Genetic Diversity and Physicochemical Characteristics of Different Wheat Species (Triticum aestivum L., Triticum monococcum L., Triticum spelta L.) Cultivated in Romania

Abstract

Thirty-one varieties of wheat cultivated in Romania were analyzed regarding the genetic diversity and physicochemical properties, including the following determinations: moisture, ash, protein, wet gluten, lipid, starch content, falling number and damaged starch, considering the particularity of each species, its biological status and origin. The physicochemical data showed that the wheat samples presented large variability. The physicochemical properties of wheat flour were assessed by multivariable data analysis, using principal component analysis (PCA). All wheat samples clustered together according to their physicochemical data showed an association between all wheat species. The protein and ash contents were more related to the ancient wheat species, while the amounts of starch and damaged starch were associated with the modern ones. Positive correlations were obtained between protein and wet gluten content and between lipid and ash content. ISSR markers were used to analyze and compare genetic diversity among selected wheat cultivars. The obtained data were analyzed using NTSYSpc software considering the coefficients of similarity (Jaccard) and dissimilarity (Neighbor joining). The Jaccard coefficients varied from 0.53 to 1, reflecting the high genetic diversity characteristic of all wheat varieties.

1. Introduction

The continuous growth of the world population highlights one of the most delicate problems of our century: providing food for mankind. Wheat is one of the most important cereal crops in the world, originally cultivated approximately 12,000 years ago in the Fertile Crescent [1]. In a course of history, about 8000 years ago, wheat became the main food source of most communities, so today it is widely cultivated around the world, covering 17% of the world’s cultivated area and feeding 40% of the world’s population [2]. Nowadays, wheat is a crop with multiple and diverse genetic resources available because of the general evolution of people and particularly the evolution of the agricultural segment, as there are currently many species of wheat cultivated by thousands of populations all over the world. Known functional genetic studies regarding the wheat crop are mostly focused on identifying natural variations, therefore contributing to the assembly and enrichment of wheat genetic stock [3,4]. By implementing different breeding programs, cultivators aim to obtain wheat crops with an increased agronomic performance, characterized by an improved yield, better quality and increased resistance to disease [4]. Sustainable wheat cultivation involves the conservation of species and the evaluation and characterization of genetic resources, as genetic diversity can nowadays be considered the key for many agricultural challenges.

Romania, due to its geographical location and temperate–continental climate, offers favorable conditions for wheat cultivation. Romanian wheat culture has been known since ancient times, from the Upper Neolithic and the Bronze Age [5]. Due to the food-friendly chemical composition, consisting of proteins, carbohydrates, lipids, minerals and vitamins but also its bread-making properties, wheat is used in human nutrition in the form of bread, pasta, biscuits and pastries [6]. As these products are very common for most countries globally, wheat occupies the first place in worldwide cereal production. The main wheat producers are considered to be European Union countries, China, India, Russia, USA, Canada, Ukraine and Pakistan [7]. In the EU, Romania achieved the 4th rank in wheat production after France, Germany and Poland [6], being one of the most important producers from Europe and the world. Among cereals, it occupies one of the largest areas of the total arable land, being of great economic importance in Romania and contributing to the country’s food security [8]. In 2000, the wheat production in Romania was about 4.4 million tons per year. In 2020, this amount increased to 52.27%, and it is estimated to grow even more by 2030, up to 138%, which is 16 million tons of wheat annually [9]. The milling and bakery sectors depend on wheat production and grain quality. Romanian wheat varieties, such as Izvor, Glosa, Miranda, Pitar, Putna and Arieșan, must be adapted to the climatic conditions and soils on which they are cultivated to produce grains of as high of a quality as possible [10].

