Received: 4 March 2020 Revised: 3 April 2020 Accepted: 4 April 2020 DOI: 10.1002/pca.2945

SPECIAL ISSUE ARTICLE

Discrimination of common from Egypt based on their genetic and metabolic profiling

Mona M. Okba1 | Passent M. Abdel Baki1 | Amal E. Khaleel1 | Moshera M. El-Sherei1 | Mohamed A. Salem2

1Department of Parmacognosy, Faculty of Pharmacy, Cairo University, Cairo, Egypt Abstract 2Department of Pharmacognosy, Faculty of Introduction: Irises have been medicinally used in Ancient Egyptians, Anatolian, Chi- Pharmacy, Menoufia University, Menoufia, nese, British and Irish folk medicine. They are also well-known ornamental that Egypt have economic value in the perfume industry. The main obvious diagnostic difference Correspondence between the different species is based on the morphology of the flowers. The Mona M. Okba, Department of Parmacognosy, Faculty of Pharmacy, Cairo University, Cairo flowering cycle is very short as well as the persistence of the fully opened flowers 11562, Egypt. extends for a few days only. Moreover, the climatic conditions significantly causes Email: [email protected] fluctuation in their blooming time from year to year. This makes the morphological discrimination very difficult. The discrimination of different iris species is of a great importance, as each species is reported to possess different folk medicinal activities. Objectives: Finding genetic and metabolic markers for differentiation between Sealy (Subgen. Limniris Sect. Lophiris), I. pseudacorus L. (Subgen. Limniris Sect. Limniris) and I. germanica L. (Subgen. Iris Sect. Iris) on levels other than traditional tax- onomic features. Material and Methods: Inter-simple sequence repeat (ISSR) and gas chromatography–mass spectrometry (GC–MS) analyses were performed. Results: The highest similarity was found between I. pseudacorus L. and I. germanica L. and the least similarity was between I. confusa Sealy and I. pseudacorus L. The met- abolic profiling of the leaves confirmed genetic profiling discriminating I. confusa from the other two species. The primary metabolites of the underground parts showed clear discrimination between the three species. Conclusions: This study represents the sole complete map for distinguishing the three Iris species on genetic and metabolic bases.

KEYWORDS DNA extraction, GC–MS, genetic fingerprint, genetic profiling, Iris, ISSR markersmetabolic fingerprint

Abbreviations: C, degrees Celsius; 1X, one-fold concentrated; AFLPs, amplified fragment length polymorphisms; bp, base pair; dNTPs, deoxyribonucleotide triphosphates; FAME, fatty acid methyl ester; g, gravity; GC–MS, gas chromatography–mass spectrometry; IRAP, inter-retrotransposon amplified polymorphism; ISSR, inter-simple sequence repeat; Kb, kilobase; LP-PCR, long primer polymerase chain reaction; m, metre; m/z, mass/charge; mg, milligram; mg/mL, milligram/millilitre; MgCl2, magnesium chloride; min, minute; mL/s, millilitre per second; mM, millimolar; MSTFA, N-methyl-N-(trimethylsilyl)trifluoroacetamide; ng, nanogram; PC1, principal component one; PCA, principal component analysis; PCR, polymerase chain reaction; RAPD, rapid amplified polymorphic DNA; REMAP, retrotransposon-microsatellite amplified polymorphism; RGAP, resistance gene analogue polymorphism; SPSS, statistical package for social sciences; Taq, Thermus aquaticus; TBE, Tris borate EDTA; TOF, tetralogy of Fallot; U, enzyme unit; UPGMA, unweighted pair-group method with arithmetic mean; UV, ultraviolet; μg/mL, microgram/millilitre; μL, microlitre; μM, micromolar.

Phytochemical Analysis. 2020;1–11. wileyonlinelibrary.com/journal/pca © 2020 John Wiley & Sons, Ltd. 1 2 OKBA ET AL.

