Genetic diversity of a native population of Myrcia ovata (Myrtaceae) using ISSR molecular markers
Received: May 07, 2018
Accepted: July 06, 2018
Published: July 27, 2018
Genet.Mol.Res. 17(3): http://dx.doi.org/gmr18022
Myrcia ovata Cambess. (Myrtaceae) is a medicinal and aromatic plant that has analgesic, bactericidal and fungicidal properties. Even though this plant has economic potential, nothing is known about the variability and genetic diversity of this species. This information is necessary to establish conservation strategies and allow prospection of natural resources. The aim of this study was to evaluate the genetic diversity of M. ovata individuals of a native population in the municipality of Japaratuba, Sergipe State, Brazil, using Inter-Simple Sequence Repeat molecular markers (ISSR). Nine primers were tested, resulting in 99 polymorphic bands. The 24 individuals evaluated were clustered in two groups by the software Structure. The Jaccard similarity ranged from 0.21 (MYRO-034 and MYRO-159) to 0.82 (MYRO-178.1 and MYRO-178.2), with an average of 0.38. The genetic diversity of M. ovata was considered of medium level. The individuals MYRO-154, MYRO-175 and MYRO- 175.1 presented the most variability.
Myrtaceae; Medicinal plant; Genetic variability
The Myrcia genus is composed of more than 300 species and belongs to the subtribe Myrciinae of the Myrtaceae family. Myrcia ovata is an aromatic and medicinal species originally from the South American tropics. In Brazil, it is popularly known as laranjinha-do-mato (small forest orange) and is used in traditional medicine against diseases such as gastritis and diarrhea (Limberger et al., 2004; Lucas et al., 2007, Cândido et al, 2010). Myrcia ovata has been documented in the Brazilian states of Alagoas, Amazonas, Bahia, Ceará, Espírito Santo, Mato Grosso, Pará, Pernambuco, Rio de Janeiro, São Paulo (SpeciesLink, 2018) and Minas Gerais (Torres, 2012). In Sergipe, plants of this species were found in an area exposed to human disturbance and fire, therefore, at risk of becoming locally extinct.
Works involving M. ovata are quite recent, mainly after 2010, when it was proven that its essential oil has antibacterial activity, thus generating economic interest (Cândido et al., 2010). Since then, new studies discovered that it also has analgesic (Santos et al., 2014) and fungicidal properties (Sampaio et al., 2016).
Several species of the Brazilian flora have already been studied with the aim of obtaining bioactive molecules potentially useful to man. Unfortunately, many other species became extinct before their capabilities were evaluated. The anthropic destruction of forests and natural ecosystems, the habitat of numerous medicinal species, justifies the need to conduct research that will aid in the conservation of these resources currently in risk of genetic erosion. The establishment of conservation and management strategies to maximize the genetic variability within species is only possible through the measurement of the genetic variability in the populations (Lima et al., 2015a).
A study of the chemical diversity in M. ovata essential oil was already done in plants from the Brazilian state of Sergipe (Sampaio et al., 2016). However, this is the first study that characterizes the genetic diversity of the species. This characterization identifies the degree of polymorphism between individuals and populations, regardless of the phenotypic variation and the stage of development (Grattaplagia and Ferreira, 1998).
The analysis of genetic variation of individuals can be obtained by molecular characterization using molecular markers. Developed in the 90s, the molecular marker Inter Simple Sequence Repeats (ISSR) is a dominant marker (binary) that performs an amplification of the DNA chain by PCR, without a need of prior knowledge of the gene sequence, generating polymorphic standards (Zietkiewicz et al., 1994). The ISSR marker has already been used in studies of variability and genetic diversity in species of plants of the Myrtaceae family, such as Eucalyptus spp. (Ballesta et al., 2015), Psidium spp. (Oliveira et al., 2014), Eugenia spp. (Brunchault et al., 2014) and Myrcia spp. (Brandão et al., 2015; Alves et al., 2016).
