Exploring phytochemical variability in Iranian Acanthophyllum (Caryophyllaceae) species: insights for enhanced medicinal and industrial utilization

Abstract

Background

Acanthophyllum species are notable for their medicinal and industrial importance due to the presence of valuable bioactive compounds, especially saponins. Assessing genetic diversity is crucial for the strategic development and selection of superior ecotypes in Acanthophyllum species. The present study aimed to investigate eight different phytochemical traits of ten species of the Acanthophyllum genus, including Acanthophyllum bracteatum Boiss (7 ecotypes), Acanthophyllum diezianum Hand-Mazz (1 ecotype) Acanthophyllum glandulosum Bunge ex Bioss (22 ecotypes), Acanthophyllum heratense Schiman-Czeika (1 ecotype), Acanthophyllum korshinskyi Schischk (5 ecotypes), Acanthophyllum laxiusculum Schiman-Czeika (1 ecotype), Acanthophyllum microcephalum Boiss (12 ecotypes), Acanthophyllum sordidum Bunge ex Boiss (7 ecotypes), Acanthophyllum spinosum C.A.Mey (6 ecotypes) and Acanthophyllum squarrosum (6 ecotypes), collected from different natural habitats of Acanthophyllum in Iran.

Results

The investigated ecotypes have significant differences in terms of all the measured as revealed using analysis of variance (P <0.01). The highest levels of total phenolics (28.83 mg gallic acid equivalent g⁻¹ dry weight), total flavonoids (9.08 mg quercetin g⁻¹ DW), and total flavonols (5.8 mg quercetin g⁻¹ DW) were recorded in ecotypes of A. bracteatum3, A. glandulosum11, and A. glandulosum4, respectively. The highest total tannins contents in roots (194.36 mg catechin equivalent g⁻¹ DW) and leaves (94.97 mg tannic acid equivalent g⁻¹ DW) were found in A. glandulosum21 and A. squarrosum2, respectively. The highest contents for total polysaccharide (15.14 mg glucose equivalent g⁻¹ DW) and total saponins (158.75 mg saponin equivalent g⁻¹ DW) in roots were observed in A. heratense1 and A. glandulosum17, respectively. The highest antioxidant activity, (2,2-diphenyl-1-picrylhydrazyl), was measured as 93.28% in A. microcephalum12 and A. bracteatum7 ecotypes. A significant and positive correlation (r = 0.37^**), between total phenolics content and antioxidant activity was observed. Cluster analysis based on phytochemical traits grouped the ecotypes into three distinct clusters, demonstrating high genetic variability: group I (26 ecotypes), group II (20), and group III (22), which highlights the high potential for selecting advantageous traits according to their cluster classification.

Conclusion

These findings emphasize significant phytochemical diversity between and within Acanthophyllum species, positioning them as promising natural sources of antioxidants and bioactive compounds. A. squarrosum and A. laxiusculum demonstrated the greatest convergence in phytochemical traits, while A. glandulosum and A. spinosum showed the greatest divergence. Superior Acanthophyllum species and their associated ecotypes are promising candidates for breeding programs and future economic exploitation in different food industries and pharmaceutical aims.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-026-08426-9.

Introduction

Acanthophyllum, a large genus of the Caryophyllaceae family, includes approximately 82 perennial subshrub species distributed across the Irano-Turanian floristic region [1]. Acanthophyllum species are recognized as important medicinal plants in Iran, Turkey, Afghanistan, Pakistan, and Central Asia [2]. Iran hosts some 40 species of Acanthophyllum [3] that grow naturally in various geographical regions of it [4]. Acanthophyllum species, commonly known as ‘Chubak’ in Iran [5]. Traditionally, their roots have been used by local populations as natural soaps (particularly for woolen fabrics), biosurfactants, detergents, and nasal stimulants [6]. Additionally, these roots act as emulsifying and stabilizing agents in oil–water systems and serve as stabilizers in traditional desserts such as ‘Halva Ardeh [7]. Extracts from Acanthophyllum exhibit foaming and emulsifying properties which are employed in the preparation of traditional sweet pastes in Iran and Turkey [8]. Acanthophyllum species have emerging potentials in bioremediation and nanotechnology [5, 7].

Phytochemical analyses have revealed that Acanthophyllum roots contain saponins (notably triterpenoid saponins), gums (hydrocolloids), polysaccharides and monosaccharides [9]. Triterpenoid saponins act as surface-active agents, contributing to foam formation and antimicrobial activity [5]. Consequently, the roots serve as a natural source of biosurfactants and emulsifiers. Industrial applications of these saponins include the production of malt beverages [5], foaming agents, emulsifiers for food and non-food products such as cosmetics (e.g., soaps, lotions, shampoos, and lipsticks), detergents, and biosurfactants [6, 10], as well as their use in bioremediation and nanotechnology [7]. Furthermore, saponins from Acanthophyllum exhibit multiple pharmacological effects, including immune stimulation, hypoglycemic activity, anticancer, antioxidant, anti-inflammatory, hemolytic, antifungal, and antiviral properties [8, 10, 11].

Medicinal plants synthesize various secondary metabolites (SMs) which possess diverse pharmacological activities [12]. The phytochemical composition of SMs in medicinal plants is influenced by both genetic and environmental factors [13].

Evaluation of genetic diversity in plants is the first and fundamental step to identifying, preserving, and maintaining the genetic resources, which are considered the basis for breeding programs [14]. Assessing plant genetic diversity through morphological, phytochemical, and molecular markers is vital for medicinal plant germplasm [14]. Identifying superior ecotypes with enhanced phytochemical profiles is crucial for improving both quality and safety of plant-derived products [15], germplasm conservation and its management [14] and leads to more attention of farmers and plant breeders.

According to literature a few studies has done to evaluate the phytochemical diversity in Caryophyllaceous family. For instance, evaluation the diversity of SMs in the genus Silene L., identified a wide range of compounds such as triterpene saponins, volatile terpenoids, and phenolics [16]. Phytochemical diversity of saponins in Gypsophila L. species, showed a significant correlation between phytochemical variation and genetic differentiation [17]. According to literature, a few studies have addressed phytochemical traits across Acanthophyllum species [5, 7, 18]. The diversity of major bioactive constituents in Acanthophyllum genus has not yet been adequately characterized. Given the broad spectrum of valuable natural compounds, especially in roots, comprehensive research is needed to elucidate phytochemical diversity and its applied aspects in Acanthophyllum.

The main objective of this study was to evaluate the phytochemical variability between Acanthophyllum species and intra-species ecotypes grown in different natural habitats of Iran and identifying their potential for utilization in pre-breeding programs. Also, this study aimed to evaluate the impact of climatic and soil conditions on Acanthophyllum species diversity based on phytochemical traits.

The findings of this study will facilitate the identification of superior ecotypes with higher concentrations of bioactive compounds, supporting their potential applications in pharmaceutical, food, and cosmetic industries as well as contributing to conservation and sustainable development strategies in Acanthophyllum species.

Materials and methods

A total number of 68 ecotypes from ten different species of the genus Acanthophyllum were collected at the full flowering stage from several natural provinces of Iran based on their natural distribution across various habitat between June and August 2022. Distinct species of Acanthophyllum genus, including A. bracteatum Boiss, A. diezianum Hand-Mazz., A. glandulosum Bunge ex Boiss, A. heratense Schiman-Czeika, A. korshinskyi Schischk, A. laxiusculum Schiman-Czeika, A. microcephalum Boiss, A. sordidum Bunge ex Boiss, A. spinosum C.A.Mey, and A. squarrosum Boiss, were taxonomically identified by Dr. A. Pirani (a botanist from the Department of Biology at Ferdowsi University of Mashhad, Iran) according to Flora Iranica [19]. Voucher specimens’ number were deposited in the Herbarium of Department of Natural Resources at Isfahan University of Technology (HIUT), Iran. The geographical characteristics of the collection sites are summarized in Table 1, and the sampling locations are also mapped to illustrate their spatial distribution (Fig. 1). The aerial and root parts of ecotypes were collected from each geographical location. The aerial parts of different Acanthophyllum species at the flowering stage in their natural habitats are shown in Fig. 2.