1 | INTRODUCTION been used in dropsy and is used as a cosmetic for removal of freckles from skin.15 Family consists of about 60 genera and 800 species widely Owing to the earlier mentioned; the taxonomic classification and distributed mainly in tropical and temperate regions. Iris is the largest relationship among the species are controversial and vague.18 In addi- genus with the richest taxonomic diversity in family Iridaceae repre- tion, it is not surprising to find confusion and misuse of irises, as dif- sented in six subgenera, 12 sections, about 301 species and ferent may erroneously have the same name or different 17 hybrids.1–3 They are perennial plants distinguished among the names of the same .6 Therefore, the taxonomic methods based other genera of family Iridaceae by their basal fan unifacial leaves, col- on botany and horticulture methods are not reliable making the sys- ourful of three horizontal and three upright petals that tematics of the species in genus Iris problematic.19 The use of molecu- are basally fused into a tube. Their underground organs are diverse lar markers is a powerful tool in the genetic study of populations with , stolons, bulbs and/or tuberous roots.1 The diagnostic elucidating the relationships among the species and providing infor- morphological characters are largely based on characters such as mation about the of the genus.20 Chemotaxonomy also is beards and/or crests, seed arils, and the type of underground organs used to classify and identify organisms, mainly plants, according to present that were previously proved by Wilson et al,4 to be misleading perceptible differences and similarities in their biochemical composi- in the classification of the genus as these characters are tions by studying either their primary metabolites or secondary homoplasious. Correct identification of Iris genus could be possible if metabolites. Chemotaxonomy can serve as a useful tool in classifica- the flowers are present, but it is very confusing before that because it tion of plants when the classical botanical methods show uncertainty. requires phenotypic data that is difficult to assess and may be variable Thus, it can be considered as a fusion science between the morpho- by environmental changes.5 logical and molecular data to upgrade plant systematics. Several pri- The taxonomy of Iris is complicated as the classification mary metabolites as fatty acids, proteins, amino acids, carbohydrates, schemes and determination keys are based on morphological alkanes, alkynes and carotenoids were previously used as chemotaxo- descriptions which are not reliable in Iris species having various nomic markers in various plant species.21–23 Therefore, finding genetic species, similar traits and convergent morphology.6 Most irises now markers and metabolic markers of the different plant species could be growing in Egyptian gardens are introduced species or hybrids cul- interesting and serve in plant taxonomy. However, the isolation of tivated for ornamental purposes because of their beautifully DNA in good quantity and quality is a critical step towards the devel- coloured flowers beside their recently discovered pharmacological opment of the genetic markers.24–26 Extracting the genomic DNA in activities. Among the Egyptian cultivated species are the three good state can be influenced by the tissue type and also by how the most common and available rhizomatous plants; I. germanica sample is manipulated by the researcher.26,27 This may justify the L. (German iris), I. pseudacorus L. (yellow flag) and I. confusa Sealy integration of the metabolomics with the genetic profiling to obtain a ( iris).4,7 Rhizomatous irises do not have great interspecific distinct, reliable and reproducible proper fingerprint of each plant variations in their morphology, except for their flowers which have species. a very short flowering cycle (5–10 days) and short persistence of In this study, the genetic profiling of the most common Iris spe- the fully opened flowers reaching 5 days at most. Moreover, the cies in Egypt was conducted using inter-simple sequence repeat (ISSR) blooming time is significantly altered and may fluctuate from one which commonly use polymerase chain reaction (PCR) based approach year to the next depending on the climatic conditions.8 to study the genetic variations among the species. The discrimination of these three Iris species is of a great impor- Moreover, the metabolic profiling using gas chromatography– tance, as each species is reported to possess different folk medicinal mass spectrometry (GC–MS) analysis was utilised to provide a com- activities. Irises have been medicinally used since the Ancient Egyp- prehensive characterisation of the primary metabolites and detect tians as evidence has been found engraved on a marble panel in a their variations in the three Iris species. temple at Karnak.9–11 Iris confusa rhizomes have been in Chinese folk medicine to treat acute tonsillitis and bronchitis.12 and I. germanica rhizomes have been used as a diuretic, carminative and 2 | EXPERIMENTAL against constipation and stomachache in Anatolian folk medicine.13 The rhizomes of I. pseudacorus were used in British and Irish folk med- 2.1 | Plant material icine as a purgative, for treatment of colds, throat inflammations and toothache.14 The infusion of I. pseudacorus is reputed of value in dys- The flowering I. confusa Sealy (Subgen. Limniris Sect. Lophiris) was col- menorrhea and leucorrhoea. Its juice has been found effective in the lected from Al-Mansouria, Giza, Egypt while I. germanica (Subgen. Iris case of obstinate cough, convulsions, ‘evil spleens’, dropsies and ser- Sect. Iris) and I. pseudacorus (Subgen. Limniris Sect. Limniris) were col- pents’ bites.15 In the traditional European folk medicine, the peeled lected from the Experimental Station of Medicinal Plants of the Fac- and dried rhizomes of I. germanica was used as emetic, cathartic, ulty of Pharmacy, Assiut University, Egypt in March 2017. The diuretic, stimulant, and expectorant. The powdered dried rhizomes of collected plants were cultivated in sunny sites in well drained gravelly several irises especially I. germanica were used as an ingredient in soils with moderated moisture. About 30 plants from each species toothpowders.16,17 The juice of the fresh roots of I. germanica has were collected. The three species were identified and authenticated OKBA ET AL. 3 by Dr Nina Davies, the curator of the African Iridaceae, Royal Botani- (0.2 mg/mL) as an internal standard solution at 70C for 15 min. After cal Gardens, Kew, London, UK. Voucher specimens were deposited in centrifugation at 20800 × g for 10 min, methylene chloride (375 μL) the Herbarium of the Pharmacognosy Department, Cairo University and water (750 μL) were added to each supernatant, mixed and cen- under the registration numbers 15.1.2019 (1–3). The leaves and the trifuged at 15300 × g for 15 min. The clear supernatant was dried underground parts of each species were separated. Each plant mate- overnight in a speed vacuum. Methoxyamine hydrochloride (dissolved rial was kept cold separately after collection and snap-frozen in liquid in pyridine, 20 mg/mL) was used for derivatisation of the dried plant nitrogen within three hours, then kept at −80C until time of analysis. material. The mixture was shaken for 2 h at 37C and then 70 μLof MSTFA–FAME mixture 50:1 (v/v) was added [N-methyl-N-(tri- methylsilyl)trifluoroacetamide (MSTFA) was obtained from Macherey- 2.2 | ISSR-PCR reactions Nagel (Düren, Germany) and fatty acid methyl ester (FAME) mixture was purchased from Sigma-Aldrich (Hamburg, Germany). After a brief Each species was reduced to a pool of 15 plants and 100 mg of young shaking for 30 min at 37C the samples were subjected to GC–MS leaves from each plant was used to create the pool. Total pooled analysis. The GC–MS system used was coupled to a time-of-flight genomic DNA from young fresh leaves of each species was separately mass spectrometer (Pegasus III, Leco, St Joseph, MI, USA) and fitted extracted using the DNeasy Plant Mini-kit (Qiagen, Germantown, MD, with a 30 m DB-35 column. Helium was used as a carrier gas at a con- USA). The quality and quantity of DNA were determined spectropho- stant flow rate of 2 mL/s. The injection temperature was 230C and tometrically. Visual comparison of DNA dilutions electrophoresed on the transfer line and ion source were set on 250C. Oven temperature 1% agarose gel in comparison to 10 μL of a DNA size marker (lambda was set initially on 85C and increased with 15C/min to 360C. The DNA Hind III digest Phi X 174/HaeIII digest). The degree of fluores- mass spectra were recorded at 20 scans/s with m/z 70–600 scanning cence of the DNA sample was compared to the different bands in range. DNA size marker to estimate the DNA concentration. A set of 20 ISSR primers obtained from Agricultural Genetic Engineering Research Institute, Cairo, Egypt was screened for polymorphism on DNA sam- 2.4 | Data analysis ples from the three Iris species under investigation. ISSR primers were selected based on the number of polymorphic bands produced, band The banding patterns generated by ISSR-PCR marker analyses were size, amplification intensity, and reproducibility. Primers were compared to determine the genetic relatedness of the samples under excluded if banding patterns were difficult to score accurately on study. Clear and distinct amplification products were scored as ‘1’ for 1.5% agarose gels. The amplification reaction was carried out in 25 μL presence and ‘0’ for absence of bands. Bands of the same mobility reaction volume containing one-fold concentrated (1X) PCR buffer, were scored as identical. Cluster analysis was performed using the

