Developing Simple Sequence Repeat (SSR) Markers For Mangos

Abstract

Mangos are an important tropical fruit with an abundance of species resources. SSR molecular markers have been widely used to identify and distinguish the genetic relationship of mango cultivars. The statistical results of SSR loci information in mango simplified genome sequencing show the number of dinucleotide repeat elements to be the largest, which accounts for 40% of the total SSR loci. The repetition times for each nucleotide element were mainly six. The nucleotide types of AT/AT and A/T are 17.8% and 21.2%, respectively, which accounts for 39% of total SSR. The length of SSR loci concentrated in 15bp, with 2,931 loci, which accounts for 20.12%. At the same time, the genetic diversity and phylogenetic relationship of 71 mango cultivars are analyzed. The MISA is used for searching SSR loci, designing and screening SSR primers with good polymorphism, and 20 pairs of primers are chosen following the screening of 200 pairs of primers by gel electrophoresis. The genetic diversity of mango germplasm from two different distribution areas is analyzed, and the genetic similarity coefficients are clustered by unweighted group average method (UPGMA). Genetic distance cluster analysis shows that 71 mango germplasm are divided into seven categories when the genetic similarity coefficient is 0.89, and the similarity coefficient range is 0.55 to 1, polymorphism information content (PIC) values ranging from 0.2334 to 0.7997. In summary, our findings could be used for genetic diversity analysis and the marker-assisted breeding of mango germplasm.

Introduction

The mango, Mangifera indica L., is one of the most important and popular fruits in terms of fresh consumption and preserved fruit consumption globally and is a self-fertilizing diploid species from the Anacardiaceae R. Br. family with 2n = 2x = 20 chromosomes. It is a fruit that is cultivated mostly in tropical and subtropical regions and had 5,510 tons (Mt) of production in 2021 (http://fao.org/faostat/).

Almost every edible mango species belongs to this species (Mangifera indica L.) from the Indian subcontinent, and there are several wild types in China (Xu Biyu 1998). Mango classification is generally based on fruit shape, fruit color, number of embryos, and rachis color (Wang Caifa 2000), but its morphological characteristics are susceptible to environmental influences, so defining a uniform standard is difficult. Therefore, the senior synonym and name sakes phenomenon occurs frequently. To solve this phenomenon from the perspective of molecular biology, molecular markers can be used.

Several types of biochemical and molecular markers: random amplified polymorphic DNA (RAPD) (Diao Yi 2019), Sequence—related amplified polymorphism (SRAP) (Liu Rong 2019), amplified fragment length polymorphism (AFLP) (Wang Yuan 2009), simple sequence repeats (SSR) (Huang Lifang 2011) genotyping with molecular markers is used for cultivar fingerprinting, detection of genetic diversity, assessment of population structure, mapping genes of interest, and for selection of desirable genotypes in breeding programs.

Fingerprinting of plant cultivars is frequently conducted with SSR markers (microsatellites) as they are co-dominant, multi-allelic, and more informative than dominant types of markers. SSR molecular markers have been successfully used in watermelons, apples, pears, kiwi fruit, pineapples, and other fruits, their molecular genetic maps having been constructed successfully. However, the development of SSR markers is both costly and time-consuming, so only an incredibly limited number of SSR markers are publicly available for mangos.

Previously, a quite limited amount of breed varieties have been put into developing SSR markers for mangos (Ren Liang 2004). This work uses 71 breed varieties to develop it from simplified genomic DNA for fingerprinting mango cultivars. This work has several objectives: 1) developing a set of genomic SSR markers; 2) testing marker polymorphism on a diverse set of mango cultivars; and 3) the genetic similarity coefficients are clustered by unweighted group average method (UPGMA), which will lay a foundation for molecular marker-assisted selection and efficient mango breeding.

