.E., Hollingsworth, M.L., Barraclough, T.G., Kelly, L. & Wilkinson, M. 2007. A proposal for a standardised protocol to barcode all land plants. TAXON, 56(2): 295-299. https://doi.org/10.1002/tax.562004.
8. 8. Chedid, E., Rizou, M. & Kalaitzis, P. 2020. Application of high resolution melting combined with DNA-based markers for quantitative analysis of olive oil authenticity and adulteration. Food chemistry: X, 6: 100082. https://doi.org/10.1016/j.fochx.2020.100082

9. 9. Chung, C.T., Niemela, S.L. & Miller, R.H. 1989. One-step preparation of competent Escherichia coli: transformation and storage of bacterial cells in the same solution. Proceedings of the National Academy of Sciences, 86(7): 2172-2175. https://doi.org/10.1073/pnas.86.7.2172
10. 10. Dawan, J. & Ahn, J. 2022. Application of DNA barcoding for ensuring food safety and quality. Food Science and Biotechnology, 31(11): 1355-1364. https://doi.org/10.1007/s10068-022-01143-7
11. 11. Dhivya, S., Ashutosh, S., Gowtham, I., Baskar, V., Harini, A.B., Mukunthakumar, S. & Sathishkumar, R. 2020. Molecular identification and evolutionary relationships between the subspecies of Musa by DNA barcodes. BMC genomics, 21: 1-11. https://doi.org/10.1186/s12864-020-07036-5
12. 12. di Rienzo, V., Fanelli, V., Miazzi, M.M., Savino, V., Pasqualone, A., Summo, C., Giannini, P., Sabetta, W. & Montemurro, C. 2017. A reliable analytical procedure to discover table grape DNA adulteration in industrial wines and musts. Acta Hortic, 1188: 365-370 https://doi.org/10.17660/ActaHortic.2017.1188.49
13. 13. Fanelli, V., Mascio, I., Miazzi, M. M., Savoia, M. A., De Giovanni, C. & Montemurro, C. 2021. Molecular approaches to agri-food traceability and authentication: An updated review. Foods, 10(07): 1644. https://doi.org/10.3390/foods10071644
14. 14. Fazekas, A.J., Burgess, K.S., Kesanakurti, P.R., Graham, S.W., Newmaster, S.G., Husband, B.C., Percy, D.M., Hajibabaei, M. & Barrett, S.C.H. 2008. Multiple multilocus DNA barcodes from the plastid genome discriminate plant species equally well. PLoS ONE, 3(7): p.e2802. https://doi.org/10.1371/journal.pone.0002802.
15. 15. Feng, S., Jiao, K., Zhu, Y., Wang, H., Jiang, M. & Wang, H. 2018. Molecular identification of species of Physalis (Solanaceae) using a candidate DNA barcode: the chloroplast psbA–trnH intergenic region. Genome, 61(1): 15-20. https://doi.org/10.1139/gen-2017-0115
16. 16. Ford, R., Rubeena, Redden, R.J., Materne, M., Taylor, P.W.J. 2007. Lentil. pp. 91-108 In: Kole, C. (eds) Pulses, Sugar and Tuber Crops. Genome Mapping and Molecular Breeding in Plants, Vol. 3. Springer, Berlin, Heidelberg. xxiv + 306 pp. https://doi.org/10.1007/978-3-540-34516-9_5
17. 17. Ganopoulos, I., Argiriou, A. & Tsaftaris, A. 2011. Adulterations in Basmati rice detected quantitatively by combined use of microsatellite and fragrance typing with High Resolution Melting (HRM) analysis. Food Chemistry, 129(2): 652-659. https://doi.org/10.1016/j.foodchem.2011.04.109
18. 18. Genievskaya, Y., Abugalieva, S., Zhubanysheva, A., & Turuspekov, Y. (2017). Morphological description and DNA barcoding study of sand rice (Agriophyllum squarrosum, Chenopodiaceae) collected in Kazakhstan. BMC Plant Biology, 17(1): 1-8. https://doi.org/10.1186/s12870-017-1132-1
19. 19. Gismondi, A., Fanali, F., Labarga, J.M.M., Caiola, M.G. & Canini, A. 2013. Crocus sativus L. genomics and different DNA barcode applications. Plant Systematics and Evolution, 299: 1859-1863. http://doi.org/10.1007/s00606-013-0841-7
20. 20. Gomes, S., Breia, R., Carvalho, T., Carnide, V. & Martins‐Lopes, P. 2018. Microsatellite High‐Resolution Melting (SSR‐HRM) to Track Olive Genotypes: From Field to Olive Oil. Journal of food science, 83(10): 2415-2423. https://doi.org/10.1111/1750-3841.14333
21. 21. Hamilton, M.B. 1999. Four primer pairs for the amplification of chloroplast intergenic regions with intraspecific variation. Molecular ecology, 8(3): 521-523.