1.5 mM magnesium chloride (MgCl2), 0.2 mM deoxyribonucleotide tri- unweighted pair-group method with arithmetic mean (UPGMA) based phosphates (dNTPs), 1 μM primer, 1 U Thermus aquaticus (Taq) DNA on Dice coefficient29 using SPSS program V1.6. polymerase and 30 ng template DNA. PCR amplification was per- For GC–MS data analysis, Chroma TOF 1.0 (Leco) and TagFinder formed in a Perkin-Elmer/GeneAmp® PCR System 9700 (PE Applied 4.0 software were used for chromatograms and mass evaluation. In Biosystems, Foster City, CA, USA) to fulfill 35 cycles after an initial addition, the Golm Metabolome database was used for cross- denaturation cycle for 5 min at 94C. Each cycle consisted of a dena- referencing of the mass spectra. The relative concentration of metab- turation step at 94C for 1 min, an annealing step at 45C for 1 min, olites was determined by normalising the integrated areas of the char- and an elongation step at 72C for 1.5 min. The primer extension seg- acteristic fragment ion traces to the integrated area of ribitol (m/z ment was extended to 7 min at 72C in the final cycle. The amplifica- 319). tion products were resolved by electrophoresis in a 1.5% agarose gel containing ethidium bromide (0.5 ug/mL) in 1X Tris borate EDTA (TBE) buffer at 95 V. A 1 kb DNA ladder was used as a molecular size 3 | RESULTS AND DISCUSSION standard. PCR products were visualised under ultraviolet (UV) light and photographed using a Gel Documentation System (BIO-RAD 3.1 | Genetic profiling of cultivated Iris species 2000, Hercules, CA, USA). In this study, genetic analysis of the three most commonly cultivated Iris species found in Egypt was established with the aim of differenti- 2.3 | Metabolites analysis ating them. The genetic profiling was conducted using ISSR markers. From a total of 20 assayed ISSR primer pairs, 13 primers that were Metabolites extraction and derivatisation, followed by GC–MS were consistent for the presence and intensity of bands were selected for performed as described by Harb et al.28 Briefly, 50 mg of each fresh distinguishing the three Iris species. A total of 155 generated bands material of the leaves and the underground parts of each species were clear and stable, ranging in size from 150 bp (with primer C7) to (50 mg each/five biological replicates/condition) were homogenised 560 bp (with primer C11), of which 136 bands (87.51%) were poly- separately with 1400 μL of 100% methanol and 60 μL of ribitol; morphic (Table 1 and Figure 1). The highest polymorphic percentage 4 OKBA ET AL.

TABLE 1 ISSR polymorphic and monomorphic bands generated by 13 primers in Iris confusa Sealy, I. pseudacorus L. and I. germanica L.

Primer Primer sequence To. MP P% Amplified bands in (bp) (minimum–maximum) C7 5’-GTCCCGACGA-3’ 14 1 13 92.86 150–550 C9 5’-CTCACCGTCC-3’ 14 1 13 92.86 180–510 C10 5’-TGTCTGGGTG-3’ 8 0 8 100 250–530 C11 5’-AAAGCTGCGG-3’ 13 0 13 100 190–560 C16 5’-CACACTCCAG-3’ 11 2 9 81.82 180–420 C19 5’-GTTGCCAGCC-3’ 12 2 10 83.33 190–480 C20 5’-ACTTCGCCAC-3’ 11 2 9 81.81 200–470 G1 5’-CTACGGAGGA-3’ 10 1 9 90 170–490 G2 5’-GGCACTGAGG-3’ 12 2 10 83.33 170–500 O1 5’-GGCACGTAAG-3’ 10 3 7 70 170–480 O5 5’-CCCAGTCACT-3’ 13 0 13 100 160–490 O6 5’-CCACGGGAAG-3’ 15 2 13 86.67 180–490 O7 5’-CAGCACTGAC-3’ 12 3 9 75 190–480 Total 155 19 136 Mean 11.92 1.46 10.46 87.51

A, adenine; C, cytosine; G, guanine; T, thymine. bp, base pair; M, number of monomorphic bands; P, number of polymorphic bands; P%, polymorphism percentage; To., total number of bands.