Results

Genetic Variation in 71 Mango Cultivars

14,564 SSR markers were obtained from 33 mango samples by simplified genome sequencing. Over 200 markers with more than 20bp repeats were randomly chosen (Supplemental Table 3). primer5.0 software was used for designing primers. Following primer synthesis, six mango varieties were chosen at random from the 71 varieties that were used in the experiment, and their primers were screened using agarose gel electrophoresis. 20 primers (Table 2) were found to generate clear polymorphic bands (Fig. 1).

The 20 primer pairs were sifted using nondenatured polyacrylamide gel electrophoresis and then stained with silver nitrate method to reveal the results. All SSR markers exhibited high polymorphism (Fig. 2). The PIC was between 0.2334 ~ 0.7997, and the gene diversity was between 0.2574 ~ 0.8185. The PIC and gene diversity of marker 120 were the highest in this study, while the PIC and gene diversity of marker 50 were the lowest. Cluster analysis divided almost all the varieties into different groups, and the similarity coefficient was between 0.55 ~ 1 (Fig. 3), which indicates high variability and the existence of outbreeding.

Discussion

RAPD, AFLP, and other universal DNA markers exhibited defects in genetic diversity analysis and were gradually replaced by SSR markers due to their codominant highly polymorphic and reproducible nature with crops such as ginger (Vidya 2021), SSR molecular markers have developed quite well and they play a crucial role in molecular breeding and other fields (P.K., Gupta, and Varshney R.K. 2000). Similarly, SSR molecular markers technology is widely used in some bulk fruits, such as apples (Zhang Chunyu 2008) and pears (Han Mingli 2010). Mango genetic research is relatively underdeveloped and published SSR markers are extremely limited (Zhiheng DU 2016). As a result of the rich genetic diversity in domestic mango germplasm resources (Lei Xintao 2009), SSR primer utilization efficiency and the level of polymorphism will be reduced. 20 pairs of SSR primers with rich polymorphism were screened in this study, and the average PIC was 0.5861, a highly polymorphic locus (Kuan-wei 2016). This lays an excellent foundation for the SSR analysis of the genetic diversity of mango germplasm resources, which is helpful for germplasm innovation and the breeding of new varieties. Many reports on mango genetic diversity have been published that include phenotypic data (He Xinhua 2008) and DNA molecular markers (Lei Xintao 2009). All the results have shown there to be rich genetic diversity in mango germplasm resources and certain geographical differentiation (Ravishankar 2015).

According to the SSR amplification results in this experiment, 71 mango germplasm resources displayed rich polymorphism differences, and some of the tested materials had large genetic differences, meaning that they could be roughly divided into four regional groups, which is in accordance with previous results. The germplasm resources used in this study in particular will provide a new understanding of the genetic diversity and selective utilization of mango rootstock groups. As Fig. 2 shows, the similarity coefficient of 78 mango fruits was between 55% and 100%, which indicates rich genetic diversity and provides a favorable prerequisite for the conservation and utilization of fruit resources. However, the similarity coefficient of some materials is close to or equal to 1, which indicates very close relatives, or even completely consistent, which indicates that these materials may be of the same origin, or workers have the obvious phenomenon of different names and homonymous foreign bodies in the introduction process. This is similar to the results of the study by Olawale et al. (2022), which found 48–98% genetic similarity among 15 mango cultivars using 15 SSR markers. It is also similar to the work of Shamili (2012) where 35–100% genetic similarity was reported among 41 mango cultivars using 16 SSR markers. At the same time, Fig. 3 shows that the mango germplasm used in this experiment was collected from two mango planting resource nurseries with different geographical locations, among which a small number of mango varieties that originated from Florida in the United States and Guangxi in China were highly similar in the cluster analysis, including Zillate and Keitt, Lippens and Tommy Atkins, and Guangxi No.4 and Guangxi No.8, which indicates that “Marker 120” may play an important role in the identification of these varieties. At the same time, the similarity of the majority of mango varieties was found to be very low in the cluster analysis of “Marker 120”. This may be because during the long-term introduction and domestication process of various planting resource nurseries, the varieties were affected by environmental influences, hybridization, or grafting. Therefore, integrating and identifying the fruiting resources of mangos are necessary.

