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Article

Genomic Analysis of Romanian Lycium Genotypes: Exploring BODYGUARD Genes for Stress Resistance Breeding

by
Roxana Ciceoi
1,
Adrian Asanica
2,
Vasilica Luchian
2 and
Mihaela Iordachescu
1,*
1
Research Center for Studies of Food Quality and Agricultural Products, University of Agronomic Sciences and Veterinary Medicine of Bucharest, 59, Mărăști Bd., 011464 Bucharest, Romania
2
Faculty of Horticulture, University of Agronomic Sciences and Veterinary Medicine of Bucharest, 59, Mărăști Bd., 011464 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(4), 2130; https://doi.org/10.3390/ijms25042130
Submission received: 28 December 2023 / Revised: 30 January 2024 / Accepted: 6 February 2024 / Published: 9 February 2024
(This article belongs to the Special Issue State-of-the-Art Molecular Biology in Romania)
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Abstract

:
Goji berries, long valued in Traditional Chinese Medicine and Asian cuisine for their wide range of medicinal benefits, are now considered a ‘superfruit’ and functional food worldwide. Because of growing demand, Europe and North America are increasing their goji berry production, using goji berry varieties that are not originally from these regions. European breeding programs are focusing on producing Lycium varieties adapted to local conditions and market demands. By 2023, seven varieties of goji berries were successfully registered in Romania, developed using germplasm that originated from sources outside the country. A broader project focused on goji berry breeding was initiated in 2014 at USAMV Bucharest. In the present research, five cultivated and three wild L. barbarum genotypes were compared to analyse genetic variation at the whole genome level. In addition, a case study presents the differences in the genomic coding sequences of BODYGUARD (BDG) 3 and 4 genes from chromosomes 4, 8, and 9, which are involved in cuticle-related resistance. All three BDG genes show distinctive differences between the cultivated and wild-type genotypes at the SNP level. In the BDG 4 gene located on chromosome 8, 69% of SNPs differentiate the wild from the cultivated genotypes, while in BDG 3 on chromosome 4, 64% of SNPs could tell the difference between the wild and cultivated goji berry. The research also uncovered significant SNP and InDel differences between cultivated and wild genotypes, in the entire genome, providing crucial insights for goji berry breeders to support the development of goji berry cultivation in Romania.

1. Introduction

Goji berry plants have long been used for both Asian culinary and medicinal traditions, with their use extending back over thousands of years [1], and, currently, the berries are acknowledged as one of the most recognised ‘superfruits’ of the 21st century [2,3,4], being considered as a functional food [1,5]. The goji berry has attracted significant attention in Western countries due to its nutritional profile, especially for its abundant vitamins and antioxidants. Its oxygen radical absorbance capacity values, which lie between 25,000 and 30,000, surpass those of other nutritionally beneficial fruits like pomegranates and blueberries, indicating its superior antioxidant capacity [6]. Its medicinal uses range from improving visual acuity [5,7,8], abdominal pain [5], dry cough, fatigue and headache [5], immune system support, cancer prevention [7,8], and antidiabetic activity [7,8] to increased longevity [8,9,10] and enhanced fertility [10,11,12,13].
In China, out of the existing nine Lycium taxa [14], only four are traditionally utilised, with L. barbarum and L. chinense being the main species traded worldwide [14,15]. In World Flora Online, the genus Lycium comprises 436 species names, and out of which 92 are accepted species, 241 are considered synonyms, and 103 are unplaced [16]. Yao and al. name 97 Lycium species, and out of which 35 species and 2 varieties are used as food and/or medicine worldwide [14]. The Plants of the World Online platform includes 101 officially accepted Lycium species in 71 countries, across 130 regions, including Romania [17]. Such taxonomical debate could also be explained by the fact that the genetic foundation of the germplasm resources of wild Lycium species in the world, and also in China, remains poorly understood [18].
The flora of Romania recognised Lycium halimifolium L. as a native species for decades [19] before L. barbarum became the accepted name [17,20]. A manuscript from 1867 documents the traditional usage of Lycium vulgare Dun. in Romania, and mention its identification as L. barbarum in the Transylvania region [21,22]. Although L. halimifolium is a synonym of L. barbarum [20,23,24], widespread public belief still treats L. halimifolium and L. barbarum as separate species, attributing them different culinary and toxicological properties [25,26]. Traditionally, the plant has been used extensively to make fences in the countryside, but has also had folk medicinal uses, such as in treating conditions related to fear and anxiety and for epilepsy and spasms, indicating psychological and neurological benefits [27]. In a few Romanian regions, it is considered an invasive plant, such as in Oltenia, the Danube riverbanks, and Dobrogea [28,29].
Due to goji berries’ increased fame, the market demand has grown exponentially in the last two decades [30,31]. China dominates goji berry production, particularly in the northwest regions like Ningxia and Xinjiang, the two main exporting regions [9,32,33]. In contrast, production in North America and Europe is limited due to a lack of traditional use, knowledge, and adapted varieties [34,35,36,37]. Romania has emerged as a significant producer of goji berries [38], also focusing on plant material for cultivation [39], with a market that is showing a rising trend [40]. Especially in the difficult context of climate change constraints, goji berry planting material which has adapted to local conditions is required by European farmers. Therefore, Lycium breeding programs have been launched, together with initiatives on identifying promising genitors and new crop production processes [41].
By 2023, seven varieties of goji berry had been registered in the Official Catalogue of Cultivated Plant Varieties: ‘Erma’, ‘Transilvania’, ‘Kirubi’, ‘Kronstadt’, ‘Bucur’, ‘Sara’, and ‘Anto’, belonging to both L. barbarum and L. chinense [42].
Having a deeper understanding of native goji berry genetic resources is important both for preserving local biodiversity and for the breeding sector [18,33,43]. With growing market demand for goji berries, comprehensive molecular research has been initiated to identify valuable genes in both cultivated and wild goji berry plants, aiming to enhance future breeding programs [1,33,43,44,45,46]. Crop breeding aims to develop new plant varieties with improved traits such as increased yield, disease resistance, and nutritional quality [47]. High-throughput technologies, including genomics, transcriptomics, and metabolomics, have opened up a new phase in crop breeding, enhancing the efficiency and precision of this process [47,48]. The last two decades have seen a significant growth in both the volume and quality of publicly available plant genomes, with a higher efficiency of genome sequencing, assembly, and annotation [48,49,50].
In the Solanaceae family, which includes around 3000 species, 170 full genomes of 46 species have been sequenced [49]. Among them are Lycium barbarum [47,51] and its invasive relative, L. ferocissimum [52]. The L. barbarum genome contains 12 chromosomes [31] (2n = 2x = 24) and it is 1.8 Gb in size, with a level of heterozygosity of approximately 1% [51]. The sequenced and annotated genome ASM1917538v2 [53] was obtained by sequencing a haploid plant developed from pollen culture, using PacBio Sequel technology [51]. The annotation allowed for the identification of 47,740 genes and 34,339 protein-coding sequences. The availability of another annotated genome of L. ferocissimum, of 1.2 Gb size, 40,291 genes, and 30,549 protein-coding genes [52], will ease the characterisation of the future goji berry sequenced genomes even more, allowing for the identification of new genes of interest.
The current research marks the initial phase of a broader project focused on genes related to resistance to abiotic and biotic stress. The present study is a preliminary exploratory step that aimed to discover regions with high SNP and InDel polymorphism as sources of wild-type resistance genes that could be introgressed into future varieties. A case study on the genomic coding sequence of BDG genes, focusing on cuticle thickness, is presented to demonstrate the utility of the research. Analysing the genetic diversity of cultivated and wild goji plant genes has the final aim of providing information required by goji berry breeders, supporting the development of goji berry production in Romania.

