Evaluation of molecular diversity analysis and relation with environmental factors in accessions of Cumin (Cuminum cyminum L.) in Iran, revealed by inter-simple sequence repeat (ISSR) markers and start codon targeted (SCoT) markers

doi:10.21203/rs.3.rs-35133/v1

Download PDF

Research

Evaluation of molecular diversity analysis and relation with environmental factors in accessions of Cumin (Cuminum cyminum L.) in Iran, revealed by inter-simple sequence repeat (ISSR) markers and start codon targeted (SCoT) markers

https://doi.org/10.21203/rs.3.rs-35133/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Cuminum cyminum (as an aromatic herb) is the sources of cumin, which has been commonly used in traditional medicine for the treatment of various diseases especially in tropical Asia. Thus, the current experiment was performed to accesses molecular genetic diversity among 22 collected accessions of cumin from different area of Iran, based on inter simple sequence repeat (ISSR) markers and start codon targeted (SCoT) markers. Analysis of molecular variance revealed that a significant genetic difference between the studied accessions reach to 43%. Mantel test between genetic diversity and geographical distance indicated that an increase in geographical distance of collected accessions did not influence on genetic differentiation. Results about all agro-morphological traits showed a significant difference between the ecotypes except the number of branches. Canonical correspondence analysis ballot of genetic features and environmental factors included 5 geographic and climatic factors for seed origin’s habitat, which have been show that the mentioned parameters had no strong effect on the plant genotypes. However, difference in elevation and latitude of accession origins had effect on genetic clustering in two major groups. The consensus tree of both molecular and morphological data relieved that the accessions Chah-Dashi, Damghan, Ferdows and Nehbandan formed discreet clusters based on genetic and agro-morphological features. In conclusion, the results confirmed that both ISSR and SCoT markers were reliable and useful tools for analyzing the genetic diversity of cumin in Iran.

Plant Molecular Biology and Genetics

Cuminum cyminum

Genetic diversity

Canonical correspondence analysis

ISSR

SCoT

Iran

Cumin (Cuminum cyminum L.), the second most popular spice in the world, is belong to Apiaceous or Umbelliferae family, which has been regard as a medicinal herb, economical crop plant, and part of food culture in in wide geographical areas (KAFIE et al., 2002; LODHA and MAWAR, 2014) especially in tropical Asia (HASHEMIAN et al., 2013). There are numerous usages of cumin seed due to its phyto-chemicals components, minerals (like Fe, Cu, Ca, K, Mg, Se, Zn and Mn), vitamins, and many flavonoid phenolic anti-oxidants (PARASHAR et al., 2014). Geographical distribution of cumin in Iran confined mainly to arid and semi-arid regions in eastern, south-eastern, and central provinces (HASHEMIAN et al., 2013).

Estimation of genetic diversity of related plant sample is a requirement towards the helpful utilization and conservation of plant genetic resources (WEISING et al., 1995; SHAHLAEI et al., 2014).

On the other hand, morphological and molecular surveys are used for genetic diversity, genotypes, and ecotypes of crop plants for upgrading the cultivars or varieties in purpose of breeding programs and conservation (AFSHAR et al., 2015). Molecular markers have been widely and effectively used for assessment of genetic diversity, phylogenetics, fingerprinting, variation, and differentiation in biology (XIONG et al., 2011; AMIRMORADI et al., 2012; MULPURI et al., 2013). Molecular markers, as a reliable method in detecting the polymorphism at DNA level, could be helpful in genetic relationships within and between species and ecotypes (MARSJAN and OLDENBROEK, 2007). DNA fingerprinting of all the genetic stocks is imperative in order to preparing a powerful molecular database of medicinal plants (WJITKUS et al., 1994). The most important characteristics of molecular markers are neutrality, not influenced by age, environment, economical beneficiary and more informative than morphological traits (SAMANTARAY et al., 2010; RAHIMI et al., 2018). Inter simple sequence repeat (ISSR) marker is preferred by many researchers due to many advantages such as simplicity, quickly, less costly and high reproducibility (PRADEEP REDDY et al., 2002; XU et al., 2018). ISSR markers rapidly reveal high polymorphic fingerprints and have been used frequently to determine the genetic diversity among date palm cultivars (AYESH, 2017). Also, SCoT (start codon targeted), as a novel marker, can be evaluating as the strong polymorphic markers which indicate differences between individuals extremely well (COLLARD and MACKILL, 2009).

As yet, genetic relationships between cumin ecotypes were investigated in terms of agro-morphological traits (BAHRAMINEJAD et al., 2011) and molecular polymorphism by markers like RAPD, SCoT, CCMP and SSR (BAHRAMINEJAD et al. 2012; BAHRAMINEJAD and MOHAMMADINEJAD, 2013; ROSTAMI-AHMADVANDI et al., 2013; PARSSHAR et al., 2014). Considering the worth of medicinal plants in industry and commerce, the present study was survey to undertaken the agro-morphological traits and genetic differentiation (by ISSR and SCoT markers) of 22 cumin accessions collected from different regions of Iran. This study would be helpful in selecting parents in the cumin breeding programs and conservation of this medicinal plant. Furthermore, relationships between ecological factors and genetic differentiation will be discussed due to poor information about environmental influence on different accessions of cumin.