In order to describe the genetic diversity within and between populations or groups of individuals, molecular markers are used. Molecular markers are capable of detecting high levels of polymorphism [11]. These markers are provided by the wheat seed protein complex, which is mostly associated with the bakery quality of a product [12]. Gliadin, as an important protein compound, plays a primordial role in creating the gluten network, which is decisive in terms of dough rheological properties, especially when it comes to its elasticity and viscosity [13]. Gliadin is mainly responsible for dough viscosity, whereas glutenin, another important gluten protein, is responsible for dough elasticity [14]. Although there are other grains that contain gluten-type proteins, wheat is the only grain that can form dough when mixed. Chemically, wheat flour contains gluten proteins in proportions of 75–85% of the total protein content and there are also more proteins in the wheat endosperm [15]. Gliadin and glutenine have a major impact on the bread-making process and therefore on the technological properties and bread quality [14]. During the mixing process, gluten-type proteins influence dough rheological properties, such as strength, extensibility, elasticity and consistency [14,16]. However, during fermentation, they influence the porosity of the bakery products by keeping the resulting gases in the dough [17]. Moreover, during baking, gluten plays an important role in forming the shape and volume of the bread. They also contribute to the formation of flavoring substances, increase the shelf life of the bread and affect the crust color [17,18]. Wheat grains contain 8–20% of proteins, which is an average content of 11–12% [19]. In addition, wheat grains are also a rich source of carbohydrates, mainly related to the starch molecules but also to other compounds, such as lipids, minerals, etc. [1]. Starch has important properties for bread-making. It plays a significant role during the fermentation process, where it is hydrolyzed by the action of amylolytic enzymes, forming maltose, the main fermentable carbohydrate presented in the dough [20]. Maltose forms during the enzymatic hydrolysis of starch grains and participates in the formation of crust color and flavor substances in bread [21]. In addition, starch has an essential role in the baking process due to its ability to gelatinize, as well as in the bread retrogradation process [17]. When mixing the dough, the starch also hydrates the dough; this important role is attributed to the mechanically damaged starch granules. The higher the degree of mechanical damage, the more the amylolytic hydrolysis of starch increases [22]. The most commonly used methods used to measure wheat flour damaged starch granules and the activity of α-amylase are SDmatic and Hagberg Falling Number methods, which have been used in this study [23,24]. In addition to these, the evaluation of fat, ash and moisture content of wheat flours gives us complete information regarding their quality for bread-making purposes [25]. Wheat flour consists of water, which represents 13–15% of its mass and dry matter [26], which includes proteins, carbohydrates, lipids, mineral substances, vitamins, pigments and enzymes [27]. Although there are only small amounts of lipids, these compounds play an important technological role in bread-making because they form complexes with proteins and starch granules in the dough, influencing its rheological properties, the bread quality itself and its freshness [28]. Ash content is an indicator of the mineral substances present in wheat flour [25]. Therefore, the higher the mineral content, the better the nutritional value. Wheat chemical composition can be genetically modified, so it may have a tremendous impact on human health and diet. According to Feldman et al., there are more than 25,000 cultivated forms of wheat in the whole world, and this number may be at least twice as high as estimated [29]. According to Shewry and Hey, modern wheat cultures have a different composition than ancient ones, being lower in bioactive compounds and higher in other components such as dietary fibers. Some wheat components show high heritability due to their genetic effects on modern species, such as arabinoxylan presented in flour, alkylresorcinols in whole meal, sterols, tocols, etc. [30]. In order to obtain wheat with an optimal/average chemical composition and to overcome the variation between certain cultures due to the genetic impact, different genotypes of wheat can be blended in one [31].

The existence of reliable genetic markers is beneficial for evaluating the diversity of wheat germplasm but also for highlighting its possible temporal changes, which could be caused by selection for its quality. Thus, the following molecular DNA markers: inter-simple sequence repeat (ISSR), amplified fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), microsatellites and single nucleotide polymorphisms (SNP) have been developed and used to assess the relationships and levels of genetic diversity in wheat germplasm [32].

ISSR (Inter-simple sequence repeat) is a molecular biology technique discovered in 1994 by Zietkiewicz et al., which aims at DNA fingerprinting, by amplifying, using the PCR technique, repetitive regions within the nuclear and organelle genomes (chloroplasts and mitochondria) [33]. ISSR primers have a dominant character, but occasionally, they show codominance [34]. Interest in using ISSR primers is caused by the reproducibility of the results and the low costs of development and usage [35].

In this study, we evaluated the genetic diversity by utilizing ISSR markers (Inter-Simple Sequence Repeats) of 31 wheat varieties cultivated in Romania, with different origins and kept in the active collection of the “Mihai Cristea” Suceava Plant Genetic Resources Bank. The vast majority of common wheat varieties, taken in this study, of Romanian origin, such as Izvor, Glosa, Andrada and Dumbrava, or foreign ones, such as Sosthene, Amicus, Apache and Anapurna, were listed in the Romanian Official Variety Catalog, published in 2021. The varieties listed in this catalog are admitted for certification and commercialization based on tests of distinction, uniformity and stability for agronomic value and use. These wheat varieties are representative for Romania, and in particular, for the intensive wheat cultures in the northeast region, which resulted from applying breeding programs that aimed to develop wheat with certain characteristics such as higher yield, superior quality for bread-making, and resistance to biotic and abiotic stress. Additionally, the use of ancient wheat species from the gene bank collection, such as Triticum monococcum and Triticum spelta, highlights important aspects regarding ancient wheat species. To our knowledge, no other study has been reported so far on the genetic diversity of wheat varieties cultivated in Romania, one of the most important wheat producers in Europe. Moreover, the physicochemical characteristics of wheat samples of different species (Triticum aestivum L., Triticum monococcum L., Triticum spelta L.) cultivated in Romania have been determined in order to allow us to compare them by using multivariate principal component analysis (PCA) and to analyze the differences between wheat species. The physicochemical characteristics analyzed in this study, namely moisture, ash, protein, wet gluten, lipid, starch, falling number, and damaged starch, are the most relevant ones for the bread-making industry. This allows us to recommend the best wheat species and varieties that may be used in the future by farmers in order to obtain raw materials of a high quality for bread-making.

2. Materials and Methods

2.1. Plant Material

The genetic material consisting of thirty-one wheat samples is shown in

Table 1. Identification of genotypes regarding species, accession name, origin and biological status.