2. Results

2.1. NGS Data Analysis

2.1.1. Sequencing Data Quality Control

The genomes of eight Romanian L. barbarum varieties, out of which five were cultivated varieties that were part of a population obtained from Chinese seeds [54] and three were spontaneous plants growing in the wild in three different Romanian counties [28,55], were sequenced using NGS technology. The distribution of sequencing quality was analysed across the entire length of all sequences to identify any locations with abnormally low sequencing quality that could indicate the inclusion of incorrect bases at higher-than-normal rates. Novogene Co., Ltd. (Cambridge, UK), analysing base calling (Casava 1.8 software), had Qphred scores between 30 and 40, indicating error rates between 1:1000 and 1:10,000, with the Qphred usually being higher than 35 (Supplementary File S1, Sequencing Quality Distribution). The sequencing error rate for all samples was around 0.02 at the beginning of the data acquisition and between 0.04 and 0.06 at the end of the reading (Supplementary File S1, Sequencing Error Rate). When performing sequencing data filtration, the percentage of clean reads was between 99.52% and 99.72% (Supplementary File S1, Classification of the Sequenced Reads). Regarding the statistics of the sequencing data, for 1,669,720,889 base pair (bp) reference genome, the mapping rate of each sample ranged from 96.66% to 99.36% (Supplementary File S1, CleanData_QCsummary). The proportion of clean data relative to raw data, referred to as the effective rate, was higher than 99.52% for all reads. Referring to the reference genome (without Ns), the average depths were between 10.01 X and 9.29 X and the 1 X coverages ranged from 77.43% to 97.41%; the results therefore fell within the acceptable normal range and could be utilised in variation detection and genetic analysis (Supplementary File S1, Allsample_allinfo).

2.1.2. SNP Detection, Distribution, and Mutation Frequency

SNP (Single Nucleotide Polymorphism) variations were observed in all eight genotypes, but the quantity and genomic distribution of these variations differed across the genotypes. A total of 108,290,958 SNPs were identified within the eight genotypes, with an average ranging from 14,079,300.6 SNPs/genome for the cultivated specimens and 12,631,485 SNPs/genome for the wild specimens. The Lb2 genome exhibited the largest quantity of SNPs, totalling 15,983,773.
In the eight genotypes, the transitions—point mutations that change one purine nucleotide to another or one pyrimidine to another—were more frequent, with an average count of 8,559,837.5. This was higher than the number of transversions, which are mutations that switch a purine for a pyrimidine or vice versa, averaging at 4,976,532.25. This resulted in an average ts/tv ratio (transitions to transversions) of 1.72, which was relatively consistent across the two categories of genotypes, cultivated and wild. However, there was a difference between the cultivated and wild-grown plants: the average ts/tv ratio was higher in the cultivated genotypes at 1.736, compared to 1.688 in the wild-grown plants.
The genotype Lb7w exhibited the highest heterozygosity rate, measured as 5.037‰ (per thousand), whereas the lowest rate was found in genotype Lb4, at 3.245‰. On average, the wild plants showed a higher heterozygosity rate, averaging at 4.851‰, compared to the cultivated plants, which had an average heterozygosity rate of 3.607‰.
Figure 1 illustrates the distribution of the six types of SNP mutations. Genotypes Lb1 and Lb2 showed the highest number of SNPs across all six SNP types, while genotype Lb8w had the lowest count. Among these six types of SNP mutations, for every genotype, the most common was the C:G>T:A mutation, followed by T:A>C:G. Conversely, the least frequent type of SNP was the C:G>G:C mutation. For the C:G>T:A mutation type, the Lb2 genotype had 5,289,157 SNPs, Lb1 had 5,256,956 SNPs, and LB8w had 3,784,249 SNPs. For the T:A>C:G, the Lb2 genotype had 4,877,268 SNPs, Lb1 had 4,854,020 SNPs, and LB8w had 3,768,683 SNPs. For the C:G>G:C, the Lb2 genotype had 934,963 SNPs, Lb1 had 931,895 SNPs, and LB8w had 731,795 SNPs (Supplementary File S1, SNP.frequency and SNP_Annotation_Statistics).