22. 22. Hebert, P.D.N., Cywinska, A., Ball, S.L. & de Waard, J.R. 2003. Biological identifications through DNA barcodes. Proceedings Biological sciences, 270(1512): 313-321. https://doi.org/10.1098/rspb.2002.2218.
23. 23. Hilu, K.W. & Liang, G. 1997. The matK gene: sequence variation and application in plant systematics. American Journal of Botany, 84(6): 830-839. https://doi.org/10.2307/2445819.
24. 24. Hollingsworth, P.M., Forrest, L.L., Spouge, J.L., Hajibabaei, M., Ratnasingham, S., van der Bank, M., Chase, M.W., Cowan, R.S., Erickson, D.L., Fazekas, A.J., Graham, S.W., James, K.E., Kim, K.-J., Kress, W.J., Schneider, H., van AlphenStahl, J., Barrett, S.C.H., van den Berg, C., Bogarin, D. & Burgess, K.S. 2009. A DNA barcode for land plants. Proceedings of the National Academy of Sciences, 106(31): 12794-12797. https://doi.org/10.1073/pnas.0905845106.
25. 25. Hollingsworth, P.M., Graham, S.W. & Little, D.P. 2011. choosing and using a plant DNA barcode. PLoS ONE, 6(5): p.e19254. https://doi.org/10.1371/journal.pone.0019254.
26. 26. Intharuksa, A., Sasaki, Y., Ando, H., Charoensup, W., Suksathan, R., Kertsawang, K., Sirisa-Ard, P. & Mikage, M. 2020. The combination of ITS2 and psbA-trnH region is powerful DNA barcode markers for authentication of medicinal Terminalia plants from Thailand. Journal of natural medicines, 74: 282-293. https://doi.org/10.1007/s11418-019-01365-w
27. 27. Kalia, R.K., Rai, M.K., Kalia, S., Singh, R. & Dhawan, A.K. 2010. Microsatellite markers: an overview of the recent progress in plants. Euphytica, [online] 177(3): 309-334. https://doi.org/10.1007/s10681-010-0286-9.
28. 28. Khilare, V., Tiknaik, A., Prakash, B., Ughade, B., Korhale, G., Nalage, D., Ahmed, N., Khedkar, C. & Khedkar, G. 2019. Multiple tests on saffron find new adulterant materials and reveal that Ist grade saffron is rare in the market. Food chemistry, 272: 635-642. https://doi.org/10.1016/j.foodchem.2018.08.089
29. 29. Kress, W.J. & Erickson, D.L. 2007. A two-locus global DNA barcode for land plants: the coding rbcL gene complements the non-coding trnH-psbA spacer region. PLoS ONE, 2(6): p.e508. https://doi.org/10.1371/journal.pone.0000508.
30. 30. Kress, W.J., Wurdack, K.J., Zimmer, E.A., Weigt, L.A. & Janzen, D.H. 2005. Use of DNA barcodes to identify flowering plants. Proceedings of the National Academy of Sciences of the United States of America, [online] 102(23): 8369-8374. https://doi.org/10.1073/pnas.0503123102.
