DOI: https://doi.org/10.21203/rs.3.rs-1439317/v1
Melons from the famous Silk Road have not been studied for their population structure, phylogenetic relationship, and diversity, which suggests their need for genetic study and breeding. Measurements of fruit and seed traits and a phylogenetic analysis of five chloroplast genome markers, 17 RAPD markers and 11 SSR markers were conducted for 87 Kazakh melon accessions with reference accessions. Fruit weight and shape was highly variable across Kazakh melons and was highly correlated with fruit length, fruit width, and pulp thickness. Fruit length could characterize Kazakh melon groups, while pulp thickness associated with fruit width was conserved due to culinary use, as was pulp sweetness. Molecular phylogeny showed that two unique genetic groups, STIa-2 with Ib-1/-2 cytoplasm and STIa-1 with Ib-3 cytoplasm, and one admixed group, STIAD combined with STIa and STIb, were prevalent across all Kazakh melon groups. STIAD melons that phylogenetically overlapped with STIa-1 and STIa-2 melons were frequent in the eastern Silk Road region, including Kazakhstan. Evidently, a small population contributed to melon development and variation in the historical record of the eastern Silk Road. Conscious preservation of fruit traits specific to Kazakh melon groups, with andromonoecy, is thought to play a role in the conservation of Kazakh melon genetic variation during melon production, where hybrid progenies were generated through open pollination. This overview of fruit phenotypic and molecular variation supports the belief that recurrent selection for edible parts after crop domestication involves varietal variation.
Melon (Cucumis melo L.) is a widely consumed crop from the Cucumis genus, similar to cucumber (Cucumis sativus L.), West Indian gherkin (Cucumis anguria L.) and horned melon (Cucumis metuliferus E. Mey. Ex Naudin), and it has various uses as a fruit or vegetable and in medicine, and aroma therapy (Pitrat 2016). Melon originated in Africa and parts of India and has been distributed to wide geographical areas across a long history of melon utilization of more than four thousand years (Zohary and Hopf 2000, Walters 1989, Sebastian et al. 2010). During its historically long utilization, melon fruit phenotypes, such as fruit size, fruit sutures, epicarp colour, pulp colour, pulp bitterness, sourness, and sweetness, have been improved to suit human needs (e.g., Zhao et al. 2019, Liu et al. 2020, Wang et al. 2021). Based on these phenotypic traits, at least 19 horticultural groups have been proposed: Agrestis, Kachri, Chito, Tibish, Acidulus, Momordica, Conomon, Makuwa, Chinensis, Flexuosus, Chate, Dudaim, Chandalak, Indicus, Ameri, Cassaba, Ibericus, Inodorus, and Cantalupensis (Pitrat 2016). Numerous local varieties, market classes and melons with phenotypes that do not fit traits specific to known horticultural groups exist worldwide (e.g., Stepansky et al. 1999) because of the ability to generate hybrid progeny via crossing. Hence, melon classifications provide beneficial information for genetic resource management and utilization in melon breeding.
Classification of Central Asian melons has been conducted with morphological and physiological traits (e.g., Pangalo 1950, Filov 1960). However, molecular phylogenetic classification was rare and limited to Iranian melons and northwestern Chinese melons, even though Central Asian melons are important genetic resources considering their long history on the famous Silk Road (Aierken et al. 2011, Raghami et al. 2014, Zhang et al. 2017). According to Aierken et al. (2011), the classification of melons by seed and fruit phenotypes and RAPD, SSR, and chloroplast DNA marker genotyping indicates selection during and after transmission based on the phylogenetic relationship between northwestern Chinese melons and Central Asian melons and their genetic diversity. However, a lack of Central Asian melons included in the classification led to less resolution for melon development in Central Asian countries of the Silk Road.
Kazakhstan is on the Silk Road through which the trade of goods, flow of people and introduction of cultures has occurred. During the long history of the Silk Road, animals and plants such as horses, apples, and apricots have undergone domestication and improvement via genomic changes (Librado et al. 2017, Duan et al. 2017). Melons have also developed along the historic footstep and landscape of the Silk Road. This region has a desert climate with little rain, low humidity, high day and low night air temperatures, and strong and persistent sunshine, such as in Uzbekistan and northwestern China, as well as neighbouring areas in Kazakhstan; this region has suitable conditions for sweet melon cultivation (Mavlyanova et al. 2005, Aierken et al. 2011, Zhang et al. 2017). Melons are cultivated mainly in southern Kazakhstan under irrigation from rivers, such as the Syr Darya, and are sold on the road side and in commercial markets alongside imported varieties from Uzbekistan and Kyrgyzstan. Melons are eaten raw, and sweetness is generally preferred in Kazakhstan. Fruit appearance, such as fruit size, fruit shape, fruit surface morphology and colour, and flesh traits are recognized by consumers and are useful for Kazakh melon classification. Kazakh cultivated melons include various groups, including known horticultural groups Agrestis, Ameri, Cantalupensis, Cassaba, Chandalak, and Inodorus and local melon groups Zard and Zurbek. Some melon groups are found in Uzbekistan and northwestern China (Mavlyanova et al. 2005, Aierken et al. 2011), indicating relationships between Kazakh melons and those of nearby areas. By assessing phenotypic and molecular data, we can provide an overview of their phylogenetic relationships and population structure, which suggest a reduced sample size in line within genetic studies, and assist in discussing Kazakh melon development.