Table 1.

The name of different ecotypes of Acanthophyllum species, collection sites and their geographical characteristics from Iran

NO.	Ecotypes abbreviations	Collection area	Longitude	Latitude	Elevation (m)	Voucher numbers
1	A. bracteatum (A.br1)	Qasrcham, Shahreza, Isfahan, Iran	51° 49′ 11″	31° 51′ 12″	2000	100 (HIUT)
2	A. bracteatum (A.br2)	Cheshme Sard, Semirom, Isfahan, Iran	51° 34′ 4″	31° 42′ 56″	2442	101 (HIUT)
3	A. bracteatum (A.br3)	Podeh, Dehaqan, Isfahan, Iran	51° 39′ 30″	32° 3′ 31″	1905	102 (HIUT)
4	A. bracteatum (A.br4)	Gonharan, Isfahan, Iran	50° 42′ 32″	32° 59′ 18″	2517	103 (HIUT)
5	A. bracteatum (A.br5)	Borujen, Chaharmahal and Bakhtiari, Iran	51°15′ 26″	31° 56′ 41″	2269	104 (HIUT)
6	A. bracteatum (A.br6)	Sefidasht, Chaharmahal and Bakhtiari, Iran	51°11′ 47″	32° 6′ 24″	2168	105 (HIUT)
7	A. bracteatum (A.br7)	Robat Sefid, Razavi Khorasan, Iran	59° 24′ 02″	35° 48′ 11″	1477	106 (HIUT)
8	A. diezianum (A.di1)	Torbat Heydarieh, Razavi Khorasan, Iran	59° 14′ 46″	35° 44′ 40″	1662	107 (HIUT)
9	A. glandulosum (A.gl1)	Kesheh, Isfahan, Iran	51° 46′ 7″	33° 22′ 42″	2275	108 (HIUT)
10	A. glandulosum (A.gl2)	Ab Senjad, Natanz, Isfahan, Iran	51° 45′ 2″	33° 18′ 42″	2160	109 (HIUT)
11	A. glandulosum (A.gl3)	Borujen, Chaharmahal and Bakhtiari, Iran	51°15′ 26″	31° 56′ 41″	2269	110 (HIUT)
12	A. glandulosum (A.gl4)	Gandoman, Chaharmahal and Bakhtiari, Iran	51°10′ 54″	31° 53′ 52″	2335	111 (HIUT)
13	A. glandulosum (A.gl5)	Borujen, Chaharmahal and Bakhtiari, Iran	51°13′ 1″	31° 54′ 11″	2394	112 (HIUT)
14	A. glandulosum (A.gl6)	Ardakan, Sepidan, Fars, Iran	51° 58′ 3″	30° 17′ 48″	2635	113 (HIUT)
15	A. glandulosum (A.gl7)	Chalehgah, Sepidan, Fars, Iran	51° 57′ 59″	30° 19′ 20″	2897	114 (HIUT)
16	A. glandulosum (A.gl8)	Chalehgah, Sepidan, Fars, Iran	51° 56′ 51″	30° 20′ 39″	2868	115 (HIUT)
17	A. glandulosum (A.gl9)	Bon Rud, Sepidan, Fars, Iran	51° 55′ 27″	30° 21′ 51″	2818	116 (HIUT)
18	A. glandulosum (A.gl10)	Sepidan, Fars, Iran	51° 51′ 49″	30° 18′ 03″	2512	117 (HIUT)
19	A. glandulosum (A.gl11)	Ardakan, Sepidan, Fars, Iran	51° 58′ 00″	30° 17′ 28″	2501	118 (HIUT)
20	A. glandulosum (A.gl12)	Kang to Zoshk road, Razavi Khorasan, Iran	59° 16′ 01″	36° 20′ 11″	1477	119 (HIUT)
21	A. glandulosum (A.gl13)	Dehbar, Razavi Khorasan, Iran	59° 20′ 11″	36° 17′ 12″	1505	120 (HIUT)
22	A. glandulosum (A.gl14)	Farizi village, Razavi Khorasan, Iran	59° 05′ 14″	36° 33′ 02″	1322	121 (HIUT)
23	A. glandulosum (A.gl15)	Amrudak, Razavi Khorasan, Iran	59° 19′ 49″	36° 52′ 29″	1325	122 (HIUT)
24	A. glandulosum (A.gl16)	Hesar, Razavi Khorasan, Iran	59° 28′ 10″	36° 42′ 04″	1475	123 (HIUT)
25	A. glandulosum (A.gl17)	Hesar, Razavi Khorasan, Iran	59° 28′ 10″	36° 42′ 04″	1355	124 (HIUT)
26	A. glandulosum (A.gl18)	Zoshk, Razavi Khorasan, Iran	59° 07′ 10″	36° 16′ 08″	2403	125 (HIUT)
27	A. glandulosum (A.gl19)	Binalud Heights, Razavi Khorasan, Iran	59° 07′ 14″	36° 16′ 08″	2235	126 (HIUT)
28	A. glandulosum (A.gl20)	Farizi Dareh Darmeh, Razavi Khorasan, Iran	58° 53′ 02″	36° 29′ 12″	1969	127 (HIUT)
29	A. glandulosum (A.gl21)	Kohgiluyeh and Boyer Ahmad, Iran	51° 48′ 32″	30° 26′ 3″	2507	128 (HIUT)
30	A. glandulosum (A.gl22)	Kohgiluyeh and Boyer Ahmad, Iran	51° 48′ 20″	30° 25′ 5″	2600	129 (HIUT)
31	A. heratense (A.he1)	Tar, Isfahan, Iran	51° 44′ 44″	33° 23′ 29″	2365	130 (HIUT)
32	A. korshinskyi (A.ko1)	Golbahar, Razavi Khorasan, Iran	59° 08′ 07″	36° 33′ 15″	1277	131 (HIUT)
33	A. korshinskyi (A.ko2)	Sadd-e Ţoroq, Razavi Khorasan, Iran	59° 32′ 11″	36° 08′ 11″	1316	132 (HIUT)
34	A. korshinskyi (A.ko3)	Robat Sefid, Razavi Khorasan, Iran	59° 24′ 03″	35° 48′ 11″	1677	133 (HIUT)
35	A. korshinskyi (A.ko4)	Robat Sefid, Razavi Khorasan, Iran	59° 24′ 03″	35° 48′ 11″	1477	134 (HIUT)
36	A. korshinskyi (A.ko5)	Golbahar, Razavi Khorasan, Iran	59° 08′ 13″	36° 33′ 17″	1266	135 (HIUT)
37	A. laxiusculum (A.la1)	Abchuiyeh, Nayin, Isfahan, Iran	52° 49′ 9″	32° 41′ 17″	2241	136 (HIUT)
38	A. microcephalum (A.mi1)	Qarneh, Isfahan, Iran	52° 2′ 43″	32° 24′ 30″	1769	137 (HIUT)
39	A. microcephalum (A.mi2)	Kesheh, Tar Road, Isfahan, Iran	51° 46′ 7″	33° 22′ 42″	2275	138 (HIUT)
40	A. microcephalum (A.mi3)	Kahrouye, Shahreza, Isfahan, Iran	51° 45′ 23″	31° 42′ 11″	2404	139 (HIUT)
41	A. microcephalum (A.mi4)	Bardkan, Semirom, Isfahan, Iran	51°35′ 45″	31° 29′ 18″	2608	140 (HIUT)
42	A. microcephalum (A.mi5)	Dezej, Dehaqan, Isfahan, Iran	51° 33′ 5″	31° 55′ 34″	2174	141 (HIUT)
43	A. microcephalum (A.mi6)	Kordesofla, Tiran and Karvan, Isfahan, Iran	50° 47′ 21″	32° 54′ 36″	2268	142 (HIUT)
44	A. microcephalum (A.mi7)	Goldare, Najaf Abad, Isfahan, Iran	50° 41′ 42″	33° 2′ 3″	2555	143 (HIUT)
45	A. microcephalum (A.mi8)	Seng- e Sefid, Khansar, Isfahan, Iran	50° 22′ 20″	33° 11′ 42″	2476	144 (HIUT)
46	A. microcephalum (A.mi9)	Borujen, Chaharmahal and Bakhtiari, Iran	51°15′ 26″	31° 56′ 41″	2269	145 (HIUT)
47	A. microcephalum (A.mi10)	Gandoman, Chaharmahal and Bakhtiari, Iran	51°10′ 54″	31° 53′ 52″	2335	146 (HIUT)
48	A. microcephalum (A.mi11)	Borujen, Chaharmahal and Bakhtiari, Iran	51°13′ 1″	31° 54′ 11″	2394	147 (HIUT)
49	A. microcephalum (A.mi12)	Sefidasht, Chaharmahal and Bakhtiari, Iran	51°12′ 13″	32° 05′ 8″	2180	148 (HIUT)
50	A. sordidum (A.so1)	Akbarabad, Nayin, Isfahan, Iran	52°49′ 49″	32° 41′ 48″	2341	149 (HIUT)
51	A. sordidum (A.so2)	Sereshk, Nayin, Isfahan, Iran	52° 53′ 14″	32° 40′ 25″	2298	150 (HIUT)
52	A. sordidum (A.so3)	Akbarabad, Nayin, Isfahan, Iran	52° 49′ 52″	32° 41′ 50″	2355	151 (HIUT)
53	A. sordidum (A.so4)	Mayan-e Sofla, Razavi Khorasan, Iran	59° 23′ 10″	36° 16′ 05″	1447	152 (HIUT)
54	A. sordidum (A.so5)	Dehbar, Razavi Khorasan, Iran	59° 20′ 11″	36° 17′ 12″	1505	153 (HIUT)
55	A. sordidum (A.so6)	Robat Sefid, Razavi Khorasan, Iran	59° 24′ 15″	35° 48′ 11″	1700	154 (HIUT)
56	A. sordidum (A.so7)	Zoshk, Razavi Khorasan, Iran	59° 17′ 01″	36° 20′ 14″	1423	155 (HIUT)
57	A. spinosum (A.sp1)	Qarneh, Isfahan, Iran	52° 2′ 40″	32° 24′ 34″	1709	156 (HIUT)
58	A. spinosum (A.sp2)	Tangleh, Shahreza, Isfahan, Iran	51° 48′ 30″	31° 48′ 3″	2082	157 (HIUT)
59	A. spinosum (A.sp3)	Kahrouye, Shahreza, Isfahan, Iran	51° 45′ 23″	31° 42′ 11″	2404	158 (HIUT)
60	A. spinosum (A.sp4)	Cheshme Sard, Semirom, Isfahan, Iran	51° 34′ 4″	31° 42′ 56″	2442	159 (HIUT)
61	A. spinosum (A.sp5)	Gonharan, Tiran and Karvan, Isfahan, Iran	50° 42′ 31″	32° 59′ 19″	2516	160 (HIUT)
62	A. spinosum (A.sp6)	Gonharan, Tiran and Karvan, Isfahan, Iran	50° 42′ 52″	33° 00′ 56″	2593	161 (HIUT)
63	A. squarrosum (A.sq1)	Tangleh, Shahreza, Isfahan, Iran	51° 48′ 46″	31° 47′ 22″	2116	162 (HIUT)
64	A. squarrosum (A.sq2)	Dezej, Dehaqan, Isfahan, Iran	51° 33′ 5″	31° 55′ 34″	2174	163 (HIUT)
65	A. squarrosum (A.sq3)	Kordesofla, Tiran and Karvan, Isfahan, Iran	50° 47′ 25″	32° 54′ 35″	2264	164 (HIUT)
66	A. squarrosum (A.sq4)	Goldare, Najaf Abad, Isfahan, Iran	50° 41′ 42″	33° 2′ 3″	2555	165 (HIUT)
67	A. squarrosum (A.sq5)	Khansar, Isfahan, Iran	50° 31′ 54″	33° 14′ 28″	2234	166 (HIUT)
68	A. squarrosum (A.sq6)	Laijand, Khansar, Isfahan, Iran	50° 24′ 57″	33° 15′ 22″	2330	167 (HIUT)

