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2024 Volume 3
Article Contents
Abstract
Introduction
Types of functional rice
High resistant starch rice
Colored rice
Giant embryo rice
Low glutelin rice
Golden rice
Fe and Zn fortified rice
Breeding technology of functional rice
Traditional polymerization breeding
Transgenic polymerization breeding
Molecular marker-assisted selection polymerization breeding
Cultivation and regulation technology of functional rice
Effect of light on the accumulation of active components in rice
Effect of temperature on the accumulation of active components in rice
Effect of water management on the accumulation of active components in rice
Effect of fertilizer application on the accumulation of active components in rice
Effect of harvesting time on the accumulation of active components in rice
Prevention and control of pests and diseases for functional rice
Conclusions
Acknowledgments
Conflict of interest
Supplementary information
Rights and permissions
References
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ARTICLE   Open Access    

Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers

  • 1.

    Cash Crops Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, Guangxi 530007, China

  • 2.

    College of Pharmacy, Guangxi Medical University, Nanning, Guangxi 530021, China

  • # Authors contributed equally: Pingli Shi, Yun Zhou

More Information
  • Received: 30 November 2023
    Revised: 22 February 2024
    Accepted: 14 March 2024
    Published online: 23 April 2024
    Tropical Plants  3,  Article number: e012 (2024)  |  Cite this article

  • 272 individuals of Pueraria species in Guangxi were divided into two main clusters in all analysis.

    118 alleles were identified and 112 alleles were polymorphic.

    Overall genetic diversity was moderate.

    A core collection of 20 Pueraria accessions was constructed when the samples collected reached 7.35% (20/272).

  • Pueraria, extensively cultivated in Guangxi, China, stands as a pivotal commercial crop and a valuable supplement for human health. Despite its significance, the core germplasm and genetic diversity within Guangxi's Pueraria populations remain largely unexplored. This study delves into the genetic diversity of a comprehensive collection of 272 Pueraria germplasm accessions from Guangxi, utilizing 23 simple sequence repeat (SSR) markers. The average number of SSR alleles per locus was 5.13, ranging from 2 to 11, with four primers (PtSSR121, PtSSR196, PtSSR155, and PtSSR222) consistently producing at least two polymorphic bands, while PtSSR122 yielded an impressive 11 polymorphic bands. The analysis revealed 118 alleles, 112 of which were polymorphic. The average gene flow (Nm) was estimated at 1.7690, and the average predicted heterozygosity per location was 0.1841. Principal component and STRUCTURE cluster analyses corroborated the division of the 272 accessions into two main clusters. However, no significant statistical correlation was observed between geographic and genetic distances. The study identified a moderate level of genetic diversity. A core collection comprising 20 Pueraria accessions that encompass 105 alleles was proposed. These findings provide a theoretical basis for the strategic conservation of Pueraria's genetic resources, laying the groundwork for future breeding programs.
    Graphical Abstract

  • Introduction

    With the development of the world economy, people's lifestyles have changed dramatically, and long-term high-intensity work has put many people's bodies in a sub-healthy state. The increasing incidence of various chronic diseases has not only put enormous pressure on society's healthcare systems but also caused endless suffering to people[1]. Therefore, people's demands on the functionality and safety of food are increasing, and it has become the consensus of people that 'not just eating enough, but more importantly eating well'.

    Rice is the staple food for more than half of the world's population and the main economic source for a large number of rural people[2]. However, due to the rising cost of rice cultivation, farmers are gaining less and less economic benefits from growing rice, which seriously undermines their incentive to grow rice and poses a serious threat to world food security. Increasing the added value of rice not only helps to increase farmers' income but also helps to ensure world food security. The presence of a large number of functional ingredients in rice makes it possible to increase the added value of rice, and functional rice has therefore been widely noticed.

    Functional rice refers to rice containing certain specific components that play a regulatory and balancing role in human physiological functions in addition to the nutrients necessary for human growth and development in the endosperm, embryo, and rice bran. They can increase human physiological defense mechanisms, prevent certain diseases, help recovery, delay aging, and boost physical strength and energy levels[3]. Rice is a staple food for more than half of the world's population[4], and its functional components have a great potential to be exploited for human welfare. Using functional rice as a carrier to address health problems and realize 'medicine-food homology' is an excellent motivation for promoting functional rice. The current typical functional rice is introduced in this paper. It also summarizes the breeding and cultivation technologies of functional rice.

    Types of functional rice

    High resistant starch rice

    Rice has a high glycemic index. Its long-term consumption leads to obesity, diabetes, and colon disease in many people[5]. However, the consumption of rice rich in resistant starch (RS) can greatly reduce the risk of these diseases[6]. Therefore, breeding rice varieties with high RS content has attracted considerable attention from breeders in various countries. However, the variability of RS content between different rice varieties is low, and there are few germplasm resources available for selection, thus making it challenging to breed rice varieties with high RS content using traditional breeding methods. Combining traditional and modern molecular breeding techniques can greatly improve the successful production of high RS rice breeds. Nishi et al.[7] selected a high RS rice variety EM10 by treating fertilized egg cells of Kinmaze with N-methyl-N-nitrosourea. However, its yield was very low, and it was not suitable for commercial production. Wada et al.[8] crossed 'Fukei 2032' and 'EM129' as parents and selected Chikushi-kona 85, a high RS rice variety with a higher yield than EM10. Miura et al.[9] bred ultra-high RS BeI-BEIIB double mutant rice by crossing the Abe I and Abe IIB mutant strains, and the content of RS in the endosperm reached 35.1%. Wei et al.[10] found that the simultaneous inhibition of starch branching enzyme (SBE) genes SBEIIb and SBEI in Teqing by antisense RNA could increase the RS content in rice to 14.9%. Zhu et al.[11] used RNAi technology to inhibit the expression of SBEI and SBEII genes in rice, which increased the content of RS in rice endosperm from 0 to 14.6 %. Zhou et al.[6] found that rice RS formation is mainly controlled by soluble starch synthase (SSIIA). However, its regulation is dependent on the granule-bound starch synthase Waxy (Wx), and SSIIA deficiency combined with high expression of Wxa facilitates the substantial accumulation of RS in the rice. The results of Tsuiki et al.[12] showed that BEIB deficiency was the main reason for the increased accumulation of RS in rice. Itoh et al.[13] developed new mutant rice lines with significantly higher levels of RS in rice by introducing genes encoding starch synthase and granule-bound starch synthase in the rice into the BEIB-deficient mutant line be2b.

    Colored rice

    The accumulation of anthocyanins/proanthocyanidins in the seed coat of the rice grain gives brown rice a distinct color[14]. Most common rice varieties lack anthocyanins in the seed coat, and so far, no rice variety with colored endosperm in its natural state has been identified. However, Zhu et al.[15] bred rice with purple endosperm using transgenic technology. Red rice contains only proanthocyanidins, while black and purple rice contain anthocyanidins and proanthocyanidins[16]. Red seed coat of rice was found to be controlled by the complementary effects of two central effect genes Rc and Rd. The loss of function of the Rc gene prevented the synthesis of proanthocyanidins, while the Rd gene could enhance the effect of the Rc gene in promoting proanthocyanidins synthesis[17]. Purple seed coat color is controlled by two dominant complementary genes Pb and Pp. Pb determines the presence or absence of seed coat color, and Pp determines the depth of seed coat color[18]. In addition, phycocyanin synthesis is also regulated by transcription factors such as MYB, bHLH, HY5, and WD40[14], but the exact regulatory mechanism is not clear. Colored rice is rich in bioactive components, such as flavonoids, phenolic acids, vitamin E (VE), glutelin, phytosterols, and phytic acid (PA). It also contains large amounts of micronutrients such as Ca, Fe, Zn, and Se[19], and has a much higher nutritional and health value than ordinary white rice. In addition, Zhu et al.[20] successfully developed rice with enriched astaxanthin in the endosperm by introducing the genes sZmPSY1, sPaCrtI, sCrBKT, and sHpBHY. This achievement has laid a solid foundation for the further development of functional rice industry.

    Giant embryo rice

    Giant embryo rice refers to rice varieties whose embryo volume is more than twice that of ordinary rice[21]. Rice embryo contains more nutrients than the endosperm; therefore, the nutritional value of giant embryo rice greatly exceeds that of ordinary rice. Studies have found that the levels of γ-aminobutyric acid (GABA), essential amino acids, VE, γ-oryzanol, phenols, and trace elements in giant embryo rice are considerably higher than that in ordinary rice[21]. Satoh & Omura[22] used the chemical mutagen N-methyl-N-nitrosourea to treat the fertilized egg cells of the rice variety Kinmaze to obtain a 'giant embryo' mutant. The mutants’ embryo occupied 1/4–1/3 of the rice grain volume and was 3–4 times larger than normal rice embryo[23]. Its GABA content increased dramatically after the rice was soaked in water. Maeda et al.[24] crossed the giant embryo mutant EM40 of Kinmaze with the high-yielding variety Akenohoshi to produce the giant embryo rice variety 'Haiminori'. The embryo size of 'Haiminori' is 3–4 times that of ordinary rice, and the GABA content of its brown rice is 3–4 times higher than that of 'Nipponbare' and 'Koshihikari' after soaking for four hours in water. A few genes that can regulate the size of rice embryos have been identified, and GE is the first identified rice giant embryo gene[25]. Nagasawa et al.[26] found that the loss of GE gene function resulted in enlarged embryos and smaller endosperm in rice. Lee et al.[27] found that the inhibition of LE gene expression by RNAi technology could lead to embryo enlargement in rice, but the regulatory mechanism remains to be investigated.