Random amplified polymorphic DNA (RAPD) markers can be used to evaluate plant materials with simple experimental procedures and instruments. The low reproducibility of RAPD markers varies by reagent, experimental instrument, and experimenter. Nevertheless, molecular evaluation was successful for melons from wide geographical ranges based on two series of RAPD markers: one from Staub et al. (2000) (e.g., Dhillon et al. 2007, Luan et al. 2008) and another from Tanaka et al. (2007) (e.g., Aierken et al. 2011, Duong et al. 2021). These two series of RAPD markers can be used for the high-confidence molecular evaluation of melons. Simple sequence repeat (SSR) markers have a higher resolution to detect melon variation than RAPD markers and have higher reproducibility (Staub et al. 2000, Aierken et al. 2011, Duong et al. 2021). Useful SSR markers have been constructed for genotyping melons from a wide geographical area (e.g., Fukino et al. 2007, Kong et al. 2007, Fernandez-Silva et al. 2008). These SSR markers have been used to classify the diversity within melons (e.g., Tzitzikas et al. 2009, Raghami et al. 2014, Zhang et al. 2017, Chikh-Rouhou et al. 2021). Two standard SSR marker sets have also been constructed and applied to the classification of melons from a wide geographical area (López-Sesé et al. 2003, Escribano et al. 2012; Nhi et al. 2010, Duoug et al. 2021). Another useful marker for melon classification is the chloroplast genome, which is transmitted maternally in melon (Havey et al. 1998). This marker enables us to assess melon development from the maternal lineage perspective. Chloroplast sequence polymorphisms are useful for proposing the origin and phylogenetic relationships of melons (Sebastian et al. 2010, Endl et al. 2018). For large-scale screening, PCR-based markers such as cleaved amplified polymorphic sequence (CAPS) markers and derived cleaved amplified polymorphic sequence (dCAPS) markers were developed by Nhi et al. (2010) and Shigita et al. (2022). These markers were used to classify melons into three cytoplasmic genotypes, Ia, Ib-1/-2 and Ib-3, which were designated by Tanaka et al. (2013) based on melons from a wide geographical area. Here, molecular markers were used to reveal the phylogenetic relationships and population structure of Kazakh melons.
In this study, we measured fruit traits and seed size and genotyped chloroplast DNA, SSR, and RAPD marker loci across 87 melon accessions from southern Kazakhstan. We analysed their phylogenetic relationship and population structure with 115 reference accessions from Aierken et al. (2011) to assess Kazakh melon development, diversity, selection, and on-farm conservation.
A total of 87 melon accessions from Kazakhstan (Cucumis melo L.) were used in this study (Supplementary Table 1). To understand melon fruit preference and genetic structure in Kazakhstan, the fruits and seeds from specific accessions were used for trait measurement and molecular analysis in this study. These accessions were collected in four provinces, Almaty, Zhambyl, South Kazakhstan, and Kyzylorda, in southern Kazakhstan in 2011 and maintained as genetic resources at Okayama University. Based on fruit characteristics, as shown in Supplementary Table 2, and communication with Mr. Mamypbelov Z. about Kazakh melon classification, 63 of 87 melon accessions were classified into six known horticultural groups proposed by Pitrat (2016), including Agrestis, Ameri, Cantalupensis, Cassaba, Chandalak, and Inodorus and two local variety groups, Zard and Zurbek (Fig. 2). Group Zard was classified into six subgroups: Basvaldy, Guliabi, Kalaysan, Kara Guliabi, Sali Guliabi, and Zard. The remaining 24 cultivated melon accessions had admixed fruit phenotypes specific to 13 melon groups and were classified as the unknown melon group. Among those 14 Kazakh melon groups, including the unknown melon group, four groups had similar fruit characteristics to the following melon horticultural groups: Group Zurbek appeared to correspond to Subgroup Honeydew of Group Inodorus, and subgroups Guliabi, Kara Guliabi, and Sali Guliabi were related to subgroups Rochet, Tendral, and Amarillo, respectively, of Group Ibericus.
A total of 115 melon accessions, including two Kazakh melon accessions, were used as reference accessions for an analysis of molecular data from Aierken et al. (2011) (Supplementary Table 1). Thus, a total of 202 melon accessions were included in the data analysis.
A single fruit per accession was collected and used for the measurement of fruit weight, fruit length and fruit width. Fruit shape was calculated as the ratio between height and width. Pulp thickness values were collected from a fruit pulp section from around the centre of the melon fruit perpendicular to the longitudinal axis, and then soluble solids content (SSC) was analysed on the inside of the fruit pulp section with a digital hand refractometer (Atago Co. Ltd., Tokyo, Japan).
The length and width of 10 randomly selected melon seeds per accession were measured by using a hand-held CD-AX calliper (Mitutoyo, Japan); these seeds were harvested from a single mature fruit. Based on the average three-seed length, each accession was classified as a large-seed melon (≥ 9.00 mm) or a small-seed melon (< 9.00 mm) after Akashi et al. (2002).