Since assessment of genetic variability can be used for conservation and use in genetic resources programs, the aim of this study was to determine the genetic diversity of M. ovata found in the municipality of Japaratuba-SE, using ISSR molecular marker.
Material and Methods
For the extraction of DNA, fresh leaves of 24 individuals of M. ovata were collected in silica from the municipality of Japaratuba, in the State of Sergipe, Brazil on 09/15/2016 (Figure 1 and Table 1). This region has rain forest and dune vegetation. The average of annual rainfall is 1400 mm (Sergipe, Semarh/SRH, 2014) and the rainy season is from March to August. The average annual temperature is 25.3°C and the climate is dry sub-humid mesothermal type (Sergipe, Seplantec/Supes, 2000).
Table 1: Identification of 24 Myrcia ovata individuals from a native population located in the municipally of Japaratuba, in the state of Sergipe, Brazil.
DNA extraction and ISSR amplification
DNA extraction was carried out as described by Nienhuis et al. (1995), with modifications. For the PCR-ISSR reaction, nine primers from Invitrogen were used (Thermo Fisher Scientific, Carlsbad, CA, USA) (Table 2).
The amplifications were carried out in a PTC-100 Thermocycler (MJ Research Inc., Quebec, Canada) programmed under the following protocol: initial denaturation for 5 min at 94°C; 35 cycles each comprising denaturation for 40 s at 94°C, 30 s for each primer annealing temperature (Table 2) and extension for 60 s at 72°C; and, a final extension for 7 min at 72°C.
|Name||Sequence (5’-3’)||Length (bp)||Annealing temp.||Total bands||Polymorphic bands||Polymorphism (%)|
|UBC807||(AG)8-T||1500 - 700||43°C||10||10||100%|
|UBC808||(AG)8-C||2000 - 400||47°C||12||12||100%|
|UBC810||(GA)8-T||1500 - 400||45.4°C||12||11||92%|
|UBC811||(GA)8-C||2000 - 500||45°C||9||9||100%|
|UBC813||(CT)8-T||2000 - 500||47°C||10||10||100%|
|UBC825||(AC)8-T||2000 - 500||47°C||13||13||100%|
|UBC827||(AC)8-G||2000 - 500||47°C||13||12||92%|
|UBC834||(AG)8-YT||2000 - 700||46°C||9||8||89%|
R = purine (A or G) e Y = pyrimidine (C or T)
Table 2: Primer information and amplified products from the genetic diversity analysis of Myrcia ovata individuals in a native population located in the municipality of Japaratuba, in the state of Sergipe, Brazil.
The fragments were subjected to electrophoresis on a 1.5% agarose gel (1X TBE: 89 mM Tris, 89 mM boric acid, 2.5 mM EDTA, pH 8.3) in a horizontal electrophoresis system (Loccus Biotecnologia LCH 20 x 25) at 120 V for 2 h. Each sample was stained with 2μL of GelRed® dye (Biotium) and the amplification products were visualized under UV light.
From the analysis and interpretation of the agarose gel, a binary matrix was constructed based on the presence and absence of the fragments, represented by “1” and “0” respectively. The optimal number of fragments was estimated by the GENES software (Cruz, 2001), in order to obtain the correlation and stress value. The average values of Polymorphic Information Content (PIC) (Botstein et al., 1980) and Hardy-Weinberg Expected Heterozygosity (HE) (Nei, 1973) for dominant molecular markers, and the Jaccard similarity (Sneath and Sokal, 1973) and the Bootstrap analysis for 100 simulations analysis were also performed using GENES software. The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram was constructed by the NTSYS-pc 2.0 software (Rohlf, 2001). The percentage of polymorphic loci, the number of different alleles (Na), the number of effective alleles (Ne) and the Shannon Index were calculated using the GeneAlEx 6.5 version (Peakall and Smouse, 2012).
Another cluster analysis, using Bayesian method, was performed in the STRUCTURE software, version 2.3.4 (Pritchard et al., 2012). The admixture model was used with correlated allele frequencies, and simulations were carried out with a burn-in period and a MCMC number of 104 each. The choice for the best fit clustering number (K) was evaluated using ΔK, from the Evanno et al. (2005) method, in the on-line STRUCTURE HAVERSTER software (Earl and vonHoldt, 2012).