Fig. 1.

Geographic locations of collection sites of the studied ecotypes from different Acanthophyllum species in Iran

Fig. 2.

Aerial parts of (a) A. glandulosum Bunge ex Boiss from Fars, (b) A. microcephalum Boiss from Chaharmahal and Bakhtiari, (c) A. bracteatum Boiss from Isfahan (d) A. squarrosum Boiss from Isfahan (e) A. spinosum C.A.Mey from Isfahan (f) A. heratense Schiman-Czeika from Isfahan (g) A. sordidum Bunge ex Boiss from Razavi Khorasan (h) A. laxiusculum Schiman-Czeika from Isfahan (i) A. korshinskyi Schischk from Razavi Khorasan (j) A. diezianum Hand-Mazz (Adapted from Pirani et al. (2014) with permission)

Phytochemical traits assay

Methanolic extract preparation

Methanolic extraction was performed by adding 10 mL of acidic methanol (1:99 acetic acid: methanol; Merck, Germany) to 100 mg of dried and pulverized leaves from different samples [20]. The samples were kept at room temperature (24 ± 2 ^°C) for 24 h. The resulting mixtures were centrifuged at 4000 rpm for 20 min (Rotina 380, Hettich, Germany), and the supernatants were filtered using Whatman No. 1 filter paper (125 mm). The prepared extracts were stored at 4 ^°C for further analyses.

Total phenolics content assay

Total phenolics content (TPC) was assessed using the Folin-Ciocalteu method [20]. Briefly, 500 µL of each methanolic extract was mixed with 2.5 mL of 10% (w/v) Folin–Ciocalteu reagent (Sigma-Aldrich), followed by the addition of 2 mL of 7.5% (w/v) sodium carbonate (Na₂CO₃) after 5 min. The mixture was incubated in the dark at room temperature for 90 min. Absorbance was measured at 760 nm using a UV-VIS spectrophotometer (JASCO V-530, Japan). TPC was calculated based on a standard curve of gallic acid (GAE) and expressed as mg GAE per gram (g) of extract dry weight (mg GAE g^− 1 DW).

Total flavonoids and flavonols contents assay

Total flavonoids (TFD) content was determined according to Chang et al. [21], with minor modifications. For total flavonoids, 500 µL of methanolic extract was mixed with 100 µL of 10% AlCl₃, 100 µL of 1 M potassium acetate, and 2.5 mL of distilled water. After 30 min at room temperature, absorbance was measured using a UV-VIS spectrophotometer at 415 nm. For total flavonols assay [21], 1 mL of extract was combined with 2 mL of 2% AlCl₃ and 6 mL of 5% sodium acetate. After 150 min of incubation at room temperature, absorbance was recorded using a spectrometer (JASCO V-530, Japan) at 440 nm. Quantification for both TFD and TFL contents was based on a quercetin standard curve and results were expressed as mg quercetin equivalents (QE) per g of dry weight (mg QE g⁻¹DW).

Total tannin content in leaves (TTCL) assay

To extract tannins from leaves, 200 mg of dried leaf powder was suspended in 10 mL of acetone (70%) and centrifuged at 3000 rpm for 5 min. Total tannin content was estimated using insoluble polyvinylpolypyrrolidone (PVPP) [22]. For total phenolics, 100 µL of extract was mixed with 900 µL of distilled water, 500 µL of 1 M Folin-Ciocalteu reagent, and 2.5 mL of 20% sodium carbonate, then incubated for 35 min at room temperature before the absorbance was read at 725 nm using a UV- VIS spectrophotometer (JASCO V-530, Japan). For non-tannin phenolics: 1 mL of extract was mixed with 100 mg PVPP, vortexed, incubated at 4 ^°C for 10 min, and centrifuged at 3000 rpm for 10 min. The clear supernatant was analyzed as above. Tannin content was calculated as the difference between total and non-tannin phenolic content. A standard curve was prepared using tannic acid (0–1000 µg/mL). Total tannin content in leaves was expressed as mg tannic acid equivalent (TAE) per g of dry weight (mg TAE g^− 1 DW).