Conclusion

The morphological and microsatellite (SSR) results that were obtained for the mango cultivars demonstrated that there was valuable diversity among the mango cultivars studied, potentially due to the outcrossing ability of mangos. This study provides information on interesting traits that are unique to the mango cultivars studied that will suit various different consumer preferences and industrial uses.

Materials And Methods

Plant materials

59 mango cultivars were collected from South Subtropical Crop Research Institute, China Academy of Tropical Agricultural Sciences, Guangzhou, China, and 12 were collected from Mango Germplasm Nursery, Danzhou Campus, Hainan University, Sanya, Hainan, China. (Table 1)

DNA extraction and quantification

Genomic DNA was extracted from young leaves using a DNeasy plant kit (Qiagen, Hilden, Germany), which was used according to the instructions of the manufacturer.

The quality and quantity of DNA were assessed by gel electrophoresis using 1% agarose with known concentrations of undigested lambda DNA (Sigma, St. Louis, MO, USA). The quantification of DNA was performed using a spectrophotometer (Beckman Coulter DU530) at 260 nm. Extracts were diluted with sterile water as a means of obtaining DNA concentrations of nearly 25 ng/µL.

SSR detection

Some mango materials have previously been sequenced using simplified genome sequencing technology. MISA software was used for searching for the tags obtained by all GBS (Genotyping-by-Sequencing), and for finding the SSR fragments in the tags. The total table of SSR statistical results was then obtained. The statistical map of SSR types was obtained by sorting and summarizing the contents of this table (Supplemental Table 2). SSRs were defined as dinucleotide motifs with over 10 to 20 repeats, trinucleotide motifs with over 5 to 15 repeats, and tetraconucleotide, hexanucleotide, and pentanucleotide motifs with over 5 to 10 repeats. For dinucleotide motifs, SSRs with AC/GT motifs, AT/TA motifs, and AG/CT motifs were randomly chosen for PCR primer design using Primer Premier 5.0 (Ren Liang 2004).

SSR marker development

The available primers were chosen from six randomly selected varieties of 71 mango varieties in order to determine the available primers (cultivars Renung No.1, R2E2, HongXiangYa, Kett, Panxi Red, and DaBaiYu). Template DNA was amplified by PCR with primers, and the amplified products were electrophoresed on 1× TAE buffer (90 V) on 2% (w/ V) agarose gel. The results of electrophoresis were observed using UV light to check for any obvious polymorphic bands. Primers with polymorphic bands were screened out, and each pair of primers amplified the template DNA of the 71 varieties. The amplified products were detected using non-denaturing polyacrylamide gel (15%) electrophoresis. Following electrophoresis, the formaldehyde-silver nitrate chromogenic method was used for staining the amplified products, and the visible polymorphic bands were resolved in a naturally-lit environment. Primers with obvious polymorphisms were determined as being the final available primers.

Polymerase chain reaction

20 pairs of SSR primers were screened out in this study. Automatic thermal cycler (model: Peltier Thermal Cycler 200) was used for early screening and later detection. PCR was performed with 20 µL system of SSR reaction in a PCR tube. The reaction volume was 25 ng of template DNA, 3 mM of DNApreMix, and 0.5 µm of forward and reverse primers. PCR operation conditions were as follows: 94°C for 2 minutes, followed by 30 cycles of 94°C for 30 seconds, 55°C for 45 seconds, 72°C for 45 seconds, and a final extension step of 72°C for 7 minutes.

Abbreviations

Declarations

Author Contributions

All authors contributed to the study conception and design. Hongliang wrote the main manuscript text and prepared all figures. Liufeng, Zhan Rulin, Hu jiangfang put forward valuable suggestions during the experiment, and all authors commented on previous versions of the manuscript. All authors reviewed the manuscript. 