Plant materials

At current study 22 accessions of cumin from 9 Iran province were investigated (Figure 1, Supplementary 1). The accession number, origin and ecological condition of these accessions are presented in Table 1. The seeds were cultivated using randomized complete block design at the Research Field of Agriculture (Semnan, Iran; Table 2). And, the information about environmental factors of the study area was given from State Meteorological Organization of Iran (http://reports.irimo.ir/jasperserver/login.html).

The measured agro-morphological traits were including: plant height, number of branches, number of umbel, mini-umbel and seed, 1000-seed weight, seed weight per plant and grain yield. These traits were measured through morphometric method. The photos of accession were given as Supplementary data 1.

Table 1. Accession number, origin information of 22 accessions of cumin and their ecological factors

F Code.	Province	Locality	Accession .No	Longitude	Latitude	Elevation (m)	AMT (° C)	AP (mm)
1	Razavi-Khorasan	Khaf	100	60° 8' 10.509''	34° 34' 17.431''	972	16.7	200
2	Razavi-Khorasan	Khoshab	101	57° 59' 26.287''	36° 25' 30.785''	1187	13.8	207
3	South-Khorasan	Chah-Dashi	102	59° 43' 20.238''	31° 28' 14.567''	1065	20.05	86
4	South-Khorasan	Ayesk	103	58° 22' 58.742''	33° 53' 14.868''	1368	16.7	135
5	East-Azarbayjan	Tabriz- Ilkhchi	104	45° 58' 35.354''	37° 56' 19.467''	1308	11.6	318
6	East-Azarbayjan	Shabistar-Shendabad	105	45° 37' 44.730''	38° 8' 39.281''	1309	10.9	320
7	Isfahan	Khur and Biabanak	106	55° 5' 6.655''	33° 46' 25.764''	831	20.2	79
8	Isfahan	Mobarakeh-Talkhuncheh	107	51° 33' 36.569''	32° 15' 44.604''	1731	15.1	154
9	Kerman	Rig-Mahan	108	57° 17' 29.504''	30° 3' 40.064''	1901	15.4	156
10	Kerman	Kuhbanan	109	56° 16' 58.527''	31° 24' 37.072''	1990	14.1	146.1
11	Yazd	Yazd	110	54° 21' 20.827''	31° 53' 15.208''	1222	18.9	55
12	Semnan	Dlạzyạn	111	53° 24' 23.810''	35° 29' 53.300''	1042	17.2	130
13	Semnan	Damghan	112	54° 20' 27.193''	36° 9' 49.639''	1154	15.4	213
14	Semnan	Sorkheh-Biabanak	113	53° 16' 6.488''	35° 24' 42.841''	1034	17.4	129
15	South-Khorasan	Birjand1	2001	59° 13' 0.940''	32° 52' 24.009''	1454	17	129
16	South-Khorasan	Birjand3	2003	59° 13' 0.940''	32° 52' 24.009''	1454	17	129
17	South-Khorasan	Boshruyah	2004	57° 25' 41.322''	33° 52' 6.185''	879	19.7	96
18	South-Khorasan	Ferdows	2005	58° 10' 0.473''	34° 0' 20.273''	1269	17.2	130
19	South-Khorasan	Nehbandan	2006	60° 3' 1.431''	31° 32' 26.144''	1185	18.4	93
20	South-Khorasan	Faizabad	2007	58° 47' 33.697''	35° 1' 10.119''	942	26	220
21	Tehran	Tehran	2008	51° 22' 50.599''	35° 42' 2.604''	1214	16.4	220
22	North-Khorasan	Bojnord	2014	57° 19' 44.809''	37° 28' 20.124''	1071	13.2	257

F (Features); AMT (mean year temperature); AP (mean year rainfall).

Table 2. Geographical condition of seeds were cultivated

Parameters

Longitude

Latitude

Altitude (m)

Temperature (mean year, ° C)

Rainfall

(mean year, mm)

Semnan

53^°23^' 20.077^''

35^° 35^' 0.752^''

1164

17.2

130-145

DNA Extraction and ISSR and SCoT PCR amplification

Total genomic DNA was extracted from young leaves using cetyl trimethylammonium bromide method described by Doyle and Doyle (1990) with a little modification (Ghasemzadeh-Barkaki et al., 2018) that included combination of β-mercaptoethanol, polyvinyl pyrrolidone, sodium N-lauroyl sarcosine and sodium dodecyl sulfate. To determine the quality of DNA, 0.8% agarose gel electrophoresis were used. In this study, 5 SCoT primers commercialized by UBC (the University of British Columbia) using for amplification (COLLARD and MACKILL, 2009). Specific primers (Table 3) were provided by Invitrogen, USA. Polymerase chain reaction was performed in 25 μL reaction volume containing 10 mM Tris-HCl, pH 8.3, 2.5 mM Mg Cl₂, 1 mM dNTP_s mix, 0.2 μM of each primer, 1U of Taq DNA polymerase, and 25 ng of template DNA.