For each accession, genomic DNA was extracted from a single ten-day-old seedling after Murray and Thompson (1980) with minor modifications. One plant from each accession was used for chloroplast genotyping and RAPD and SSR analyses.
Chloroplast genome type, assigned as the cytoplasm genotype, was determined based on an insertion or deletion in the region of psbC to trnS (InDel1 assigned by Tanaka et al. 2013) and four single nucleotide polymorphisms in the regions of trnK to matK (SNP2), rpl16 to rpl14 (SNP18), ndhF to rpl32 (SNP19), and ndhA intron 1 (SNP30). Screening for large-scale materials was performed using PCR-based markers as follows: a marker for InDel1, ccSSR-7, was established in the study of Chang and Staub (2003), markers for SNP30 were converted into cleaved amplified polymorphic markers (CAPS), and markers for the remaining three SNPs were converted into derived cleaved amplified polymorphic sequence markers (dCAPS) in the studies of Aierken et al. (2011) and Shigita et al. (2022) (Supplementary Table 3). The genotyping procedure was performed according to Aierken et al. (2011).
Thirteen RAPD and 11 SSR markers were selected for their reproducibility and ability to detect polymorphisms in Kazakh melon accessions (Tanaka et al. 2007, Nhi et al. 2010, Aierken et al. 2011).
Fruit phenotypic data and seed size data were used for ANOVA, Tukey–Kramer multiple comparison test, correlation analysis, principal coordinate (PCO) analysis, and Pearson's chi-square test which were performed using IBM SPSS Statistics for Windows, version 26 (IBM Corp., Armonk, NY, USA).
The cytoplasmic genome type of each melon accession was determined based on the genotypes of five chloroplast sequence polymorphisms, according to Tanaka et al. (2006). RAPD marker bands were scored as 1 for a positive band and 0 for a null band. For SSRs, marker fragments were scored based on their size from smallest (1) to largest (2–7, depending on the marker). From these data, calculations for the number of effective alleles (Ne), expected heterozygosity (He) and fixation index (FST) value were performed by using GenALEX v6.503 (Peakall and Smouse 2012), and then the polymorphic information content (PIC) and gene diversity within each group were calculated according to Botstein et al. (1980) and Nei (1973). The genetic distance (GD) among accessions and among populations was calculated as described by Apostol et al. (1993) and Nei (1972), respectively. Based on the GD matrix, a dendrogram by using the unweighted pair group method with arithmetic averages (UPGMA) cluster analysis was constructed by using Phylip v3.69 programs and was compared with that constructed by the neighbour-joining (NJ) method. The model-based software program STRUCTURE v2.3.4. (Pritchard et al. 2000) was used to infer population structure with a Bayesian approach using the RAPD and SSR marker dataset. The optimal value of K (the number of clusters) was deduced by evaluating K = 1–10 and determined by an admixture model with an allele frequencies correlated model. The length of the burn-in for the Markov chain Monte Carlo (MCMC) iterations was set to 5,000, and data were collected over 5,000 MCMC iterations in each run. Twenty iterations per K were conducted. The optimal value of K was identified using the ad hoc procedure introduced by Pritchard et al. (2000) and the method developed by Evanno et al. (2005), which were carried out in the online program ‘Structure Harvester’ (Earl and vonHoldt 2012). Data plotting after STRUCTURE simulation was conducted with CLUMPP (Jakobsson and Rosenberg 2007). For visualization of melon evolution and admixture within the ancestral group (main group), substructures within each main group were hierarchically detected by the same approach, according to Jia et al. (2013) and Liu et al. (2019). Membership was carried out with assignment probabilities (Q) > 0.70, which was a looser threshold value than the optimal value of 0.85 in another model study (Vähä and Primmer 2006), which allowed us to apply more accessions to the substructure analysis.
Variation in six fruit phenotypic traits, seed length and seed width was observed between and within the 13 Kazakh melon groups and one unknown melon group (Table 1). Kazakh melons had a seed length greater than 9.76 mm, classifying them as large-seeded melons, with the exception of two small-seeded melon accessions from Group Agrestis with a seed length of 5.76 mm. The variance coefficient across melons was larger for fruit weight, fruit length, and fruit shape, in order. The relative variance between the fruit trait values was also supported by the F values from ANOVA, although the highest F value was detected for fruit length. Fruit length showed a high correlation with both fruit weight and shape, with a correlation coefficient greater than 0.70 (Supplementary Table 4). The gradual increase in fruit length appeared in the following order, Chandalak, Cassva, Sary Guliabi/Basvaldy, Kara Guliabi and Ameri, implying that fruit weight increased and that fruit shape changed from a globular shape to an oblong shape (Fig. 1A). Soluble solids content (SSC) was found to be an independent trait compared to other five phenotypic traits based on correlation coefficient values (r = -0.128 – -0.058). Group Chandalak showed higher SSC values in the fruit pulp than other melon groups (Fig. 1B), while little difference between melon groups, including the hybrid population, was obtained based on the variance coefficient (Table 1). PCO of the above six fruit phenotypic traits produced three coordinates that represented 93.8% of the cumulative variance (Supplementary Table 5), and the first two coordinates or the first and 3rd coordinates showed clear separation of Group Agrestis from remaining Kazakh melon groups which were mixed each other (Supplementary Fig. 2AB). Consequently, the fruit trait values showed specific characteristics in Kazakh melons: gradual differentiation of fruit length together with fruit weight and fruit shape changes and conserved fruit width, thickness, and SSC among Kazakh melon groups.