FIGURE 1 ISSR analysis of three Iris species. (A) Amplification profiles by primers C7, C9, C10, C11 and C16; (B) amplification profiles by primers C19, C20 and G1; (C) amplification profiles by primers G2, O1, O5, O6 and O7; (D) dendrogram. I.c., Iris confusa; I.g., I. germanica; I.p., Iris pseudacorus;M, marker. [Colour figure can be viewed at wileyonlinelibrary.com]

was 100% (primers C10, C11 and O5) while the lowest polymorphic bands per primer combination was close to 100%, indicating that the percentage was 70% (primer O1). The number of bands amplified per three Iris species have rich genetic diversity.6 primer ranged from eight (primer C10) to 15 (primer O6) (Figure 1), The UPGMA dendrogram revealed a high level of genetic dis- with an average of 11.92 and the average number of polymorphic tances (Figure 1D). The examined accessions were divided into two bands per primer was 10.46 (Table 1). The polymorphic percentage of groups at a similarity coefficient of 0.30. The first group had OKBA ET AL. 5

I. pseudacorus (Subgen. Limniris Sect. Limniris) and, I. germanica were found frequently across all species and responsible for the dis- (Subgen. Iris Sect. Iris) while the second group had I. confusa (Subgen. crimination of the three species. It was divided into two main clusters: Limniris Sect. Lophiris). In addition, the three species appeared as three metabolites that were higher in content in I. germanica and distinct groups at a similarity coefficient of 0.40. The highest similarity I. pseudacorus leaves and lower in I. confusa leaves, and metabolites was observed between I. pseudacorus and I. germanica (33%), followed that were lower in content in I. germanica and I. pseudacorus leaves by the similarity between I. confusa and I. germanica (30%) and the and higher in I. confusa leaves. Among the compounds which showed lowest similarity was between I. confusa and I. pseudacorus (21%). a relatively lower abundance in I. confusa leaves were organic acids (2-oxo glutaric, fumaric, isocitric, glycolic, pyruvic and succinic), phe- nolic acids (caffeic), vitamins (riboflavin, 1-dehydroascorbic acid and 3.2 | Metabolite profiling for chemotaxonomy of ascorbic acid), amino acids (trans-4-hydroxy proline, leucine, adenine, different Iris species tyramine, isoleucine and methionine), sugars (raffinose, melibiose, α,α- trehalose, fucose, threose and fructose), sugar alcohols (galactinol, In this study, GC–MS was utilised to cover the major metabolites pre- glycerol-3-phosphate and mannitol), sugar acids (glyceric), sugar deriv- sent in the leaves of the three Iris species under investigation. The atives (myo-inositol and glucuronic acid), diterpene alcohols (phytol), principal component analysis (PCA) was used to find if we can discrim- inorganic acids (ortho phosphoric) and heterocyclic compounds (uric inate the three Iris species based on their metabolite profiles. PCA acid). showed that I. confusa leaves were discriminated from those of the Iris confusa leaves were rich in several metabolites belonging to other two species along principal component one (PC1) which different classes as: amino acids (glutamic acid, methionine sulfoxide, explaining 77% of the total variance. The PCA scores revealed that proline, tryptophan, lysine, alanine, histidine, arginine, ornithine the metabolomes of both I. germanica and I. pseudacorus were strongly 1,5-lactam, threonine, glutamine, serine, glycine, phenylalanine, tyro- related due to the overlapping clusters of their leaves (Figure 2A). sine, ornithine, aspargine and valine), amino alcohols (ethanolamine), From the loading plot, several metabolites belonging to different clas- organic acids (aspartic, allantoic and oxalic), carboxylic acids (benzoic ses and metabolic pathways have contributed to this correlation and 4-hydroxy benzoic), sugars (xylose and rhamnose), sugar alcohols between I. germanica and I. pseudacorus. Among those metabolic clas- (erythritol, xylitol and glycerol), sugar acids (gluconic), vitamins (nico- ses were amino acids, organic acids and sugars (sugars, sugar alcohols tinic acid), organic nitrogenous compounds (urea) and nucleotides and sugar derivatives). (uracil), while they were detected in I. germanica leaves in lower abun- This study enabled the detection of more than 60 metabolites in dance. The heatmap profiles of I. germanica and I. pseudacorus leaves the leaves of the tested Iris species (Supporting Information Table S1). showed considerable similarity between their respective metabolomes These metabolites were classified into six major metabolite than with that of I. confusa (Figure 3). groups; amino acids, carbohydrates, organic acids, amines, phosphates To determine if the same metabolic markers can be found in the and lipids which covered different metabolic pathways. A quantitative underground tissues of the aforementioned species, GC–MS analysis metabolomic heatmap constructed to visualise and assess qualitative on the underground parts was also studied. PCA revealed the discrimi- and quantitative differences across the three Iris species leaves dis- nation of the three species. Along PC1 which explained 49% of vari- played red colour for high content and blue colour for low content ance, I. pseudacorus was discriminated from I. confusa and I. germanica. (Figure 3). The heatmap was generated for the metabolites which Along principal component two (PC2) which explained 29% of

FIGURE 2 Three Iris species leaves primary metabolites. (A) PCA illustrating their different levels; (B) loading plot showing the metabolites that caused difference between the three species [Colour figure can be viewed at wileyonlinelibrary.com] 6 OKBA ET AL.