Thermocycling conditions (BIORAD.T100) were as follows: after initial denaturation at 94^oC for 300s; 36 amplification cycles were performed at 94^oC for 60s, 60^oC for 60s; Finally 60 ^oC for 60s, 72 ^oC for 90s, and 72 ^oC for 10 min. QRT-PCR for ISSR marker was similar to above condition. The thermocycler (BIORAD.T100) was used for first denaturation in 94^oC for 5 min followed by 40 cycles consisting of denaturation at 94^oC for 60 sec, annealing at 57^oC for 60 sec and extension at 72^oC for 120 sec and 72^oC for 10 min for final extension. Thermocycling conditions (BIORAD.T100) were as follows: after initial denaturation at 94^oC for 300s; 40 amplification cycles were performed at 94^oC for 60s, 57^oC for 60s; Finally 72 ^oC for 120s, and 72 ^oC for 10 min. The 2^-σσCt method was used to analyze the data.

Table 3. Primer sequences of target genes for QRT-PCR.

Primer	Sequence 5′-3′	%GC	Annealing temperature (^oC)
SCoT1	CAACAATGGCTACCACCA	50	60
SCoT2	CAACAATGGCTACCACCC	55	60
SCoT3	CAACAATGGCTACCACCG	55	60
SCoT7	CAACAATGGTCACCACGG	56	60
SCoT18	ACCATGGCTACCACCGCC	66	60
ISSR810	GAGAGAGAGAGAGAGAT	47	57
ISSR834	AGAGAGAGAGAGAGAGYT	44	57
ISSR(AGC)5GG	AGCAGCAGCAGCAGCGG	70	57
ISSR(AGC)5GC	AGCAGCAGCAGCAGCGC	70	57
ISSR(GA)9C	GAGAGAGAGAGAGAGAGAC	52	57

Data analyzing

The analysis of variance (ANOVA) of agro-morphological data was carried out for 3 plant per accessions by SPSS 19.0 software. The obtained SCOT and ISSR bands were coded as binary characters (presence=1, absence=0). The genetic diversity parameters like allele diversity (Weising et al., 1995), Nei’s gene diversity, Shannon information index, number of effective alleles, and percentage of polymorphism (FREELAND et al., 2011) were determined for each population. Nei’s genetic distance was used for clustering (WEISING et al., 1995; FREELAND et al., 2011). Neighbour Joining clustering were used for grouping (FREELAND et al., 2011).

The Mantel test was performed to check correlation between geographical and genetic distances of the studied accessions (PODANI, 2000). PAST ver. 2.17 (HAMER et al., 2001) and GeneALEx 6.4 (PEAKALL and SMOUSE, 2006) programs were used for these analyses.

The Pearson coefficient of correlation was determined between geographical features (longitude and latitude) and genetic diversity parameters. Genetic differentiation of the studied species and populations was studied by Analysis of molecular variance (AMOVA) test (with 1000 permutations) as performed in GenAlex 6.4 (PEAKALL and SMOUSE, 2006).

Canonical correspondence analysis (CCA) was done using PAST software v. 2.17 (HAMER et al., 2001), to determine the relative importance of geographical factors in the spatial organization of genetic diversity between accessions.

DARwin program (Version 5; PERRIER and JACQUEMOUD-COLLET, 2006) were used to compare the accessions that are genetically and agro-morphologically differentiated from the others, a consensus tree was conducted from agro-morphological and genetic obtained trees.

Phenotypic data

ANOVA results showed significant differences among the accessions for all the studied traits except number of branch (Table 4), that indicated high genetic diversity among these accessions. Accessions 17 (Boshruyah) indicated highest average seed weight per plant and grain yield, while accession 20 (Faizabad) had the lowest average. The average number of umbel and mini-umbel in accession 16 (Birjand 3) was highest and the lowest was observed in accessions 20 (Faizabad) and 8 (Talkhuncheh), respectively. On average, higher 1000-seed weight and number of seed per plant were seen in accessions 8 (Mobarake) and 9 (Rig), while the lowest were observed in accessions 6 (Shabistar-Shendabad) and 20 (Faizabad), respectively.

In the WARD tree of morphological characters, two major clusters were formed (Figure 2). PCA biplot supported the grouping made by WARD tree and also revealed agro-morphological traits including seed weight per plant, grain yield and number of seed per plant separated these two groups (Figure 3). PCA analysis revealed that about 76% of total variance was described by the first two components. The loading of morphological characters on the first PCA axis showed that traits like seed weight per plant and grain yield had the highest positive correlation (>0.54) with the first PCA component. Number of mini-umbel in umbel and the number of seed per plant had the highest positive correlation (>0.42) with the second PCA axis.

Table 4. ANOVA of the agro-morphological traits in 22 accessions (RCBD with 3 replicates)

S.O.V	Df	Mean of square
S.O.V	Df	PH	NB	NU	NMU	NS	SW	SWP	GY
Rep	2	1.93^ns	0.53^ns	10.65^*	60.36^ns	87.31^ns	0.82^*	0.002^ns	1939.48^ns
Treat	21	66.82^*	1.47^ns	13.68^*	196.9^*	2843.71^*	1.02^*	0.035^*	35142.63^*
Error	42	2.36	0.87	2.56	25.36	240.36	0.12	0.004	3880.77
CV		0.23	0.18	0.24	0.27	0.33	0.18	0.37	0.39

ns,*: non-significant and significant at 5%, respectively.