Total tannin content in roots (TTCR) assay

Tannin content in roots was determined using the vanillin–HCl method [23]. Briefly, 0.5 g of root powder was extracted with 10 mL of 100% methanol for 20 min at room temperature. Then, 1 mL of the supernatant was reacted with 5 mL of vanillin reagent (1:1 mixture of 1% vanillin in methanol and 8% HCl in methanol) for 20 min at 28 °C. Absorbance was measured at 500 nm through UV- VIS spectrophotometer (JASCO V-530, Japan).Tannin concentration was determined from a standard curve (0, 0.25, 0.5, 1, 1.5, 2 mg/mL) prepared with catechin (Merck, Com.). Total tannin content in roots was expressed as mg catechin equivalent (CE) per g of dry weight (mg CE g^− 1 DW).

Total saponin content (TSC) assay

Saponin extraction from roots was performed according to Crozier et al. [24]. One gram of dried root powder was extracted with 40 mL of methanol and 10 mL of 6 M HCl, then heated under reflux at 90 °C for 2 h. The mixture was filtered using Whatman filter paper. Saponin content was measured using the vanillin–sulfuric acid method [25]. Briefly, 100 µL of extract was mixed with 5 mL of 0.7% vanillin in 65% sulfuric acid and incubated at 60 ^°C for 1 h in a water bath. After cooling in an ice bath, absorbance was measured at 473 nm through UV- VIS spectrophotometer (JASCO V-530, Japan). Saponin content was expressed as mg saponin equivalents per gram dry weight, using saponin (Merck, Com.) as standard. The standard curve was calculated based on different concentrations of saponin (0, 50, 100, 200, 300, 400 µg/mL). Total saponin content was expressed as mg saponin equivalent (SE) per g of dry weight (mg SE g^− 1 DW).

Total polysaccharides (TPS) assay

Total polysaccharides were measured using the phenol-sulfuric acid method [26]. Root samples (0.11 g) were extracted with 15 mL of 80% ethanol and vortexed for 20 s. After centrifugation at 3000 rpm for 10 min, the supernatant was concentrated at 50 ^°C. The residue was dissolved in 40 mL of distilled water, followed by addition of 5 mL of 5% zinc sulfate and 4.7 mL of 3% barium hydroxide to remove impurities. After centrifugation (3000 rpm, 10 min), 2 mL of the extract was mixed with 1 mL of 5% phenol and 5 mL of 98% sulfuric acid. Absorbance was measured at 490 nm after 45 min. D-glucose (Merck, Com.) was used to construct the standard curve (0, 20, 40, 60, 80, 100 µg/mL). Total polysaccharides content was expressed as mg glucose equivalent (GE) per g of dry weight (mg GE g^− 1 DW).

Antioxidant activity assay

The antioxidant activity of the extracts was determined using the DPPH (2,2-diphenyl-1-picrylhydrazyl) radical-scavenging method [27]. For this assay, 1.5 mL of 0.1 mM DPPH and 3 ml of methanol were prepared as the control solution, and the absorbance was measured at 517 nm. To each sample, 3 ml of methanolic extract was added to 1.5 ml of 0.1 mM DPPH solution. The mixture was then incubated in the dark for 20 min, after which the absorbance at 517 nm was measured. The antioxidant activity (AA) (%) was calculated using the formula as [(A _control - A _sample) /A _control] × 100, where A _sample is the absorbance of the methanolic extract and A _control is the absorbance of the control reaction. Three number of three replicates was considered for all mentioned phytochemical traits including TPC, TFD, TFL, TTCL, TTCR, TSC, TPS and antioxidant activity.

Environmental factors assay

During plant sampling, soil samples were randomly collected from the top 0–20 cm layer at 32 distinct locations. The samples were subsequently transported to the Soil Laboratory at IUT for analysis. Prior to measurement, all samples were air-dried, crushed, and sieved through a 2 mm mesh. Standard analytical procedures were employed to assess a comprehensive set of physical and chemical soil properties. In addition, site-specific climatic variables-including mean annual temperature and precipitation-were obtained from the nearest meteorological station to each sampling site. The selection of stations was based on spatial proximity and representativeness. Following sample preparation, a comprehensive set of soil parameters were analyzed. These included the electrical conductivity (EC) of the saturation extract (Jenway 4310 conductivity meter, UK), expressed in deciSiemens per meter (dS/m) [28]. Total nitrogen (TN) content was determined using the Kjeldahl (UDK 120, Italy (digestion method [29], while available phosphorus (P) was measured following the Olsen extraction procedure [30]. Calcium carbonate (CaCO₃) was quantified via titration with sodium hydroxide [31]. Available potassium (K) was extracted using 1 N ammonium acetate [32] and measured accordingly. Finally, organic matter (OM) content was determined by titration with ammonium ferrous sulfate, as described by Carter and Gregorich [33]. Each soil sample was measured with three number of replications.

Statistical analysis

Before conducting then analysis of variance (ANOVA), assumptions of normality and homogeneity of variance were evaluated. Normality of residuals was assessed using Shapiro–Wilk’s normality test (P ≤ 0.05), while homogeneity of variance was tested using Levene’s F test across treatment groups. The experimental design was performed using a completely randomized design with three replications for all phytochemical traits and soil analysis in SAS software (version 9.3.1). Mean comparisons were conducted using the using the Duncan’s multiple range test at P < 0.01. Mean and standard error (SE) were calculated for each trait with corresponding P-values. The analyses, including heatmap, hierarchical clustering, and principal component analysis, were performed on standardized quantitative data to ensure comparability across variables using covariance matrix. Hierarchical cluster analysis based on Ward’s method and Euclidean distance coefficient was done to classify species and ecotypes. Heatmap, principal component analysis, and Pearson’s correlation were performed and visualized using R software (version 3.4.3) [34].

Results

Different phytochemical traits were evaluated across 10 Acanthophyllum species and 68 ecotypes (Table 2). Analysis of variance (ANOVA) indicated highly significant differences (P < 0.01) among all studied traits.

Table 2.

Analysis of variance for total phenolics content, total flavonoids, total flavonols, total tannin content of leave, total tannin content of roots, total saponin content, total polysaccharides and diphenyl-1-picrylhydrazyl activity in different species of Acanthophyllum

Source of variation			Mean square of studied traits
	df¥	TPC	TFD	TFL	TTCL	TTCR	TSC	TPS	DPPH
Species	9	148.52** (P < 0.001)	22.98** (P < 0.001)	4.38** (P < 0.001)	1292.43** (P < 0.01)	21045.3** (P < 0.001)	10735.7** (P < 0.001)	44.62** (P < 0.001)	239.23** (P < 0.001)
Species (ecotypes)	58	46.62** (P < 0.001)	6.07** (P < 0.001)	2.38** (P < 0.001)	1656.69** (P < 0.001)	2570.32** (P < 0.001)	3532.6** (P < 0.001)	18.86** (P < 0.001)	46.47** (P < 0.001)
Residual	136	0.83	0.06	0.11	9.08	0.01	159.2	0.001	1.04

^¥ Abbreviations: df degree of freedom, TPC total phenolics content, TFD total flavonoids, TFL total flavonols, TTCL total tannin content of leave, TTCR total tannin content of roots, TSC total saponin content, TPS total polysaccharides, DPPH 2, 2- diphenyl-1-picrylhydrazyl. The exact P-values for each trait is showed in parenthesis for each trait. ** significant at P < 0.01

Phytochemical traits among Acanthophyllum species

Mean comparisons revealed significant variation in all studied phytochemical traits among the Acanthophyllum species. The highest total phenolics content was observed in A. bracteatum (16.80 mg GAE g^− 1 DW), while the lowest was in A. laxiusculum (8.82 mg GAE g^− 1 DW) (Table 3). Total flavonoids content was highest in A. glandulosum (7.22 mg QE g^− 1 DW) and lowest in A. laxiusculum (3.32 mg QE g^− 1 DW). Total flavonol content ranged from 1.56 mg QE g^− 1 DW in A. diezianum to 3.75 mg QE g^− 1 DW in A. sordidum. The highest tannin content in roots was found in A. glandulosum (108.43 mg CE g^− 1 DW), whereas the lowest was in A. microcephalum (25.87 mg CE g^− 1 DW). Total tannins in leaves were highest in A. squarrosum (58.57 mg TAE g^− 1 DW) and lowest was observed in A. korshinskyi (29.46 mg TAE g^− 1 DW). Total polysaccharides content in roots ranged from 2.96 mg GE g^− 1 DW (A. microcephalum) to 9.8 mg GE g^− 1 DW (A. laxiusculum). Total saponin content in roots varied between 49.55 mg SE g^− 1 DW (A. microcephalum) and 124.8 mg SE g^− 1 DW (A. diezianum). Antioxidant activity, assessed via DPPH assay, ranged from 80.77% (A. laxiusculum) to 92.52% (A. diezianum) (Table 3).