2.1.3. Insertion/Deletion Detection and Distribution

InDel (insertion and deletion) variations were detected in all the studied genotypes, amounting to a total of 11,225,960 InDels, averaging 1,403,245 InDels per genome. The genotype Lb2 had the highest number of InDels at 1,773,325, whereas Lb8w had the fewest, totalling 1,197,605. For the cultivated specimens, the average InDels per genome were 1,495,506.4, compared to 1,249,476 for the wild specimens. When examined individually, the overall count of insertions, which was 5,166,205, was less than the total number of deletions, totalling 6,043,098. However, the mean count of insertions in the cultivated specimens, at 684,984.8, exceeded the average insertion count in the wild specimens, which was 580,427. In the case of deletions, the average number of deletions in the cultivated specimens, amounting to 808,410.6, was greater than the average deletion counts in the wild specimens, recorded at 667,015.
The InDel heterozygosity rate, expressed in per mille (‰) and calculated as the ratio of InDels to the total genomic bases, was 0.381‰. This value was lower on average for the cultivated specimens, at 0.335‰, compared to the higher average rate for the wild specimens, which was 0.458‰.
InDel distribution within the genome (Figure 2) showed that almost 50% of all insertions and deletions (InDels) had the length of 1 base pair, around 13% of InDels were 2 bp long, around 7% of InDels were 3 bp long, and, thereafter, the percentage continued to decrease with the increase in InDel length. The highest percentages of 1 bp InDels were observed in the Lb5 (49.40%), Lb4 (49.32), and Lb3 (49.28%) cultivated genotypes, while the lowest percentages were observed in the wild genotypes Lb8w (48.42%), Lb6w (48.32%), and Lb7w (48.21%). On the contrary, the highest percentages of 2 bp InDels were observed in the wild genotypes Lb7w (14.12%), Lb6w (14.11%), and Lb8w (14.05%), while the lowest percentages were observed in the Lb1 and Lb2 genotypes (13.29%). The InDels longer than 12 bp were below 1%, and the ones longer than 32 bp were below 0.1% (Supplementary File S1, InDel.GENOME percentage and InDel_Annotation_Statistics).
In the analysis of the eight genotypes, the densities of SNPs (Figure 3) and InDels (Figure 4) across each chromosome appeared to be relatively similar (Supplementary File S2). However, a visible reduction in InDel density was observed (Figure 3). Additionally, distinct differences were evident between the genomes of the cultivated and wild plants. Generally, the ratio of SNPs to InDels was around 10. This ratio was slightly lower in the cultivated plants, ranging from 9.29 to 9.52, and was somewhat higher in the wild plants, with values ranging from 10.03 to 10.16.
For both SNPs and InDels, it was visible that the density of variation was higher at the end of all 12 chromosomes. In addition, (1) all genomes of the cultivated specimens had high variation density, for both SNPs and InDels, almost in the middle of the 5th chromosome, with the same area also being observed in the genomes of the wild plants, but with a lower density; (2) all genomes had the first half of the 12th chromosome with a very low density of both SNPs and InDels, with the exception of the wild plants, where SNPs were present at a very high density; (3) the 1st chromosome, despite being the longest one, had the longest area with a low density of SNP and InDel variations, except for Lb8w; (4) there was a clear distinction between the cultivated and wild plant genomes, which was more easily observed at the level of SNPs.

2.1.4. Sequence Analyses of BODYGUARD Genes in Romanian Goji Berry Genomes

In both Lycium species with available reference genomes, three BODYGUARD (BDG) genes were identified (Table 1). However, these genes are situated on different chromosomes in each species, on chromosomes 4, 8, and 9 in Lycium barbarum and on chromosomes 1, 3, and 9 on Lycium ferocissimum.
Analysing the eight studied genomes using Genome Workbench, a distinct divergence was noted between the genomes of the cultivated and wild plants.
Regarding the BDG gene situated at LOC132634709 (Table 2) on chromosome 4, 22 SNPs were identified within its coding region. Out of these, 14 are synonymous mutations, meaning they do not change the amino acid sequence. With the exception of the SNP at position 1312, situated within a codon that encodes for lysine/arginine (basic amino acids), the rest of the SNPs are situated within codons that encode for either nonpolar (10) or polar amino acids (11), and none of these SNPs change the polarity or charge of the encoded amino acid. A key finding was the apparent distinction in most SNPs between the cultivated and wild varieties of the plant, with differences being observed as homozygous versus heterozygous SNPs.
In the BDG gene at LOC132607278 (Table 3) on chromosome 8, the analysis revealed 28 SNPs within its coding sequence, including 4 synonymous mutations. Notably, SNPs at positions 287–288 and 392–294 each impact a single codon, changing AAA to CGA (Lysine to Arginine) and AAA to GAC (Lysine to Aspartic Acid), respectively. A distinct pattern was observed between the cultivated and wild genotypes at 17 specific SNP locations: 174, 209, 288, 392–394, 617, 648, 688, 694, 799, 891, 1030, 1049, 1076, 1150, 1151, 1266, and 1304, predominantly presenting as homozygous versus heterozygous SNPs. Apart from the SNPs at positions 146 and 1274, which alter the charge of the encoded amino acids (Lysine to Glutamic Acid and Glutamic Acid to Lysine, respectively) from basic to acidic and vice versa, the other SNPs do not cause any changes in either the polarity or charge of the encoded amino acids.
The BDG gene at LOC132609965 (Table 4) on chromosome 9 features 29 SNPs within its coding region, with 18 of these being silent mutations. Two SNPs at positions 555–557 lead to the formation of four different codons: TCT, GCT, TCA, and TCG, which correspond to the amino acids Serine, Alanine, Serine, and Serine, respectively. In a similar manner, the two SNPs at positions 1671–1672 result in the codons GGA, AGA, and GAA, which encode for the amino acids Glycine, Arginine, and Glutamic Acid, respectively. The SNPs at positions 555–557 are unique in that they alter the polarity of the encoded amino acid from Serine (as found in the reference genome) to Alanine (as observed in the cultivated genotypes). The other SNPs, however, do not cause any changes in the polarity or charge of the amino acids that they encode. Approximately half of these SNPs show sequence-level differences between the cultivated and wild-type genotypes.