Genotype Number	Scientific Name	Accession Name	Country	Biological Status
TA1	T. aestivum L.	Izvor	Romania	Modern variety 1
TA2	T. aestivum L.	Glosa	Romania	Modern variety 1
TA3	T. aestivum L.	Miranda	Romania	Modern variety 1
TA4	T. aestivum L.	Andrada	Romania	Modern variety 1
TA5	T. aestivum L.	Dumbrava	Romania	Modern variety 1
TA6	T.aestivum L.	Aurelius	Austria	Modern variety 1
TA7	T. aestivum L.	Sofru	France	Modern variety 1
TA8	T. aestivum L.	Sosthene	France	Modern variety 1
TA9	T. aestivum L.	Amicus	Austria	Modern variety 1
TA10	T. aestivum L.	Sothys	France	Modern variety 1
TA11	T. aestivum L.	Flavor	France	Modern variety 1
TA12	T. aestivum L.	Solindo	France	Modern variety 1
TA13	T. aestivum L.	Izalco	France	Modern variety 1
TA14	T. aestivum L.	Tonnage	Austria	Modern variety 1
TA15	T. aestivum L.	Sophie	France	Modern variety 1
TA16	T. aestivum L.	Apache	France	Modern variety 1
TA17	T. aestivum L.	Anapurna	France	Modern variety 1
TA18	T. aestivum L.	Illico	Switzerland	Modern variety 1
TA19	T. aestivum L.	Sf. Ilie	Romania	Modern variety 1
TA20	T. aestivum L.	Lucăcești	Romania	Modern variety 1
TA21	T. aestivum L.	Udești 1	Romania	Modern variety 1
TA22	T. aestivum L.	Udești 2	Romania	Modern variety 1
TA23	T. aestivum L.	Udești 3	Romania	Modern variety 1
TA24	T. aestivum L.	Frumoasa	Romania	Modern variety 1
TA25	T. aestivum L.	Tișăuți	Romania	Modern variety 1
TM26	T. monococcum L.	SVGB-11842	Romania	Landrace 2
TM27	T.monococcum L.	SVGB-11861	Romania	Breeding line 3
TS28	T. spelta L.	Ebners Rotkorn	Austria	Modern variety 1
TS29	T. spelta L.	Frankenkorn	Austria	Modern variety 1
TS30	T. spelta L.	Alkoran	Russia	Modern variety 1
TS31	T. spelta L.	Oberkulmer Rotkorn	Germany	Modern variety 1

¹ Currently cultivated variety, which is distinguished by superior characteristics of quality, productivity, uniformity and stability compared to a primitive variety and is widely used as a parent in the breeding program. ² Local variety of a plant species that has been obtained under the action of natural and/or artificial empirical selection, in specific environmental conditions and that presents a series of distinct individual characteristics in order to be associated with a specific geographical region. ³ Biological material obtained by breeders through artificial selection, based on conscious selection schemes.

. Fourteen samples originated from Romania, nine from France, five from Austria, one from Switzerland, one from Germany and one from Russia. The seeds of foreign varieties of wheat, cultivated in Romania, come from the northern and central regions of France, from northwestern Switzerland, from eastern Austria for Triticum aestivum and from the areas of northwestern Austria, southern Germany and southern Russia for Triticum spelta. Additionally, all 31 wheat samples belonged to three different species: twenty-five were Triticum aestivum L., two were Triticum monococcum L. and four were Triticum spelta L. The analyzed samples had various biological statuses: modern variety, landrace and breeding line. All wheat samples were grown in the northeastern region of Romania in 2020 by different farmers. The direct collection of genetic material from them was carried out by organizing our own exploration/collection missions in 2021, focused on local varieties, modern and old varieties, which lend themselves very well to the pedo-climatic and socio-economic conditions characteristic of this region. It was considered that this region, in which we encountered a transitional temperate continental climate, presented similar pedo-climatic conditions. Thus, local climate differences in the northeast region are due to altitude and latitude, resulting in an average annual temperature that slightly decreased from 10.41 °C in 2020 to 9.96 °C in 2021. The annual amount of precipitation was much lower in 2021 (276.9 mm) than in 2020 (493.9 mm). The favorable agricultural period for cultivating and harvesting autumn wheat in Romania is September 2020–July 2021. After harvesting, the wheat samples were subjected to a drying process until the moisture content of the seeds reached a maximum of 7% and then they were stored before being analyzed in a cold room at a variable temperature between 3 and 5 °C, for more than 12 months. Afterwards, the samples were ground using a laboratory mill 3100 (Perten Instruments, Hagersten, Sweden) and prepared for further analysis. The moisture, ash, protein, total starch, damaged starch, fat content, wet gluten and falling number values were measured.

2.2. Wheat Physiochemical Characteristics

The wheat flour physicochemical characteristics were analyzed according to the international standard methods: moisture content according to ICC 110/1, ash content according to ICC 104/1, protein content according to ICC 105/2, wet gluten content according to ICC 137/1, lipid content according to ICC 136, starch content according to AACC 76-13.01, falling number according to ICC 107/1, and damaged starch according to AACC 76-33.

2.3. Genomic DNA Isolation

Genomic DNA isolation was performed using the CTAB method of Doyle and Doyle [36] from 200 mg of seeds of each sample. After grinding to a thin powder using a laboratory mill 3100 (Perten Instruments, Hagersten, Sweden), wheat samples were mixed with 1200 μL CTAB buffer (20 g/L CTAB, 7.44 g/L EDTA·NA₂·2H₂O, 81.82 g/L Sodium chloride, 12.11 g/L TRIS ultrapure, PanReac Applichem, A4150,0500). The samples were incubated for 1 h at 65 °C with periodic mixing, followed by a centrifugation process at 14,000 rpm for 10 min at room temperature (RT). The supernatant was mixed with 200 μL freshly prepared chloroform: isoamyl alcohol solution (24:1) and centrifuged at 14,000 rpm for 10 min at RT. The upper phase was transferred into new tubes, mixed with 200 μL isopropyl alcohol and centrifuged at 13,500 rpm, 10 min, RT. The supernatant was carefully removed, and the pellet washed with 70% ethanol, followed by a centrifugation process at 13,500 rpm for 10 min at RT. The pellet was therefore dried for 15 min at RT and then dissolved in 200 μL nuclease-free water. The samples were kept overnight at 4 °C and stored at −20 °C for further examination.