The nine ISSR primers that were used generated 99 polymorphic bands. The fragments number varied from 9 to 13 per primer, with an average of 11.3 bands per primer (Figure 2).
The optimization analysis showed correlation and stress values of 0.9986 and 0.0127, respectively. These values confirm the stability among the number of primers and the number of fragments obtained. Furthermore, the content of PIC ranged from 0.1094 to 0.3469, with an average of 0.2594.
The genetic variability for the population was estimated as moderate. The average number of different alleles (Na) and number of effective alleles (Ne) were 1.971 and 1.412, respectively. The Shannon Information Index (IS) was 0.4 and the Expected Heterozygosity (HE) ranged from 0.1162 to 0.4466, with an average of 0.3097. The percentage of polymorphic loci was 97.06%.
The similarity coefficient of Jaccard between each pair of individuals ranged from 0.21 to 0.82, with an average of 0.38 (Table 3). The pair formed by the individuals MYRO-178.1 and MYRO-178.2 (0.82), followed by the pairs MYRO-032 and MYRO-033 (0.71), and MYRO-159 and MYRO-159.2 (0.69) presented the highest genetic similarity. Moreover, the pairs of individuals MYRO-034 and MYRO-159 (0.21), MYRO-029 and MYRO-093 (0.24), and MYRO-030 and MYRO-162 (0.25) presented the lowest genetic similarity
Table 3: Jaccard similarity coefficient of 24 Myrcia ovata individuals from a native population located at the municipally of Japaratuba, in the state of Sergipe, Brazil.
The UPGMA dendrogram separated two groups (I and II) of individuals. According to this analysis, group I was formed by six individuals representing 25% of the population (MYRO-159, MYRO-159.1, MYRO-159.2, MYRO-093 and MYRO-162), and group II was formed by 18 individuals representing 75% of the population. The Bootstrap repeatability analysis showed a range from 19 to 100%. The junctions between MYRO-154 and MYRO-155 (100%); MYRO-178.1 and MYRO-178.2 (100%); and, MYRO-159 and MYRO-159.1 (97%) individuals showed higher consistencies (Figure 3).
The Bayesian cluster analysis from the STRUCTURE software divided the population into two groups. The individuals MYRO-154 and MYRO-175 had the most variability (Figure 4).
The ISSR marker detected a relatively high level of polymorphism among the 24 plants of M. ovata in a native population located in the municipality of Japaratuba, in the state of Sergipe, Brazil. This is the first study to evaluate the genetic variability of M. ovata.
The number of fragments amplified by the ISSR markers was included in a lower range than would be expected for species of the Myrtaceae tribe (Lima et al., 2015b). In addition, the 99 polymorphic bands obtained with the nine primers were sufficient to find a reliable number of fragments to estimate genetic variability (Dudley, 1994). Also, using the optimization analysis, it was proved that the number of primers used were sufficient to evaluate the genetic variability of M. ovata individuals, with correlation and stress closer to 1.0 and below 0.05, respectively (Kruskal, 1964).
The PIC content represents the probability of finding each marker present and/or absent in each band, revealing allelic variation. It ranges from 0 to 0.5 and lower values can correspond to very rare or abundant markers (Roldan-Ruiz et al., 2000). In this paper, the PIC content (0.259) was considered to have moderate discriminatory power. In addition, the PIC content was lower than the HE, as expected (Cruz, 2001).
Regarding the genetic variability, the mean number of different alleles (Na) obtained for dominant markers was 1.97, close to the highest it can be, and among these 1.41 were considered as effective alleles (Ne). This means that 72% of the alleles can contribute to the construction of the genetic information of this native population of M. ovata in the Sergipe state.
The Shannon Index (IS) measures the certainty of predicted genetic proximity between individuals, ranging from 0 to 1. The lower the number, the higher the certainty degree and the lower the population diversity (Estopa et al., 2006). The average HE is associated with low diversity and consequently, reduced capacity of the remaining population for adaptation (Álvares-Carvalho et al., 2016).