The DNA fingerprints of the CAPS and dCAPS markers and insertion or deletion markers corresponded with the respective nucleotide sequences in five regions of the chloroplast genome (Supplementary Table 6). Eighty-seven Kazakh melon accessions, excluding two reference Kazakh accessions, were classified into three cytoplasm genotypes by those markers: 2 accessions were classified into Ia, 52 were classified into Ib-1/-2, and 33 were classified into Ib-3. The Ib-1/-2 and Ib-3 cytoplasm genotypes were dominant.
Thirteen random markers and 11 SSR markers generated a total of 92 alleles in the 202 melon accessions examined, of which 70 alleles were detected in 87 Kazakh melon accessions excluding the two reference accessions from Kazakhstan (Table 2). The number of alleles per SSR locus ranged from two to four in the Kazakh melon accessions, for which no unique alleles were obtained. The expected heterozygosity (He) ranged from 0.022 to 0.763, corresponding to the polymorphic information content results (PIC; r = 0.758). The mean He was higher in SSR markers than in RAPD markers in both the Kazakh melon accessions (0.390 and 0.133, respectively) and reference accessions (0.580 and 0.383, respectively), and the mean He was lower in Kazakh melon accessions than in reference accessions. Heterozygosity within SSR loci was observed in the Kazakh melon accessions, although the Ho values ranged from 0.011 to 0.213 (Mean: 0.055) and were lower than the He values (Range: 0.022 to 0.634, Mean: 0.390). With regard to the potential to detect polymorphisms, the SSR markers were more efficient.
To clarify the genetic relationships in melons between Kazakhstan and neighbouring countries and their genetic variation, genetic classification was carried out for melon accessions, including reference accessions. The pairwise genetic distances between 202 melon accessions were calculated from the RAPD and SSR data and ranged from 0 to 0.936, with an average of 0.393 (data not shown). The GDs calculated by combining the RAPD and SSR data was also related to the GDs calculated by RAPD data (r = 0.966, P < 0.01) and SSR data (r = 0.874, P < 0.01) alone. The GDs calculated from RAPD and SSR data showed a high correlation (r = 0.718, P < 0.01). The genetic relationships between the 202 melon accessions were visualized by UPGMA cluster analysis based on the RAPD genetic distance and SSR allele frequency (Fig. 2). The 202 melon accessions were grouped into seven groups, which were assigned as Cluster I to Cluster VII.
The STRUCTURE simulations of the admixture model-based calculations were performed using all 202 accessions. The LnP (D) value and Delta K value suggested the presence of two groups in the 202 accessions; 181 accessions were allocated into the two groups designated STI and STII, with the remainder in the admixed group STAD, with assignment probabilities (Q) > 0.70. Substructuring under the topmost hierarchy was detected for the accessions in STI using a similar approach. Consequently, using model-based classification, the 202 accessions were divided into STI (159 accessions), STII (22) and the admix group STAD (21), and STI was separated into five subgroups: STIa-1 (21), STIa-2 (20), STIa-3 (11), STIb-1 (26), and STIb-2 (10), with three admixed subgroups: STIAD (44), STIaAD (17) and STIbAD (10) (Fig. 2). This model-based classification was significantly correlated with the distance-based classification by the UPGMA cluster analysis (χ2 = 643.83, P < 0.01; Cramer’s V = 0.73, P < 0.01). The combined results of these two classifications provided the following phylogenetic overview: STIa located in Clusters I–III showed some divergence from STIb, which was mainly found in Clusters III–V; STIAD overlapped with STIa and STIb; STII in Cluster VII was grouped alone; and STAD in Clusters V and VI was an intermediate group between STI and STII.
To visualize the genetic groups associated with Kazakh melon development, the cytoplasm genotype representative of the maternal lineage was combined with the subgroups by model-based classification and distance-based classification (Fig. 3). Trends for the cytoplasm genotype were obtained from model-based classification as follows: divergence of East Asian melons from European and American melons and a close relationship between Kazakh melons and those from nearby areas in Central Asia and Russia (Fig. 3A). STIb-1 melons and/or Ia cytoplasm melons were rare in Kazakh melons (two accessions). In contrast, in the Ib cytoplasm, the subgroup STIAD of the admixed group with STIa and STIb was frequent in Central Asian (Turkmenistan, Uzbekistan, and Tajikistan), Russian, northwestern Chinese, and Kazakh melons and thought to be a key genetic group for melon development in these areas. Subgroups STIa-1 and STIa-2 were specific to Kazakh melons, and in combination with Ib cytoplasm genotypes, STIa-1 with Ib-3 cytoplasm and STIa-2 with Ib-1/-2 cytoplasm were unique genetic groups. STIa-1 with Ib-3 cytoplasm was frequent in Cluster I and rare in Cluster II (χ2 = 279.4, P < 0.01), whereas subgroup STIa-2 with Ib-1/-2 cytoplasm was evenly spread across Cluster I to Cluster III (χ2 = 5.4, P = 0.25; Fig. 3B). Similar to STIAD, these two subgroups were detected in the Kazakh melon groups Ameri, Cantalupensis, Chandalak, Zard and Cassaba and subgroups Basvaldy, Guliabi, and Kara Guliabi, as well as in the unknown melon group, which was prevalent in Kazakh melon (Fig. 4). Thus, the three subgroups were indicative of a close relationship between each Kazakh melon group.