2.4. Spectrophotometric Analysis of DNA

The extracted DNA was measured both quantitatively and qualitatively using ThermoScientific NanoDrop One. The quantitative determination refers to the concentration of DNA obtained in 1 μL and the qualitative analysis measures the purity (A260/A2680 ratio) of the samples.

2.5. PCR Analysis

The PCR reaction was performed using according to GoTaq G2 Green Master Mix protocol (Promega, M7822). The DNA samples were amplified using 11 ISSR markers, as shown in

Table 2. ISSR primer sequence used in the PCR analysis.

Primer	Sequence (5′-3′) 1	Tm (°C)	(%) GC
UBC841	GAGAGAGAGAGAGAGAYC	58	50
UBC843	CTCTCTCTCTCTCTCTRA	56	44.4
UBC854	TCTCTCTCTCTCTCTCRG	58	50
UBC855	ACACACACACACACACYT	56	44.4
UBC857	ACACACACACACACACYG	58	50
UBC859	TGTGTGTGTGTGTGTGRC	58	50
UBC880	GGAGAGGAGAGGAGA	48	60
UBC808	AGAGAGAGAGAGAGAGC	52	52.9
UBC810	GAGAGAGAGAGAGAGAT	50	47.1
UBC834	AGAGAGAGAGAGAGAGYT	56	44.4
UBC890	VHVGTGTGTGTGTGTGT	52	41.2

¹ A = Adenine; T = Thymine; C = Cytosine; G = Guanine; H = (A, T or C); R = (A or G); V = (A, C or G) e Y = (C or T); Tm = melting temperature.

. The primer’s sequences were identified in the literature [37] and they were synthetized at Eurogenetec, Belgium. The PCR mix was prepared in a final volume of 25 μL. Each reaction was comprised of GoTaq G2 DNA Polymerase, 2X Green GoTaq^® G2 Reaction Buffer (pH 8.5), 3 mM MgCl₂, 400 μM each dNTPs, 0.5 μM primer and 30 ng DNA. Amplifications were performed in an Eppendorf Mastercycler under the following conditions: 94 °C for 2 min, followed by 30 cycles, each cycle consisting of three steps: (1) 94 °C for 30 s, (2) 48–58 °C for 30 s (depending on primer’s Tm), (3) 72 °C for 1 min, and a final step of 72 °C for 7 min.

2.6. Agarose Gel Electrophoresis

Amplified DNA fragments were separated using 2% agarose gel. For electrophoresis, the following components were used: agarose (BioRad, 1613101), TBE (0.89 M Tris-Borate, 0.02 M EDTA, pH 8.3, Lonza, BE50843), SYBR Safe DNA Gel Stain (Invitrogen, S33102), Gene Ruler 100 bp (Thermoscientific, SM#0323) and a constant power supply (PowerPac Basic, BioRad). Gels were run at 70 V for 100 min and subsequently analyzed using the GelDoc Go Imaging System, BioRad.

2.7. Statistical Analysis

The obtained data were analyzed considering the presence/absence of the amplified fragments. The presence was marked as 1 and the absence as 0. The obscure DNA fragments under UV exposure were not used in the statistical interpretation. All data were analyzed using NTSYSpc software, considering similarity (Jaccard) and dissimilarity (Neighbor joining) coefficients. To analyze the correlations between wheat samples and their physicochemical characteristics, principal component analysis (PCA) was performed using the software XLSTAT (version 2020.3.1., Addinsoft, Paris, France).

3. Results

3.1. Wheat Sample Characteristics

The physicochemical characteristics of wheat samples are shown in

Table 3. Physicochemical characteristics of wheat flour.

Chemical Data	Minimum	Maximum	Mean	Standard Deviation	Variance
Moisture (%)	10.80	12.90	11.86	0.55	0.31
Ash (%)	1.18	2.06	1.45	0.23	0.05
Protein (%)	9.90	16.90	13.09	1.67	2.80
Wet gluten (%)	21.70	39.70	28.74	3.47	12.05
Lipid (%)	1.50	2.42	1.73	0.24	0.06
Starch (%)	53.45	64.10	58.55	5.11	26.21
Falling Number (%)	83.0	404	265.43	8.32	69.42
Damaged starch (UCDc)	1.40	19.40	9.92	3.24	10.54

. The physicochemical data of each cultivar and line can be seen in the supplementary material—Table S1. All data indicated large variability among the wheat samples.

The moisture content presented a maximum value of 12.90% indicating the fact that all wheat samples presented good storage stability. From the protein content point of view of which values varied between 9.90 and 16.90%, all wheat samples were good for bread- making. However, the wet gluten content varied between 21.70 and 39.70%, indicating that some wheat samples of which wet gluten values were less than 22% were difficult to be used by bakery producers [25]. The lowest protein and gluten content were recorded for the TA8 and TA5 samples, whereas the highest was recorded for the TS 31 genotype number. The falling number values varied between 83 and 404 s, the lowest one being recorded for the TA24 genotype number and the highest one being recorded for TA4. This data means that wheat flours varied from the falling number point of view, from low, normal and high α-amylase activity [38]. The large variation of starch, damaged starch, fat and ash content may be due to many factors, such as wheat vegetation period, climatic conditions and their species [30].