    Low glutelin rice

    Protein is the second most crucial nutrient in rice, accounting for 7–10% of the grain weight, and glutenin accounts for 60%–80% of the total protein content in rice grains[28]. Compared to other proteins, glutenin is more easily digested and absorbed by the body[29]. Therefore, higher glutenin content in rice can improve its nutritional value. However, people with renal disease (a common complication of diabetes) have impaired protein metabolism, and consumption of rice with lower glutelin content can help reduce their protein intake and metabolic burden[30]. Japanese breeders treated Nihonmasari with the chemical mutagen ethyleneimine and selected the low-glutelin rice mutant NM67[31]. Iida et al.[31] developed a new rice variety LGC-1 (Low glutelin content-1) with a glutelin content of less than 4% by backcrossing the NM67 mutant with the original variety 'Nihonmasari'. According to Miyahara[32], the low glutelin trait in LGC-1 is controlled by a single dominant gene Lgc-1 located on chromosome 2. Subsequently, Nishimura et al.[33] produced two rice varieties, 'LGC Katsu' and 'LGC Jun' with lower glutelin content by crossing LGC1 with a mutant line Koshikari (γ-ray induction) lacking 26 kDa globulin (another easily digestible protein).

    Golden rice

    Vitamin A (VA) is one of the essential nutrients for the human body[34]. However, rice, a staple food, lacks VA, leading to a VA deficiency in many people. β-carotene is a precursor for VA synthesis and can be effectively converted into VA in the human body[35]. Therefore, breeding rice varieties rich in β-carotene has attracted the attention of breeders in various countries. Ye et al.[36] simultaneously transferred phytoene synthase (psy), phytoene desaturase (crt I), and lycopene β-cyclase (lcy) genes into rice using the Agrobacterium-mediated method and produced the first generation of golden rice with a β-carotene content of 1.6 µg·g−1 in the endosperm. However, due to the low content of β-carotene in rice, it is difficult to meet the human body's demand for VA. To increase β-carotene content in rice, Paine et al.[37] introduced the phytoene synthase (psy) gene from maize and the phytoene desaturase (crt I) gene from Erwinia into rice. They obtained the second generation of golden rice with 37 µg g−1 of β-carotene in the endosperm, with nearly 23-fold increase in β-carotene content compared to the first generation of golden rice.

    Fe and Zn fortified rice

    Fe and Zn are essential trace elements for human beings. The contents of Fe and Zn in common rice are about 2 μg·g−1 and 16 μg·g−1, respectively[38], which are far from meeting human needs. In 2004, to alleviate micronutrient deficiencies among underprivileged people in developing countries, the Consultative Group on International Agricultural Research launched the HarvestPlus international collaborative program for improving Fe, Zn, and β-carotene levels in staple crops, with breeding targets of 13 μg·g−1 and 28 μg·g−1 for Fe and Zn in rice, respectively. Masuda et al.[39] found that expression of the nicotianamine synthase (NAS) gene HvNAS in rice resulted in a 3-fold increase in Fe and a 2-fold increase in Zn content in polished rice. Trijatmiko et al.[38] overexpressed rice OsNAS2 gene and soybean ferritin gene SferH-1 in rice, and the Fe and Zn content in polished rice of rice variety NASFer-274 reached 15 μg·g−1 and 45.7 μg·g−1, respectively. In addition, it has been found that increasing Fe intake alone does not eliminate Fe deficiency but also decreases the amount of Fe absorption inhibitors in the diet or increases the amount of Fe absorption enhancers[40]. The negatively charged phosphate in PA strongly binds metal cations, thus reducing the bioavailability of Fe and Zn in rice[41], while the sulfhydryl group in cysteine binds Fe, thereby increasing the absorption of non-heme Fe by the body[42]. To improve the bioavailability of Fe and Zn, Lucca et al.[40] introduced a heat-tolerant phytase (phyA) gene from Aspergillus fumigatus into rice and overexpressed the cysteine-rich protein gene (rgMT), which increased the content of phytase and cysteine residues in rice by 130-fold and 7-fold, respectively[40].

    Breeding technology of functional rice

    The functional quality of rice is highly dependent on germplasm resources. Current functional rice breeding mainly adopts transgenic and mutagenic technologies, and the cultivated rice varieties are mainly enriched with only one functional substance and cannot meet the urgent demand by consumers for rice enriched with multiple active components. The diversity of rice active components determines the complexity of multifunctional rice breeding. In order to cultivate multifunctional rice, it is necessary to strengthen the application of different breeding technologies. Gene polymerization breeding is a crop breeding technology that can polymerize multiple superior traits that have emerged in recent years, mainly including traditional polymerization breeding, transgenic polymerization breeding, and molecular marker-assisted selection polymerization breeding.

    Traditional polymerization breeding

    The transfer of beneficial genes in different species during traditional polymeric breeding is largely limited by interspecific reproductive isolation, and it is challenging to utilize beneficial genes between different species effectively. Gene transfer through sexual crosses does not allow accurate manipulation and selection of a gene and is susceptible to undesirable gene linkage, and in the process of breed selection, multiple backcrosses are required[43]. Thus, the period of selecting target plants is long, the breeding cost is high, and the human resources and material resources are costly[44]. Besides, it is often difficult to continue the breakthrough after a few generations of backcrossing due to linkage drag. Thus, there are significant limitations in aggregating genes by traditional breeding methods[45].

    Transgenic polymerization breeding

    Transgenic technology is an effective means of gene polymerization breeding. Multi-gene transformation makes it possible to assemble multiple beneficial genes in transgenic rice breeding rapidly and can greatly reduce the time and workload of breeding[46]. The traditional multi-gene transformation uses a single gene transformation and hybridization polymerization method[47], in which the vector construction and transformation process is relatively simple. However, it is time-consuming, laborious, and requires extensive hybridization and screening efforts. Multi-gene-based vector transformation methods can be divided into two major categories: multi-vector co-transformation and multi-gene single vector transformation[47]. Multi-vector co-transformation is the simultaneous transfer of multiple target genes into the same recipient plant through different vectors. The efficiency of multi-vector co-transformation is uncertain, and the increase in the number of transforming vectors will increase the difficulty of genetic screening, resulting in a reduced probability of obtaining multi-gene co-transformed plants. Multi-gene single vector transformation constructs multiple genes into the T-DNA region of a vector and then transfers them into the same recipient plant as a single event. This method eliminates the tedious hybridization and backcrossing process and solves the challenges of low co-transformation frequency and complex integration patterns. It can also avoid gene loss caused by multi-gene separation and recombination in future generations[47]. The transgenic method can break through the limitations of conventional breeding, disrupt reproductive isolation, transfer beneficial genes from entirely unrelated crops to rice, and shorten the cycle of polymerizing target genes significantly. However, there are concerns that when genes are manipulated, unforeseen side effects may occur, and, therefore, there are ongoing concerns about the safety of transgenic crops[48]. Marker-free transgenic technology through which selective marker genes in transgenic plants can be removed has been developed. This improves the safety of transgenic crops, is beneficial to multiple operations of the same transgenic crop, and improves the acceptance by people[49].

    Molecular marker-assisted selection polymerization breeding

    Molecular marker-assisted selection is one of the most widely used rice breeding techniques at present. It uses the close linkage between molecular markers and target genes to select multiple genes directly and aggregates genes from different sources into one variety. This has multiple advantages, including a focused purpose, high accuracy, short breeding cycle, no interference from environmental conditions, and applicability to complex traits[50]. However, few genes have been targeted for the main effect of important agronomic traits in rice, and they are mainly focused on the regulation of rice plant type and the prevention and control of pests and diseases, and very few genes related to the synthesis of active components, which can be used for molecular marker-assisted selection are very limited. Furthermore, the current technical requirements and costs for analyzing and identifying DNA molecular markers are high, and the identification efficiency is low. This greatly limits the popularization and application of functional rice polymerization breeding. Therefore, to better apply molecular marker-assisted selection technology to breed rice varieties rich in multiple active components, it is necessary to construct a richer molecular marker linkage map to enhance the localization of genes related to functional substance synthesis in rice[51]. Additionally, it is important to explore new molecular marker technologies to improve efficiency while reducing cost.