This close relationship was supported by the phylogenetic analysis of 17 melon populations and their fixation index (FST) values. Kazakh melon groups clustered on the UPGMA tree, although groups Ameri, Cassaba, and Zard had similar fruit phenotypes to the northwestern Chinese Group Ameri, Spanish Group Cassaba, and northwestern Chinese Group Zard, respectively (Fig. 5). This clustering was in agreement with the FST values between the 17 melon populations as follows: the mean FST values between Kazakh melon groups with the remaining Kazakh melon groups ranged from 0.094–0.186 and were smaller than those with melons from nearby areas of Kazakhstan (FST = 0.138–0.272) and Spain and the USA (FST = 0.280–0.462) (Supplementary Table 7). Thus, Kazakh melon groups had genetic similarity to each other, which indicates their low genetic diversity. This low genetic diversity was well supported by the mean gene diversity, which was 0.224 for Kazakh melons and 0.158 for northwestern Chinese melons, and the values for Kazakh melon groups (0.099 to 0.253) were lower than those for Iranian, Afghanistan, Pakistan, and Central Asian melons (0.331 to 0.363) (Fig. 3A). Thus, the Kazakh melons examined here showed lower genetic variation than those other melons. Even between the three Kazakh provincial melon populations from Zhambyl, South Kazakhstan, and Kyzylorda, low divergence was detected by AMOVA, where 3% of the total variance was generated among the populations (Supplementary Fig. 3). The FST values among these three provincial melon populations was less than 0.036, indicating similar genetic components among them.
The two subgroups STIa and STIAD were dominant in Kazakh melons (Fig. 3A), while the admixed group STAD with STI and STII was a distantly related group to Kazakh melons (Fig. 2). Two accessions from the Kazakh Group Agrestis were allocated to STAD but shared Ib-1/-2 cytoplasm with cultivated Kazakh melons. Their location on the UPGMA tree was Cluster V, where one accession with Ib-1/-2 and two accessions with Ib-3 were included among cultivated Kazakh melons. Thus, the Kazakh Group Agrestis examined here showed phylogenetic similarity with a few cultivated Kazakh melons.
Melons are cultivated in southern Kazakhstan and exported to or imported from nearby areas, such as Kyrgyzstan, Turkmenistan, Tajikistan, and Uzbekistan, which are on the Silk Road. The evaluation of melon fruits sold in Kazakhstan is expected to provide an overview of fruit trait preferences, characteristics, and current conditions of on-farm conservation in these areas. Here, we discuss Kazakh melon development, gene diversity, and fruit phenotype selection, taking into account current melon on-farm conservation.
The phylogenetic classification in the current study clearly shows that genetic groups contributed to Kazakh melon development. Model-based classification showed a correlation with distance-based classification and even cytoplasm genotyping. STIAD with Ib-1/-2 and Ib-3 cytoplasm was frequent in Kazakhstan, Central Asia (Turkmenistan, Uzbekistan, and Tajikistan), Russia, and northwestern China (Fig. 3A). These results support the suggestion of an ancestral relationship among melons on the Silk Road (Luan et al. 2008, Aierken et al. 2012, Zhang et al. 2017) and are agreement with historical records that Kazakh melon has been inbred in the constituent countries of the former Russian Federation in the Soviet Union era, similar to Kazakh wheat and barley (Turuspekov et al. 2017, Lister et al. 2018, Almerekova et al. 2021). Thus, Kazakh melon is thought to be a progenitor of melons produced through the Silk Road and/or during the Soviet Union era.
Taking into account their phylogenetic relationship with STIb melons and STIa melons (Fig. 2), STIAD melons appear to be a contributor to Kazakh melon development and are related to both STIb-1 melons and STIa melons. Two genetic groups, STIa-1 with Ib-3 cytoplasm and STIa-2 with Ib-1/-2 cytoplasm, were frequent and unique in Kazakh melon groups (Fig. 4), suggesting involvement in early Kazakh melon development. The dominance of these two genetic groups in the phylogenetic cluster suggested that the evolution of STIa-2 with Ib-1/-2 cytoplasm was prevalent in Clusters I to III, prior to the emergence of STIa-1 with Ib-3 cytoplasm (Fig. 3B). However, STIa-1 and STIa-2 Kazakh melons shared alleles with other Kazakh melons, as well as reference accessions. Frequent recombination events in the whole melon genome are demonstrated by low linkage disequilibrium within diverse melons (Esteras et al. 2013, Gonzalo et al. 2019). STIa-2 and STIa-1 Kazakh melons may have been generated by allelic exchange and chromosome recombination.