31. 31. Kumar, S., Kahlon, T. & Chaudhary, S. 2011. A rapid screening for adulterants in olive oil using DNA barcodes. Food Chemistry, 127(3): 1335-1341. https://doi.org/10.1016/j.foodchem.2011.01.094
32. 32. Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. 2018. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Molecular Biology and Evolution, 35(6): 1547–1549. https://doi.org/10.1093/molbev/msy096.
33. 33. Mahadani, P. & Ghosh, S.K. 2014. Utility of indels for species-level identification of a biologically complex plant group: a study with intergenic spacer in Citrus. Molecular biology reports, 41: 7217-7222. https://doi.org/10.1007/s11033-014-3606-7
34. 34. Mower, J.P., Touzet, P., Gummow, J.S., Delph, L.F. & Palmer, J.D. 2007. Extensive variation in synonymous substitution rates in mitochondrial genes of seed plants. BMC Evolutionary Biology, 7(1): 135. https://doi.org/10.1186/1471-2148-7-135.
35. 35. Parvathy, V.A., Swetha, V.P., Sheeja, T.E., Leela, N.K., Chempakam, B. & Sasikumar, B. 2014. DNA barcoding to detect chilli adulteration in traded black pepper powder. Food Biotechnology, 28(1): 25-40. https://doi.org/10.1080/08905436.2013.870078
36. 36. Parveen, I., Techen, N. & Khan, I.A. 2019. Identification of species in the aromatic spice family Apiaceae using DNA mini-barcodes. Planta medica, 85(02): 139-144. https://doi.org/10.1055/a-0664-0947
37. 37. Peakall, R. & Smouse, P.E. 2006. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes, 6(1): 288-295. https://doi.org/10.1111/j.1471-8286.2005.01155.x
38. 38. Peakall, R. & Smouse, P.E. 2012. GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research. Bioinformatics, 28(19): 2537-2539. https://doi.org/10.1093/bioinformatics/bts460
39. 39. Perrier X. & Jacquemoud-Collet, J.P. 2006. DARwin - Dissimilarity analysis and representation for windows. In: darwin.cirad.fr. http://darwin.cirad.fr/. (Date accessed: 07.12.2020)
40. 40. Pinczinger, D., von Reth, M., Hanke, M.V. & Flachowsky, H. 2020. SSR fingerprinting of raspberry cultivars traded in Germany clearly showed that certainty about the genotype authenticity is a prerequisite for any horticultural experiment. European Journal of Horticultural Science, 85(2): 79-85.
41. 41. Ratnasingham, S., & Hebert, P. D. (2007). BOLD: The Barcode of Life Data System (http://www.barcodinglife.org). Molecular ecology notes, 7(3), 355-364. http://doi.org/10.1111/j.1471-8286.2007.01678.x
42. 42. Robson, K., Dean, M., Haughey, S. & Elliott, C. 2021. A comprehensive review of food fraud terminologies and food fraud mitigation guides. Food Control, 120: 107516. https://doi.org/10.1016/j.foodcont.2020.107516
43. 43. Rozas, J., Ferrer-Mata, A., Sánchez-DelBarrio, J.C., Guirao-Rico, S., Librado, P., Ramos-Onsins, S.E. & Sánchez-Gracia, A. 2017. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Molecular Biology and Evolution, 34(12): 3299-3302. https://doi.org/10.1093/molbev/msx248
44. 44. Sang, T., Crawford, D.J. & Stuessy, T.F. 1997. Chloroplast DNA phylogeny, reticulate evolution and biogeography of Paeonia (Paeoniaceae). American Journal of Botany, 84(8): 1120-1136. https://doi.org/10.2307/2446155
45. 45. Shaw, J., Lickey, E.B., Beck, J.T., Farmer, S.B., Liu, W., Miller, J., Siripun, K.C., Winder, C.T., Schilling, E.E. & Small, R.L. 2005. The tortoise and the hare II: relative utility of 21 noncoding chloroplast DNA sequences for phylogenetic analysis. American Journal of Botany, 92(1): 142-166. https://doi.org/10.3732/ajb.92.1.142
46. 46. Shaw, J., Lickey, E.B., Schilling, E.E. & Small, R.L. 2007. Comparison of whole chloroplast genome sequences to choose noncoding regions for phylogenetic studies in angiosperms: the tortoise and the hare III. American Journal of Botany, 94(3): 275-288. https://doi.org/10.3732/ajb.94.3.275
47. 47. Silletti, S., Morello, L., Gavazzi, F., Gianì, S., Braglia, L. & Breviario, D. 2019. Untargeted DNA-based methods for the authentication of wheat species and related cereals in food products. Food chemistry, 271: 410-418. https://doi.org/10.1016/j.foodchem.2018.07.178
48. 48. Sneath, P.H.A. & Sokal, R.R. 1973. Numerical taxonomy: the principles and practice of numerical classification. WF Freeman & Co., San Francisco, 573p.