The Shannon Index and the average HE found for this native population (0.40; 0.30), was lower than those found by Brandão et al. (2015) (0.48; 0.33) and by Alves et al. (2016) (0.46; 0.30) who worked with M. splendes and M. lundiana, respectively. Knowing that the range of HE is expected to be similar between species that present similar characters, including biological, reproductive and distribution characteristics (Lima et al., 2015b), the numbers of the Shannon Index and HE can be, in part, explained because of the origin of the plants used in their research, which was a conserved vegetation. This contrasts with the M. ovata location, an anthropized vegetation (Santana et al., 2012), which could result in a lower gene flow.
The Jaccard analysis showed a moderate similarity (0.38), which can be influenced by a cross-pollination reproduction system (Kageyama et al., 2003; Sampaio et al., 2016) and the absent of domestication (Silva et al., 2011). Nevertheless, the existence of moderate genetic diversity does not justify the lack of conservation activities for this specie, mainly because it was not found in other locations of the state of Sergipe besides the study area, and the genetic variability shows a tendency to decay if no action is taken.
It’s important to emphasize that the most similar pairs of individuals are shrubs, each located side-by-side, and clustered within the same group in the UPGMA and STRUCTURE analysis, implying that they probably have the same progenitors. This observation is applied to other shrubs that are side-by-side, for example MYRO-159 and MYRO-159.1, MYRO-159.1 and MYRO-159.2 and MYRO-178 and MYRO-178.1. Furthermore, concerning the population genetic structure, the 24 individuals of M. ovata were clustered in two groups by the UPGMA and STRUCTURE analyses, which presented the same arrangement.
The choice of matrices to describe the variability and/or genetic diversity of individuals within and between populations is a prerequisite for genetic characterization of the species, since this characterization is a common procedure in the conservation of natural resources and genetic improvement programs. Based on this study, it was determined that the individuals MYRO-154, MYRO-175 and MYRO-175.1 of M. ovata present in the state of Sergipe should be selected as priorities for conservation of the species.
Regarding the comparison of chemical and genetic analyses, 12 individuals of M. ovata used in Sampaio et al. (2016) were also used in this paper. A match of the chemical and genetics groups described in the clustering analysis was not found. For example, each of these individuals: MYRO-174 and MYRO-176, and MYRO-159 and MYRO-160 were chemically grouped in the same clusters, but genetically clustered in different groups. This differentiation could be because different samples were used, from different collection periods for these studies. The chemical variation is commonly found in the chemical composition of plants, because it is influenced not only by gene composition, but also by dynamic factors such as rainy season, drought, temperature and pests, as well as by the extraction method (Scheffer, 1993; Ribeiro et al., 2016). This comparison could be improved with other molecular makers that also are influenced by dynamic factors, such as enzyme producers (Faleiro, 2007).
The authors thank CNPq, FAPITEC/SE, CAPES, and FINEP for their financial support of this study
Conflicts of interest
The authors declare no conflict of interest.
About the Authors
- Ahmadi J, Mohammadi A and Mirak N (2012). Targeting promising bread wheat (Triticum aestivum L.) lines for cold climate growing environments using AMMI and SREG GGE Biplot analyses. J. Agric. Sci. Technol. 14: 645-657.
- Allard RW (1971).Princípios do Melhoramento Genético das Plantas. USAID/Edgard Blucher, Rio de Janeiro.
- Alvarez MP and Eyhérabide GH (1996). Estabilidad del rendimento de cultivares de híbidoscomerciales de maíz em la área de la EEA Pergamino. Rev. Tecnol. Agropec 5: 17-21.
- Amorim EP, Camargo CEO, Filho AWPF, Junior AP, et al. (2006). Adaptabilidade e estabilidade de linhagens de trigo no Estado de São Paulo. Bragantia 65: 575-582 https://doi.org/10.1590/S0006-87052006000400007.