In the case of Group Agrestis, which was introduced as “wild melon” (Pitrat 2013) after Naudin (1859), two Kazakh accessions examined were collected in a ridge or grassland habitat beside a cultivated field. These two Kazakh accessions grouped into Cluster V with Ib-1/-2 cytoplasm. Among Kazakh cultivated melons, one accession of Subgroup Zard was obtained in Cluster V, which contained both Ib-1/-2 cytoplasm and Ib-3 cytoplasm, and one accession each from Group Cassava and Subgroup Zard were found in Cluster V (Supplementary Table 1). Wild Kazakh melons appear to be genetically similar to a few cultivated Kazakh melons. Genetic similarity between wild and cultivated melons from the origin area has been found across wide geographical areas in phylogenetic studies using a larger number of molecular markers (e.g., Hu et al. 2014, Gonzalo et al. 2019, Zhao et al. 2019). Large genetic regions are nearly identical between wild and cultivated plants from the same origin, similar to the results of genome comparisons between weedy rice and cultivated rice (Li et al. 2017, Qiu et al. 2020) and between Tibetan semiwild wheat and Tibetan landraces (Guo et al. 2020). Wild melon has uniform traits of small leaves, a small fruit size, pulp sourness, and pulp bitterness (Supplementary Table 2; Pitrat 2013), and 19 quantitative trait loci have been identified for these wild traits (Zhao et al. 2020). From these results, the few genetic regions associated with these uniform traits are thought to be sustainable in nature, and if cross-pollination with a cultivated melon occurs, the remaining genetic regions could be substituted with those from cultivated melon in their progenies. Given this perspective, it was not surprising that the wild Kazakh melons examined showed a relationship with the cultivated melons; conversely, genetic introgression of wild melon DNA into cultivated melons may occur, although the specific fruit traits of cultivated melons were preserved. Thus, wild Kazakh melons are contributors to the genetic variation in cultivated Kazakh melons.
Model-based classification suggests that the genetic variation in Kazakh melons formed one admixed group STIAD and two unique genetic groups: STIa-1 with Ib-3 cytoplasm and STIa-2 with Ib-1/-2 cytoplasm. A wide variety of Kazakh melons were classified into these three genetic groups (Fig. 4) and showed low mean gene diversity values, similar to northwestern Chinese melons (Fig. 3A). The geographical frequency of STIAD increased in Central Asia, Kazakhstan, and northwestern China along the eastern Silk Road. These results are supported by the study of Aierken et al. (2011), who found that northwestern Chinese melons showed less genetic variation than those of Iran, Afghanistan, Pakistan, and Central Asia, suggesting a small founding population during Kazakh melon development. In the case of barley, central and northern populations showed genetic distinction from the southern population based on SNPs tightly linked to genes for plant adaptation traits, such as heading date and plant height (Almerekova et al. 2021). However, Zhambyl, South Kazakhstan, and Kyzylorda Provinces in southern Kazakhstan, where we collected our plant materials, share suitable conditions for sweet melon production such as low air humidity, large differences in day-and-night air temperature, strong sunshine, and long sunshine duration; these conditions are also found in Uzbekistan and northwestern Chinese areas on the Silk Road (Mavlyanova et al. 2005, Aierken et al. 2011, Zhang et al. 2017). Due to these advantageous conditions for sweet melon cultivation, the selective pressure on melon was weaker and could not drive the decrease in gene diversity in Kazakh melon. Therefore, there are other causes leading to low gene diversity in Kazakh melon, such as genetic drift when melon was introduced into Kazakhstan.
However, the low genetic variation in Kazakh melon is also explained by the observed heterozygosity, the basal value for the gene diversity. The mean Ho value ranged from 0 to 0.213 in Kazakh melon accessions, which was lower than the He value (range: 0.114–0.574) (Table 2). This reduction in Ho has also been found for Greek and Cypriot melons (Staub et al. 2004, Tzitzikas et al. 2009), Iranian melons (Raghami et al. 2014), and northwestern Chinese melons (Zhang et al. 2017). These previous studies suggest the potential for small sample sizes, high levels of selection, or homozygosity by self-pollination causing the reduction in Ho or the low genetic variation in Kazakh melon. Thus, we discuss this potential scenario further.