FIGURE 3 Heatmap and dendrogram illustrating the different primary metabolite levels in Iris confusa, I. pseudacorus and I. germanica leaves. Blue colour: low content; red colour: high content. [Colour figure can be viewed at wileyonlinelibrary.com]

variance, the three species were discriminated from each other detected belonging to different classes of metabolites as: organic (Figure 4). Iris confusa was discriminated from the other two species acids (2-oxo-glutaric and pyruvic), amino acids (methionine and gluta- because of its highest sucrose content. Iris germanica was distin- mine), sugars (glucose and fructose), sugar alcohols (mannitol and guished on the basis of the highest content of citric acid, glucose and glycerol-3-phosphate) and vitamins (riboflavin). The metabolites mannitol markers. Riboflavin, glutamine and allantoic acid were the grouped in the middle cluster were mainly amino acids (threonine and discriminators of I. pseudacorus as it showed their highest content. phenylalanine). The metabolic profiles of the tested underground parts revealed three In addition to organic acids (malic and succinic), phenolic acids main clusters: metabolites least abundant in I. confusa, metabolites (caffeic), carboxylic acids (benzoic), amino alcohols (ethanolamine), least abundant in I. pseudacorus and metabolites least abundant in sugars (threose and α,α-trehalose), sugar alcohols (galactinol and xyli- I. germanica (Figure 5). In the upper cluster several metabolites were tol), sugar acids (glyceric and threonic), sugar derivatives (myo-inositol OKBA ET AL. 7

FIGURE 4 Three Iris species underground parts primary metabolites. (A) PCA illustrating their different levels; (B) loading plot showing the metabolites that caused the difference between the three species [Colour figure can be viewed at wileyonlinelibrary.com]

and glucuronic acid), vitamins (ascorbic acid and 1-dehydro-ascorbic Previous data determined the genetic diversity among several acid), nucleotides (adenine and uracil), organic nitrogenous com- Iris species collected from different localities used RAPD (rapid pounds (urea), heterocyclic compounds (uric acid), diterpene alcohols amplified polymorphic DNA) and/or ISSR.41–47 Different genetic (phytol) and inorganic acids (orthophosphoric) were also detected markers [ISSR, inter-retrotransposon amplified polymorphism (IRAP), (Table S2). The cluster at the bottom of the heat map consisted of retrotransposon-microsatellite amplified polymorphism (REMAP), organic acids (allantoic), carboxylic acids (4-hydroxy benzoic), sugars resistance gene analogue polymorphism (RGAP) and long primer (sucrose) and sugar alcohols (erythritol). The heatmap profiles of the (LP)-PCR] were also used to study the genetic diversity of some underground parts showed different metabolic profile of each Iris species, where the ISSR-markers were found to be the most species. efficient.48 Amplified fragment length polymorphisms (AFLPs) was Each species appeared as a distinct group in the dendrogram con- used to determine diversity among I. pseudacorus population and structed on the metabolites of the three species underground parts other 14 Iris species.6,49 Iris pseudacorus and I. germanica genetic (Figure 5). profiling was studied before using RAPD.50 Upon comparing the genetic polymorphism among regenerated and mother plants of I. germanica using ISSR and RAPD techniques, it was found that 4 | DISCUSSION ISSR primers generated a higher number of clearly detected bands than RAPD.51 Plants belonging to the genus Iris have gained interest as ornamental In this study, it is reported for the first time the use of the ISSR plants in the cut-flower industry. They also have economical value as genotyping technique in measuring genetic variation and determina- iris oil is one of the most precious ingredients in perfumery.30–32 Not tion of genetic relationships among three Iris species cultivated in only is the genus Iris of ornamental and economic value, but it has also Egypt. It is a technique, which involves the use of simple sequence been shown to have several interesting biological activities including repeats (SSRs), also called microsatellites as primers in a PCR to gener- anti-inflammatory, antimicrobial, antioxidant, phytoestrogenic, anti- ate multilocus markers without requiring prior knowledge of genome diabetic, hepatoprotective, antihepatitis B, anticholinesterase, anti- sequence information.19 This method is highly reproducible due to its plasmodial, molluscicidal, antimicrobial, antineoplastic and cytotoxic primer length and the high stringency achieved by the annealing tem- activities.12,33–38 perature.20 It involves amplification of DNA segment present at an Several species are cultivated in Egypt among them: I. confusa, amplifiable distance between two identical microsatellites repeat I. pseudacorus and I. germanica which are rhizomatous plants difficult regions oriented in opposite direction. The microsatellites used usually to distinguish morphologically without their flowers which have a very 16–25 bp long, as primers in a single primer PCR targeting multiple short life cycle. Considering the fact that the genetic material is the genomic loci to amplify mainly the inter-SSR sequences of different same in all plant tissues and younger tissues, especially the leaves, to sizes. The microsatellite repeats used as primers can be di-, tri-, tetra- produce DNA of good quality and quantity due to large numbers of or penta-nucleotides.52 ISSR technique has been proven suitable for cells, small amounts of secondary metabolites and lignin that enable distinguishing between closely related species53 and even between the isolation of the genetic material with ease and do not hinder the different populations.54 ISSR markers have been found to provide a PCR used to amplify the loci of interest,26,39,40 the young leaves were highly polymorphic fingerprint.20 the organ of choice for extracting the DNA to conduct the genetic The obtained data provide evidence of genetic diversity among profiling. and within the tested species. ISSR fingerprints clearly distinguished 8 OKBA ET AL.