    It is worth noting that the effects of gene aggregation are not simply additive. There are cumulative additive effects, greater than cumulative epistatic effects, and less than cumulative epistatic effects among the polymerization genes, and the effects are often smaller than the individual effect. Only with a clearer understanding of the interaction between different QTLs or genes can functional rice pyramiding breeding be carried out reasonably and efficiently. Except for RS and Se, other active components of rice mainly exist in the rice bran layer, and the content of active components in the endosperm, the main edible part, is extremely low. Therefore, cultivating rice varieties with endosperm-enriched active components have broad development prospects. In addition, because crops with high quality are more susceptible to pests and diseases[52], the improvement of rice resistance to pests and diseases should be considered during the polymerization breeding of functional rice.

    Cultivation and regulation technology of functional rice

    The biosynthesis of active components in rice is influenced by rice varieties but also depends on cultivation management practices and their growth environment.

    Effect of light on the accumulation of active components in rice

    Environmental conditions have a greater effect on protein content than genetic forces[53]. Both light intensity and light duration affect the synthesis and accumulation of active components in rice. Low light intensity in the early stage of rice growth is not conducive to the accumulation of glutelin in rice grains but favors the accumulation of amylose, while the opposite is true in the late stage of rice growth[54]. Low light intensity during the grain-filling period reduces the accumulation of total flavonoids in rice[55] and decreases Fe ions' movement in the transpiration stream and thereby the transport of Fe ions to rice grains[56]. An appropriate increase in light intensity is beneficial to the accumulation of flavonoids, anthocyanins, and Fe in rice, but the photostability of anthocyanins is poor, and too much light will cause oxidative degradation of anthocyanins[57]. Therefore, functional rice is best cultivated as mid-late rice, which would be conducive to accumulating active components in rice.

    Effect of temperature on the accumulation of active components in rice

    The temperature has a great influence on the synthesis of active components in rice. An appropriate increase in the temperature is beneficial to the accumulation of γ-oryzanol[58] and flavonoids[59] in rice. A high temperature during the grain-filling period leads to an increase in glutelin content in rice[60], but an increase in temperature decreases the total phenolic content[61]. The results regarding the effect of temperature on the content of PA in rice were inconsistent. Su et al.[62] showed that high temperatures during the filling period would increase the PA content, while Goufo & Trindade[61] reported that the increase in temperature would reduce the PA content. This may be due to the different growth periods and durations of temperature stress on rice in the two studies. The synthesis of anthocyanins/proanthocyanidins in colored rice requires a suitable temperature. Within a certain range, lower temperatures favor the accumulation of anthocyanins/proanthocyanidins in rice[63]. Higher temperatures will lead to degradation, and the thermal stability of proanthocyanidins being higher than that of anthocyanins[64]. In addition, cold or heat stress facilitates GABA accumulation in rice grains[65]. Therefore, in actual production, colored rice and low-glutelin rice are best planted as late rice, and the planting time of other functional rice should be determined according to the response of its enriched active components to temperature changes.

    Effect of water management on the accumulation of active components in rice

    Moderate water stress can significantly increase the content of glutelin[66] and GABA[67] in rice grains and promote the rapid transfer of assimilation into the grains, shorten the grain filling period, and reduce the RS content[68]. Drought stress can also induce the expression of the phytoene synthase (psy) gene and increase the carotenoid content in rice[69]. Soil moisture is an important medium in Zn diffusion to plant roots. In soil with low moisture content, rice roots have low available Zn, which is not conducive to enriching rice grains with Zn[70]. Results from studies on the effect of soil water content on Se accumulation in rice grains have been inconsistent. Li et al.[71] concluded that flooded cultivation could significantly increase the Se content in rice grains compared to dry cultivation. However, the results of Zhou et al.[72] showed that the selenium content in rice grains under aerobic and dry-wet alternative irrigation was 2.44 and 1.84 times higher than that under flood irrigation, respectively. This may be due to the forms of selenium contained in the soil and the degree of drought stress to the rice that differed between experiments[73]. In addition, it has been found that too much or too little water impacts the expression of genes related to anthocyanin synthesis in rice, which affects the accumulation of anthocyanins in rice[74]. Therefore, it is recommended to establish different irrigation systems for different functional rice during cultivation.

    Effect of fertilizer application on the accumulation of active components in rice

    Both the amount and method of nitrogen application affect the accumulation of glutelin. Numerous studies have shown that both increased and delayed application of nitrogen fertilizer can increase the accumulation of lysine-rich glutelin to improve the nutritional quality of rice (Table 1). However, this improvement is not beneficial for kidney disease patients who cannot consume high glutelin rice. Nitrogen stress can down-regulate the expression of ANDs genes related to the anthocyanins biosynthesis pathway in grains, resulting in a decrease in anthocyanins synthesis[55]. Increased nitrogen fertilizer application can also increase the Fe, Zn, and Se content in rice[75,76]. However, some studies have found that increased nitrogen fertilizer application has no significant effect on the Fe content of rice[77], while other studies have shown that increased nitrogen fertilizer application will reduce the Fe content of rice[78]. This may be influenced by soil pH and the form of the applied nitrogen fertilizer. The lower the soil pH, the more favorable the reduction of Fe3+ to Fe2+, thus promoting the uptake of Fe by rice. Otherwise, the application of ammonium fertilizer can improve the availability of soil Fe and promote the absorption and utilization of Fe by rice. In contrast, nitrate fertilizer can inhibit the reduction of Fe3+ and reduce the absorption of Fe by rice[79].

    Table 1.  Effect of nitrogen fertilizer application on glutelin content of rice.
    SampleN level
    (kg ha−1)    		Application timeGlutelin content
    (g 100 g−1)    		References
    Rough rice05.67[66]
    270Pre-transplanting : mid tillering : panicle initiation : spikelet differentiation = 2:1:1:16.92
    300Pre-transplanting : mid tillering : panicle initiation : spikelet differentiation = 5:2:2:16.88
    Brown rice05.35[83]
    90Pre-transplanting : after transplanting = 4:16.01
    Pre-transplanting : after transplanting = 1:16.60
    180Pre-transplanting : after transplanting = 4:16.53
    Pre-transplanting : after transplanting = 1:17.29
    270Pre-transplanting : after transplanting = 4:17.00
    Pre-transplanting : after transplanting = 1:17.66
    Rough rice05.59[84]
    187.5Pre-transplanting : after transplanting = 4:16.47
    Pre-transplanting : after transplanting = 1:16.64
    300Pre-transplanting : after transplanting = 4:17.02
    Pre-transplanting : after transplanting = 1:17.14
    Polished rice03.88[85]
    90Pre-transplanting : tillering : booting = 2:2:14.21
    180Pre-transplanting : tillering : booting = 2:2:14.43
    270Pre-transplanting : tillering : booting = 2:2:16.42
    360Pre-transplanting : tillering : booting = 2:2:14.87
    Brown rice09.05[86]
    120Flowering22.14
     | Show Table
    DownLoad: CSV

    Appropriate application of phosphorus fertilizer is beneficial in promoting the translocation of Fe and Zn from leaves to rice grains, thus increasing the content in rice grains[80]. However, the excessive application of phosphate fertilizer will reduce the availability of Fe and Zn in soil, resulting in less uptake by the roots and a lower content in the rice grains[81]. The content of PA in rice increased with a higher phosphorus fertilizer application rate[80]. Increasing the phosphorus fertilizer application rate would increase the values of [PA]/[Fe] and [PA]/[Zn] and reduce the effectiveness of Fe and Zn in rice[80]. Currently, there are few studies on the effect of potassium fertilization on the synthesis of active components in rice. Available studies report that increased application of nitrogen fertilizer can increase the Zn content in rice[82]. Therefore, the research in this area needs to be strengthened.

    Because the iron in soil mainly exists in the insoluble form Fe3+, the application of iron fertilizer has little effect on rice biofortification[87]. There are different opinions about the effect of Zn fertilizer application methods. Phattarakul et al.[88] believed that foliar spraying of Zn fertilizer could significantly improve the Zn content in rice grains. Jiang et al.[89] concluded that most of the Zn accumulated in rice grains were absorbed by the roots rather than from the reactivation of Zn in leaves. In contrast, Yuan et al.[90] suggested that soil application of Zn fertilizer had no significant effect on Zn content in rice grains. The different results may be affected by the form of zinc fertilizer applied and the soil conditions in the experimental sites. Studies have found that compared with the application of ZnEDTA and ZnO, zinc fertilizer in the form of ZnSO4 is most effective for increasing rice's Zn[70]. In addition, the application of zinc fertilizer reduces the concentration of PA in rice grains[70].