PH: plant height; NB: number of branches; NU: number of umbel; NMU: number of mini-umbel; NS: number of seed; SW: 1000-seed weight; SWP: seed weight per plant; GY: grain yield.

Table 4. Genetic diversity parameters of SCoT and ISSR markers in cumin accessions.

GP eco	PPB%			Na			Ne			I			He			UHe
GP eco	SCoT	ISSR	S&I	SCoT	ISSR	S&I	SCoT	ISSR	S&I	SCoT	ISSR	S&I	SCoT	ISSR	S&I	SCoT	ISSR	S&I
1	18.68	10.89	14.58	0.63	0.46	0.54	1.13	1.07	1.1	0.11	0.06	0.08	0.07	0.04	0.06	0.1	0.06	0.08
2	21.98	24.75	23.44	0.65	0.69	0.67	1.15	1.17	1.16	0.13	0.15	0.14	0.09	0.1	0.09	0.12	0.13	0.12
3	23.08	23.76	23.44	0.69	0.7	0.69	1.16	1.16	1.16	0.14	0.14	0.09	0.09	0.09	0.09	0.12	0.13	0.12
4	23.08	12.87	17.71	0.61	0.48	0.54	1.16	1.09	1.12	0.14	0.07	0.1	0.09	0.05	0.07	0.12	0.07	0.09
5	21.98	12.87	17.19	0.68	0.6	0.64	1.15	1.09	1.1	0.13	0.07	0.1	0.09	0.05	0.07	0.12	0.07	0.09
6	23.08	10.89	12.50	0.42	0.46	0.44	1.1	1.07	1.08	0.08	0.06	0.07	0.05	0.04	0.05	0.07	0.06	0.06
7	21.98	9.9	14.58	0.59	0.47	0.53	1.1	1.07	1.1	0.12	0.06	0.08	0.08	0.04	0.06	0.1	0.05	0.08
8	14.29	22.77	18.75	0.57	0.68	0.63	1.1	1.16	1.13	0.08	0.13	0.11	0.05	0.09	0.07	0.07	0.12	0.1
9	23.08	19.8	21.35	0.69	0.64	0.66	1.16	1.14	1.15	0.14	0.12	0.12	0.09	0.08	0.08	0.12	0.1	0.11
10	29.67	24.75	27.08	0.82	0.77	0.79	1.21	1.17	1.19	0.17	0.15	0.16	0.12	0.1	0.11	0.16	0.13	0.15
11	16.48	17.82	17.19	0.59	0.6	0.58	1.11	1.12	1.12	0.1	0.1	0.1	0.06	0.07	0.07	0.09	0.09	0.09
12	23.08	25.74	24.48	0.61	0.74	0.68	1.16	1.18	1.17	0.14	0.15	0.14	0.09	0.1	0.1	0.12	0.14	0.13
13	24.18	11.88	17.71	0.6	0.57	0.58	1.17	1.08	1.12	0.14	0.07	0.1	0.1	0.04	0.07	0.13	0.06	0.09
14	19.78	20.79	20.31	0.57	0.72	0.65	1.14	1.14	1.14	0.12	0.12	0.12	0.08	0.08	0.08	0.1	0.11	0.11
15	3.3	4.95	4.71	0.33	0.44	0.39	1.02	1.03	1.02	0.02	0.03	0.02	0.01	0.02	0.01	0.01	0.02	0.02
16	0	9.9	5.21	0.2	0.52	0.37	1	1.07	1.03	0	0.06	0.03	0	0.04	0.02	0	0.05	0.02
17	1.1	8.91	5.21	0.33	0.4	0.37	1	1.06	1.03	0.007	0.05	0.03	0.005	0.03	0.02	0.006	0.04	0.02
18	2.2	12.87	7.81	0.37	0.5	0.44	1.01	1.09	1.05	0.01	0.07	0.04	0.009	0.05	0.03	0.01	0.07	0.04
19	8.79	9.9	9.38	0.41	0.38	0.4	1.06	1.07	1.06	0.05	0.06	0.05	0.03	0.04	0.03	0.04	0.05	0.05
20	4.4	20.79	13.02	0.42	0.62	0.53	1.03	1.14	1.09	0.02	0.12	0.07	0.01	0.08	0.05	0.02	0.11	0.07
21	7.69	18.81	13.54	0.39	0.59	0.5	1.05	1.13	1.09	0.04	0.11	0.08	0.03	0.07	0.05	0.04	0.1	0.07
22	4.4	7.92	6.25	0.39	0.5	0.45	1.03	1.05	1.04	0.02	0.04	0.03	0.01	0.03	0.02	0.02	0.04	0.03
Total	14.79	15.62	15.22	0.52	0.57	0.55	1.1	1.1	1.1	0.08	0.09	0.09	0.06	0.06	0.06	0.08	0.08	0.08

PPB: genetic polymorphism percentage; Na: the mean number of different alleles over all loci; Ne: the highest value for effective No. of alleles; I: Shannon Information Index; He: gene diversity; UHe: unbiased expected heterozygosity; S & I: SCoT and ISSR.