49. 49. Swetha, V.P., Parvathy, V.A., Sheeja, T.E. & Sasikumar, B. 2014. DNA barcoding for discriminating the economically important Cinnamomum verum from its adulterants. Food Biotechnology, 28(3): 183-194. https://doi.org/10.1080/08905436.2014.931239
50. 50. Tate, J. & Simpson, B. 2003. Paraphyly of tarasa (malvaceae) and diverse origins of the polyploid species. Systematic Botany 28: 723-737.
51. 51. Thongkhao, K., Tungphatthong, C., Phadungcharoen, T. & Sukrong, S. 2020. The use of plant DNA barcoding coupled with HRM analysis to differentiate edible vegetables from poisonous plants for food safety. Food Control, 109: 106896. https://doi.org/10.1016/j.foodcont.2019.106896
52. 52. Thompson, J.D., Higgins, D.G. & Gibson, T.J. 1994. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research, 22(22): 4673-4680. https://doi.org/10.1093/nar/22.22.4673
53. 53. Uncu, A.T., Uncu, A.O., Frary, A. & Doganlar, S. 2017. Barcode DNA length polymorphisms vs fatty acid profiling for adulteration detection in olive oil. Food chemistry, 221: 1026-1033. https://doi.org/10.1016/j.foodchem.2016.11.059
54. 54. Vassou, S. L., Kusuma, G. & Parani, M. 2015. DNA barcoding for species identification from dried and powdered plant parts: a case study with authentication of the raw drug market samples of Sida cordifolia. Gene, 559(1): 86-93. https://doi.org/10.1016/j.gene.2015.01.025
55. 55. Verdone, M., Rao, R., Coppola, M. & Corrado, G. 2018. Identification of zucchini varieties in commercial food products by DNA typing. Food Control, 84: 197-204. https://doi.org/10.1016/j.foodcont.2017.07.039
56. 56. Yu, J., Xue, J.-H. & Zhou, S.-L. 2011. New universal matK primers for DNA barcoding angiosperms. Journal of Systematics and Evolution, 49(3): 176-181. https://doi.org/10.1111/j.1759-6831.2011.00134.x
57. 57. Zambianchi, S., Soffritti, G., Stagnati, L., Patrone, V., Morelli, L., Vercesi, A. & Busconi, M. 2021. Applicability of DNA traceability along the entire wine production chain in the real case of a large Italian cooperative winery. Food Control, 124: 107929. https://doi.org/10.1016/j.foodcont.2021.107929
58. 58. Zhang, Y., Mo, M., Yang, L., Mi, F., Cao, Y., Liu, C., Tang, X., Wang, P. & Xu, J. 2021. Exploring the species diversity of edible mushrooms in Yunnan, Southwestern China, by DNA barcoding. Journal of Fungi, 7(4): 310. https://doi.org/10.3390/jof7040310