    The form of selenium fertilizer and the method and time of application will affect the accumulation of Se in rice grains. Regarding selenium, rice is a non-hyperaccumulative plant. A moderate application of selenium fertilizer can improve rice yield. However, the excessive application can be toxic to rice, and the difference between beneficial and harmful supply levels is slight[91]. Selenite is readily adsorbed by iron oxide or hydroxide in soil, and its effectiveness in the soil is much lower than selenite[92]. In addition, selenate can migrate to the roots and transfer to rice shoots through high-affinity sulfate transporters. In contrast, selenite is mainly assimilated into organic selenium in the roots and transferred to the shoots in smaller amounts[93]. Therefore, the biological effectiveness of Se is higher in selenate-applied soil than in selenite application[94] (Table 2). Zhang et al.[95] found that the concentration of Se in rice with soil application of 100 g Se ha-1 was only 76.8 μg·kg-1, while the concentration of Se in rice with foliar spray of 75 g Se ha-1 was as high as 410 μg·kg-1[73]. However, the level of organic selenium was lower in rough rice with foliar application of selenium fertilizer compared to soil application[96], while the bioavailability of organic selenium in humans was higher than inorganic selenium[97]. Deng et al.[73] found that the concentrations of total selenium and organic selenium in brown rice with selenium fertilizer applied at the full heading stage were 2-fold higher than those in brown rice with selenium fertilizer applied at the late tillering stage (Table 2). Although the application of exogenous selenium fertilizer can rapidly and effectively increase the Se content of rice (Table 2), it can easily lead to excessive Se content in rice and soil, which can have adverse effects on humans and the environment. Therefore, breeding Se-rich rice varieties is a safer and more reliable way to produce Se-rich rice. In summary, functional rice production should include the moderate application of nitrogen and phosphorus fertilizer and higher levels of potassium fertilizer, with consideration to the use of trace element fertilizers.

    Table 2.  Effect of selenium fertilizer application on the selenium content of rice.
    SampleSe level (g Se ha−1)Selenium fertilizer formsApplication methodSe content (μg·g−1)References
    Rough rice00.002[98]
    18SeleniteFoliar spray at full heading0.411
    Polished rice00.071[99]
    20SeleniteFoliar spray at full heading0.471
    20SelenateFoliar spray at full heading0.640
    Rough rice75SeleniteFoliar spray at late tillering0.440[73]
    75SeleniteFoliar spray at full heading1.290
    75SelenateFoliar spray at late tillering0.780
    75SelenateFoliar spray at full heading2.710
    Polished rice00.027[100]
    15SeleniteFoliar spray at full heading0.435
    45SeleniteFoliar spray at full heading0.890
    60SeleniteFoliar spray at full heading1.275
     | Show Table
    DownLoad: CSV

    Effect of harvesting time on the accumulation of active components in rice

    The content of many active components in rough rice is constantly changing during the development of rice. It was found that the content of total flavonoids in brown rice increased continuously from flowering stage to dough stage and then decreased gradually[101]. The γ-oryzanol content in rice decreased by 13% from milk stage to dough stage, and then gradually increased to 60% higher than milk stage at full maturity[101]. The results of Shao et al.[102] showed that the anthocyanin content in rice reached its highest level at two weeks after flowering and then gradually decreased. At full ripeness, and the anthocyanins content in brown rice was only about 50% of the maximum level. The content of total phenolics in rice decreased with maturity from one week after flowering to the fully ripe stage, and the loss of total phenolics reached more than 47% by the fully ripe stage. In contrast, the content of total phenolics in black rice increased with maturity[102]. Moreover, RS content in rough rice decreases during rice maturation[68]. Therefore, the production process of functional rice should be timely and early harvested to obtain higher economic value.

    Prevention and control of pests and diseases for functional rice

    Pests and diseases seriously impact the yield and quality of rice[103]. At present, the two most effective methods to control pests and diseases are the use of chemical pesticides and the planting of pest and disease-resistant rice varieties. The use of chemical pesticides has greatly reduced the yield loss of rice. However, excessive use of chemical pesticides decreases soil quality, pollutes the environment, reduces soil biodiversity[104], increases pest resistance, and aggravates the adverse effects of pests and diseases on rice production[105]. It also increases residual pesticide levels in rice, reduces rice quality, and poses a severe threat to human health[106].

    Breeding pest and disease-resistant rice varieties are among the safest and effective ways to control rice pests and diseases[107]. In recent years, many pest and disease resistance genes from rice and microorganisms have been cloned[47]. Researchers have used these genes to breed rice varieties resistant to multiple pests and diseases through gene polymerization breeding techniques. Application in production practices delivered good ecological and economic benefits[108].

    Green pest and disease control technologies must consider the synergies between rice and water, fertilizer, and pest and disease management. In this regard, the rice-frog, rice-duck, and other comprehensive rice production models that have been widely used in recent years are the most representative. These rice production models significantly reduced chemical pesticide usage and effectively controlled rice pests and diseases[109]. The nutritional imbalance will reduce the resistance of rice to pests and diseases[110]. Excessive application of nitrogen fertilizer stimulates rice overgrowth, protein synthesis, and the release of hormones, increasing its attractiveness to pests[111]. Increased soluble protein content in rice leaves is more conducive to virus replication and increases the risk of viral infection[112]. Increasing the available phosphorus content in the soil will increase crop damage by pests[113], while insufficient potassium supply will reduce crop resistance to pests and diseases[114]. The application of silica fertilizer can boost the defense against pests and diseases by increasing silicon deposition in rice tissue, inducing the expression of genes associated with rice defense mechanisms[115] and the accumulation of antifungal compounds in rice tissue[116]. The application of silica fertilizer increases the release of rice volatiles, thereby attracting natural enemies of pests and reducing pest damage[117]. Organic farming increases the resistance of rice to pests and diseases[118]. In addition, rice intercropping with different genotypes can reduce pests and diseases through dilution and allelopathy and changing field microclimate[119].

    In conclusion, the prevention and control of rice pests and diseases should be based on chemical and biological control and supplemented by fertilizer management methods such as low nitrogen, less phosphorus, high potassium and more silicon, as well as agronomic measures such as rice-aquaculture integrated cultivation, organic cultivation and intercropping of different rice varieties, etc. The combined use of multiple prevention and control measures can improve the yield and quality of functional rice.

    Conclusions

    Functional rice contains many active components which are beneficial to maintaining human health and have high economic and social value with broad market prospects. However, the current development level of the functional rice industry is low. The development of the functional rice requires extensive use of traditional and modern polymerization breeding techniques to cultivate new functional rice varieties with endosperm that can be enriched with multiple active components and have broad-spectrum resistance to pests and diseases. It is also important to select suitable planting locations and times according to the response characteristics of different functional rice active components to environmental conditions.

    Acknowledgments

    This work is supported by the National Natural Science Foundation of China (Project No. 32060430 and 31971840), and Research Initiation Fund of Hainan University (Project No. KYQD(ZR)19104).

  • The authors declare that they have no conflict of interest.

  • Supplemental Table S1 Details of sample location of Pueraria species in the present study.
    Supplemental Fig. S1 SSR fingerprinting map analysis of Pueraria germplasms.
    Supplemental Fig. S2 SSR fingerprinting map analysis of 20 Pueraria accessions of core germplasms.
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  • Cite this article

    Shi P, Zhou Y, Shang X, Xiao L, Zeng W, et al. 2024. Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers. Tropical Plants 3: e012 doi: 10.48130/tp-0024-0012
    Shi P, Zhou Y, Shang X, Xiao L, Zeng W, et al. 2024. Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers. Tropical Plants 3: e012 doi: 10.48130/tp-0024-0012
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Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers

  • 1.

    Cash Crops Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, Guangxi 530007, China

  • 2.

    College of Pharmacy, Guangxi Medical University, Nanning, Guangxi 530021, China

  • Received: 30 November 2023
  • Revised: 22 February 2024
  • Accepted: 14 March 2024
  • Published online: 23 April 2024
Tropical Plants  3,  Article number: e012  (2024)  |  Cite this article

Abstract: Pueraria, extensively cultivated in Guangxi, China, stands as a pivotal commercial crop and a valuable supplement for human health. Despite its significance, the core germplasm and genetic diversity within Guangxi's Pueraria populations remain largely unexplored. This study delves into the genetic diversity of a comprehensive collection of 272 Pueraria germplasm accessions from Guangxi, utilizing 23 simple sequence repeat (SSR) markers. The average number of SSR alleles per locus was 5.13, ranging from 2 to 11, with four primers (PtSSR121, PtSSR196, PtSSR155, and PtSSR222) consistently producing at least two polymorphic bands, while PtSSR122 yielded an impressive 11 polymorphic bands. The analysis revealed 118 alleles, 112 of which were polymorphic. The average gene flow (Nm) was estimated at 1.7690, and the average predicted heterozygosity per location was 0.1841. Principal component and STRUCTURE cluster analyses corroborated the division of the 272 accessions into two main clusters. However, no significant statistical correlation was observed between geographic and genetic distances. The study identified a moderate level of genetic diversity. A core collection comprising 20 Pueraria accessions that encompass 105 alleles was proposed. These findings provide a theoretical basis for the strategic conservation of Pueraria's genetic resources, laying the groundwork for future breeding programs.