- Annicchiarico P (1992). Cultivar adaptation and recommendation from alfafa trials in Northern Italy. J. Gen. Plant Breed 46: 269-278.
- Benin G, Pinnow C, Silva CL, Pagliosa ES, et al. (2012). Análises biplot na avaliação de cultivares de trigo em diferentes níveis de manejo. Bragantia 71: 28-36 https://doi.org/10.1590/S0006-87052012000100005.
- Campbell BT and Jones MA (2005). Assesment of genotype x environment interactions for yield and fiber quality in cotton performance trials. Euphytica 144: 69-78. https://doi.org/10.1007/s10681-005-4336-7
- Castillo D, Matus I, Pozzo A, Madariaga R, et al. (2012). Adaptability and genotype times environment interaction of spring wheat cultivars in Chile using regression analysis, AMMI, and SREG. Chil. J. Agric. Res. 72: 167. https://doi. org/10.4067/S0718-58392012000200001
- CONAB - Companhia Nacional de Abastecimento (2012). Acompanhamento da safra brasileira de grãos. 3° Levantamento Grãos Safra 2012/13 - Setembro 2012. Available at [http://www.conab.gov.br/OlalaCMS/uploads/ arquivos/14_01_10_10__12_36_boletim_portugues_dezembro_2012.pdf]. Accessed October 7, 2016.
- Condé ABT, Coelho MAO, Yamanaka CH and Corte HR (2010). Adaptabilidade e estabilidade de genótipos de trigo sob cultivo de sequeiro em Minas Gerais. Pesqui. Agropecu. Trop. 40: 45-52.
- Cruz CD (2013). GENES - a software package for analysis in experimental statistics and quantitative genetics. Acta Scientiarum 35: 271-276.
- Cruz CD, Carneiro PCS and Regazzi AJ (2014). Modelos biométricos aplicados ao melhoramento genético. 3 ed. Editora da UFV, Viçosa.
- Da Silva GO, De Carvalho ADF, Souza ZS, Ponijaleki RS, et al. (2016). Desempenho genotípico de clones de batata via modelos mistos. Agraria 11: 259-266 https://doi.org/10.5039/agraria.v11i4a5390.
- Duarte JB and Vencovsky R (1999). Interação genótipo x ambiente: uma introdução à análise “AMMI”. Sociedade Brasileira de Genética, Ribeirão Preto.
- Junior OP (2014). Aptidão, adaptabilidade e estabilidade fenotípica de genótipos de trigo. Dissertação (Mestrado em Agronomia) - Departamento de Produção Vegetal da Universidade Estadual do Centro-Oeste (UNICENTRO), Guarapuava.
- Ma BL, Yan W, Dwyer LM, Fregeau-Reid J, et al. (2004). Graphic analysis of genotype, environment, nitrogen fertilizer, and their interactions on spring wheat yield. Agron. J. 96: 169-180.
- Oliveira AB, Duarte JB and Pinheiro JB (2003). Emprego da análise AMMI na avaliação da estabilidade produtiva em soja. Pesqui. Agropecu. Bras. 38: 357-364 https://doi.org/10.1590/S0100-204X2003000300004.
- Oliveira DM, Souza MA, Rocha VS, Assis JC, et al. (2011). Desempenho de genitores e populações segregantes de trigo sob estresse de calor. Bragantia 70: 25-32 https://doi.org/10.1590/S0006-87052011000100005.
- Pereira HS, Melo LC, Peloso MJD, Faria LC, et al. (2009). Comparação de métodos de análise de adaptabilidade e estabilidade fenotípica em feijoeiro-comum. Pesqui. Agropecu. Bras. 44: 374-383 https://doi.org/10.1590/S0100-204X2009000400007.
- Pereira TCV, Schmit R, Haveroth EJ, Melo RC, et al. (2015). Reflexo da interação genótipo x ambiente sobre o melhoramento genético de feijão. Cienc. Rural 46: 411-417 https://doi.org/10.1590/0103-8478cr20130998.