3. Discussion

Exploring the genetic diversity in Romanian wild and cultivated Lycium species can advance breeding by developing new varieties tailored to specific environmental conditions and market needs, highlighting key genetic markers for desired traits. The Romanian homologated varieties were developed based on Chinese varieties’ germplasm, due to their high fruit quality traits [56,57], without using the local germplasm. Generally, the goji berry in Romania only has three major biotic threats, powdery mildew, goji berry gall mite, and stink bugs [58,59], making it much more suitable for organic production than that in China [37,60]. The escalating threat of extreme weather events caused by climate change is set to pose an increasingly serious challenge to goji berry production, with the major threats being extreme drought and insolation [61,62]. The cuticle is a protective, hydrophobic layer covering the epidermis of leaves, stems, and fruits in plants [63,64]. Cuticle primary roles include water regulation, protection against biotic stress, defence against abiotic stress, and the facilitation of gas exchange and photosynthesis, enhancing pollution tolerance [65,66,67,68,69,70]. The cuticle type also impacts the fruits’ postharvest storage [71], as demonstrated for the goji berry [67]. By 2013, Yeats and Rose had mentioned almost 50 discovered cuticle-associated genes, with most of them belonging to Arabidopsis, tomato, rice, barrel clover, and maize [63]. Among these are BODYGUARD genes that encode proteins like α/β hydrolase, crucial for plant defence and cutin biosynthesis in Arabidopsis [67,72,73,74]. Studies on goji berry cuticles have revealed that certain varieties have enhanced resistance to Alternaria alternata. This offers valuable preliminary data for breeding and selecting cultivars for better postharvest storage [64].
The sequencing and annotation project of the goji berry genome in 2023 [51] represents a crucial resource for future resequencing projects. The advancements in next-generation sequencing/whole genome sequencing (NGS/WGS) [50] are poised to generate a wealth of data, which will be instrumental in developing new goji berry varieties.
Following SNP and InDel density analysis, it became apparent that SNP polymorphism is more spread from the chromosome ends towards their middle part, whereas InDel polymorphism is more concentrated in the chromosomes’ ends. In addition, in the beginning of chromosome 12, there is much less InDel polymorphism compared to the rest of the chromosome ends (Figure 3 and Figure 4). Higher SNP and InDel densities have been observed in other plant species such as Sorghum spp. [75,76], Solanum lycopersicum L. [77,78], and Capsicum spp. [79]. The observed increased polymorphism near the ends of chromosomes can be attributed to the higher frequency of recombination in these areas [80]. In addition to chromosome ends, the wild-type genotypes Lb6w and Lb7w present a high degree of SNP polymorphism in the central regions of chromosomes 9 and 10 (Figure 3). The examination of variations in density at the genomic level, particularly for SNPs and InDels, highlights specific genome areas that warrant further investigation, to identify potentially beneficial genes from wild genotypes that could be integrated into new varieties.
Romanian breeding efforts have led to the registration of seven new goji berry varieties in the Official Catalogue of Cultivated Plants in Romania. These include ‘Erma’ and ‘Transilvania’ registered in 2017, ‘Kirubi’ in 2018, ‘Kronstadt’ in 2019, ‘Bucur’ and ‘Sara’ in 2020, and ‘Anto’ in 2021 [40]. This development has enabled Romanian farmers to establish commercial L. barbarum and L. chinense plantations using certified plants. Presently, commercial plantations and branded products are established in several Romanian counties, including Bihor, Brașov, Călărași, Cluj, Constanța, Dâmbovița, Hunedoara, Prahova, Satu Mare, Sibiu, and Vaslui, and this trend is on the rise, so new varieties are being requested by the market. Present research is dedicated to enriching the diverse gene pool found in wild germplasm, potentially enhancing the unique characteristics of Romanian goji berries. By examining the morphological and phenological traits of wild goji berries and correlating them with genetic data, characteristics like early or late flowering, high drought tolerance, and strong resistance to low temperatures, as well as features like thicker cuticles and leaves, could become valuable assets in breeding programs.
In earlier research, the morpho-anatomical features of leaves and flowers of both wild and cultivated goji berries in the Bucharest region were analysed. One study aimed to identify the key traits of interest to both goji berry breeders and taxonomists [28]. Another study involved mapping the spontaneous genetic resources found across Romania [55]. Notable morphological distinctions were observed in the leaf shape, orientation, and width of Romanian L. barbarum, results that are similar with findings reported in the Republic of Moldova, in a similar study between cultivated and wild goji berries [81]. Leaf anatomical characteristics are particularly significant in relation to biotic and abiotic stress factors, with wild plants having leaves covered with a thick cuticle, prominently developed vascular bundles, and sheaths surrounding the vascular bundles within the mesophyll. Additionally, the palisade cells in these plants were observed to be considerably larger than those in the cultivated plants [28]. These findings motivate further investigation into genes putatively linked to these phenotypic differences. The formation of the plant cuticle involves several proteins that play crucial roles in the biosynthesis and regulation of cutin and waxes, such as BDG, CER, KCS, VLCFAs, GPAT, LACS, ABC, SHN/WIN, LTPs, and CD1 [63,67,72,82].
Arabidopsis BDG1 proved to be involved in multiple processes: cuticle development [67,72,83,84], cutin biosynthesis and response to osmotic stress [85], defence response to the fungus Botrytis cinerea [83], lateral root development [84], the positive regulation of cutin biosynthesis, suberin biosynthesis, and transpiration [67]. All of these studies used the Arabidopsis bdg mutant phenotype to prove BDG1’s functions. For instance, bdg mutant plants are dwarfed and have abnormal leaves, collapsed cells, a reduced number of trichomes, and an abnormal cuticle, as they accumulate more cell-wall-bound lipids and epicuticular waxes than wild-type plants and have activated defence responses, making them immune to Botrytis cinerea attack [72,83]. However, bdg mutant plants are extremely sensitive to osmotic stress [85].
Three BDG genes, similar to Arabidopsis BDG 1 which encodes a protein involved in cutin biosynthesis and cuticle development and morphogenesis [69,71], were selected for a detailed analysis. In the reference genome, the BDG genes are located in high-SNP and -InDel polymorphism areas. The gene LOC132634709, a probable lysophospholipase BODYGUARD 3, is located at the beginning of chromosome 4, position 426077–430655. The gene LOC132607278, a probable lysophospholipase BODYGUARD 4, is located at the end of chromosome 8, position 127610658–127620151. The gene LOC132609965, a probable lysophospholipase BODYGUARD 3, is located at the end of chromosome 9, position 126096652–126103895.
In analysing the sequences of three BODYGUARD (BDG) genes in Romanian goji berry genomes, notable differences between the cultivated and wild types are evident, as observed in Table 2, Table 3 and Table 4. For the BDG 3 gene on chromosome 4, 14 out of 22 SNPs (64%) can distinguish wild from cultivated types. Eight SNPs (positions 590, 749, 1208, 1247, 1352, 1439, 1715, and 1886), all of them silent, do not differentiate between cultivated and wild types. On chromosome 8’s BDG 4 gene, 17 out of 26 SNPs (69%) do so. For chromosome 9’s BDG 3 gene, 15 out of 28 SNPs (56%) differentiate between the two types. Seven of the thirteen SNPs that do not differentiate between the cultivated and the wild-type genotypes are silent. These findings highlight significant genetic variations between cultivated and wild goji berry plants. It is not yet certain how each of these SNP variations at the gene sequence level translates into phenotypical differences between the wild-type and cultivated plants. In Arabidopsis, the use of loss-of-function mutant plants obtained by transposon insertion led to the discovery of BDG1 multiple roles. Previous studies demonstrated morphological differences between wild and cultivated goji berry plants [55,81]. It remains to be seen in future studies if these differences in morphology are directly linked to the gene sequence variations, if indeed the wild plants are resistant to various pathogens, and to what extent they are affected by abiotic factors, such as osmotic stress [85].
The sequence analyses of the BDG genes in Romanian goji berry genomes revealed several differences among the three genes. The genes located on chromosomes 4 and 9 encode probable lysophospholipase BODYGUARD 3 proteins, whereas the gene located on chromosome 8 encodes a probable lysophospholipase BODYGUARD 4 protein [51]. The BDG 4 gene from chromosome 8 is shorter than the BDG 3 genes from chromosomes 4 and 9. Although located on different chromosomes, two of the genes presented SNPs affecting the same amino acid, such as in the 12, 65, 84, 235, 254, 410, 426, 467, and 473 positions. Even if some SNPs are located within conserved regions, many of them are silent (Figure 5).
Recent advances in genetic research have significantly enhanced our understanding of both goji berries and other important crop species. Regarding the goji berry, a comprehensive analysis of the relationships and origins of various Lycium species, including wild and cultivated varieties in China, was proposed by Qian et al. [86], while quantitative trait loci for fruit size in goji berries, employing specific-locus amplified fragment sequencing for SNP detection, were determined by Rehman et al. [87]. For soybean, genome resequencing and the development of SNP markers provided a framework that could be adapted for goji berry genetic studies and breeding [88,89]. For groundnut, Pandey et al. developed a high-density SNP array, a technique that can also be applied to goji berries to explore genetic diversity [90]. In tomatoes and apples, two teams demonstrated the utility of genomic libraries and reduced representation genome sequencing offering valuable methods that could be employed in goji berry genetic research [91,92]. These studies collectively indicate a growing trend of employing advanced genomic techniques to enhance crop breeding and genetic analysis, with potential applications in understanding and improving goji berries.