Table 3.

Mean comparison for total phenolics content, total flavonoids, total flavonols, total tannin content of leave, total tannin content of roots, total saponin content, total polysaccharides and diphenyl-1-picrylhydrazyl activity in different Acanthophyllum species

	Mean of studied traits
Acanthophyllum species	TPC(mg GAE g−1 DW)	TFD(mg QE g−1 DW)		TFL(mg QE g−1 DW)	TTCL(mg TAE g−1 DW)	TTCR(mg CE g−1 DW)	TSC(mg SE g−1 DW)	TPS(mg GE g−1 DW)	DPPH(%)
A. bracteatum	1.29±16.80a	0.29±6.20c, d		0.28±3.64a	35.51d, e±6.70	81.17g±4.871	99.59bc±6.82	5.57f±0.60	82.98d±1.16
A. diezianum	0.14±9.99c, d	0.01±4.09f		0.10±1.56d	39.91b, c±1.90	101.37c±0.03	124.8a±17.77	5.67e±0.00	92.52a±0.80
A. glandulosum	0.53±16.31a	0.17±7.22a		0.11±3.66a	42.22b±2.74	108.43a±4.85	89.63 cd ±4.43	5.71d±0.40	88.82b±0.33
A. heratense	0.25±13.52b	0.10±5.87d		0.15±2.67c	34.41def±2.24	105.39b±0.04	88.68cd±5.16	7.00b±0.00	87.09c±0.10
A. korshinskyi	0.46±16.18a	0.38±6.55b		0.16±2.92b c	29.46g±2.80	90.95e±4.72	100.33b c±3.80	5.38g±0.17	91.92a±0.17
A. laxiusculum	0.59±8.82d	0.09±3.32g		0.08±1.86d	34.53def±2.47	59.51h±0.06	81.31d e±6.35	9.80a±0.00	80.77e±1.04
A. microcephalum	0.36±10.83c	0.21±6.05d		0.12±3.38ab	37.16c, d±4.20	25.87j±0.84	49.55f±6.86	2.96j±0.22	82.14d, e±0.86
A. sordidum	0.44±13.18b	0.23±6.41bc		0.21±3.75a	30.77f, g±3.21	47.99i±7.31	66.71e±9.40	3.32i±0.27	87.68b, c±0.85
A. spinosum	1.25±10.31c	0.45±4.19f		0.25±2.91b, c	32.48efg±4.80	92.26d±4.091	116.77ab±5.85	4.80h±0.36	83.37d±1.13
A. squarrosum	0.56±13.92b	0.14±5.05e		0.07±2.88c	58.57a±4.80	83.04f±3.89	111.40ab±5.76	6.95c±0.57	86.33c±0.65

Means followed by the same letter in each column are not significantly different at P <0.01 according to using the Duncan’s multiple range test

¥ Abbreviations: TPC total phenolics content, TFD total flavonoids, TFL total flavonols, TTCL total tannin content of leave, TTCR total tannin content of roots, TSC total saponin content, TPS total polysaccharides, DPPH 2 2- diphenyl-1-picrylhydrazyl

Phytochemical traits among Acanthophyllum ecotypes

Among the studied ecotypes, TPC ranged from 5.98 mg GAE g^− 1 DW in ecotype A.sp6 to 28.83 mg GAE g^− 1 DW in ecotype A.br3 (Table 4). The highest TFD and TFL contents ranged from 2.66 to 9.08 mg QE g^− 1 DW and 1.57 to 5.8 mg QE g^− 1 DW, respectively. The highest TFD and TFL were observed in ecotypes A.gl11 and A.gl4, while the lowest values were found in A.sp2 and A.di1 (Table 4). Total tannins in roots showed wide variation, ranging from 16.86 to 194.36 mg CE g^− 1 DW. The lowest total tannin value (16.86 mg CE g^− 1 DW) was reported in A.so2, while the highest value was observed at A.gl21 (194.36 mg CE g^− 1 DW). Total tannins in leaves ranged from 7.24 mg TAE g^− 1 DW (A.br5) to 94.97 mg TAE g^− 1 DW (A.sq2) (Table 4). Total polysaccharide content in roots ranged from 1.16 mg GE g^− 1 DW (A.gl14) to 15.14 mg GE g^− 1 DW (A.gl2) in roots (Table 4). Total saponin content in roots ranged from 8.05 mg SE g^− 1 DW (A.mi7) to 158.75 mg SE g^− 1 DW (A.gl17) (Table 4). Likewise, different antioxidant activities have been reported in Acanthophyllum species as 87-90.6% in A. glandulosum roots [10] and 60.56% in A. laxiusculum [5] by the same method, here, the DPPH values ranged from 72.79% (A.mi12) to 93.28% (A.br7) (Table 4) which indicated that all species of Acanthophyllum possess potent antioxidant activity.

Table 4.

Mean comparison of phytochemical traits for 68 different ecotypes of Acanthophyllum species