Molecular markers polymorphism in Cumin

Genetic diversity parameters determined for ISSR, SCoT and combined ISSR-SCoT data markers are presented in Table 5. The highest value for number of effective alleles, Shannon Information Index and gene diversity, occurred in accession 10 (Kuhbanan) equal with 1.19, 0.16 and 0.11, respectively. While the lowest level of the effective number of effective alleles and Shannon Information Index occurred in accessions 16 (Birjand 1) equal with 1.02 and 0.02, respectively, and in ecotype 16 gene diversity (He) is zero.

When compared of genetic diversity parameters between SCoT and ISSR primers results indicated differences among them. So that in some accessions SCoT markers had more efficiency than ISSR marker and in others vice versa. For example, polymorphism percentage in SCoT marker was higher value for accessions 1 (Khaf), 4 (Ayesk), 5 (Ilkhchi), 6 (Shabistar), 7 (Khur), 9 (Rig), 11 (Kuhbanan), 12 (Dlazyan) and 13 (Damghan) than ISSR markers, while in other accession ISSR markers showed higher value of polymorphism percentage. The similar status was exhibited for other genetic diversity parameters (Table 5).

AMOVA results revealed that a significant genetic difference (PhiPT=0.56, P=0.01) among the studied accessions. It also AMOVA results indicated that 57% of total genetic variation was due to diversity within accession and 43% was due to genetic differentiation among populations. Pairwise AMOVA produced a significant difference among these populations (Figure 4).

PCOA plot of combined SCoT-ISSR data (Figure 5) showed both intra- and inter-accessions. The grouping of the accessions obtained by these two procedures produced two major clusters. Accessions Khaf, Khoshab, Chah-Dashi, Ayesk, Ilkhchi, Shabistar, Khur, Mobarakeh, Rig, Kuhbanan, Dlazyan, Damghan, Sorkheh and Nehbandan comprised the first major cluster. In this cluster, accessions Dlazyan, Damghan, Sorkheh and Nehbandan showed higher genetic similarity and were joined to each other. The same holds true for accessions Khur, Mobarakeh, Rig and Kuhbanan. Accession Shabistar had the most distinction in this cluster. In cluster 2, accessions Yazd, Birjand 1, Birjand 3, Boshruyah, Ferdows, Faizabad, Tehran, and Bojnord showed higher genetic similarity and were joined to each other. The photos of banding pattern in ISSR and SCoT analysis are given in Supplementary data 2.

Environmental factors and genetic diversity

Based on the Mantel test that was performed between genetic distance and geographical distance of the origin locality of studied accessions the results showed that produced no significant correlation (R2=0.005, Figure 6), which have been indicate an increase in geographical distance of collected seeds of accessions did not influence in genetic differentiation. Therefore, no isolation by distance (IBD) exists between them.

The main aims of the present study was to compare the effect of different environmental factors on the genetic features of cumin ecotypes, 5 geographic and climatic factors of seed origin’s habitat were examined. CCA biplot (Figure 7) of genetic features and environmental factors showed that the mentioned parameters had no strong effect on the plant genotypes (Axis 1= 32.1% of variance; Axis 2= 28.7 %). However, it seems that difference in elevation of sea and latitude of ecotype origins had impacts on genetic clustering in two major groups. The clustering of accessions Ilkhchi and Shabistar (East Azarbayjan province) was related to influence of altitude. These two accessions had high altitude than others accessions. Also, longitude influence on grouping accessions Mobarakeh, Rig, Kuhbanan and Damghan.

Combined molecular and morphological results

The consensus tree of both molecular and morphological data is presented in Figure 8. The specimens of accessions Chah-Dashi, Damghan, Ferdows and Nehbandan formed separate clusters. These accessions belong to south Khorasan province except ecotype Damghan that belong to Semnan province. The results differed from the other studied populations in both genetic and morphological features. This showed that agro-morphological differences in these four accessions had genetic background.

Generally, in the plant breeding programs and genotype conservation the population structure and genetic diversity information are valuable keys. Agro-morphological traits are prerequisite to classify and describe genotype diversity (LOU et al., 2015). In the current study, all agro-morphological traits showed a significant difference between the ecotypes (except the number of branches). The results of the present study a line with some previous studies that it's were done about genetic diversity of cumin in Iran (AMINPOOR and MOUSAVI, 1997; BAHRAMINEJAD et al., 2011; ROSTAMI-AHMADVANDI et al., 2013). There is an obvious differentiation between studied accessions on the basis of agro-morphological traits. These accessions divided into 2 main clusters. The grouping of these accessions is not thoroughly math to origin geographic region of seeds. It seems that planting all ecotypes in Semnan locality and difference in cultivated condition influence on agro-morphological traits. In contrast, examining of 49 cumin ecotypes by BAHRAMINEJAD et al. (2011) indicated categorizing of them in to 3 groups in line with geographical distance provinces. Basically, morphological variation does not always indicate true genetic variation. Because of interaction between genotype and environment, and large unbeknown genetic control of polygenic morphological and agronomic traits (XU et al., 2018). However, morphological traits are advantageous for primary assessment, because of their fast and simple application and can be used as a typical way for estimating genetic diversity among morphologically differentiable populations (RAHIMI and KORDROSTAMI, 2017). In this study, the most important traits in discriminate accessions including seed weight per plant, grain yield and number of mini-umbel that indicated the best potential for increasing seed yield in cumin breeding in most part of Iran.