3.2. DNA Amplification

DNA migration patterns characteristic of all 31 wheat samples, as a result of agarose gel electrophoresis, indicate that only 6 ISSR primers out of 11 presented clear and well-defined bands (UBC808, UBC810, UBC855, UBC857, UBC859 and UBC880). The migration pattern for UBC 808 can be seen in Figure 1, and the results obtained for the other five primers can be found in Supplementary Materials—Figures S1–S5.

The number of DNA bands per primer varied from 3 (for UBC859 and UBC880) to 8 (for UBC808), with a mean value of 4.83 bands/primer. Most of the primers had a number of polymorphic fragments equal to the number of amplified fragments, except UBC808, which had 7 polymorphic fragments from a total of 8. For each ISSR primer, we calculated the polymorphic information content (PIC) as proposed by Roldan-Ruiz et al. [39]. The formula was PIC_i = 2f_i (1 − f_i), where f_i is the number of amplified fragments and 1 − f_i is the frequency of absent fragments. The polymorphic information content varied from 0.32 for UBC859 to 0.42 for UBC857, with a mean of 0.36. The total number of amplified fragments, fragment length, polymorphism percentage and PIC value can be found in

Table 4. Number of amplified fragments, number of polymorphic bands and polymorphism percentage of each primer used in ISSR analysis.

Primer	Number of Amplified Fragments	Number of Polymorphic Fragments	Fragment Length (bp)	Polymorphism Percentage	Polymporphic Information Content (PIC)
UBC808	8	7	280–2250	87.5	0.34
UBC810	4	4	400–1000	100	0.39
UBC855	5	5	300–2500	100	0.37
UBC857	6	6	200–1500	100	0.42
UBC859	3	3	250–1000	100	0.32
UBC880	3	3	650–2000	100	0.33

.

The analysis of 31 wheat accessions using 6 ISSR primers showed that the mean allele number was 4.83; the minimum number of alleles was obtained for UBC859 and UBC880 (3 alleles), and the maximum number was 8 alleles for UBC808.

Considering the allele frequency, the average was 0.6, with the highest value for UBC880 (0.77) and the lowest for UBC808 (0.45). The other primers registered the following allele frequencies: UBC810—0.55, UBC855—0.6, UBC857—0.64 and UBC859—0.72. Primers with the lowest number of alleles (3 for UBC859 and UBC880) showed the highest allele frequency (0.72, respectively 0.77). Information about allele number and frequency can be found in Figure 2, and the exact values are shown in Supplementary Materials, Table S2.

3.3. Clustering Using the UPGMA Method

The migration pattern of each genotype was converted in a binary system where 0 means the absence of a certain DNA fragment, and 1 means the presence of it. The obtained data were analyzed in the NTSYS 2.21w statistical package (Applied Biostat LLC, Albany, NY, USA) using the UPGMA method. We calculated the Jaccard similarity coefficient, and the results can be found below.

According to the Jaccard similarity coefficient, the genotypes were divided into five clusters (C1–C5) with values between 0.53 and 1. C5 was the most extended cluster and contained 16 genotypes: 5 from Romania, 5 from France, 4 from Austria, 1 from Russia and 1 from Germany. Additionally, within C5, there was a subcluster formed by the Triticum spelta genotypes (TS28, TS29, TS30, TS31). C4 contained 6 genotypes, 5 from Romania and 1 from Switzerland, similar to C3, which also included 6 genotypes from different countries (2 from Romania, 3 from France and 1 from Austria). In C2, there was only one genotype from France, and in C1, there were two genotypes from Romania, as may be seen in Figure 3.

3.4. Clustering Using the Neighbor Joining Method

The Neighbor joining coefficient is related to the molecular distances between the analyzed samples and the results vary between 0 and 1, where values close to 1 show a higher genetic distance [40].

Table 5. Dissimilarity Matrix among wheat samples using the NJ method and dissimilarity coefficient.