The current study utilized 89 accessions (one plant each), which generated a mean Ho value of 0.055 (Table 2). This value is lower than that of 0.12 for 360 Iranian melon plants (15 plants each from 24 accessions in Raghami et al. 2014), 0.22 for 175 northwestern Chinese melon plants (5 plants each from 35 accessions in Zhang et al. 2017), and 0.23 for 500 Indian melon plants (10 plants each from 50 accessions in Fergany et al. 2011). The smaller sample size examined in the current study might be prone to the low Ho value, but the Kazakh melon production system has the potential to increase Ho in the future. Different melon groups are cultivated alongside Kazakh melon fields, and seeds for the next round of melon cultivation are harvested from open-pollinated fruits. Cross hybridization between different plants could generate allelic heterozygosity. Andromonoecious plants, i.e., hermaphroditic and male flowers produced on the same individual, were obtained from the Kazakh melons examined, with the exception of three monoecious accessions (JP242177, JP242179, and JP242183), based on the database from the Genetic Resource Center, NARO, JAPAN (https://www.gene.affrc.go.jp/databases_en.php?section=plant). This sexual type has advantages for pollination, especially self-pollination, because it provides abundant amounts of fertile pollen from the anthers of both hermaphroditic and male flowers during insect-mediated pollination, with equal pollen fertilizing abilities (Fujishita 1959). Thus, andromonoecy leading to self-pollination may promote a decrease in allelic heterozygosity in Kazakh melon.
On the other hand, andromonoecy has the potential to play a role in cross-pollination. If male flowers increase the floral display, they can affect cross-pollination by attracting more pollinators that bring outside pollen to the stigmas of hermaphroditic flowers (Janzen 1977, Kouonon et al. 2009, Casimiro et al. 2013). It was reported in the cantaloupe melon that hermaphroditic flowers were effective at attracting honeybees based on their larger corollas and long honeydew excretion (Mann 1953). Andromonoecy can consequently increase the fruit setting rate while contributing to outcrossing with other melon plants via pollinators in melon fields where several melon groups are cultivated together. Therefore, there is potential gene introgression that leads to the generation of similar genetic components among these melon groups and the breakdown of specific traits, as observed in the unknown melon group in the current study (Fig. 4). This genetic component resemblance among melons can explain the close relationships between the Kazakh melon group with low FST values (Fig. 5); nevertheless, there were specific traits that could be used to classify Kazakh melon (Supplementary Table 2). Several phylogenetic studies display grouping patterns for melons from geographically identical origins but from different horticultural groups, market classes, or cultivar classes (e.g., Tzitzikas et al. 2009, Esteras et al. 2013, Raghami et al. 2014), suggesting that the grouping is associated with beneficial trait introgression into melons and heterogeneity in the examined genetic regions, with retention of potentially important genetic variation. Classification by fruit metabolic quality traits was in close agreement with that by DNA fingerprinting (Moing et al. 2020). Due to the low genetic variation in Kazakh melon groups, the above patterns suggested that conscious preservation may be performed on specific traits, leading to the retention of their relevant genetic regions, even though other genetic regions may have changed and show genetic similarity within each Kazakh melon group, such as those outside of the genetic region of domesticated genes (Li et al. 2017, Zhao et al. 2019, Liu et al. 2020).
Several fruit phenotypes were retained in Kazakh melon groups (Supplementary Table 2). Pulp sweetness with less sourness is an essential trait for eating the flesh pulp of cultivated Kazakh melons. Most of the cultivated Kazakh melons had more than 9.0 °Brix (Fig. 1B), which is a normally acceptable value for sweetness in melons (Burger et al. 2006). Thick fruit epicarp and hard fruit rind are important shipping-related traits in melons. The grade for fruit weight and fruit shape in different melon groups depends on fruit length (Fig. 1A). Other fruit appearance traits, such wrinkles, ribs and/or sutures on the fruit surface as well as epicarp colour, are easily discriminable traits for the different melon groups. Fruit pulp colour and texture, such as crispy texture and melting texture, are favourable traits for consumer preference. All melon genetic studies reveal major single genes controlling sutures on the fruit surface, fruit epicarp colour, fruit pulp colour and decreased sourness (Cohen et al. 2014, Tzuri et al. 2015, Zhao et al. 2019) and multigene integration for fruit weight, shape, and length and pulp sweetness (Harel-Beja et al. 2010, Zhao et al. 2019, Liu et al. 2020, Oren et al. 2000, Wang et al. 2021). A number of genes relevant to fruit phenotypes suggest that the genetic region retained in Kazakh melon was not small. Genetic variation within the Kazakh melon group may be generated by not only population size during their development but also conscious selection by farmers to preserve fruit phenotypes to satisfy consumer demands. This preservation may be performed on a fruit with traits specific to the melon group that is used for harvesting the seeds for future cultivation, leading to low heterozygosity in the Kazakh melon population (Table 2). Considering that ancient people have techniques for intensive selection for fruit quality, such as fruit sweetness and acidity in apples, on the Silk Road near Kazakhstan (Duan et al. 2017), the conscious selection of melon fruit is possible.