FIGURE 5 Heatmap and dendrogram illustrating the different primary metabolite levels in Iris confusa, I. pseudacorus and I. germanica underground parts. Blue colour: low content; red colour: high content. [Colour figure can be viewed at wileyonlinelibrary.com]

all of the tested species. ISSR markers were shown to be a good tool according to which the subgenera were divided. In subgenus Limniris for assessing genetic diversity, genetic relationships and identifica- species of each section which are I. confusa (section Lophiris) and tions in Iris. The high percentage of polymorphism indicated that I. pseudacorus (section Limniris) were well separated on the genetic genus Iris has a rich genetic diversity. The three studied Iris species level. The findings of this study are in accordance with that of Kiani can be separated from each other by a few primer combinations. et al,20 on Tulipa L. species. The results also showed a great similarity These results can be a useful tool for the conservation, classification in the genetic profile of subgenus Iris section Iris (I. germanica) and and identification of Iris flora. The integrated genetic and metabolic subgenus Limniris section Limniris (I. pseudacorus) which was in accor- fingerprinting methods have demonstrated their potential for discrimi- dance with the findings of Raycheva et al.19 nating the three Iris species. Results suggest that ISSR genetic diver- It is well documented that the primary metabolism reactions sity does not necessarily match the morphological trait differences are highly conserved, leading to a constant primary metabolic OKBA ET AL. 9 profile compared to the secondary metabolism pathways which, CONFLICT OF INTEREST STATEMENT usually showed much greater diversity.55–58 Thus, this study can The authors wish to declare that no conflicts of interest associated be used as a powerful tool for the discrimination of these Iris with this publication and there has been no financial support for species without being forced to wait for their very short this work. flowering cycle to ensure the presence of the morphological variations. ACKNOWLEDGEMENT GC–MS was proved to be one of the common analytical tools The authors gratefully acknowledge Prof. Dr Mahmoud Hafiz Assaf, widely used for profiling complex mixtures of primary metabolites, Department of Pharmacognosy, Faculty of Pharmacy, Assiut Univer- such as organic and amino acids, sugars, sugars alcohols, phosphory- sity, Egypt, for suppling the plant materials. lated intermediates, and lipophilic constituents. The primary metabo- lites from plants are extracted, derivatised via silylation and detected by GC–MS.21 ORCID The metabolic fingerprinting methods have demonstrated their Mona M. Okba https://orcid.org/0000-0003-1880-8123 potential for discriminating the three Iris species. The metabolic profiling of the leaves of the three species elicited similar result to REFERENCES that of the genetic profiling. The close genetic relation between 1. Wilson CA. Subgeneric classification in Iris re-examined using chloro- plast sequence data. Taxon. 2011;60(1):27-35. section Iris and section Limniris might be the reason for their simi- 2. Niketic M, Tomovic G, Siljak-Yakovlev S. A new spontaneous hybrid larity in the metabolic composition which was obvious in the between the cultivated and wild Iris species from Serbia. Bull Nat His- strong relation between the primary metabolites of I. germanica tory Museum. 2018;11:189-210. and I. pseudacorus leaves. 3. Barker C, Govaerts R. Iridaceae. In: World Checklist of . Kew: Royal Botanic Gardens; 2018. The primary metabolite profiles of the underground parts were 4. Wilson CA. Patterns in evolution in characters that define Iris subge- not similar to that of the leaves, indicating that the plant metabolites nera and sections. Aliso. 2006;22(1):425-433. are tissue specific. Their primary metabolite profile discriminated the 5. Al-Gabbiesh A, Hassawi DS, Afifi FU. Determination of genetic diver- three species from each other and determined their metabolic finger- sity among Iris species using random amplified polymorphic DNA print. Thus, a DNA analysis and analysis of quantitative metabolite analysis. Biotechniques. 2006;5(2):173-179. 6. Xu Y, Hussain K, Yan X, et al. Genetic diversity and relationships data may be used in conjugation with multivariate statistical treatment among 15 species of Iris based on amplified fragment length polymor- for taxonomic identification. However, the metabolite profiles are phism markers. App Ecol Env Res. 2019;17(3):5369-5381. generally unique even for different tissues of the same plant, so che- 7. Tackolom V, Drar M. Flora of Egypt: Angiospermae, Part Mon- motaxonomy based on these data usually gives clear-cut ocotyledones: Liliaceae-Musaceae. 3 Cairo: Cairo Univerity Press; 1954. 8. Cris¸an I, Stoie A, Buta E, Cantor M. Flowering phenology of some Iris classifications. species in the UASVM Cluj agrobotanical garden. Rom Biotechnol Lett. Several reports have been concerned with the secondary metabo- 2018;23(3):13702-13707. lite profiling of the genus,30,59–64 however, none dealt with their pri- 9. King C, Killens WR. A guide to species irises: their identification and cul- mary metabolite profiling. tivation. Cambridge: Cambridge University Press; 1997. 10. Crisan I, Cantor M. New perspectives on medicinal properties and This study identified genetic markers as well as metabolic uses of Iris sp. Hop Med Plants. 2016;24(1–2):24-36. markers for the selected studied Iris species that could be inte- 11. Lust J. The herb book: the most complete catalog of herbs ever publi- grated together with the purpose of discriminating the three Iris shed. Chelmsford, MA: Courier Corporation; 2014. species belonging to three different sections and establishing a dis- 12. Chen X, Zhang X, Ma Y, Deng Z, Geng C, Chen J. Iridal-type triterpenoids with anti-HBV activity from Iris confusa. Fitoterapia. tinct genetic and metabolic fingerprint for each species. To the 2018;129:126-132. best of our knowledge, this is the first study that addresses 13. Wang H, Cui Y, Zhao C. of the genus iris (Iridaceae). Mini- genetic profiling of the leaves of the three Iris species and corre- Rev Med Chem. 2010;10(7):643-661. lates it with their primary metabolic profiling. These findings aided 14. Allen DE, Hatfield G. Medicinal plants in folk tradition. Portland, OR: Timber Press; 2004. in correlating the genetic similarity among the studied species 15. Grieve M. A Modern Herbal: the medicinal, culinary, cosmetic and eco- belonging to different subgenera and sections. The ongoing never- nomic properties, cultivation and folk-lore of herbs, grasses, fungi, ending progress in the analytical, mass spectrometric methods, shrubs, & trees with all their modern scientific uses. 2 Chelmsford, MA: computer tools and software will make the metabolite profiling a Courier Corporation; 1971. more powerful candidate in the future for plant taxonomy provid- 16. Uphof J. Dictionary of economic plants. 2nd ed. Lehre, Germany: Ver- lag Von J. Cramer; 1968. ing a unique fingerprint of each organ of each plant in the Plant 17. Steinegger E. Lehrbuch der Pharmakognosie und Phytopharmazie. Kingdom. 4. vollst. neubearb. Aufl. von Rudolf Hänsel,[ua]. Berlin: Springer Verlag; 1988. 18. Yu X-F, Zhang H-Q, Yuan M, Zhou Y-H. Karyotype studies on ten Iris species (Iridaceae) from , . Caryologia. 2009;62(3): FUNDING 253-260. This research did not receive any specific grant from funding agencies 19. Raycheva T, Stoyanov K, Denev I. Genetic diversity and molecular in the public, commercial, or not-for-profit sectors. taxonomy study of three genera from Iridaceae family in the 10 OKBA ET AL.