NO.		Traits mean
	Acanthophyllum ecotypes	TPC mg GAE g− 1 DW	TFD mg QE g− 1 DW	TFL mg QE g− 1 DW	TTCL mg TAE g− 1 DW	TTCR mg CE g− 1DW	TSC mg SE g− 1DW	TPS mg GE g− 1DW	DPPH (%)
1	A.br1	20.68 ± 0.21	7.05 ± 0.02	4.45 ± 0.27	25.3 ± 2.69	44.48 ± 0.09	92.08 ± 9.65	8.15 ± 0.01	81.08 ± 1.6
2	A.br2	15.6 ± 0.92	6.94 ± 0.05	5.28 ± 0.26	75.54 ± 1.01	98.99 ± 0.04	123.47 ± 4.08	4.8 ± 0.01	84.79 ± 0.64
3	A.br3	28.83 ± 0.43	7.33 ± 0.02	4.84 ± 0.21	87.53 ± 2.96	96.46 ± 0.09	112.1 ± 15.15	9.39 ± 0.01	84.46 ± 0.67
4	A.br4	13.37 ± 0.19	6.68 ± 0.13	3.13 ± 0.11	13.05 ± 1.56	62.83 ± 0.07	116.66 ± 21.54	5.31 ± 0.001	78.72 ± 2.2
5	A.br5	11.86 ± 0.37	3.63 ± 0.1	2.24 ± 0.01	7.24 ± 0.43	74.36 ± 0.03	43.2 ± 0.15	2.46 ± 0.001	76.98 ± 0.82
6	A.br6	16.4 ± 0.71	7.06 ± 0.09	3.94 ± 0.05	24.81 ± 0.76	77.83 ± 0.07	96.22 ± 2.06	1.61 ± 0.001	81.59 ± 0.51
7	A.br7	10.93 ± 0.51	4.73 ± 0.01	1.64 ± 0.03	15.11 ± 1.46	113.27 ± 0.09	113.4 ± 15.13	7.34 ± 0.01	93.28 ± 0.04
8	A.di1	9.99 ± 0.14	4.1 ± 0.01	1.57 ± 0.1	39.91 ± 1.9	101.38 ± 0.03	124.81 ± 17.77	5.67 ± 0.001	92.53 ± 0.81
9	A.gl1	19.69 ± 1.3	6.67 ± 0.06	4.73 ± 0.13	38.6 ± 1.01	96.87 ± 0.06	54.85 ± 1.81	5.61 ± 0.001	92.58 ± 0.17
10	A.gl2	15.42 ± 1.2	3.81 ± 0.09	3.75 ± 0.25	13.62 ± 0.43	69.64 ± 0.05	51.13 ± 0.03	15.14 ± 0.01	88.48 ± 0.11
11	A.gl3	11.73 ± 0.48	5.09 ± 0.24	2.76 ± 0.1	8.61 ± 0.45	118.97 ± 0.04	115.38 ± 21.45	3.38 ± 0.001	85.09 ± 0.72
12	A.gl4	15.1 ± 0.83	7.72 ± 0.01	5.8 ± 0.001	50.28 ± 1.9	80.99 ± 0.09	18.38 ± 0.13	6.13 ± 0.001	84.68 ± 0.06
13	A.gl5	10.06 ± 0.001	6.19 ± 0.16	3.58 ± 0.06	49.77 ± 0.93	149.6 ± 0.09	148.26 ± 5.58	5.13 ± 0.001	80.05 ± 0.07
14	A.gl6	12.37 ± 0.01	8.19 ± 0.08	3.65 ± 0.09	93.77 ± 2.78	109.52 ± 0.04	87.61 ± 0.32	10.63 ± 0.001	89.04 ± 0.11
15	A.gl7	13.65 ± 0.6	8.59 ± 0.13	4.16 ± 0.29	15.37 ± 0.59	69.85 ± 0.06	81.74 ± 0.6	8.52 ± 0.001	92.71 ± 0.1
16	A.gl8	15.22 ± 0.001	8.05 ± 0.001	4.16 ± 0.03	80.05 ± 2.96	153.17 ± 0.04	92.6 ± 1.93	4.62 ± 0.001	88.43 ± 0.25
17	A.gl9	14.81 ± 1.05	8.56 ± 0.26	4 ± 0.09	34.38 ± 2.31	50.34 ± 0.05	91.15 ± 6.31	3.79 ± 0.001	88.84 ± 0.46
18	A.gl10	13.1 ± 0.42	7.97 ± 0.08	2.71 ± 0.27	34.43 ± 2.15	121.28 ± 0.03	90.65 ± 2.27	6.24 ± 0.01	89.61 ± 0.05
19	A.gl11	16.33 ± 0.001	9.08 ± 0.26	4.54 ± 0.05	59.74 ± 1.2	163.27 ± 0.19	107.15 ± 2.49	3.92 ± 0.01	91.1 ± 0.06
20	A.gl12	15.46 ± 0.99	7.75 ± 0.19	3.87 ± 0.31	46.66 ± 1.75	116.28 ± 0.2	104.78 ± 2.44	5.35 ± 0.001	88.34 ± 0.19
21	A.gl13	26.32 ± 0.04	7.39 ± 0.13	2.81 ± 0.18	21.96 ± 0.66	129.12 ± 0.03	90.82 ± 2.17	5.89 ± 0.001	91.24 ± 0.21
22	A.gl14	15.03 ± 0.81	3.83 ± 0.001	2.76 ± 0.1	23.81 ± 0.39	111.82 ± 0.05	100.81 ± 17.23	1.16 ± 0.001	89.67 ± 0.13
23	A.gl15	22.6 ± 0.61	7.99 ± 0.22	3.83 ± 0.44	77.68 ± 3.29	51.23 ± 0.1	143.2 ± 9.66	12.29 ± 0.01	89.51 ± 0.01
24	A.gl16	17.76 ± 0.31	6.89 ± 0.07	2.75 ± 0.01	37.66 ± 2.13	30.1 ± 0.05	19.36 ± 0.29	3.07 ± 0.01	88.94 ± 0.01
25	A.gl17	15.54 ± 0.33	5.94 ± 0.14	2.57 ± 0.11	54.02 ± 3.62	106.65 ± 0.09	158.75 ± 6.49	2.76 ± 0.01	89.77 ± 0.02
26	A.gl18	17.14 ± 0.05	7.88 ± 0.08	2.2 ± 0.46	53.69 ± 2.17	103.8 ± 0.06	95.28 ± 7.26	2.5 ± 0.001	89.5 ± 0.08
27	A.gl19	20.42 ± 1.78	8.33 ± 0.29	4.06 ± 0.54	54.03 ± 0.85	128.43 ± 0.09	71.77 ± 0.82	6.07 ± 0.01	90.43 ± 0.15
28	A.gl20	13.86 ± 0.001	8.22 ± 0.31	4.58 ± 0.05	33.71 ± 1.8	85.83 ± 0.09	67.86 ± 0.31	3.66 ± 0.001	89.25 ± 0.05
29	A.gl21	25.64 ± 0.51	6.71 ± 0.03	3.96 ± 0.03	17 ± 1.53	194.36 ± 0.05	93.09 ± 0.15	5.99 ± 0.001	89.14 ± 0.32
30	A.gl22	11.56 ± 0.001	8.05 ± 0.001	3.45 ± 0.001	30.22 ± 0.52	144.43 ± 0.05	87.34 ± 0.67	3.8 ± 0.001	87.78 ± 0.1
31	A.he1	13.53 ± 0.25	5.88 ± 0.11	2.67 ± 0.16	34.41 ± 2.24	105.39 ± 0.04	88.69 ± 5.17	7 ± 0.001	87.1 ± 0.11
32	A.ko1	17.76 ± 0.16	8.82 ± 0.45	3.75 ± 0.32	21.29 ± 0.87	113.64 ± 0.07	111.53 ± 10.31	5.01 ± 0.02	91.97 ± 0.01
33	A.ko2	14.15 ± 0.76	4.4 ± 0.01	2.39 ± 0.15	24.18 ± 0.91	98.19 ± 0.03	95.72 ± 6.49	6.36 ± 0.001	92.15 ± 0.07
34	A.ko3	18.41 ± 0.02	6.08 ± 0.03	2.56 ± 0.21	27.4 ± 1.49	77.4 ± 0.08	99.29 ± 2.49	5.9 ± 0.01	92.31 ± 0.06
35	A.ko4	15.49 ± 0.64	6.97 ± 0.03	2.47 ± 0.02	24.84 ± 2.68	101.15 ± 0.14	107.36 ± 12.19	5.15 ± 0.01	92.52 ± 0.14