4. Materials and Methods

4.1. Plant Material

In this study, five selected Romanian-goji-berry-cultivated and three spontaneous-growing genotypes were examined. The five genotypes are integral to an extensive breeding program for goji berries that commenced in 2014 at the Experimental Field of the Faculty of Horticulture, at the University of Agronomic Sciences and Veterinary Medicine in Bucharest [54,56,57]. The initial biological samples were derived from the seeds of Lycium barbarum L., including five distinct biotypes: Lb1–Lb5 [54]. The native plant samples were chosen from robust and well-established populations in the counties of Bucharest (Lb6w), Ilfov (Lb7w), and Călărași (Lb8w). Specifically in Bucharest, specimens were gathered from the shores of Morii Lake (44.453424, 26.013337), a natural area on the periphery of the western segment of the Romanian capital. This location was also selected for a comparative morpho-anatomical study of the leaves and flowers of both wild and cultivated goji berry plants [28]. The plants encountered in Ilfov county are believed to have originated from cultivated specimens within a military base, subsequently becoming naturalised in the area (44.447382, 26.019239). The specimens from Călărași were found to be proliferating along a roadside in Lehliu city (44.434389, 26.858775). Voucher specimens for all eight genotypes were stored in the Herbarium BUAG “Prof. dr. V. Ciocîrlan” of USAMV Bucharest, entry numbers 4094–4101 (Supplementary File S1, Sampling Metadata Sheet).

4.2. DNA Extraction

Genomic DNA from fresh goji berry leaves was isolated using the InnuPure C16 automated system (Analytik Jena GmbH, Jena, Germany), which employs magnetic particle separation technology for the fully automated extraction and purification of DNA. This process took place at the Research Center for Studies of Food Quality and Agricultural Products at the University of Agronomic Sciences and Veterinary Medicine in Bucharest, Romania. For genomic DNA extraction, the InnuPREP Plant DNA I Kit-IPC16 (Analytik Jena GmbH, Jena, Germany) was used, adhering to the protocols provided by the manufacturer. Initial processing involved breaking down the plant material externally, with the sample being mashed into a fine powder under liquid nitrogen and then homogenised using an SLS lysis solution (with CTAB as the detergent), proteinase K, and an RNase A solution. Following this external lysis step, the automatic DNA extraction continued in the InnuPure C16 automated system, as per the manufacturer’s guidelines. DNA quantification was performed using a NanoDrop™ 1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) [78].

4.3. Sequencing and Sequencing Data Quality Control

Whole genome sequencing (WGS) was conducted using the next-generation sequencing (NGS) technology of an Illumina platform by Novogene Co., Ltd. (Cambridge, UK). The original image data from Illumina’s high-throughput sequencing were converted into sequenced reads (raw data) through the CASAVA base recognition process (Base Calling) at Novogene Co., Ltd. These raw data were saved in FASTQ (.fq) format files [93], which included the sequencing reads along with their respective base quality scores. In NGS, as different factors (choice of sequencing platform, chemical reactants, sample quality, etc.) can influence the overall sequencing quality and the rate of base errors, an assessment across the entire length of all sequences was performed. This process allowed for the identification of specific sites or base positions that exhibited unusually low sequencing quality, which translated into high levels of incorrect base incorporation. When using Illumina platforms, the error rate of sequencing is denoted by ‘e’. The quality of sequencing, referred to as Qphred, is a score assigned to each base (Phred score) to indicate its accuracy. This Phred score is calculated using the following formula: Qphred = −10 log10(e). Essentially, this formula translates the sequencing error rate into a quality score [94]. Lower Q scores are associated with a rise in false-positive variant calls, which can lead to erroneous conclusions and additional costs for confirmatory experiments. Illumina’s sequencing technology consistently achieves Q30 or higher scores for most bases. This level of precision is particularly beneficial for various sequencing applications, including those in clinical research, where reliable data are critical [95]. In addition to sequencing quality distribution, on Illumina high-throughput sequencing platforms, the error rate has to be determined, as this increases with read extension, due to the consumption of chemical reagents during the sequencing process. Sequencing data filtration involves cleaning raw sequencing reads to enhance downstream analysis quality. This process includes removing paired reads if either contains adapter contamination, discarding paired reads where uncertain nucleotides (Ns) exceed 10% of either read, and eliminating paired reads with more than 50% low-quality nucleotides (base quality ≤ 5). All of this was performed by Novogene Co., Ltd. (Cambridge, UK) and results are provided as statistics in a sequencing data table.