Bulgarian flora based on ISSR markers. Biotechnol Biotechnol Equip. 43. Johnston JA, Wesselingh RA, Bouck AC, Donovan LA, Arnold ML. 2011;25(3):2484-2488. Intimately linked or hardly speaking? The relationship between geno- 20. Kiani M, Memariani F, Zarghami H. Molecular analysis of species of type and environmental gradients in a Louisiana Iris hybrid popula- Tulipa L. from Iran based on ISSR markers. Plant Syst Evol. 2012; tion. Mol Ecol. 2001;10(3):673-681. 298(8):1515-1522. 44. Arafeh RH, Sapir Y, Shmida A, Iraki N, Fragman O, Comes H-P. 21. Osorio S, Do PT, Fernie AR. Profiling primary metabolites of tomato Patterns of genetic and phenotypic variation in and fruit with gas chromatography/mass spectrometry. Plant Metabolomics. I. atrofusca (Iris sect. Oncocyclus = the royal irises) along an Berlin: Springer Verlag; 2011:101-109. ecogeographical gradient in and the . Mol Ecol. 2002; 22. Hao D, Gu X, Xiao P. Chemotaxonomy: a phylogeny-based approach. 11(1):39-53. Medicinal Plants. Cambridge, UK: Woodhead Publishing; 2015;1-48. 45. Zhang M, Huang S-Z, Qiu S, Sun Y-D. Analysis of RAPD and ISSR on 23. Singh R. Chemotaxonomy: a tool for plant classification. J Med Plants genetic diversity of Iris L. plants. J Plant Resour Environ. 2007;16(2): Stud. 2016;4(2):90-93. 6-11. 24. Haig SM. Molecular contributions to conservation. Ecology. 1998; 46. Wang K, Kang J, Zhou H, et al. Genetic diversity of Iris lactea var. 79(2):413-425. chinensis germplasm detected by inter-simple sequence repeat (ISSR). 25. Allen G, Flores-Vergara M, Krasynanski S, Kumar S, Thompson W. A Afr J Biotechnol. 2009;8(19):4856-4863. modified protocol for rapid DNA isolation from plant tissues using 47. Attari S, Shoor M, Neghab M, Tehranifar A, Malekzadeh-Shafaroudi S. cetyltrimethylammonium bromide. Nat Protoc. 2006;1(5):2320- Evaluation of genetic diversity of Iris genotypes (Iris spp.) using ISSR. 2325. J Hortic Sci. 2016;30(3):376-382. 26. Lucas MS, Carvalho CS, Hypolito GB, Côrtes MC. Optimized protocol 48. Bublyk OM, Andreev IO, Kalendar RN, Spiridonova KV, Kunakh VA. to isolate high quality genomic DNA from different tissues of a palm Efficiency of different PCR-based marker systems for assessment of species. Hoehnea. 2019;46(2):e942018. genetic diversity. Biologia. 2013;68(4):613-620. 27. Varma A, Padh H, Shrivastava N. Plant genomic DNA isolation: an art 49. Lamote V, Roldán-Ruiz I, Coart E, De Loose M, Van Bockstaele E. A or a science. Biotechnol J. 2007;2(3):386-392. study of genetic variation in Iris pseudacorus populations using ampli- 28. Harb J, Alseekh S, Tohge T, Fernie AR. Profiling of primary metabo- fied fragment length polymorphisms (AFLPs). Aquat Bot. 2002;73(1): lites and flavonols in leaves of two table grape varieties collected from 19-31. semiarid and temperate regions. Phytochemistry. 2015;117:444-455. 50. Makarevitch I, Golovnina K, Scherbik S, Blinov A. Phylogenetic rela- 29. Sneath PH, Sokal RR. Numerical taxonomy. The Principles and Practice tionships of the Siberian Iris species inferred from noncoding chloro- of Numerical Classification. San Francisco, CA: W. H. Freeman and Co.; plast DNA sequences. Int J Plant Sci. 2003;164(2):229-237. 1973. 51. Cerasela P, Simona L, Alexandru L, Maria B. Genetic characterization 30. Schütz C, Quitschau M, Hamburger M, Potterat O. Profiling of iso- of micropropagated Iris germanica varieties as assessed by RAPD and flavonoids in Iris germanica extracts by microprobe NMR and ISSR markers. Curr Opin Biotechnol. 2013;24:S121-S122. HPLC–PDA–MS analysis. Fitoterapia. 2011;82(7):1021-1026. 52. Reddy MP, Sarla N, Siddiq E. Inter simple sequence repeat (ISSR) 31. Kaššák P. Secondary metabolites of the chosen genus Iris species. polymorphism and its application in plant breeding. Euphytica. 2002; Acta Univ Agric et Silvic Mendel Brun. 2013;60(8):269-280. 128(1):9-17. 32. Voudouri D, Tesseromatis C. Perfumery from myth to antiquity. Int J 53. Benharrat H, Veronesi C, Theodet C, Thalouarn P. Orobanche species Med Pharmacy. 