Funding Source Declaration

This work was supported by the National Natural Science Foundation of China (Grant numbers: 31972360) and Yunnan Innovation Guidance and Technological Enterprise Cultivation Plan Project (Grant numbers:202104BI090012) 

Conflict of interest

All authors disclosed no relevant relationships.

References

1. Arogundade, Olawale, Joshua Olumide Matthew, Omolara Ifeoluwa Akinyoola, Pamela Eloho Akin-Idowu, and Sunday Oluseyi Solomom Akinyemi. "Phenotypic and Molecular Characterization of Mango Cultivars in Southwest Nigeria." International Journal of Fruit Science 22, no. 1 (2022/12/31 2022): 151-59. https://doi.org/10.1080/15538362.2021.2019652. https://doi.org/10.1080/15538362.2021.2019652.
2. DU, Zhiheng, Chunzhu XU, and Fangyong NING. "Isolation and Characterization of 15 Microsatellite DNA Loci for the Alpine Stream Frog Scutiger Boulengeri(Anura:Megophryidae)." Asian Herpetological Research 7, no. 04 (2016): 298-300. https://doi.org/10.16373/j.cnki.ahr.150073.
3. Kuan-wei, Chen, Li Hui-fang, Yu Ya-bo, Tang Qing-ping, Gu Rang, Zhu Wen-qi, Chinese Academy of Agriculture Science, et al. "Evaluation of Genetic Variability and Genetic Distance between Twelve Chinese Indigenous Chicken Breeds." Paper presented at the the tenth National Symposium on Genetic Markers of Livestock and Poultry, Beijing China, 2006.
4. P.K., Gupta, and Varshney R.K. "The Development and Use of Microsatellite Markers for Genetic Analysis and Plant Breeding with Emphasis on Bread Wheat." [In eng]. Euphytica 113, no. 3 (2000): 163-85. https://doi.org/10.1023/a:1003910819967. https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsRU5HTmV3UzIwMjMwMTAzEhFxazNlXzAwMDAyMjg2MDUyMRoIamFzbHA0NHU%3D.
5. Ravishankar, K. V., M. R. Dinesh, P. Nischita, and B. S. Sandya. "Development and Characterization of Microsatellite Markers in Mango (Mangifera Indica) Using Next-Generation Sequencing Technology and Their Transferability across Species." [In eng]. Molecular Breeding 35, no. 3 (2015). https://doi.org/10.1007/s11032-015-0289-2. https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsRU5HTmV3UzIwMjMwMTAzEiA1NjlmMDk2ZjE3NzQ3MjczYzkyNTA0Y2RjODA5ODQxYhoIZXR4eWQ4dGw%3D.
6. Shamili, M., R. Fatahi, and J. I. Hormaza. "Characterization and Evaluation of Genetic Diversity of Iranian Mango (Mangifera Indica L., Anacardiaceae) Genotypes Using Microsatellites." Scientia Horticulturae 148 (Dec 2012): 230-34. https://doi.org/10.1016/j.scienta.2012.09.031. <Go to ISI>://WOS:000315232000032.
7. Vidya, V., D. Prasath, M. Snigdha, R. Gobu, C. Sona, and C. S. Maiti. "Development of Est-Ssr Markers Based on Transcriptome and Its Validation in Ginger (Zingiber Officinale Rosc.)." [In eng]. PLoS One 16, no. 10 (2021): e0259146. https://doi.org/10.1371/journal.pone.0259146.
8. CAI Haibin, Han Guangyu, Tu Min, Wang Zhi, FU Yongwei, Guan Xin, WANG Yunyue, and Lu Baorong. "Genetic diversity analysis of weedy rice population in Jiangsu Province based on SSR fluorescent markers." Molecular Plant Breeding 20, no. 21 (2022): 7104-15. https://doi.org/10.13271/j.mpb.020.007104.