To improve the cultivars, some information about genetic polymorphism and relationship by using biotechnological tools like molecular markers, are needed. At the present experiment, the mean number of polymorphism (ISSR=25.74 and SCoT=29.67) was rather lower than ROSTAMI-AHMADVANI et al. (2013) ones that reported polymorph bands for ISSR markers 67% and for RAPD markers 54%, these accessions belong to Kerman, Esfahan and Khorasan provinces of Iran among cumin accessions. BAHRAMINEJAD and MOHAMMADINEJAD (2013) reported similar results using ISSR markers. Moreover, PARASHAR et al. (2014) showed polymorphic bands for SCoT markers about 79.8% for cumin plant.

Genetic similarity, marker technique, ecotypes, and their origins are notable parameters for affecting on genetic polymorphism in the plants (LUO et al., 2010) that could be explain low polymorphic percentage in this study. Clustering of accessions on the basis of maximum genetic distance would be useful in hybrid breeding programs (RAHIMI et al., 2018). Two main clusters were observed in all two procedures: molecular and agro-morphological-based analysis. In some accessions, this grouping confirmed each other. So that, in all two molecular and agro-morphological clusters, accessions Ferdows, Nehbandan and Faizabad nested in group I and accessions Rig, Khur, Ilkhchi, Dlazyan and Damghan nested in group II. This conformity could be remarkable in breeding programs. However, considering agro-morphological and molecular traits in consensus tree indicated that definition of ecotypes suits to accessions Chah-Dashi, Damghan, Ferdows and Nehbandan of cumin species in this study. Thus, this results indicated that different agro-morphological traits in these accessions related to different genetic background. Comparing of accessions group on the basis of agro-morphological traits and genetic data indicated that accessions with the highest and lowest performance (Boshruyah and Faizabad) discriminate in agro-morph clustering, while do not discriminate in genetic-based clustering. Accession 20 (Feyzabad) completely separated from other accessions and nested in cluster I, because of the lowest quantitative agro-morphological traits. Accessions Birjand 3, Boshruyah (south Khorasan), and Mobarakeh (Esfahan) have showed the best performance based on the phenotypic data, while accession Feyzabad (south Khorasan) had almost the lowest traits. Chah-Dashi, Damghan, Ferdows and Nehbandan accessions were introduced as ecotypes based on both agro-morphological and molecular differentiation. BAHRAMINEJAD et al. (2012) represented high variation between and within Iranian cumin accessions using phenotypic traits and RAPD markers. Also, the results of genetic variation between Iranian cumin accession using ISSR and RAPD markers indicated different molecular and morphological groups (ROSTAMI-AHMADVANDI et al., 2013).

The results of AMOVA test showed out of total genetic variation, 57% was due to within population genetic variability that similar results have been reported in different plant species and should be related to outcrossing nature of this plant species. The existence of high within population genetic variability is useful tool for adapting to local environmental changes (SHEIDAI et al., 2013).The same may hold true for the cumin accessions. Among-population differentiation in molecular variation (43%) is expected to occur as a consequence of isolation, drift, founder effects, and local selection (JOLIVET and BERNASCONI, 2007).

In Iran, cumin have various distribution regions like arid and semi-arid areas in eastern, south-eastern, central and western geographical regions that lead to adaptation to ecological impacts and variation in genetic traits (HASHEMIAN et al., 2013 ).In this study, the results of Mantel test has shown no significant correlation between geographical and genetic distances. Hence, in spite of genetic differentiation among the studied cumin accessions, they are not totally isolated and yet some amount of gene flow has occurred between them. The relationships within infra-species specimens sometimes are consistent with their geographical distance. While, in species with a wide distribution region, sometimes do not consistent with one (QIU et al., 2004; DING et al., 2013; LIU et al., 2013).

Based on our results investigation of relationship between genetic diversity and environmental factors showed impact of ecological conditions on adaptation and differentiation of cumin accessions. Also, genetic diversity of these accession were assessed in relation to ecological parameters of origin locality. The relationship between accession genetic diversity was not well accorded with ecological conditions of seed origin including latitude, altitude, longitude, the mean year temperature and rainfall. However, altitude and latitude effect on genetic variation of these accessions on the basis of CCA data.

Climate has an important role in local adaptation for plant species that could be lead to alter the genetic diversity of plan populations (MOSCA et al., 2012; AVOLIO et al., 2013). Increasing of genetic differentiation could be consequence of the environmental changing (WANG et al., 2009). These results are in line with the relationship between genetic diversity and ecological parameters in Stipa grandis from Inner Mongolia (ZHAO et al., 2006). Also this point was considered by HUANG et al. (2016) in Caragana microphylla (Fabaceae) with regard to relationships between climatic factors and genetic diversity that showed positive influence of some climatic factors on genetic diversity. Similar to our results were reported in Artemisia halodendron (Asteraceae) that indicated by HUANG et al. (2014).

The present molecular analyses with 2 genetic markers (ISSR and SCoT), indicated high genetic differentiation between cumin accessions. Also a significant variation was observed between the accessions in terms of genetic data and agro-morphological traits. Evaluation of effective ecological parameters on genetic differentiation shows influence of altitude and latitude on genetic grouping. In spite of genetic differentiation among the studied cumin accessions, they are not totally isolated and yet some amount of gene flow has occurred between them. Therefore, no IBD exists between them.