2015;3(2):41-55. and population discrimination using intersimple sequence repeat 33. Hacıbekiroglu I, Kolak U. Antioxidant and anticholinesterase constitu- (ISSR). Weed Res. 2002;42(6):470-475. ents from the petroleum ether and chloroform extracts of Iris 54. Buschmann H, Gonsior G, Sauerborn J. Pathogenicity of branched suaveolens. Phytother Res. 2011;25(4):522-529. broomrape (Orobanche ramosa) populations on tobacco cultivars. 34. Ibrahim SR, Mohamed GA, Al-Musayeib NM. New constituents from Plant Pathol. 2005;54(5):650-656. the rhizomes of Egyptian Iris germanica L. Molecules. 2012;17(3): 55. Wink M. Introduction: biochemistry, physiology and ecological func- 2587-2598. tions of secondary metabolites. Ann Plant Rev. 2010;40:1-19. 35. Kukula-Koch W, Sieniawska E, Widelski J, Urjin O, Głowniak P, 56. Beauvoit B, Belouah I, Bertin N, et al. Putting primary metabolism into Skalicka-Woźniak K. Major secondary metabolites of Iris spp. perspective to obtain better fruits. Ann Bot. 2018;122(1):1-21. Phytochem Rev. 2015;14(1):51-80. 57. Maeda HA. Harnessing evolutionary diversification of primary metab- 36. Ibrahim SR, Mohamed GA, Zayed MF, Ross SA. 8-Hydroxyirilone olism for plant synthetic biology. J Biol Chem. 2019;294(45):16549- 5-methyl ether and 8-hydroxyirilone, new antioxidant and α-amylase 16566. inhibitors isoflavonoids from Iris germanica rhizomes. Bioorg Chem. 58. Maeda HA. Evolutionary diversification of primary metabolism and 2017;70:192-198. its contribution to plant chemical diversity. Front Plant Sci. 2019; 37. Singaba ANB, Ahmed AH, Sinkkonen J, Ovcharenko V, Pihlaja K. Mol- 10:881. luscicidal activity and new flavonoids from Egyptian Iris germanica 59. Pan S, Jun-Li H, Gang W, Bo-Yang Y, Min-Jian Q. Analysis of flavo- L. (var. alba). Z Naturforsch C. 2006;61(1–2):57-63. noids and phenolic acids in by HPLC-DAD-ESI-MSn. Chin 38. Singab ANB, Ayoub IM, El-Shazly M, et al. Shedding the light on J Nat Med. 2010;8(3):202-207. Iridaceae: ethnobotany, phytochemistry and biological activity. Ind 60. Bhat G, Shawl AS, Shah Z, Tantry M. HPLC-DAD-ESI-MS/MS identifi- Crop Prod. 2016;92:308-335. cation and characterization of major constituents of , Iris 39. Murray M, Thompson WF. Rapid isolation of high molecular weight germanica and growing in Kashmir , . J Anal plant DNA. Nucleic Acids Res. 1980;8(19):4321-4326. Bioanal Tech. 2014;5(6):1-10. 40. Williams CE, Ronald PC. PCR template-DNA isolated quickly from 61. Xie G-Y, Zhu Y, Shu P, et al. Phenolic metabolite profiles and antioxi- monocot and dicot leaves without tissue homogenization. Nucleic dants assay of three Iridaceae medicinal plants for traditional Chinese Acids Res. 1994;22(10):1917-1918. medicine “she-Gan” by on-line HPLC–DAD coupled with chemilumi- 41. Zhuravlev YN, Kozyrenko M, Artyukova E, Reunova G, Ilyushko M. nescence (CL) and ESI-Q-TOF-MS/MS. J Pharm Biomed Anal. 2014; Fingerprinting genomes of the far eastern species of the genus Iris 98:40-51. L. by RAPD-PCR. Russ J Genet. 1998;34(3):368-372. 62. Abdel-Mageed WM, Al-Wahaibi LH, Al-Saleem MS, Gouda YG, 42. Artyukova E, Kozyrenko M, Ilyushko M, Zhuravlev YN, Reunova G. Abdel-Kader MS, Ibraheim ZZ. and chemotaxonomic Genetic variability of . Mol Biol. 2001;35(1):134-138. study on Lange leaves. Biochem Syst Ecol. 2018;76:32-34. OKBA ET AL. 11

63. Alperth F, Mitic B, Mayer S, et al. Metabolic profiling of rhizomes of native populations of the strictly endemic Croatian species Iris How to cite this article: Okba MM, Abdel Baki PM, adriatica. Plant Biosyst. 2019;153(2):317-324. Khaleel AE, El-Sherei MM, Salem MA. Discrimination of 64. Chen D, Meng Y, Zhu Y, et al. Qualitative and quantitative analysis of C-glycosyl-flavones of Iris lactea leaves by liquid chromatography/- common Iris species from Egypt based on their genetic and tandem mass spectrometry. Molecules. 2018;23(12):3359. metabolic profiling. Phytochemical Analysis. 2020;1–11. https://doi.org/10.1002/pca.2945

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