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    Introduction

    • Kudzu (Pueraria montana var. lobata (Ohwi) Maesen & S. M. Almeida) (2n = 2x = 22) is a semi-woody, perennial liana, that belongs to the Leguminosae family and is widely distributed throughout Asia, including China, Japan, Korea and other regions in Southeast Asia, as well as in North and South America. As an economic crop, it contains puerarin and other functional components and is used in the production of both pharmaceuticals and health foods. Pueraria montana var. thomsonii is another variety that shows higher starch content and thus is called starch kudzu. The roots of both P. montana var. lobata and P. montana var. thomsonii have been long used for treating fever, toxicosis, indigestion, and liver damage from alcohol abuse in traditional Chinese medicine[1], which was recorded in The Divine Husbandman's Classic of Materia Medica (Shen Nong Ben Cao Jing) compiled in the Eastern Han Dynasty (25−250 AD). China is probably the origin and distribution center of Pueraria species; however, for a long time, the identification and the breeding of germplasm resources has not received enough attention. Guangxi is a hotspot of Pueraria genetic resources in China. Kudzu is a traditional crop cultivated in Guangxi, with abundant germplasm resources at elevations of 100−199 m[2]. At present, the cultivation area of kudzu and starch kudzu in Guangxi accounts for 20% of the whole country[3]. However, the genetic diversity and core germplasm of the Pueraria species in Guangxi are not well understood.

      With the development of urban society and excessive mining, many germplasm resources are facing the risk of loss or extinction. Genetic diversity provides a basis for the improvement of the crop for different desirable traits, evolutionary capability, species survival, management of germplasm collections, and breeding programs[4−6]. Therefore, it is necessary to fully understand the genetic diversity and genetic information of Pueraria core germplasm resources of the representative individuals, which can protect key genetic resources and shorten the breeding process[7,8]. Most recently, RAPD (random amplified polymorphic DNA), ISSR (inter-simple sequence repeat), SRAP (sequence-related amplified polymorphic), SCoT (start condon targeted polymorphism), and SSR (simple sequence repeats) markers have been used to analyze the genetic diversity in Pueraria[3,9−14]. Genic-SSRs have the most advantages among these five markers because of the more comprehensive genetic information in the genome[15,16]. Genic-SSRs were used to evaluate the diversity of Pueraria, however, the population is just 44 lines[17]. Although genetic analysis of Pueraria on some accessions of Pueraria or some germplasm resources in Guangxi has been done[3], the core germplasm resource and the overall evaluation on the genetic diversity has not yet been systematically evaluated.

      Lack of systematic study of the genetic diversity and the core germplasm resource seriously restricts its efficient management, conservation and further utilization[5]. In the present study, 272 individuals of Pueraria collected in Guangxi were used to estimate the extent of genetic diversity and construct the core germplasm. The findings of this study will be utilized for conservation and management of genetic resources in Guangxi, association mapping, and traits-based kudzu breeding.

    Materials and methods

      Plant material and DNA extraction

    • A total of 272 individuals of Pueraria were collected in Guangxi from September 2017 to April 2019 (Supplemental Table S1). Three to five fresh young leaves of each accession were collected and immediately frozen in liquid nitrogen and stored at −80 °C until DNA isolation.

      Total genomic DNA was extracted from young leaf tissue of individual representative plants of each accession using a Plant DNA Isolation Reagent Kit (TaKaRa, Dalian, China). We measured the concentration and purity of the total DNA using both 1% agarose gel electrophoresis and a Nanodrop instrument (UV-2700). The total DNA extracts were stored at −20 °C until required for experiments.

      SSR genotyping

    • The final concentration of DNA was adjusted to 50 ng/μl for PCR reaction. Based on the transcriptome of P. montana var. lobata, 28 SSR primers were designed and scored in six Pueraria collections from 229 SSRs[17]. Ultimately, 23 polymorphic markers were chosen for genetic diversity analysis (Table 1). SSR amplification was carried out in a thermal cycler by Bio-Rad (MyCycler TM), in a final volume of 20 μl containing: 100 ng of genomic DNA, 10 μl of Taq DNA polymerase mix (TaKaRa, Dalian, China), and 10 μM each, forward and reverse non-fluorescent primers. The program used for PCR amplification was as follows: initial denaturation at 94 °C for 5 min; 30 cycles of denaturation at 94 °C for the 30 s, annealing at 50 °C for 30 s, extension at 72 °C for 30 s, and a final extension at 72 °C for 10 min. Amplified products were separated in 6% non-denaturing polyacrylamide gel electrophoresis (PAGE). The SSR markers amplified at sizes between 100 and 400 bp were converted into '0' and '1' codes denoting 'absence' and 'presence', respectively.

      Table 1.  Amplification results and polymorphism information of 23 SSR primers.

      No.Primer nameSequences (5'-3')Total number
      of lands      		Number of
      polymorphic bands      		Polymorphism
      rate (%)
      1PtSSR36Fw: CTGAGTCTCTGCAAAGCCCA1010100
      Rv: TGTCACTGTGCTCCAACTCC
      2PtSSR98Fw: CATTCGGACCTCCATACCCG111090.9
      Rv: CCGCATCCAACCCTGATCAA
      3PtSSR99Fw: GCTTTCCGCTGCTACCATTC77100
      Rv: GCAACCCCAATGCTTCACAG
      4PtSSR104Fw: CACCCTCCCACCACTACAAC33100
      Rv: GCAATGTCCTCCTCAGCTGT
      5PtSSR108Fw: AGCGTGCCCAACTCAGTTAA33100
      Rv: CGACGGAGAAGGAGGGAATG
      6PtSSR109Fw: CAACCTGGCTTCTGTTGTGC5480
      Rv: CTCTGAAACGCTGGGCAATG
      7PtSSR121Fw: ACACTCAACACTCCACCACC3266.67
      Rv: AGGGTTTCCACCTTGAACCG
      8PtSSR122Fw: GGGGTTTCTTCTCGGCTGAA1111100
      Rv: CACCCCCTTCACGCTTCATA
      9PtSSR130Fw: ATCAGTGTCTACGTGGGGGA5480
      Rv: CACTGCAGCCACAACAACAT
      10PtSSR135Fw: GATCCGCACCCTATCTGTGG88100
      Rv: CTGCGACAGCTCCGATCTTA
      11PtSSR144Fw: TGTTGCTTTGAACACTAACATGCT33100
      Rv: TGCCCTTGTCAGACACAACA
      12PtSSR155Fw: TTCAACATTCCCCCAACCCC22100
      Rv: AAGAAGAGGAACACCAGGCC
      13PtSSR168Fw: GATCCCACCCACCACTTCTG55100
      Rv: GGCTCTAGTTCTGGTGCTGG
      14PtSSR172Fw:TCTCCAAAACAAGAAGGAAACTCC4375
      Rv: TCTTTCCTCTTCTGGTATCCCA
      15PtSSR174Fw: CAAAGAAGAAGCAGCCGCAG66100
      Rv: GTCAATCCCGAAGCACTTGC
      16PtSSR175Fw: CTGAGTCTCTGCAAAGCCCA77100
      Rv: TGTCACTGTGCTCCAACTCC
      17PtSSR186Fw: TGTTGCTTTGAACACTAACATGCT44100
      Rv: TGCCCTTGTCAGACACAACA
      18PtSSR187Fw: TGTTGCTTTGAACACTAACATGCT44100
      Rv: TGCCCTTGTCAGACACAACA
      19PtSSR190Fw: AACTGCAGGAGGAGCATGAC55100
      Rv: GAGCCTCCAGGTTCTTGTCC
      20PtSSR191Fw: GGAAGCATTGCGGTTTGGTT33100
      Rv: TCACATCACATGCTGCCACT
      21PtSSR196Fw: GCAAGAACCTGTGCTCCTCT3266.67
      Rv: TGCCAATGCCATTGTGGTTG
      22PtSSR201Fw: GCCTCTTCCAGCGAGAACTT44100
      Rv: TGATCCTCCCCAACAAGCTG
      23PtSSR222Fw: TGTGCAAGAAGGATGGGTGA22100
      Rv: GGTTGCATTCGGAAGCAACA
      Total118112
      Avarage5.134.8794.91

      Statistical analysis

    • For each SSR locus, Popgene32 version 1.32 was used to analyze the gene frequency, number of allele (Na), effective number of alleles (Ne), polymorphic loci, Nei's genetic distance (D), Shannon–Weaver diversity index (I), Homogeneity test index (H) and gene flow (Nm).