4.4. Computational Data Processing and Sequencing Analysis

BWA software was utilised to align the effective sequencing data with the reference sequence, using the following parameters: mem -t 4 -k 32 -M [96]. The alignment outcomes were used to calculate the mapping rate and coverage.
The reads were aligned with the reference genome of the goji berry, GCF_019175385.1, downloaded from the NCBI database [51]. This process produced sequence alignment format files, which were subsequently transformed into binary sequence alignment format (*.bam) files. These were then processed to generate a variant file containing SNP (Single Nucleotide Polymorphism) data. The mapping rates for the samples indicate the degree of resemblance between each sample and the reference genome. Additionally, depth and coverage serve as metrics for the consistency and extent of correspondence to the reference genome, as conducted by Novogene Co., Ltd.

4.5. SNP Detection and Annotation

SNP detection was conducted using SAMtools with the specified parameter ‘mpileup -m 2 -F 0.002 -d 1000’ [96], facilitated by Novogene Co., Ltd. To minimise the likelihood of errors in SNP identification, the data underwent a two-step filtration process: an SNP was only considered if it was supported by over four reads, and its mapping quality had to exhibit a root mean square value exceeding 20, based on the supporting reads’ mapping qualities. The overall heterozygosity rate of SNPs across the genome (het. rate, denoted in permille ‰) was determined by the number of heterozygous SNPs over the total count of genomic bases. SNPs were assorted into six mutation classifications: T:A>C:G, T:A>G:C, C:G>T:A, C:G>A:T, T:A>A:T, and C:G>G:C. Take, for instance, mutations from T:A to C:G, which entail alterations from T to C and A to G. A T-to-C mutation on one strand of the DNA double helix will correspond to an A-to-G mutation at the identical position on the opposite strand. As a result, mutations of T>C and A>G were grouped together into one category.

4.6. Insertion/Deletion (InDel) Detection and Annotation

An InDel was identified as either an insertion or a deletion of a DNA sequence that is 50 base pairs (bp) in length or shorter. The detection of InDels was carried out using SAMTOOLS with the parameter set to ‘mpileup -m 2 -F 0.002 -d 1000′ [96], annotated with ANNOVAR software [97], by Novogene Co. The criteria for filtering InDels to enhance detection accuracy were consistent with those applied during SNP detection. The length distribution of InDels was examined as a proportion of the entire genome.

4.7. Sequence Analysis of the BDG Genes

Next-generation-sequenced BAM files containing the nucleotide sequence data for the eight goji berry genotypes were uploaded onto NCBI genome Workbench software, version 3.9.0, and aligned to the reference genome [98]. For each variety, the differences in nucleotide sequence were noted. Amino acid sequences of the three probable BDG proteins were aligned using MultAlin software, version 5.4.1 [99].

5. Conclusions

The present study re-sequenced the whole genome for eight L. barbarum genotypes, both cultivated and wild-type, and analysed the variability of three BDG genes, involved in cuticle biosynthesis, at the coding sequence level. NGS sequencing revealed clear differences between the cultivated and wild-type genotypes, not only in the whole genome, but also among the BDG genes. Future studies will be conducted to confirm the role of BDG genes in cuticle biosynthesis and, furthermore, their implication in resistance to biotic/abiotic stress. In addition, the data generated by the whole genome resequencing of these genotypes will allow for the analysis of additional genes which, if found to be useful, could be introgressed from the wild type into future varieties in goji berry breeding programs in Romania.

Supplementary Materials

The supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijms25042130/s1.

Author Contributions

Conceptualisation, R.C. and M.I.; methodology, R.C. and M.I.; software, R.C. and M.I.; investigation, R.C., M.I., A.A. and V.L.; writing—original draft preparation, R.C. and M.I.; writing—review and editing, R.C., M.I., A.A. and V.L.; project administration, R.C.; funding acquisition, R.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research work was carried out with the support of a grant of the University of Agronomic Science and Veterinary Medicine of Bucharest, project number 1268/30.07.2021, acronym ProtectGoji, within IPC 2021.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Acknowledgments