9. Diao Yi. "RAPD analysis of genetic diversity of mango germplasm in panzhihua area." Jiangsu Agricultural Sciences 47, no. 13 (2019): 35-38. https://doi.org/10.15889/j.issn.1002-1302.2019.13.009. https://kns.cnki.net/kcms/detail/32.1214.S.20190728.2350.007.html.
10. Fan Jian-Guang, ZHANG Hai-ying, GONG Guo-Yi, Xiao Jing, GUO Shao-gui, REN Yi, ZHANG Jie, et al. "Construction and application of SSR fingerprint database of the example varieties in watermelon DUS testing." Journal of Plant Genetic Resources 14, no. 05 (2013): 892-99. https://doi.org/10.13430/j.cnki.jpgr.2013.05.035. https://kns.cnki.net/kcms/detail/11.4996.S.20130809.1121.004.html.
11. Han Ming-li, LIU Yong-li, ZHENG Xiao-Yan, Yang Jian, Wang Long, WANG Suke, LI Xiu-gen, and TENG Yuan-wen. "Construction of a genetic linkage map and QTLanalysis for some fruit traits in pear." Journal of Fruit Science 27, no. 04 (2010): 496-503. https://doi.org/10.13925/j.cnki.gsxb.2010.04.005.
12. He Xinhua, Guo Yongze, Luo Cong, and Tang Zhipeng. "Relative analysis between ISSR molecular markers and fruit physiological diseases of mango." Southeast Horticulture, no. 03 (2008): 5-8.
13. Lei Xintao, YAO Quansheng, Xu Xuerong, and Wang Jiabao. "The wild mango germplasm resources in China and their AFLP molecular markers." Chinese Journal of Tropical Crops 30, no. 10 (2009): 1408-12.
14. Liu Rong, Gong Deyong, Liu Qingguo, Huang Hai, and Fan Jianxin. "Genetic diversity analysis of thirteen mango germplasm resources based on SRAP molecular markers." Chinese Journal of Tropical Crops 40, no. 01 (2019): 87-91.
15. Tang Jia-le, HUANG Chun-hui, Wu Han, Lang Bin-bin, QU Xue-Yan, and Xu Xiao-Biao. "Genetic diversity of wild Actinidia eriantha germplasm based on fruit traits and SSR markers." Acta Horticulturae Sinica 41, no. 06 (2014): 1198-206. https://doi.org/10.16420/j.issn.0513-353x.2014.06.009. https://kns.cnki.net/kcms/detail/11.1924.s.20140627.0945.003.html.
16. Tong Helin, Feng Suping, HE Junhu, Wang Jingyi, Chen Yeyuan, Sun Guangming, Yu Fei, and Wu Yao-ting. "Establishment of fingerprinting for pineapple(Ananas somosus) by SSR marker." Journal of Fruit Science 28, no. 02 (2011): 240-45. https://doi.org/10.13925/j.cnki.gsxb.2011.02.014.
17. Wang Caifa. "Overview of world mango industry development." Agricultural Research and Application, no. 03 (1995): 34-36.
18. Wang Yuan, Jin Zhiqiang, Chen Yeyuan, and Lei Xintao. "Construction of AFLP technological system in mangifera indica L." Journal of Anhui Agricultural Sciences 37, no. 33 (2009): 16305-07. https://doi.org/10.13989/j.cnki.0517-6611.2009.33.114.
19. Xu Biyu, Jin Zhiqiang, Peng Shiqing, Xu Shupei, and Zheng Xueqin. "Genome analysis of main mango cultivars in Hainan." Chinese Journal of Tropical Crops, no. 03 (1998): 33-37.
20. Zhang Chunyu. "Population genetic structure and method of constructing core collection for Malus sieversii."doctor, Shandong agricultural university, 2008. Cnki.

Tables

Table 2: Characteristics of polymorphic SSR markers used in the molecular analysis of 71 mango cultivars