      Genetic structure was inferred by STRUCTURE version 2.3.1[18]. The number of genetic clusters (K) was set from 1 to 20 with a burn-in period of 50000 steps followed by a run with 100000 iterations. Twenty independent runs were undertaken for each K value. Later, three replicates of the analysis were implemented in CLUMPP software[19]. The mean posterior probabilities [Lnp(D)] values of each K were calculated according to Pritchard et al., along with ∆K[18,20] to explore the optimum number of clusters (K). The most likely number of clusters was determined using a structure harvester (http://taylor0.biology.ucla.edu/structureHarvester/)[21]. Cluster analysis by the unweighted pair group method with arithmetic mean (UPGMA) based on the jaccard method was also developed using the NTSYS-pc 2.10e software[22]. A principal component analysis (PCA) was performed using NTSYS 2.10.

      The core collection was developed employing software Core hunter in R package[23]. To assess the core germplasm set, maximum Shannon's diversity index was estimated.

    Results

      SSR polymorphisms

    • Encoding binary digit format for genotyping sequence format to exploit the utility of potential core SSRs to fingerprint Pueraria accessions. The utilization efficiency and 23 primers information are shown in Table 1 & Supplemental Fig. S1. A total of 118 alleles were detected among 272 Pueraria individuals, leading to a mean number of alleles per locus of 5.13 (ranging from two for PsSSR155 and PtSSR222, to 11 for PtSSR98 and PtSSR122). A total of 112 polymorphic alleles (94.91%) was identified with an average of 4.87 effective alleles per locus. Among the 118 alleles, 11 (9.3%) were rare alleles with frequency less than 1% and four of them were found to be only once in one individual. The average of the observed number of alleles (Na) and the effective number of alleles (Ne) were 1.9492 and 1.2841, respectively.

      Population genetic diversity

    • The population-level genetic diversity of the Pueraria accessions under study is presented in Table 2. Nei's gene diversity ranged from 0 to 0.5 and Shannon's information index (I) ranged from 0 to 0.6931 across all 23 SSR loci with an average of 0.1778 and 0.2858, respectively. The average value of total expected heterozygosity (Ht) and Nm were recorded at 0.1841 and 1.7690, respectively.

      Table 2.  Genetic characteristics for 112 polymorphic microsatellite loci in 272 individuals of Pueraria species in the present study.

      LocusSample sizeNaNehIHtHsGstNm
      36-127221.01120.0110.03440.01290.01280.006675.8301
      36-227221.01120.0110.03440.01290.01280.006675.8301
      36-327221.04630.04430.10820.05170.05030.027317.8197
      36-427221.00740.00740.02440.00860.00860.0043114.4978
      36-527221.10170.09230.19410.10630.10070.05318.9206
      36-627221.08430.07780.16960.09050.0860.04999.5227
      36-727221.26540.20980.36500.23940.20250.15412.7446
      36-827221.93220.48240.67550.49150.41570.15432.7395
      36-927221.0340.03290.08490.03090.03080.0032156.1603
      36-1027221.00740.00740.02440.00860.00860.0043114.4978
      98-127221.01880.01840.05270.02160.02130.01144.8945
      98-227221.14420.1260.24730.1450.13360.07865.8651
      98-327221.14990.13040.25390.15090.13740.08955.0838
      98-427221.05840.05520.12910.06340.06160.027517.6531
      98-527221.03830.03690.09330.04310.04210.022521.6887
      98-627221.03830.03690.09330.04310.04210.022521.6887
      98-727221.06710.06290.14330.07330.07040.039612.1273
      98-827221.06710.06290.14330.07330.07040.039612.1273
      98-927221.160.13790.26520.15950.14420.09574.7224
      98-1027211.00000.00000.000000
      98-1127221.01490.01470.04360.01610.0160.003166.8934
      99-127221.01880.01840.05270.02160.02130.01144.8945
      99-227221.06150.0580.13430.05920.05920.0007711.3620
      99-327221.2880.22360.38300.20290.18570.08495.3899
      99-427221.20650.17120.31290.18550.17810.0412.0065
      99-527221.140.12280.24240.14220.13040.08355.4886
      99-627221.36430.2670.43750.26220.2610.0043114.6222
      99-727221.53990.35060.53530.34480.34210.007665.5416
      104-127221.0340.03290.08500.03520.03510.0039128.2046
      104-227221.95580.48870.68180.49640.39860.19692.0392
      104-327221.99920.49980.69290.49570.37380.24591.5335
      108-127221.62920.38620.57460.3930.38820.012240.3512
      108-227221.47280.3210.50160.33310.32340.029216.6414
      108-327221.68310.40580.59580.39720.38830.022321.9303
      109-127221.29340.22690.38720.25680.21670.15622.7010
      109-227221.01490.01470.04360.01610.0160.003166.8934
      109-327221.29340.22690.38720.25680.21670.15622.7010
      109-427221.01490.01470.04360.01610.0160.003166.8934
      109-52721100.000000
      121-127221.07530.070.15610.08050.07750.037412.8581
      121-227221.07530.070.15610.08050.07750.037412.8581
      121-32721100.000000
      122-127221.03040.02950.07780.03450.03390.017927.4911
      122-227221.13510.1190.23670.13790.12680.08055.7097
      122-327221.03040.02950.07780.03450.03390.017927.4911
      122-427221.08870.08150.17600.09480.08980.05259.0192
      122-527221.08870.08150.17600.09480.08980.05259.0192
      122-627221.06710.06290.14330.07330.07040.039612.1273
      122-727221.03430.03320.08560.03880.0380.020224.2677
      122-827221.42530.29840.47520.29290.29120.005885.4766
      122-927221.00370.00370.01340.00430.00430.0022230.4989
      122-1027221.51870.34150.52500.36730.31460.14352.9847
      122-1127221.29730.22910.39020.26080.21520.17482.3604
      130-127221.20250.16840.30900.1940.17030.12213.5945
      130-227221.20250.16840.30900.1940.17030.12213.5945
      130-327221.03830.03690.09330.04310.04210.022521.6887
      130-427221.03830.03690.09330.04310.04210.022521.6887
      130-52721100.000000
      135-127221.01490.01470.04380.01720.01710.008856.4956
      135-227221.05870.05540.12960.06470.06240.034613.9494
      135-327221.34770.2580.42640.29310.22950.21711.8032
      135-427221.01110.0110.03430.01180.01180.0013397.0141
      135-527221.31270.23820.40170.27160.2190.19342.0859
      135-627221.01490.01470.04360.01610.0160.003166.8934
      135-727221.77850.43770.62950.47060.18850.59940.3341
      135-827221.96560.49130.68440.49990.15580.68840.2263
      144-127221.03430.03320.08560.03880.0380.020224.2677
      144-227221.92510.48050.67360.4790.47760.0029174.8282
      144-327221.69980.41170.60200.38770.32130.17132.4193
      155-127221.06980.06520.14750.06870.06840.0049101.9746
      155-227221.24830.19890.35070.22810.19360.15122.8079
      168-127221.5010.33380.51620.31240.28250.09564.7296
      168-227221.92540.48060.67360.48920.43210.11663.7876
      168-327221.51130.33830.52140.31810.29040.08695.2557
      168-427221.95690.4890.68210.49540.43280.12623.4613
      168-527221.00740.00730.02430.00750.00750.00012000.0000
      172-127221.23680.19150.34070.21980.18820.14382.9771
      172-227221.01120.0110.03440.01290.01280.006675.8301
      172-32721100.000000
      172-427221.00370.00370.01340.00320.00320.0016310.4992
      174-127221.00740.00740.02440.00860.00860.0043114.4978
      174-227221.0740.06890.15420.07410.07340.009950.0991
      174-327221.12490.1110.22410.12780.11920.06696.9725
      174-427221.00370.00370.01340.00430.00430.0022230.4989
      174-527221.75650.43070.62210.46210.24460.47060.5625
      174-627221.97220.49290.68610.50.20540.58920.3486
      175-127221.00740.00740.02440.00860.00860.0043114.4978
      175-227221.09320.08520.18230.09910.09370.05528.5594
      175-327221.00740.00740.02440.00860.00860.0043114.4978
      175-427221.20690.17140.31320.19650.17350.1173.7725
      175-527221.85040.45960.65220.47690.36540.23371.6394
      175-627221.70470.41340.60380.390.32520.16592.5132
      175-727221.03010.02920.07710.02980.02980.00031480.2742
      186-127221.04210.04040.10050.04620.04530.018127.1466
      186-227221.77260.43590.62760.40590.27390.32521.0373
      186-327221.78480.43970.63160.46980.23250.50510.4899
      186-427221.69770.4110.60130.38080.27930.26641.3765
      187-127221.0420.04030.10020.04490.04440.012240.4142
      187-227221.76410.43310.62470.40210.26550.33970.9718
      187-327221.8750.46670.65940.49050.15710.67960.2357
      187-427221.66360.39890.58830.36870.27790.24651.5286
      190-127221.09930.09030.19080.08330.0820.016429.9125
      190-227221.0460.0440.10760.03850.03770.0224.4900
      190-327221.15350.13310.25800.12110.11660.03713.0124
      190-427221.60240.37590.56340.40760.29960.26511.3862
      190-527221.99990.50.69310.49560.3230.34830.9354
      191-127221.92840.48140.67450.4980.11260.77380.1462
      191-227221.67890.40440.59420.37080.25410.31461.0893
      191-327221.16410.1410.26960.12610.11920.05528.5656
      196-127221.22950.18670.33420.21360.18560.13123.3100
      196-227221.00740.00740.02440.00860.00860.0043114.4978
      196-32721100.000000
      201-127221.00370.00370.01340.00430.00430.0022230.4989
      201-227221.99780.49950.69260.49890.35310.29231.2103
      201-327221.4040.28770.46250.32240.25090.22191.7536
      201-427221.40330.28740.46210.27980.27680.01145.0326
      222-127221.30390.2330.39510.26510.21760.17912.2910
      222-227221.77110.43540.62710.46170.30310.34350.9556
      Mean2721.94921.28410.17780.28580.18410.14350.22041.7690
      St. Dev0.22060.32770.17410.23970.03050.0166
      Na = Observed number of alleles; Ne = Effective number of alleles[56]; h = Nei's (1973) gene diversity; I = Shannon's Information index[57]; Gst = coefficient of gene differentiation; Nm = estimate of gene flow from Gst or Gcs. E.g., Nm = 0.5(1 - Gst)/Gst; Ht = Total expected heterozygosity; Hs = the average expected heterozygosity within subpopulations.