36	A.ko5	15.1 ± 0.1	6.53 ± 0.09	3.46 ± 0.15	49.63 ± 0.87	64.41 ± 0.07	87.78 ± 5.08	4.52 ± 0.001	90.68 ± 0.18
37	A.la1	8.82 ± 0.59	3.33 ± 0.1	1.86 ± 0.08	34.54 ± 2.48	59.52 ± 0.07	81.32 ± 6.35	9.8 ± 0.01	80.77 ± 1.05
38	A.mi1	10.85 ± 0.29	5.65 ± 0.22	3.03 ± 0.01	35.18 ± 1.15	19.18 ± 0.04	13.82 ± 0.13	5.21 ± 0.001	89.59 ± 0.2
39	A.mi2	6.16 ± 0.001	2.89 ± 0.1	2.79 ± 0.001	9.18 ± 1.2	19.6 ± 0.03	34.5 ± 0.11	2.89 ± 0.001	86.63 ± 0.01
40	A.mi3	13.94 ± 0.27	6.69 ± 0.09	3.86 ± 0.1	85.51 ± 2.9	32.5 ± 0.07	100.1 ± 0.78	4.67 ± 0.01	84.22 ± 0.21
41	A.mi4	13.13 ± 0.01	7.12 ± 0.07	5.39 ± 0.03	72.95 ± 0.39	26.52 ± 0.04	22.48 ± 0.01	3.06 ± 0.01	74.02 ± 0.74
42	A.mi5	9.97 ± 0.5	6.14 ± 0.22	3.73 ± 0.15	52.35 ± 2.01	31.25 ± 0.05	113.17 ± 3.21	1.3 ± 0.001	84.94 ± 0.49
43	A.mi6	9.67 ± 0.19	6.69 ± 0.02	2.71 ± 0.07	17.11 ± 1.77	22.42 ± 0.05	113.74 ± 0.08	1.53 ± 0.001	79.79 ± 0.65
44	A.mi7	10.29 ± 0.03	6.26 ± 0.03	3.5 ± 0.001	23.97 ± 1.28	23.26 ± 0.06	8.05 ± 0.18	2.52 ± 0.01	82.19 ± 0.19
45	A.mi8	11.69 ± 0.001	6.82 ± 0.18	3.65 ± 0.2	27.76 ± 0.19	23.04 ± 0.06	19.72 ± 0.24	1.74 ± 0.001	87.25 ± 0.14
46	A.mi9	14.18 ± 0.3	7.52 ± 0.2	3.21 ± 0.11	13.98 ± 1.46	30.48 ± 0.06	15.25 ± 0.13	1.62 ± 0.001	77.09 ± 0.56
47	A.mi10	9.33 ± 0.78	4 ± 0.21	2.68 ± 0.001	32.51 ± 0.7	20.12 ± 0.05	15.16 ± 0.09	2.27 ± 0.001	81.78 ± 0.54
48	A.mi11	10.2 ± 0.001	6.1 ± 0.14	3.28 ± 0.2	65.4 ± 0.14	29.18 ± 0.11	46.18 ± 0.23	4.67 ± 0.01	85.39 ± 1.12
49	A.mi12	10.57 ± 0.16	6.74 ± 0.16	2.8 ± 0.1	10.06 ± 1.05	32.93 ± 0.02	92.49 ± 1.03	4.13 ± 0.001	72.79 ± 0.17
50	A.so1	13.62 ± 0.001	6.96 ± 0.26	5.17 ± 0.09	34.75 ± 2.14	51.2 ± 0.08	135.53 ± 11.14	2.45 ± 0.01	84.55 ± 0.49
51	A.so2	9.63 ± 0.001	5.14 ± 0.17	3.87 ± 0.23	14.2 ± 0.21	16.86 ± 0.08	12.92 ± 0.19	2.19 ± 0.01	83.13 ± 0.38
52	A.so3	13.16 ± 0.48	7.03 ± 0.11	2.75 ± 0.04	33.42 ± 2.74	124.29 ± 0.09	90.64 ± 7.85	5.52 ± 0.01	82.7 ± 1.27
53	A.so4	13.88 ± 0.16	5.65 ± 0.01	2.89 ± 0.18	14.54 ± 2.08	43.04 ± 0.07	69.45 ± 2.19	3.83 ± 0.01	91.15 ± 0.07
54	A.so5	15.09 ± 0.25	6.79 ± 0.03	3.76 ± 0.4	28.18 ± 0.11	38.85 ± 0.05	66.27 ± 0.56	1.77 ± 0.01	91.71 ± 0.1
55	A.so6	11.25 ± 0.07	5.23 ± 0.001	3.26 ± 0.34	30.67 ± 1.21	29.58 ± 0.12	84.14 ± 4.53	3.06 ± 0.001	90.98 ± 0.25
56	A.so7	15.7 ± 0.31	8.07 ± 0.16	4.61 ± 0.63	59.69 ± 1.39	32.17 ± 0.05	8.07 ± 0.22	4.45 ± 0.01	89.56 ± 0.16
57	A.sp1	20.4 ± 0.3	6.49 ± 0.04	4.46 ± 0.02	76.35 ± 0.34	119.53 ± 0.09	126.24 ± 5.47	2.82 ± 0.001	91.04 ± 0.12
58	A.sp2	6.13 ± 0.5	2.66 ± 0.04	1.86 ± 0.18	21.58 ± 0.66	98.86 ± 0.05	116.28 ± 2.38	3.72 ± 0.001	79.06 ± 0.45
59	A.sp3	7.68 ± 0.32	2.77 ± 0.13	2.3 ± 0.17	22.06 ± 1.32	80.15 ± 0.02	91.07 ± 3.27	6.2 ± 0.001	80.87 ± 1.63
60	A.sp4	8.37 ± 0.32	3.31 ± 0.18	2.38 ± 0.02	25.13 ± 2.39	80.5 ± 0.07	129.43 ± 14.94	3.56 ± 0.001	88.17 ± 0.87
61	A.sp5	13.32 ± 0.22	7.16 ± 0.04	4.21 ± 0.13	24.07 ± 1.72	70.17 ± 0.03	88.59 ± 5.31	6.72 ± 0.001	81.04 ± 0.1
62	A.sp6	5.98 ± 0.57	2.77 ± 0.15	2.28 ± 0.02	25.72 ± 2.63	104.4 ± 0.07	149.04 ± 8.82	5.8 ± 0.001	80.06 ± 0.49
63	A.sq1	17.41 ± 0.01	4.83 ± 0.11	3.06 ± 0.03	43.79 ± 0.72	103.34 ± 0.04	93.07 ± 0.98	4.5 ± 0.001	87.77 ± 0.48
64	A.sq2	16.39 ± 0.15	4.98 ± 0.08	2.55 ± 0.18	94.97 ± 1.38	60.2 ± 0.05	92.18 ± 2.86	6.66 ± 0.001	89.72 ± 0.71
65	A.sq3	13.28 ± 0.2	4.64 ± 0.04	2.98 ± 0.13	58.92 ± 2.09	66.49 ± 0.05	124.55 ± 4.08	5.9 ± 0.01	82.72 ± 0.31
66	A.sq4	12.93 ± 0.78	6.11 ± 0.03	2.81 ± 0.21	85.93 ± 2.57	98.97 ± 0.1	152.01 ± 1.6	5.94 ± 0.001	83.25 ± 1.17
67	A.sq5	11.24 ± 0.38	4.31 ± 0.01	2.64 ± 0.17	34.47 ± 1.98	90.94 ± 0.06	115.63 ± 11.23	11.96 ± 0.001	86.38 ± 0.34
68	A.sq6	12.33 ± 0.87	5.48 ± 0.08	3.28 ± 0.04	33.39 ± 0.66	78.32 ± 0.05	90.96 ± 7.87	6.76 ± 0.001	88.17 ± 0.14
Total mean	-	14.08 ± 0.32	6.2 ± 0.12	3.36 ± 0.07	38.85 ± 1.62	79.02 ± 2.86	86.71 ± 2.79	5.05 ± 0.19	86.37 ± 0.35
Max		28.83 ± 0.43	9.08 ± 0.26	5.8 ± 0.001	94.97 ± 1.38	194.36 ± 0.05	158.75 ± 6.49	15.14 ± 0.01	93.28 ± 0.04
Min		5.98 ± 0.57	2.66 ± 0.04	1.57 ± 0.1	7.24 ± 0.43	16.86 ± 0.08	8.05 ± 0.18	1.16 ± 0.001	72.79 ± 0.17