Special recognition and gratitude are extended to Liliana Bădulescu for her invaluable insights into plant physiology and biochemistry, as well as her meticulous explanations and indications during the draft preparation. Additionally, we express our sincere thanks to Dan Popescu for his dedicated efforts in taking care and supplying the cultivated plant material used for this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. SNP mutation type distribution. SNP—Single Nucleotide Polymorphism, T—Thymine, A—Adenine, C—Cytosine, G—Guanine; Lb1–Lb8w are the tested genotypes.
Figure 1. SNP mutation type distribution. SNP—Single Nucleotide Polymorphism, T—Thymine, A—Adenine, C—Cytosine, G—Guanine; Lb1–Lb8w are the tested genotypes.
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Figure 2. Length distribution of InDels in the eight Romanian goji berry genomes. Lb1–Lb8w are the tested genotypes.
Figure 2. Length distribution of InDels in the eight Romanian goji berry genomes. Lb1–Lb8w are the tested genotypes.
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Figure 3. SNP densities per chromosome, per genotype. SNP—Single Nucleotide Polymorphism; Lb1–Lb8w are the tested genotypes.
Figure 3. SNP densities per chromosome, per genotype. SNP—Single Nucleotide Polymorphism; Lb1–Lb8w are the tested genotypes.
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Figure 4. InDel densities per chromosome, per genotype. InDels—insertions and deletions; Lb1–Lb8w are the tested genotypes.
Figure 4. InDel densities per chromosome, per genotype. InDels—insertions and deletions; Lb1–Lb8w are the tested genotypes.
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Figure 5. Alignment of the three BDG proteins. Highlighted with red are the non-synonymous SNPs, with green are the synonymous SNPs. Red colour fonts denote high consensus, blue colour fonts denote low consensus, and black colour fonts denote no consensus.
Figure 5. Alignment of the three BDG proteins. Highlighted with red are the non-synonymous SNPs, with green are the synonymous SNPs. Red colour fonts denote high consensus, blue colour fonts denote low consensus, and black colour fonts denote no consensus.
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Table 1. Name, description, and location of the BODYGUARD gene in the L. barbarum and L. feroscissimum genomes.
Table 1. Name, description, and location of the BODYGUARD gene in the L. barbarum and L. feroscissimum genomes.
Name/Gene IDDescriptionLocation
LOC132634709
ID: 132634709		probable lysophospholipase BODYGUARD 3 [Lycium barbarum (goji berry)]Chromosome 4, NC_083340.1
LOC132607278
ID: 132607278		probable lysophospholipase BODYGUARD 4 [Lycium barbarum (goji berry)]Chromosome 8, NC_083344.1
LOC132609965
ID: 132609965		probable lysophospholipase BODYGUARD 3 [Lycium barbarum (goji berry)]Chromosome 9, NC_083345.1
LOC132060388
ID: 132060388		probable lysophospholipase BODYGUARD 3 [Lycium ferocissimum]Chromosome 1, NC_081342.1
LOC132049371
ID: 132049371		probable lysophospholipase BODYGUARD 4 [Lycium ferocissimum]Chromosome 3, NC_081344.1
LOC132030714
ID: 132030714		probable lysophospholipase BODYGUARD 3 [Lycium ferocissimum]Chromosome 9, NC_081350.1
Table 2. Sequence analysis of goji berry BDG gene, LOC132634709, on chromosome 4.
Table 2. Sequence analysis of goji berry BDG gene, LOC132634709, on chromosome 4.
Nr. crt.SNP Position
in Coding
Sequence		CodonAmino AcidReference
Genome
ASM1917538v2		Cultivated GenotypesWild Genotypes
Lb1Lb2Lb3Lb4Lb5Lb6wLb7wLb8w
1517AAT/AGTAsn/SerAsnh69h59h67h50h75AsnAsnAsn
2529AGC/ATCSer/IleSerh69h57h40h50h75SerSerSer
3537TTA/CTALeu/Leu (silent)Leu (TTA)Leu (CTA)Leu (CTA)Leu (CTA)Leu (CTA)Leu (CTA)h33h20h30
4590CTT/CTCLeu/Leu (silent)Leu (CTT)Leu (CTC)Leu (CTC)Leu (CTC)Leu (CTC)Leu (CTC)h37Leu (CTT)h40
5746TGC/TGTCys/Cys (silent)Cys (TGC)Cys (TGT)Cys (TGT)Cys (TGT)Cys (TGT)Cys (TGT)h50h17h30
6749CTG/CTCLeu/Leu (silent)Leu (CTG)h35h40h50h33h20h43h29h36
7767CTA/CTGLeu/Leu (silent)Leu (CTA)Leu (CTG)Leu (CTG)Leu (CTG)Leu (CTG)Leu (CTG)h50h37h30
8804CCA/ACAPro/ThrProh78h45h25h80h60ProProPro
9873GCT/TCTAla/SerAlah67h56h18h60h80AlaAlaAla
10956ATG/ATTMet/IleMeth42h35h37h40h80MetMetMet
111190TCA/TCTSer/Ser (silent)Ser (TCA)h46h92h50h45h60Ser (TCA)Ser (TCA)Ser (TCA)
121208GCA/GCCAla/Ala (silent)Ala (GCA)h50h7h50h60h40h75h40h33
131247AGT/AGCSer/Ser (silent)Ser (AGT)h50h20h57h70h25h80h45h40
141312AAA/AGALys/ArgLysArgArgArgArgArgh80h40h50
151352TGC/TGTCys/Cys (silent)Cys(TGC)h42h39h53h57h44Cys (TGC)Cys (TGC)h14
161439AAA/AAGLys/Lys (silent)Glyh59h65h60h18h60h67h57h57
171457CAG/CAAGln/Gln (silent)Gln (CAG)Gln (CAG)Gln (CAG)Gln (CAG)Gln (CAG)Gln (CAG)h67h57h57
181520ATG/ATTMet/IleMeth43h54h67h83h45MetMetMet
191631TAC/TATTyr/Tyr (silent)Tyr (TAC)Tyr (TAT)Tyr (TAT)Tyr (TAT)Tyr (TAT)Tyr (TAT)h75h25h73
201715ATA/ATTIle/Ile (silent)Ileh61h47h50h50h50h67h33h60
211761ACG/TCGThr/SerTyrh67h71h50h37h33TyrTyrTyr
221886GGC/GGGGly/Gly (silent)Gly (GGC)Gly (GGC)h36h54h86h30h83h43h75
SNP—Single Nucleotide Polymorphism, T—Thymine, A—Adenine, C—Cytosine, G—Guanine; Lb1–Lb8w are the tested genotypes. Cells highlighted with blue are the polar amino acids, green cells are the nonpolar amino acids, and yellow cells are the basic amino acids.
Table 3. Sequence analysis of goji berry BDG gene, LOC132607278, on chromosome 8.
Table 3. Sequence analysis of goji berry BDG gene, LOC132607278, on chromosome 8.