Afshar, F.; Sheidai, M.; Talebi, S.M.; Keshavari M. Bayesian and Multivariate Analyses of combined molecular and morphological data in Linum austriacum (Linaceae) populations: Evidence for infra specific taxonomic groups. Biodiversitas. 2015. 16, 179-187.

Aminpoor, R.; Mousavi S. The Effects of number of Irrigations on development stages, yield and yield components of cumin. WATER AND SOIL SCIENCE. 1997. 1, 1-8.

Amirmoradi, B.; Talebi, R.; Karami E. Comparison of genetic variation and differentiation among annual Cicer species using start codon targeted (SCoT) polymorphism, DAMD-PCR, and ISSR markers. Plant Syst Evol. 2012. 298, 1679–1688.

Avolio, M.L.; Beaulieu, J.M.; Smith, M.D. Genetic diversity of a dominant C4 grass is altered with increased precipitation variability. Oecologia. 2013. 171, 571–581.

Ayesh, B.M. Genotyping and Molecular Identification of Date Palm Cultivars Using Inter-Simple Sequence Repeat (ISSR) Markers. Methods Mol Biol. 2017. 1638, 173-183.

Bahraminejad, A.; Mohammadinejad G. Use of microsatellite markers for molecular characterization of cumin (Cuminum cyminum L.) ecotypes. Iran J Genet Plant Breed. 2013. 2, 35-41.

Bahraminejad, A.; Mohammadi-Nejad, G.; Abdul Khadir, M. Study of genetic diversity of cumin (Cumin cyminum L.) based on phenotypic characteristics. AUST J CROP SCI. 2011. 5, 301-307.

Bahraminejad, A.; Mohammadi-Nejad, G.; Abdul Khadir, M.; Bin Yusop, M. Molecular diversity of cumin (Cuminum cyminum L.) using RAPD markers. AUST J CROP SCI. 2012. 6, 194-199.

Collard, B.C.; Mackill, D.J. Start codon targeted (SCoT) polymorphism: a simple, novel DNA marker technique for generating gene-targeted markers in plants. Plant Mol. Biol Rep. 2009. 27, 86–93.

Ding, G.; Zhang, D.Z.; Yu, Y.Q.; Zhao, L.L.; Zhang, B.B. Analysis of genetic variability and population structure of the endemic medicinal Limonium sinense using molecular markers. Gene. 2013. 520, 189-193.

Doyle, J.J.; Doyle, J.L. Isolation of plant DNA from fresh tissue. Focus. 1990. 12, 13–15.

Freeland, J.R.; Kirk, H.; Peterson, S.D. Molecular Ecology, 2nd ed. Wiley-Blackwell, UK. 2011.

Ghasemzadeh Baraki, S.; Nikzat Siahkolaee, S.; Mousavi, A. Optimization of the genomic DNA extraction in some mosses. Rostaniha. 2018. 19, 165–175.

Hamer, Ø.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electronica. 2001. 4, 1-9.

Hashemian, N.; Pirbalouti, A.G.; Hashemi, M.; Golparvar, A.; Hamedi, B. Diversity in Chemical Composition and Antibacterial Activity of Essential Oils of Cumin (Cuminum cyminum L.) Diverse from Northeast of Iran. Aust. J. Crop Sci. 2013. 7, 1752-1760.

Huang, W.D.; Zhao, X.Y.; Zhao, X.; Li, Y.Q.; Lian, J.; Yun. J.Y. Relationship between the genetic diversity of Artemisia halodendron and climatic factors. Acta Oecologica. 2014. 55, 97–103.

Huang, W.D.; Zhao, X.Y.; Zhao, X.; Li, Y.L.; Lian, J.; Pan, C.C. Environmental determinants of genetic diversity in Caragana microphylla (Fabaceae) in northern China. Ecol Evol. 2016. 6, 8256–8266.

Jolivet, C.; Bernasconi, G. Molecular and quantitative genetic differentiation in European populations of Silene latifolia (Caryophyllaceae). J Hum Genet. 2007. 177, 1239-1247.

Kafie, M.; Rashed-Mohasel, M.H.; Koocheki, A.; Nassiri, M. Cumin (Cuminum cyminum) production and processing, Ferdowsi University Press, Iran. 2002.

Lodha, S.; Mawar, R. Cumin Wilt Management: A Review. J. Spice. Aromat. Crop. 2014. 23, 145-155.

Liu, F.; Guo, Q.S.; Shi, H.Z.; Wang, T.; Zhu, Z.B. Genetic diversity and phylogenetic relationships among and within populations of Whitmania pigra and Hirudo nipponica based on ISSR and SRAP markers. Biochem Syst Ecol. 2013. 51, 215-223.

Lodha, S.; Mawar R. Cumin Wilt Management: A Review. J. Spice. Aromat. Crop. 2014. 23, 145-155.

Lou, Y.; Hu, L.; Chen, L.; Sun, X.; Yang, Y.; Liu, H.; Xu, Q. Association analysis of simple sequence repeat (SSR) markers with agronomic traits in tall fescue (Festuca arundinacea Schreb.). PLoS One. 2015. 10, e0133054.