	TA1	TA2	TA3	TA4	TA5	TA6	TA7	TA8	TA9	TA10	TA11	TA12	TA13	TA14	TA15	TA16	TA17	TA18	TA19	TA20	TA21	TA22	TA23	TA24	TA25	TA26	TA27	TA28	TA29	TA30	TA31
TA1
TA2	0.042
TA3	0.026	0.016
TA4	0.02	0.022	0.006
TA5	0.004	0.038	0.022	0.016
TA6	0.01	0.052	0.036	0.03	0.014
TA7	0.033	0.009	0.007	0.013	0.029	0.043
TA8	0.004	0.038	0.022	0.016	0.018	0.014	0.029
TA9	0.064	0.022	0.038	0.044	0.06	0.074	0.031	0.06
TA10	0.024	0.018	0.002	0.004	0.02	0.034	0.09	0.02	0.04
TA11	0.016	0.058	0.042	0.036	0.02	0.006	0.049	0.02	0.08	0.04
TA12	0.014	0.028	0.012	0.006	0.01	0.024	0.019	0.01	0.05	0.01	0.03
TA13	0.056	0.098	0.082	0.076	0.06	0.046	0.089	0.06	0.12	0.08	0.04	0.07
TA14	0.006	0.036	0.02	0.014	0.002	0.016	0.027	0.002	0.058	0.018	0.022	0.008	0.062
TA15	0.036	0.078	0.062	0.056	0.04	0.026	0.069	0.04	0.1	0.06	0.02	0.05	0.02	0.042
TA16	0.088	0.046	0.062	0.068	0.084	0.098	0.055	0.084	0.024	0.064	0.104	0.074	0.144	0.082	0.124
TA17	0.078	0.036	0.052	0.058	0.074	0.088	0.045	0.074	0.014	0.054	0.094	0.064	0.134	0.072	0.114	0.01
TA18	0.069	0.027	0.043	0.049	0.065	0.079	0.036	0.065	0.005	0.045	0.085	0.055	0.125	0.063	0.105	0.019	0.009
TA19	0.094	0.052	0.068	0.074	0.09	0.104	0.061	0.09	0.03	0.07	0.11	0.08	0.15	0.088	0.13	0.006	0.016	0.025
TA20	0.008	0.034	0.018	0.012	0.004	0.018	0.025	0.004	0.056	0.016	0.024	0.006	0.064	0.002	0.044	0.08	0.07	0.061	0.086
TA21	0.006	0.048	0.032	0.026	0.01	0.004	0.039	0.01	0.07	0.03	0.01	0.02	0.05	0.012	0.03	0.094	0.084	0.075	0.1	0.014
TA22	0.009	0.033	0.017	0.011	0.005	0.019	0.024	0.005	0.055	0.015	0.025	0.005	0.065	0.003	0.045	0.079	0.069	0.06	0.085	0.001	0.015
TA23	0.009	0.033	0.017	0.011	0.005	0.019	0.024	0.005	0.055	0.015	0.025	0.005	0.065	0.003	0.045	0.079	0.069	0.06	0.085	0.001	0.015	0
TA24	0.001	0.031	0.015	0.009	0.007	0.021	0.022	0.007	0.053	0.013	0.027	0.003	0.067	0.005	0.047	0.077	0.067	0.058	0.083	0.003	0.017	0.002	0.002
TA25	0.078	0.12	0.104	0.098	0.082	0.068	0.111	0.082	0.142	0.102	0.062	0.092	0.022	0.084	0.042	0.166	0.156	0.147	0.172	0.086	0.072	0.087	0.087	0.089
TA26	0.074	0.116	0.1	0.094	0.078	0.064	0.107	0.078	0.138	0.098	0.058	0.088	0.018	0.08	0.038	0.162	0.152	0.143	0.168	0.082	0.068	0.083	0.083	0.085	0.004
TA27	0.066	0.108	0.092	0.086	0.07	0.056	0.099	0.07	0.13	0.09	0.05	0.08	0.01	0.072	0.03	0.154	0.144	0.135	0.16	0.074	0.06	0.075	0.075	0.077	0.012	0.008
TA28	0.122	0.08	0.096	0.102	0.118	0.132	0.089	0.118	0.058	0.098	0.138	0.108	0.178	0.116	0.158	0.034	0.044	0.053	0.028	0.114	0.128	0.113	0.113	0.111	0.2	0.196	0.188
TA29	0.169	0.127	0.143	0.149	0.165	0.179	0.136	0.165	0.105	0.145	0.185	0.155	0.225	0.163	0.205	0.081	0.091	0.1	0.075	0.161	0.175	0.16	0.16	0.158	0.247	0.243	0.235	0.047
TA30	0.104	0.68	0.084	0.1	0.104	0.11	0.07	0.098	0.075	0.078	0.12	0.1	0.159	0.101	0.18	0.02	0.026	0.035	0.061	0.1	0.109	0.104	0.104	0.095	0.182	0.178	0.161	0.06	0.052
TA31	0.13	0.078	0.11	0.079	0.13	0.136	0.096	0.124	0.101	0.104	0.146	0.126	0.185	0.127	0.206	0.046	0.052	0.061	0.087	0.126	0.135	0.13	0.13	0.121	0.208	0.204	0.187	0.006	0.026	0.026

shows the recorded values for all accessions. The most significant results were registered between samples 22 and 23 (coefficient = 0), where the genotypes showed molecular similarity. Additionally, the maximum value for genetic distance was obtained between samples 25 and 29 (coefficient = 0.247) due to a different genetic profile.

3.5. Principal Component Analysis of the Physicochemical Characteristics and Scores of the Wheat Samples

Principal Component Analysis (Figure 4) was carried out to underline the correlations between the physicochemical characteristics of wheat samples and wheat varieties and how these varieties were influenced by their physicochemical characteristics. The first two principal components explained 54.55% of the total variance (PC1 = 19.79% and PC2 = 34.77%). The two plots underlined a good correlation between wheat varieties Triticum spelta, Triticum monococcum and Triticum aestivum, which were grouped close to each other. Generally, the ancient species Triticum spelta and Triticum monococcum were placed on the right part of the graph, whereas the Triticum aestivum was placed on the left. On the right part of the PCA, Triticum monococcum clustered together on the top, whereas Triticum spelta was on the bottom. However, some wheat samples of Triticum monococcum were placed in the same area as Triticum monococcum and Triticum spelta, indicating that some of those show some similarities with these varieties. The quality of ancient varieties was strongly characterized by the wheat physicochemical characteristics wet gluten, protein, lipid and ash content, all of them being placed alongside the PC2 component. However, Triticum monococcum was more related to the wet gluten and protein content, whereas the ash and lipid content of Triticum spelta were underlined by the first component PC1. Triticum monococcum was more related to the wheat physicochemical characteristics of damaged starch, falling number and starch, all of which were alongside the PC2 component. The physicochemical variable lipid content was positively correlated with the ash content (r = 0.970, p < 0.05) and protein with wet gluten (r = 0.914; p < 0.05), and there was also a positive correlation between damaged starch and falling number (r = 0.672; p < 0.05). These data are in agreement with those reported by others [25,41,42]. The position of starch in the space of main components indicated a negative correlation with wheat physicochemical characteristics lipid, fat, protein and wet gluten content. Similar data have also been reported by Golea et al. in their study [25].