      Genetic structure analysis

    • The clustering analyses using STRUCTURE under the admixture model suggested the optimum K was two by STRUCTURE HARVEST[21], which divided all sampled individuals into two groups. Correspondingly, the highest of adhoc measure (∆K) analysis[20] gave a sharp peak at K = 2 (Fig. 1). Hence, the true number of groups were considered as two (Pop1 and Pop2). The accessions with a probability of more 80% were considered as pure and assigned to corresponding subgroups while less than 80% were categorized as admixture (Fig. 1). Among 272 genotypes, 259 were pure and 13 Pueraria accessions were admixture. With evidence for several admixtures within cluster I (code_collection number: 30_JCJ-30, 32_JCJ-32, 196_GL-32, 197_GL-33) or cluster II (code_collection number: 12_YZ-12, 26_LC-26, 27_LC-27, 28_HJ-28, 113_GP-21, 149_BS-13, 160_BS-24, 195_GL-31, 270_Y10), subpopulation P1 showed 152 pure (97.5%) and four admixed (2.5%) landraces, P2 had 107 pure (92.2%) and 9 (7.8%) admixed landraces. In addition, all of the 272 individuals could be clustered into one of four groups when K = 4 (Fig. 1). However, within each of the four closely related groups, a few individuals always contained an admixture of introgressed genetic material from another accession.

      Figure 1. 

      Bar plots of all 272 individuals from Pueraria germplasm grouped into two or four genetic clusters with assignment probabilities obtained from STRUCTURE analyses of polymorphisms at 23 simple sequence repeat loci. (a) Distribution of delta K = 1−20. (b), (c) Histogram of the STRUCTURE assignment test when K = 2 or K = 4, respectively. The number represents the code in Supplemental Table S1.

      Cluster analysis and Principal Component Analyses (PCA)

    • Although there was no clear demarcation in the clustering pattern in the present study, the UPGMA dendrogram (Fig. 2) showed that all the accessions were divided into two main clusters at 0.378 similarity coefficient, which showed similar results to structure analysis. Furthermore, 272 accessions were divided into four main clusters at 0.684 similarity coefficient. The minimum similarity is 0.587 for most other accessions (Fig. 2). There was no distinctive trend of accessions in these two clusters according to their place of origin (Fig. 3). For instance, accessions from Longzhou county of Chongzuo (LZ-9 to LZ-13), were covered within these two clusters with no evident bias.

      Figure 2. 

      Cluster diagram based on jaccard by UPGMA analysis calculated from alleles derived from 272 Pueraria accessions. The number represents the code in Supplemental Table S1.

      Figure 3. 

      Geographical distribution of the accessions collected in Guangxi. The number represents the code in Supplemental Table S1. The red and blue numbers represent two clusters of the 20 accessions of core germplasms. The orange squares represent the accessions of Cluster I and blue circles represent the accessions of Cluster II.

      The PCA categorized all the accessions undertaken into two groups, which was in line with the results of UPGMA based phylogenetic tree and model-based STRUCTURE analysis. The first two axes of differentiation explained 89% of the total variation. The first coordinate explained 40% of the variation and the second coordinate explained 49% of the variation (Fig. 4). The results of PCA indicated that the genetic distance does not show a relationship with geographical distribution in this study.

      Figure 4. 

      PCA of Pueraria accessions based on dissimilarity matrix (Jaccard). The number represents the code in Supplemental Table S1. The number represent 272 accessions of Pueraria. The orange circles represent the accessions of Cluster I and blue circles represent the accessions of Cluster II.

      Extraction of a core collection

    • One hundred and five SSR alleles found in this study could be represented by a core collection of 20 Pueraria accessions with 7.35% sampling proportion (Table 3, Supplemental Fig. S2). When the core selection capacity reached 20, the allele number was 105, so it captured close to 93.75% (105/112) of the total polymorphic loci. The average of the value of Na, Ne, h, I was 1.8898, 1.3716, 0.2359, and 0.3727, respectively. Based on the dendrogram, the germplasm accessions could be divided into two main groups. The value of genetic similarity indices among 20 Pueraria germplasm accessions varied between 0.31 and 0.60, which indicates that there was a relatedly narrow genetic variation within the different Pueraria accessions belonging to the diverse geographic locations across the Guangxi region (Fig. 5). In addition, our COREFINDER analysis highlighted that 10% of the entire core collection was represented by the Pueraria accessions grouped in Cluster I, while Cluster II contribute to the core collection at 90%.

      Table 3.  Summary of the extraction of a core collection.

      Sampling proportionSample numberNaNehINumber of polymorphic lociPercentage of polymorphic lociPercentage of total loci
      5%141.8644 ± 0.34381.3839 ± 0.31160.2413 ± 0.16390.3778 ± 0.224310291.07%86.44%
      7.00%191.8644 ± 0.34381.3779 ± 0.31230.2381 ± 0.16350.3736 ± 0.224210291.07%86.44%
      7.35%201.8898 ± 0.31441.3716 ± 0.30840.2359 ± 0.15980.3727 ± 0.217010593.75%88.98%
      7.70%211.8983 ± 0.30351.3658 ± 0.30630.2333 ± 0.15800.3702 ± 0.213910694.64%89.83%
      8%221.8983 ± 0.30351.3655 ± 0.30490.2333 ± 0.15810.3699 ± 0.214510694.64%89.83%
      10%271.8983 ± 0.30351.3577 ± 0.29910.2297 ± 0.15750.3648 ± 0.215410694.64%89.83%
      15%411.9068 ± 0.29201.3451 ± 0.30550.2208 ± 0.16130.3518 ± 0.220310795.54%90.68%
      20%541.9237 ± 0.26661.3384 ± 0.31060.2161 ± 0.16210.3459 ± 0.219510997.32%92.37%
      30%821.9322 ± 0.25251.3204 ± 0.30520.2060 ± 0.16250.3314 ± 0.221711098.21%93.22%
      40%1091.9407 ± 0.23721.3180 ± 0.31580.2024 ± 0.16640.3249 ± 0.227211199.11%94.07%
      50%1361.9322 ± 0.25251.3146 ± 0.32730.1980 ± 0.17030.3171 ± 0.232711098.21%93.22%
      100%2721.9492 ± 0.22061.2841 ± 0.32770.1778 ± 0.17410.2858 ± 0.239711294.92%
      Na = Observed number of alleles; Ne = Effective number of alleles[56]; h = Nei's (1973) gene diversity; I = Shannon's Information index[57].

      Figure 5. 

      Cluster diagram based on jaccard by UPGMA analysis calculated from alleles derived from 20 Pueraria accessions of core germplasm. The number represents the code in Supplemental Table S1.

    Discussion

      Polymorphism of newly developed SSR markers

    • We detected a total of 118 alleles with 23 SSRs segregating in the 272 Pueraria accessions in Guangxi, with an average of 5.13 alleles per locus. This value is higher than the number of alleles per SSR locus reported in a previous study with the 28 SSRs in the 44 Pueraria accessions from Guangxi[17]. This suggests that expanding the sample size is a powerful strategy for the analysis of genetic diversity in Pueraria germplasm in Guangxi. The number of effective alleles per locus (4.87) obtained in the Guangxi Pueraria accessions appears to be higher than the number of effective alleles per SSR locus found in 184 Pueraria accessions from Jiangxi (1.4503) and other crops, such as the value of 2.26 reported in rice[24], 3.17 in olive[25], but lower than the values of 5[26] or 7.2 in maize[27]. The results also showed that SSR allelic diversity of Pueraria germplasm was moderate (Na = 1.9492, Ne = 1.2841, h = 0.1778). Zhou et al.[14] reported an average of Ne = 1.4503 and h = 0.2865 in a collection of 184 Pueraria accessions from Jiangxi. The number of markers and individuals, the sexual propagules and type of plant material, the population size may be responsible for the level of polymorphism and discrimination power.