Nr. crt.SNP Position
in Coding
Sequence		CodonAmino AcidReference
Genome
ASM1917538v2		Cultivated GenotypesWild Genotypes
Lb1Lb2Lb3Lb4Lb5Lb6wLb7wLb8w
1129TGG/TTGTrp/LeuTrph50h67h67h57h50h33h75h75
2146AAA/GAALys/GluLysGluGluGluGluGluGluGluGlu
3174GTA/GCAVal/AlaValh45h58h17h33h71ValValVal
4202GAG/GACGlu/AspGluAsph93AspAspAsph41h62Asp
5209TTT/CTTPhe/LeuPhePhePhePhePhePheh46h62h67
6287-288AAA/CGALys/ArgLysh50h47h20h50h82Lysh14/LysLys
7392-394AAA/GACLys/AspLysh35h53h33h33h67LysLysLys
8617GAA/AAAGlu/LysGluGluGluGluGluGluh57h75h75
9648GCA/GGAAla/GlyAlaAlaAlaAlaAlaAlah57h12h14
10688AAC/AATAsn/Asn (sIlent)Asnh41h36h57h50h25AsnAsnAsn
11694TGC/TGTCys/Cys (sIlent)Cysh41h36h57h43h14CysCysCys
12799GTA/GTGVal/Val (sIlent)Val(GTA)Val(GTA)Val(GTA)Val(GTA)Val(GTA)Val(GTA)h50h40Val(GTG)
13824TCT/CCTSer/ProProProProProProProh43h45Pro
14864TAC/TTCTyr/PheTyrTyrTyrTyrTyrTyrh50h33Tyr
15891AGT/ATTSer/IleSerh53h58h56h78h67SerSerSer
161023TGG/TTGTrp/LeuTrph6TrpTrpTrpTrph50h71h43
171030GGA/GGTGly/Gly (sIlent)Gly (GGA)h58h22h43h56h33Gly (GGA)Gly (GGA)Gly (GGA)
181049TGG/GGGTrp/GlyTrph38h78h43h44h71TrpTrpTrp
191076ATT/GTTIle/ValIleIleIleIleIleIleh40h50h67
201150TTT/TTAPhe/LeuPhePhePhePhePhePheh29h50h50
211151ATG/GTGMet/ValMetMetMetMetMetMeth29h50h50
221266CCT/CTTPro/LeuProh64h75h60h64h50ProProPro
231270GAA/GATGlu/AspGluAspAspAspAspAsph60h62Asp
241274GAG/AAGGlu/LysGluGluGluGluGluGluh60h57Lys
251304ACT/TCTThr/SerThrh64h73h71h86h40ThrThrThr
261338TGT/TCTCys/SerCysh36h23h37Serh71CysCysCys
SNP—Single Nucleotide Polymorphism, T—Thymine, A—Adenine, C—Cytosine, G—Guanine; Lb1–Lb8w are the tested genotypes. Cells highlighted with blue are the polar amino acids, green cells are the nonpolar amino acids, pink cells are the acidic amino acids, and yellow cells are the basic amino acids.
Table 4. Sequence analysis of goji berry BDG gene, LOC132609965, on chromosome 9.
Table 4. Sequence analysis of goji berry BDG gene, LOC132609965, on chromosome 9.
Nr. crt.SNP Position
in Coding
Sequence		CodonAmino AcidReference
Genome
ASM1917538v2		Cultivated GenotypesWild Genotypes
Lb1Lb2Lb3Lb4Lb5Lb6wLb7wLb8w
1473CCT/CCCPro/Pro (silent)ProProProProProProh75h56Pro
2479TAC/TATTyr/Tyr (silent)Tyr (TAC)Tyr(TAT)Tyr(TAT)Tyr(TAT)Tyr(TAT)Tyr(TAT)h50h44h33
3498GCC/ACCAla/ThrAlah61h41h67h67h17h40h50Ala
4548TGT/TGCCys/Cys (silent)Cys (TGT)Cys (TGT)Cys (TGT)Cys (TGT)Cys (TGT)Cys (TGT)h50h54Cys (TGT)
5555-557TCT/GCT/TCA/TCGSer/Ala/Ser/SerSerAlaAlaAlaAlaAlah50h46h33
6563TCT/TCCSer/Ser (silent)Ser (TCT)Ser(TCC)Ser(TCC)Ser(TCC)Ser(TCC)Ser(TCC)h67h46Ser(TCT)
7625GCG/GTGAla/ValAlaValValValValValAlah70h40
8627GCT/TCTAla/SerAlaSerSerSerSerSerSerSerh40
9643TTC/TCCPhe/SerPheSerSerSerSerSerSerSerSer
10668CTT/CTCLeu/Leu (silent)Leu (CTT)Leu(CTC)Leu(CTC)Leu(CTC)Leu(CTC)Leu(CTC)h33h60h50
11713TCG/TCCSer/Ser (silent)Ser (TCG)Ser(TCC)Ser(TCC)Ser(TCC)Ser(TCC)Ser(TCC)h25h33h75
12977TCG/TCASer/Ser (silent)Ser (TCG)Ser (TCA)Ser (TCA)Ser (TCA)Ser (TCA)Ser (TCA)h33h20h60
13982TAT/TGTTyr/SerTyrh50h67h50h50h50TyrTyrTyr
14986CGG/CGAArg/Arg (silent)Arg (CGG)Arg (CGA)Arg (CGA)Arg (CGA)Arg (CGA)Arg (CGA)h33h20h63
151076GAG/GAAGlu/Glu (silent)Glu(GAG)Glu (GAA)Glu (GAA)Glu (GAA)Glu (GAA)Glu (GAA)h33h20h70
161079AAA/AAGLys/Lys (silent)Lys (AAA)h58h47h40h43h67h25h25h70
171223CCA/CCCPro/Pro (silent)Pro (CCA)h50h48h22h60h57Pro (CCA)Pro (CCA)Pro (CCA)
181256AGG/AGAArg/Arg (silent)Arg (AGG)h41h56h71h56h50h37h67Arg(AGA)
191285GTG/GCGVal/AlaValh42h44h67h60h62h37h775Ala
201304TCG/TCTSer/Ser (silent)Ser(TCG)h65h48h17h50h33Ser (TCG)Ser (TCG)Ser (TCG)
211394CTG/CTCLeu/Leu (silent)Leu(CTG)Leu(CTC)Leu(CTC)Leu(CTC)Leu(CTC)Leu(CTC)h50h50Leu(CTC)
221421TTA/TTGLeu/Leu (silent)Leu (TTA)h67h62h60Leu (TTG)Leu (TTA)Leu (TTA)Leu (TTA)Leu (TTA)
231466ACT/ACAThr/Thr (silent)Thr(ACT)Thr (ACA)Thr (ACA)Thr (ACA)Thr (ACA)Thr (ACA)h50h40h57
241550ACA/ACGThr/Thr (silent)Thr(ACA)Thr (ACG)Thr (ACG)Thr (ACG)Thr (ACG)Thr (ACG)h33h50h57
251580ATC/ATAIle/Ile (silent)Ile (ATC)Ile (ATA)Ile (ATA)Ile (ATA)Ile (ATA)Ile (ATA)h33h57h71
261671-1672GGA/AGA/GAAGly/Arg/GluGlyh16h9h16h11h6GlyGlyGly
271689GCT/TCTAla/SerAlah23h15h21h20h24AlaAlaAla
281726ACA/AAAThr/LysThrh10h17h33h18h23h57h17h67
SNP—Single Nucleotide Polymorphism, T—Thymine, A—Adenine, C—Cytosine, G—Guanine; Lb1–Lb8w are the tested genotypes. Cells highlighted with blue are the polar amino acids, green cells are the nonpolar amino acids, pink cells are the acidic amino acids, and yellow cells are the basic amino acids.
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Ciceoi, R.; Asanica, A.; Luchian, V.; Iordachescu, M. Genomic Analysis of Romanian Lycium Genotypes: Exploring BODYGUARD Genes for Stress Resistance Breeding. Int. J. Mol. Sci. 2024, 25, 2130. https://doi.org/10.3390/ijms25042130

AMA Style

Ciceoi R, Asanica A, Luchian V, Iordachescu M. Genomic Analysis of Romanian Lycium Genotypes: Exploring BODYGUARD Genes for Stress Resistance Breeding. International Journal of Molecular Sciences. 2024; 25(4):2130. https://doi.org/10.3390/ijms25042130

Chicago/Turabian Style

Ciceoi, Roxana, Adrian Asanica, Vasilica Luchian, and Mihaela Iordachescu. 2024. "Genomic Analysis of Romanian Lycium Genotypes: Exploring BODYGUARD Genes for Stress Resistance Breeding" International Journal of Molecular Sciences 25, no. 4: 2130. https://doi.org/10.3390/ijms25042130

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