Fruit weight and shape showed large variation in Kazakh melons (Table 1), which is thought to be representative of their fruit phenotypic variation. This perspective is expected based on the classification of Central Asian melon, in which fruit shape and weight or fruit size are generally utilized (Filov 1960, Mavlyanova et al. 2005, Pitrat 2016). The two traits can be resolved into three basic traits, fruit length, fruit width, and pulp thickness, which indeed showed a higher correlation in the current study (Supplementary Table 4). Among these three basic traits, fruit length showed variation within Kazakh melon groups with a larger variance coefficient than fruit width and pulp thickness (Fig. 1A, Table 1). Fruit length, similar to fruit width, explains fruit weight and shape variations via major genes, such as the flower gene locus andromonoecious relevant to longitudinal fruit growth and the carpel number gene locus pentamerous (exactly controlling five placentas) relevant to latitudinal fruit growth (Fernandez-Silva et al. 2010, Monforte et al. 2014, Galpaz et al. 2018) in melon and tomato (van del Knaap et al. 2014). It has also been shown that robust quantitative trait loci controlling fruit length and width contribute to fruit shape and weight variations (e.g., Harel-Beja et al. 2010, Díaz et al. 2014, Pereira et al. 2018, Oren et al. 2020). Nevertheless, reductions in fruit width and pulp thickness variation were obtained in Kazakh melon (Table 1). The Kazakh melons examined, with the exception of wild melons, had three locules in a central cavity (Supplementary Table 2); thus, the number of locules may have insignificant effects on fruit width, fruit shape and weight variation. Melons are eaten fresh in Central Asia, where the pulp thickness and sweetness of the edible part are essential, as shown in Supplementary Table 2, and reach a certain level of quality, which may contribute to a decrease in their variation (Fig. 1B). Kazakh melons are transported from production areas to distant markets, as are Central Asian melons (Mavlyanova et al. 2005), and have a thick or hard pulp appropriate for shipping, as shown in Supplementary Fig. 1.. The functional genetic information relevant to fruit size, culinary use and fruit phenotype characteristics for Kazakh melons supports the suggestion that fruit length rather than fruit width and pulp thickness contributes to fruit weight and shape variations characterizing the Kazakh melon group.
Three provincial melon populations derived from Zhambyl, South Kazakhstan, and Kyzylorda Provinces, the main melon production areas in Kazakhstan, showed low divergence, with an FST value of less than 0.036. This FST value was lower than that of 0.094–0.186 among Kazakh melon groups (Supplementary Table 7), which appeared to have a similar genetic composition among the three provincial melon populations. Kazakh melons are transported from distant production areas to markets where several melon groups are supplied. Thus, this low divergence reflects the dominant melons in these three provinces and implies that our plant material covers the genetic variation in Kazakh melon.
By using these plant materials, the current study provides an overview of Kazakh melon diversity. Distance-based and model-based classifications displayed two unique genetic groups and one admixed group of STIAD. The number of accessions in these groups indicates that small populations contributed to melon development and genetic variation in the eastern Silk Road. Andromonoecy leading to self-pollination also contributed to the genetic variation during Kazakh melon production, where hybrid progenies were generated through outcrossing. Gene introgression from wild and cultivated melons was occurred during Kazakh melon production; nevertheless, fruit traits specific to the current Kazakh melon groups are conserved by conscious preservation of fruit traits. Information about the diversity and coverage of genetic variation in Kazakh melons is useful for their management and utilization. This is especially true for monitoring genetic variation, because conservation of on-farm phenotypic and genetic variation is essential, similar to that for rice landraces in southwestern China (Cui et al. 2016, Wang et al. 2016). Our data confirm that ex situ conservation for the Kazakh melons examined here can cover their overall variation.
Acknowledgements
The authors thank Dr. Kathleen R. Reitsma, Iowa State University, USA, and Dr. Yoshiteru Sakata, the former NARO Institute of Vegetable and Floriculture Science, Japan, for kindly supplying the seeds and the Research Institute for the Humanities and Nature, Japan, for lending instruments for DNA analysis.
Funding
This work was partly supported by the Sasakawa Scientific Research Grant from the Japan Science Society (17-218); the Japan Society for the Promotion of Science (JSPS) Asian CORE Program entitled ‘‘Cooperative Research and Educational Center for Important Plant Genetic Resources in East Asia’’; the JSPS Grant-in-Aid for Scientific Research (A) (No. 26257409); Grant-in-Aid for Scientific Research (C) (18K01087); and a grant entitled “A Collaborative Research Project on Characterization and Evaluation of Plant Genetic Resources for Food and Agriculture (PGRAsia)” from the Ministry of Agriculture, Forestry, and Fisheries of Japan (Grant Number JPJ007117).
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
K.T., R.I., H.N., and K.K. designed the experiments; K.T. and M.S. measured fruit and seed traits; K.T. and Z.M. conducted melon group classification; K.T., G.S., R.M., T.T. D, and Y.A. performed DNA analyses; K.T. and G.S. conducted DNA data analysis; K.T., A.M.A., Z.M. and K.K. interpreted the data; K.T. and K.K. wrote the manuscript; and H.N. and K.K. modified the manuscript. All authors read the manuscript.
Ethical approval
All Kazakh melon genetic resources were stored at Okayama University after contracting with Kazagroinnovatsia Company, Ministry of Agriculture in Kazakhstan and All-Russian Institute of Plant Genetic Resources on the name of N.I.Vavilov (VIR) in the Russian Federation under the Standard Material Transfer Agreement of the International Treaty on Plant Genetic Resources for Food and Agriculture. Duplicate seed samples are stored in the Genetic Resource Centre, National Agriculture and Food Research Organization (NARO), Japan, and are available upon request.
Tables 1 and 2 are available in the Supplemental Files section