      Genetic diversity of Pueraria germplasm

    • The overall clustering patterns generated by the STRUCTURE and PCA did not clearly distinguish the sampling areas, which is consistent with the previous results[10,13,17,28]. Few admixtures (13/272) were also detected due to shared ancestry during the breeding process, which is also observed in hybrid rice[29]. Pueraria resources have a low level of genetic differentiation (Nm = 1.7690). The degree of genetic differentiation among populations may decrease due to the existence of large gene flow (Nm > 1). The low genetic differentiation indicated that geographical isolation may not restrict gene exchange among Pueraria species populations in Guangxi. It is susceptible to external factors even though there was a certain correlation between genetic variation and geographical distribution based on RAPD in several studies[12,30,31]. As a result, it is thought that Pueraria species has been cultivated and utilized for a long period in Guangxi since native cultivars of Pueraria still exist in the major regions, which is similar to Perilla in Korea[32]. The selection by humans could be responsible for this clustering pattern and moderate genetic diversity.

      Our results revealed that Pueraria accessions display moderate genetic variation throughout Guangxi, while the UPGMA dendrogram showed that 272 accessions were divided into two main clusters with 37.8% genetic similarity, four main clusters with 68.4% genetic similarity. However, previous studies revealed that Pueraria accessions or species possessed from moderate to the high level of genetic diversity with high clonal reproduction and perennial[3,14,17,28,30,33−37]. The inconsistencies observed, except for various taxon sampling and markers, could have originated from the following: 1) the populations were found by sexual propagules could contribute to the maintenance of high genetic variation in clonal populations regardless of recruitment of sexual offspring[38]; 2) introductions from across its multiple native populations into novel habitats from seed stock[37]; 3) clonal populations with fewer genotypes still maintain higher genetic diversity at each locus[39].

      Moreover, Pueraria species, as strictly self-pollinating and clonally persisting clumps plants, have considered heterozygosity (Table 2), like many clonal plants, e.g. Castanea dentata[40] and Musa balbisiana[41]. Our results showed that relatively low Ht (0.1841) and Hs (0.1435), which suggest that accessions were inbred due to little outcrossing during maintenance[42]. Moreover, we could not rule out a case that the existence of ancient clonality and the somatic mutation, which accumulates genetic variation within clonally persisting clumps may account for some of the heterozygosity, especially given rapid mutation of SSR fingerprints.

      Implications for utilization of core germplasm resources in Guangxi

    • Core germplasm plays a key role in the conservation, management, and utilization of germplasm resources, which is critical for the development of plant breeding. Individuals reflecting genetic information can be selected to build the core germplasm resources. China is the center of distribution of Pueraria, with a long history of growing Pueraria species. However, fewer excellent Pueraria germplasm have been established due to artificial over-mining, lack of conservation, and management of resources. Previous researchers have shown that a sampling proportion between 5% and 30% is enough to include at least 80% of the alleles representing the genetic diversity of the entire collection[43,44]. According to dynamic extracted results, our results revealed that when the samples collected reached 7.35% (20/272) of Pueraria accessions accounted for 105 alleles, accounting for approximately 93.75% of all alleles loci. Interestingly, the retention value of Pueraria core collection genetic diversity was lower than the allele retention values of 100%, 100%, and 97.5% in rosewood, licorice, and eggplant, with sampling ratios reaching 12.4%[45], 16.84%[46] and 12.03%[47], respectively. Pueraria species are abundant in Guangxi, especially in Tengxian and Wuzhou[2]. The most likely reason was that the breeding of a majority of Pueraria accessions in Guangxi was still from layering breeding and self-crossing, and lacked extensive gene exchanges from cross-breeding, which led to a decrease in the ratio of the core collection. Our findings will be useful in breeding programs for the introgression of noble alleles into modern cultivars by exploiting natural genetic variation existing in Pueraria genetic resources. Combined with the analysis of phenotypic diversity (e.g. puerarin, starches) of Pueraria species, we may detect the important polymorphic loci associated with the traits based on correlation analysis, which could provide a foundation for developing the molecular marker-assisted breeding or detection of target genes soon[7].

      Meanwhile, the genetic clusters were not consistent with species delimitation and geographic distribution. For instance, accession number 140 and 68 classified as P. montana var. montana, shares a close relationship with three numbers P. montana var. lobata accessions (29, 243, and 245). Pueraria plants were introduced from different regions, which may result in a certain degree of inconsistency between actual germplasm sources and clustering results[17]. Furthermore, this also implies the complex evolutionary history with the human process blur the relationship among these species.

      Complex genetic relationships among Pueraria species

    • Molecular marker based on SSR can help exploiting and utilizing plant variety resources reliably without the appraiser and environmental factors[48]. The present results include new clues in genetic relationships among Pueraria species based on SSR markers, that is moderate genetic variation and low genetic differentiation play a key role in the species delimitation of Pueraria. Pueraria DC. (Fabaceae, Phaseoleae) comprises ca. 20 species, occurring in tropical and East Asia. Eight species and two varieties have been recorded in China[49], with four groups or three sections as infrageneric classification based on morphological traits[50,51]. However, molecular studies have revealed that Pueraria is not a monophyletic group[52,53]. For example, taxonomically kudzu (P. montana var. lobata) is placed under the genus Pueraria. Pueraria montana var. thomsonii and P. montana var. lobata were treated as varieties for P. montana in flora of China. However, the phylogenetic relationship and classification among these three species are still confused based on various molecular markers and sampling taxon[54,55]. Thus, molecular markers for germplasm identification of kudzu or even Pueraria species may be limited. A wider taxon sampling with higher resolution genetic markers would increase confidence for the phylogenetic relationship among Pueraria species, efforts that are currently underway.

    Conclusions

    • In this study, we used 23 pairs of simple sequence repeat primers to evaluate the genetic diversity and construct core germplasm of the 272 individuals of Pueraria species in Guangxi. Our results revealed that Pueraria accessions display moderate genetic variation throughout Guangxi. There was a non-significant relationship between genetic distance and geographical distance. The results could provide the basis for the breeding program of Pueraria. We consider the SSR markers to be a useful tool for both genetic diversity and the core germplasm of Pueraria.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Yan H; data collection: Cao S, Zeng W, Wu Z; analysis and interpretation of results: Shi P, Zhou Y, Shang X; draft manuscript preparation: Xiao L, Zhou Y. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

      Acknowledgments

      • This work was supported by the Guangxi Natural Science Foundation Project (2021JJB130122, 2023GXNSFBA026297, 2021GXNSFBA220026), the National Natural Science Foundation of China (82204563, 31960420), the Guangxi Key R&D Program Project (Guike AB22080090), the Technology Development Project funded from Guangxi Academy of Agricultural Sciences Science (GXAAS) (Guinongke 2023JZ10), and the Special Project for Basic Scientific Research of Guangxi Academy of Agricultural Sciences (Guinongke 2021YT057).

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Supplementary Information
      Dates

      • Received 30 November 2023; Accepted 14 March 2024; Published online 23 April 2024

      Highlights

      • 272 individuals of Pueraria species in Guangxi were divided into two main clusters in all analysis.

        118 alleles were identified and 112 alleles were polymorphic.

        Overall genetic diversity was moderate.

        A core collection of 20 Pueraria accessions was constructed when the samples collected reached 7.35% (20/272).

      • # Authors contributed equally: Pingli Shi, Yun Zhou

      Supplementary information
      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press on behalf of Hainan University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (5)  Table (3) References (57)
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    Cite this article
    Shi P, Zhou Y, Shang X, Xiao L, Zeng W, et al. 2024. Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers. Tropical Plants 3: e012 doi: 10.48130/tp-0024-0012
    Shi P, Zhou Y, Shang X, Xiao L, Zeng W, et al. 2024. Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers. Tropical Plants 3: e012 doi: 10.48130/tp-0024-0012
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  • Introduction
  • Materials and methods

  • Plant material and DNA extraction

  • SSR genotyping

  • Statistical analysis

  • Results

  • SSR polymorphisms

  • Population genetic diversity

  • Genetic structure analysis

  • Cluster analysis and Principal Component Analyses (PCA)

  • Extraction of a core collection

  • Discussion

  • Polymorphism of newly developed SSR markers

  • Genetic diversity of Pueraria germplasm

  • Implications for utilization of core germplasm resources in Guangxi

  • Complex genetic relationships among Pueraria species

  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgments
  • Conflict of interest
  • Supplementary Information
  • Dates
  • Highlights
  • Supplementary information
  • Rights and permissions
  • About this article
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