Luo, C.; He, X.H.; Chen, H.; Ou, S.J.; Gao, M.P. Analysis of diversity and relationships among mango cultivars using Start Codon Targeted (SCoT) markers. Biochem Syst Ecol. 2010. 38, 1176–1184.

Marsjan, P.; Oldenbroek, J. Molecular markers, a tool for exploring genetic diversity. In: Rischkowsky, B., Pilling, D. (Eds.), The State of the World's Animal Genetic Resources for Food and Agriculture. FAO, Rome. 2007. 359–379.

Mosca, E.; Eckert, A.J.; Di, P.E.; Rocchini, D.; La, P.N.; Belletti, P.; Neale, D.B. The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps. Mol Ecol. 2012. 21, 5530–5545.

Mulpuri, S.; Muddanuru, T.; Francis G. Start codon targeted (SCoT) polymorphism in toxic and nontoxic accessions of Jatropha curcas L. and development of a codominant SCAR marker. Plan Sci. 2013. 207, 117–127.

Peakall, R.; Smouse, P.E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006. 6, 288-295.

Perrier, X.; Jacquemoud-Collet, J.P. DARwin software version 5·0·155. CIRAD: 2006. http://darwin.cirad.fr/darwin.

Podani, J. Introduction to the Exploration of Multivariate Data [English translation]. Backhuys Publ., Leiden. 2000.

Parashar, M.; Jakhar, M.L.; Malik, C.P. A review on biotechnology, genetic diversity in cumin (Cuminum cyminum). Review Article. 2014. 4, 17-34.

Pradeep Reddy, M.N.; Sarla Siddiq, E.A. Inter simple sequence repeat (ISSR) polymorphism and its application in plant breeding. Euphytica. 2002. 128, 9–17.

Qiu, Y.X.; Hong, D.Y.; Fu, C.X.; Kenneth, M.C. Genetic variation in the endangered and endemic species Changium smyrnioides (Apiaceae). Biochem Sys. Ecol. 2004. 32, 583-596.

Rahimi, M.; Kordrostami, M. Genetic Diversity Evaluation of Lemon balm (Melissa officinalis L.) Ecotypes Using Morphological Traits and Molecular Markers. J Med Plant Res. 2017. 1, 97-104

Rahimi, M.; Nazari, L.; Kordrostami, M.; Safari P. SCoT marker diversity among Iranian Plantago ecotypes and their possible association with agronomic traits. Sci Hort. 2018. 233, 302–309.

Samantaray, S.; Dhagat, U.M.; Maiti, S. Evaluation of genetic relationships in Plantago species using Random Amplified Polymorphic DNA (RAPD) markers. Plant Biotechnol. 2010. 27, 297–303.

Shahlaei, A.; Torabi, S.; Khosroshahli, M. Efficacy of SCoT and ISSR markers in assessment of tomato (Lycopersicum esculentum Mill.) genetic diversity. Int J Biosci. 2014. 5, 14-22.

Rostami-Ahmadvandi, H.; Cheghamirza, D.; Kahrizi, S.; Bahraminejad, M. Comparison of morphoagronomic traits versus RAPD and ISSR markers in order to evaluate genetic diversity among Cuminum cyminum L. Accessions. Aust J Crop Sci. 2013. 7, 361-367.

Sheidai, M.; Zanganeh, S.; Haji-Ramezanali, R.; Nouroozi, M.; Noormohammadi, Z.; Ghsemzadeh-Baraki, S. Genetic diversity and population structure in four Cirsium (Asteraceae) species. Biologia. 2013. 68, 384-397.

Wang, M.L.; Zhu, C.; Barkley, N.A.; Chen, Z.; Erpelding, J.E.; Murray, S.C.; Tuinstra, M.R.; Tesso, T.; Pederson, G.A.; Yu, J. Genetic diversity and population structure analysis of accessions in the US historic sweet sorghum collection. Theor Appl Genet. 2009. 120, 13–23.

Weising, K.; Atkinson, R.G.; Gardner, R.C. Genomic fingerprinting by microsatellite-primed PCR: A critical evaluation. PCR Meth. Appl. 1995. 4, 249-255.

Xiong, FQ.; Zhong R.C.; Han, Z.Q. Start codon targeted polymorphism for evaluation of functional genetic variation and relationships in cultivated peanut (Arachis hypogaea L.) genotypes. Mol Biol Rep. 2011. 38: 3487–3494.

Xu, J.Y.; Zhu, Y.; Yi, Z.; Wu, G.; Xie, G.Y.; Qin, M.J. Molecular diversity analysis of Tetradium ruticarpum (WuZhuYu) in China based on inter-primer binding site (iPBS) markers and inter-simple sequence repeat (ISSR) markers. Chin J Nat Med. 2018. 16(1), 1-9.

Zhao, N.X.; Gao, Y.B.; Wang, J.L.; Ren, A.Z.; Xu, H. RAPD diversity of Stipa grandis populations and its relationship with some ecological factors. Acta Ecol Sin. 2006. 26, 1312–1319.

Download PDF

Version 1

posted

You are reading this latest preprint version

Evaluation of molecular diversity analysis and relation with environmental factors in accessions of Cumin (Cuminum cyminum L.) in Iran, revealed by inter-simple sequence repeat (ISSR) markers and start codon targeted (SCoT) markers

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Conclusions

References

Status:

Version 1