4. Discussion

According to the physicochemical properties of wheat varieties, a large variability among samples was observed. All of them may be easily stored since their moisture value was less than 13%, characteristic for all wheat samples. This is an important criterion of wheat quality for bakery manufacturers, as a low moisture content means a long shelf during storage [25]. The quality content of wheat flour for bread-making is given especially by its gluten content. The technological quality of wheat flour used for bread-making is usually associated with the amount of gluten it consists of. Gluten is a protein obtained from glutenin and gliadin during mixing and is mainly responsible for dough rheological behavior during bread-making [13]. A high gluten content generally indicates flours of great quality that are suitable for bread-making [25]. According to our data, the mean value of gluten content is 28.74%, which indicates wheat samples with good bread-making quality [17]. However, the minimum gluten content value was 21.70%, which means that this type of wheat flour may be used in bread-making only after gluten addition or as a component of a mix with other kinds of flour with high gluten content [25]. Starch is the component that has the highest amount in wheat flour [20]. It is especially responsible for the formation of fermentable carbohydrates necessary during the fermentation process of the dough with baker’s yeast for the formation of carbon dioxide that loosens the dough [43]. The large variability of starch, which varied between 55.30% and 64.10%, may be due to the other wheat compounds, such as protein, whose values are interconnected during grain development [44]. From a qualitative point of view, wheat flour presents intact and damaged starch granules. According to our study, the damaged starch varied in a high amount from low to high content in wheat flour. This can be degraded to fermentable sugars by amylases. Among the amylases, the most important is alpha amylase, an endoenzyme that attacks the starch granule inside [25]. The alpha amylase activity is suggested by falling number values, which varied according to our data from low, good and high ones for bread-making. The lipid and ash content varied in a high amount, probably due to the particularities of wheat samples [45].

From the genetic diversity point of view, the degree of polymorphism shown by ISSR markers is very high (almost 100%). Thus, we verified that the primers: UBC808 [(AG)8], UBC810 [(GA)8T], UBC855 [(AC)8 YT], UBC857 [(AC)8YG], UBC859 [(TG)8RC], UBC880 [(GGAGA)3] produced high levels of ISSR polymorphism in the common wheat, emmer and spelt genomes, reflecting the lifetime and abundance of the wheat genome. The unweighted pairwise group method with arithmetic means (UP-GMA) divided all cultivars by species/ploidy, reflecting a defined genetic structure. In addition, the Jaccard coefficient variation from 0.53 to 1 reflects the high genetic diversity of these wheat varieties [32].

Many scientists studied the suitability of the ISSR technique for genetic diversity studies of wheat, including some groups from Egypt [46], India [47], Iran [48] and Turkey [49]. ISSR primers were also used in the literature in order to study wheat accessions from Azerbaijan, and the results showed that they were superior to RAPD markers, producing more bands [50]. ISSR markers are widely used in genetic diversity assessment of distinct species. Mousavifard et al. [51] analyzed the potential of nine ISSR primers for eighty-nine diploid wheat samples. They reported a different polymorphic level from the present research, at 91.2%. At the same time, the number of migrated bands in agarose gel ranged between 16 for ISSR880 and 23 for UBC873. Regarding genetic similarity, the lowest value registered was 0.44, and the highest value was 0.9. These were reported for Tr37a of Triticum boeoticum subsp. thaodar and Tr156 of Triticum urartu, respectively, and for Tr37a of Triticum boeoticum subsp. thaodar and Tr156 of Triticum urartu. In this study, the authors highlighted an elevated level of genetic diversity within different species of wheat by using ISSR markers [51]. The same thing was reported by Du et al., who found different values for genetic distance, which ranged from 0.3115 to 0.3442 [52].

Lower levels of polymorphic bands were reported in another study, where some samples of Triticum aestivum were analyzed from a genetic diversity perspective. Using 16 ISSR primers, the percentage of polymorphic bands was 57.5%. In the same research, RAPD markers generated a higher value of polymorphic bands, 86.86% [53].

Molecular analysis can be efficiently used to identify plants with high adaptability to abiotic stress, such as drought [54,55] or salt tolerance [56]. Drought is a polygenic characteristic, and ISSR primers bind randomly in the genome, which might help to describe the trait-related regions. Deshmukh et al. [54] revealed that 3 of the 90 ISSR primers analyzed showed polymorphism related to tolerant and susceptible cultivars.

The PIC is an important tool for evaluating the quality of the marker and its ability to detect genetic diversity within and between species. ISSRs are dominant markers [39], and due to their biallelic nature, they tend to have a lower PIC. According to Botstein et al. [57]. PIC values can be characterized as follows: very informative (>0.5), medium informative (0.5 > PIC > 0.25) and less informative (<0.25). For wheat samples, the average PIC values for all primers range between 0.32 and 0.42, which corresponds to the medium informative category of PIC. This shows that the most informative primers were UBC857 (average PIC = 0.42) and UBC810 (average PIC = 0.39).