- Piepho HP, Williams ER and Maddlen LV (2012). The use of two-way linear mixed models in multitreatment meta-analysis. Biometrics 68: 1269-1277.
- Pimentel Gomes F (2009). Curso de estatística experimental. 15 ed. FEALQ, Piracicaba.
- Pupin S, Dos Santos AVDA, Zaruma DUG, Miranda AC, et al. (2015). Produtividade, estabilidade e adaptabilidade em progênies de polinização aberta de Eucalyptus urophylla ST Blake. Sci. For. 43: 127-134.
- R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
- Ramalho MAP and Araújo LCA (2011). Breeding self-pollinated plants. Crop Breed. Appl. Biotechnol. 11: 1-7 https://doi.org/10.1590/S1984-70332011000500002.
- Ramalho MAP, Ferreira DF and Oliveira AC (2000). Experimentação em genética e melhoramento de plantas. UFLA, Lavras.
- Resende MDV (2002). Genética biométrica e estatística no melhoramento de plantas perenes. Embrapa Informação Tecnológica, Brasília. Embrapa Florestas, Colombo.
- Resende MDV (2007). Software SELEGEN-REML/BLUP: sistema estatístico e seleção genética computadorizada via modelos lineares mistos. Embrapa Florestas, Colombo.
- Resende MDV (2016). Software Selegen-REML/BLUP: a useful tool for plant breeding. Crop Breed. Appl. Biotechnol. 330-339 https://doi.org/10.1590/1984-70332016v16n4a49.
- Resende MDV and Duarte JB (2007). Precisão e controle de qualidade em experimentos de avaliação de cultivares. Pesqui. Agropecu. Trop. 37: 182-194.
- Rodríguez GRE, Medina PJ, Puente EO, Reyes LA, et al. (2011). Interacción genotipo-ambiente para la estabilidad de rendimiento en trigo en la región de Mexicali, BC. México. Trop. Subtrop. Agroecosyt 14: 543-558.
- Rosado AM, Rosado TB, Alves AA, Laviola BG, et al. (2012). Seleção simultânea de clones de eucalipto de acordo com produtividade, estabilidade e adaptabilidade. Pesqui. Agropecu. Bras. 47: 964-971 https://doi.org/10.1590/S0100-204X2012000700013.
- Silva RR and Benin G (2012). Análises Biplot: conceitos, interpretações e aplicações. Cienc. Rural 42: 1404-1412 https:// doi.org/10.1590/S0103-84782012000800012.
- Silva RR, Benin G, Silva GO, Marchioro VS, et al. (2011). Adaptabilidade e estabilidade de cultivares de trigo em diferentes épocas de semeadura, no Paraná. Pesqui. Agropecu. Bras. 46: 1439-1447 https://doi.org/10.1590/S0100-204X2011001100004.
- Silveira DA, Princinotto LF, Nardino M, Bahry CA, et al. (2016). Determination of the adaptability and stability of soybean cultivars in different locations and at different sowing times in Paraná state using the AMMI and Eberhart and Russel methods. Semin. Cienc. Agrar. 37: 3973-3982 https://doi.org/10.5433/1679-0359.2016v37n6p3973.
- Spilke J, Phiepho HP and Hu X (2005). Analysis of unbalanced data by mixed linear models using the Mixed Procedure of the SAS System. J. Agron. Crop Sci. 191: 47-54.
- Streck EV, Kämpf N, Dalmolin RSD, Klamt E, et al. (2008). Solos do Rio Grande do Sul. 2 ed. Emater, Porto Alegre. Yan W and Kang MS (2003). GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton.
- Yokomizo GKI, Duarte JB, Vello NA and Unfried JR (2013). Análise AMMI da produtividade de grãos em linhagens de soja selecionadas para resistência à ferrugem asiática. Pesqui. Agropecu. Bras. 48: 1372-1380 https://doi.org/10.1590/ S0100-204X2013001000009.
- Zobel RW, Wright MJ and Gauch HG (1988). Statistical analysis of a yield trial. Agron. J. 80: 388-393.
- Share This