As there is a lot of known information nowadays regarding the nature and genetic diversity of different wheat accessions, there is an important fundament in terms of knowledge for creating superior wheat varieties with increased resistance to various biotic and abiotic stress factors [58]. An increased value of PIC (Polymorphism information content) for ISSR markers suggests a high genetic diversity, but a lower value can be the result of closely related genotypes. Kumar et al. showed that ISSR markers can reveal a significant value for PIC within the Triticum aestivum genotypes [59]. Despite the fact that ISSR primers are dominant and have a biallelic character, they are able to discriminate the variability between the wheat germplasm analyzed in this study. ISSR primers are often preferred in molecular biology studies due to their high reproducibility and the fact that there is no need for prior knowledge of the genome. Additionally, they have high transferability and accessibility, and they are low cost [60].

From the physicochemical point of view, the position of wheat samples on the PCA graph indicates differences between wheat species. These differences may be due to wheat physicochemical characteristics. The ancient species are more closely related to the protein, wet gluten, lipid and ash content, whereas the modern ones are related to the falling number, damaged starch and starch physicochemical characteristics. These data are similar to those reported by others [25,45,61]. They reported that ancient wheat varieties have a higher amount of protein, wet gluten, ash and lipid compared to the modern ones. However, some varieties (TA1, TA6, TA25) of Triticum aestivum presented similar characteristics, with ancient species being closely associated with those on the PCA graph, probably due to the fact that some of them were selected for their high protein and gluten content [61]. The close association between protein and wet gluten, which indicates a strong positive correlation (p < 0.05) between these variables, is explainable since wet gluten is formed from gluten proteins, which represent almost 75–85% of the total wheat protein content [14]. A significant high correlation (p < 0.05) between fat and ash, which is shown at the bottom right of the PCA graph, is explicable since the highest amount of minerals and fat presented in a wheat grain are located in wheat bran and germ. Therefore, higher amounts of germ and bran in wheat will lead to higher ash (minerals) and lipid content of the wheat flour [25]. The starch compound was negatively correlated with the rest of the physicochemical characteristics, such as protein, wet gluten, ash and lipid content according to its position in the space of the main components, as suggested by the PCA graph. This fact is explainable since the dry substance of wheat is made up of carbohydrates, proteins, lipids, minerals. Starch is the main compound of wheat, which represents almost 75–85% of the total dry substance of wheat [62]. Therefore, an increase in starch will lead to a decrease in proteins, the second in terms of weight in relation to dry matter [44]. Falling number values are indirectly correlated with damaged starch granules and directly correlated with protein content and the amount of wet gluten [25]. The falling number is a measure of the amylolytic activity of the wheat flour, which may be retained by glutenin in amounts that become higher as glutenin increased. Therefore, wheat flours that contain high amounts of proteins and wet gluten led to a decrease in amylolytic activity [42]. Falling number value is an expression of the wheat flour slurry viscosity heating to 100 °C and depends on damaged starch, which makes the starch more easily attacked by amylases [24]. Therefore, increased α-amylase activity is due to the high amount of damaged starch granules presented in wheat samples. Consequently, there is an inverse proportionality between damaged starch granules and falling number values [25].

5. Conclusions

The present study analyzed the physicochemical characteristics and genetic diversity of different wheat varieties cultivated in Romania of various species. Our data showed significant physicochemical differences between ancient and modern wheat species regarding ash, protein, wet gluten, lipid, starch, falling number and damaged starch values. All of the wheat samples clustered together according to their physicochemical data showed an association between wheat species. For all wheat samples, there were inverse correlations between the variables starch and physicochemical characteristics protein, wet gluten, lipid and ash and between falling number and damaged starch. The ISSR technique for genetic diversity showed that only 6 out of 11 ISSR primers had significant patterns of amplified fragments with clear and well-defined bands. The number of DNA bands per primer ranged from 3 to 8, with an average of 4.83 bands/primer. Most of the primers had a number of polymorphic fragments equal to the number of amplified fragments. The obtained data were analyzed using NTSYSpc software considering the coefficients of similarity (Jaccard) and dissimilarity (Neighbor joining). The genotypes obtained were divided into 5 groups (C1–C5). C5 was the most extensive cluster and contained 16 genotypes from different countries. C4 contained 6 genotypes, 5 from Romania and 1 from Switzerland. Additionally, C3 included 6 genotypes, also from different countries. In cluster C1, 2 genotypes were identified, both from Romania, and cluster C2 contained 1 genotype from France. Over time, many studies have been carried out that have demonstrated the utility and importance of ISSR-type molecular markers in genetic diversity research. The cultivars used in this study were selected due to their high wheat consumption and large cultivated areas of wheat grains in Romania. This research is of great importance worldwide considering that Romania occupies fourth place as a wheat producer at the European level. Knowing the genetic diversity of a germplasm collection and assessing the extent and nature of genetic variation in wheat is important for breeding programs and for the conservation of genetic resources. Except for a Triticum aestivum L. variety sample (TA5), which needs gluten addition to be used for bread-making, all analyzed wheat samples may be recommended to be cultivated by farmers. From the wheat samples analyzed, the ancient ones were of the highest quality for bread-making. They also presented the highest amounts of lipids and mineral nutrients, indicating that these species are nutritionally valuable for use in the bakery industry.