The data are presented as mean ± SE (replication = 3). Refer to Table 1 for full name of Acanthophyllum ecotypes

Abbreviations: TPC total phenolics content, TFD total flavonoids, TFL total flavonols, TTCL total tannin content of leave, TTCR total tannin content of roots, TSC total Saponin content, TPS total Polysaccharide, DPPH 2 2- diphenyl-1-picrylhydrazyl

Correlation among phytochemical traits

Significant positive correlations were found between TPC and TFD (r = 0.53^**), TPC and TFL (r = 0.42^**), TPC and DPPH (r = 0.37^**), TPC and TTCR (r = 0.34^**), TPC and TTCL (r = 0.30^*) (Fig.3). Additionally, TFD and TFL (r = 0.62^**), TFD and TTCL (r = 0.32^**), TFL and TTCL (r = 0.36^**). Furthermore, antioxidant activity (DPPH) showed significant and positive correlation with TTCR (r = 0.31^*), and TPC (r = 0.37^*) (Fig.3).

Fig. 3.

Simple correlation coefficients among phytochemical traits obtained from 68 ecotypes of different Acanthophyllum species. Abbreviations TPC: total phenolics content; TFD: total flavonoids, TFL: total flavonols, TTCL: total tannin content of leave, TTCR: total tannin content of roots, TSC: total saponin content; TPS: total polysaccharide, DPPH: 2, 2- diphenyl-1-picrylhydrazyl. In this correlation figure (legend), red color indicates negative correlations while blue color indicates positive correlations. The intensity of the color reflects the strength of the correlation, with darker shades representing stronger correlation. Correlations with an absolute value of r≥0.24 were considered statistically significant at P < 0.05, while correlations with r ≥ 0.31were considered significant at P < 0.01

Principal component analysis of Acanthophyllum species and ecotypes

Principle component analysis (PCA) is particularly effective for understanding and interpreting data sets with high correlations among the studied variables [35]. The percentage of variance explained by each component is indicated on the corresponding axis (PC). Points located close to each other indicate similar species, while those farther apart indicate greater dissimilarity. Here, PCA was performed to identify the major patters of variation among the studied Acanthophyllum species and ecotypes. According to phytochemical traits, PCA, revealed considerable distances that reflect genetic differentiation among Acanthophyllum species. The first two principal components (PC1 and PC2) accounted for 69.26% of total variance, highlighting a strong and well-defined phytochemical differentiation among the Acanthophyllum species by the PC1 and PC2 (Fig. 4a). PC1 (38.9%) was negatively correlated with TPS (-0.40) and positively correlated with TFL (0.52), while PC2 (30.4%) correlated positively with TTCR (0.55). Additional PCs (PC3-PC6) explained 30.7% of variance (Table S1). Based on the PCA biplot, the species clustered into three distinct groups: (I) A. squarrosum, A. spinosum, A. laxiusculum, and A. diezianum with highest TPS, TSC, DPPH, TTCL, and TTCR; (II) A. microcephalum, A. sordidum, with relatively lower values for TPS, TSC, TPC, TFD, TFL, DPPH, TTCL and TTCR; (III) A. bracteatum, A. glandulosum, A. korshinskyi and A. heratense, with highest TPC, TFD and TFL (Fig. 4a).

Fig. 4.

Principal Component Analysis to classify phytochemical traits in different species (a) and ecotypes (b) of Acanthophyllum. In the PCA plot, each point represents a sample or ecotype. The horizontal axis (Dim1) and vertical axis (Dim2) correspond to the first and second principal components, respectively, which explain the largest portions of variance in the dataset. Refer to Table 1 for full name of Acanthophyllum species and ecotypes

PCA of studied ecotypes revealed significant differentiation based on phytochemical traits (Fig. 4b), with the first two principal components (PC1 and PC2) accounting for 54.2% of the total variance (Fig. 4b) indicating that these two axes capture only a moderate portion of total phytochemical differences among the studied ecotypes. Specifically, PC1 (32.0%) was positively correlated with TPC (0.50) and weakly with TSC (0.15), while PC2 (22.2%) showed a strong negative correlation with TSC (-0.55) and a positive correlation with TFL (0.47). Additional PCs (PC3-PC6) explained an additional 45.8% of the variance (supplementary Table 2) which implied that a significant portion of variation residues in PC3-PC6 components. The PCA biplot clearly separated the ecotypes into three distinct group (Fig. 4b), each group displayed specific phytochemical profiles, likely reflecting both genetic relatedness and environmental adaptation. Group I, included ecotypes: A.gl21, A.gl13, A.gl6, A.gl11, A.gl8, A.sp1, A.gl12, A.ko1, A.gl22, A.gl18, A.gl10, A.sq2, A.gl17, A.gl5, A.sq4, A.ko3, A.ko4, A.sq1, A.so3. These ecotypes were characterized by elevated antioxidant activity (DPPH assay), TPS, TSC, and TTCR, particularly with A.gl15 attaining the highest values in these traits. Group II comprised: A. mi1, A. mi2, A. mi4, A. mi5, A. mi6, A. mi7, A. mi8, A. mi9, A. mi10, A. mi11, A. mi12, A. so2, A. so3, A. so4, A. so5, A. so6, A. br4, A. br5, A. br6, A. br7, A. sp2, A. sp3, A. sp4, A. sp5, A. sp6, A. gl2, A. gl3, A. gl14, A. gl16, A. di1, A. la1, A. sq3, A. sq5, A. sq6, A. ko2, A. he1, that had relatively lower values for TPS, TSC, TPC, TFD, TFL, DPPH, TTCL and TTCR. Group III included: A.gl19, A.br2, A.gl1, A.gl7, A.ko5, A.gl4, A.so7, A.gl20, A.so1, A.mi3, A.br1, A.gl9. These ecotypes exhibited markedly high levels of TPC, TFD, TFL, and TTCL, with A.br3 showing the highest values in these traits (Fig. 4b). The PCA biplot of ecotypes showed that TPC, TFD, TTCL, and TFL clustered in the similar quadrant, indicating strong interrelationships among these traits, while TPS, TSC, TTCR, and DPPH grouping reflected a distinct metabolic profile.

Cluster analysis of Acanthophyllum species and ecotypes

For the ten Acanthophyllum species, cluster analysis using Ward’s method segregated them into three groups and the cophenetic correlation coefficient for the dendrogram was 0.68. The grouping of the species showed consistency with PCA findings (Fig. 5a). Group I (red color), A. laxiusculum, A. squarrosum, A. diezianum, and A. spinosum (~ 40%), was characterized by high levels of TPS, TSC, DPPH, TTCL and TTCR, but lower levels of TPC, TFD and TFL. Group II (green color), the smallest group (~ 20%), included A. microcephalum and A. sordidum, exhibited consistently lower values for TPS, TSC, TPC, TFD, TFL, DPPH, TTCL and TTCR, indicative of distinct SMs constraints possibly linked to specific ecological niches. Group III (blue color), encompassing A. heratense, A. korshinskyi, A. bracteatum, and A. glandulosum (~ 40%), displayed elevated levels of TPC, TFD, and TFL, reflecting a strong antioxidant profile suited to environments with higher oxidative stress.

Fig. 5.

Dendrogram of Acanthophyllum species (a) and Acanthophyllum ecotypes (b) using clustering method based on different phytochemical traits. The ecotypes abbreviation is presented according to Table 1

Cluster analysis based on phytochemical traits classified 68 ecotypes of Acanthophyllum into three groups (r _cophenetic= 0.64) which showed high similarity with findings from PCA analysis. Group I (red, 26 ecotypes, 38.26%) included ecotypes primarily from Razavi Khorasan (6), Isfahan (13), and Chaharmahal and Bakhtiari (7), characterized by high TPC, TFD, TFL, and TTCL (Fig. 5b). Group II (green, 20 ecotypes, 29.41%) comprised ecotypes from Isfahan (7), Razavi Khorasan (6), Fars (5), and one each from Chaharmahal and Bakhtiari and Kohgiluyeh and Boyer Ahmad provinces, characterized by elevated TPS, TSC, DPPH, and TTCR, suggesting adaptations favoring carbohydrate and saponin accumulation. Cluster III (blue, 22 ecotypes, 32.35%) included ecotypes from Isfahan (11), Razavi Khorasan (8), Chaharmahal and Bakhtiari (7), and single ecotypes from Kohgiluyeh and Boyer Ahmad and Fars, with relatively lower values for most phytochemical traits, indicating potential genetic or environmental limitations.

PCA for Acanthophyllum species based on environmental factors

For evaluation of the relation between different Acanthophyllum species with the environmental factors, the PCA was performed. The PCA analysis based on environmental variables showed that PC1 and PC2 accounted for 67.7% of total variance, indicates that the most phytochemical diversity among the ten species is represented along these two axes (Fig. 6). PC1 (49.3%) correlated negatively with latitude (-0.40) and positively with elevation (0.39), while PC2 (18.4%) correlated negatively with CaCO₃ (-0.57) and positively with TN (0.67). Remaining PCs explained 32.3% variance (Table supplementary 3). The classification of Acanthophyllum species revealed two ecologically distinct groups with contrasting environmental and soil-related traits. Group I, comprising A. laxiusculum, A. squarrosum, A. spinosum, A. heratense, A. bracteatum, and A. microcephalum, was characterized by the highest levels of TN, CaCO₃, P, K, height, and temperature, along with lower values of EC, OM, rain, latitude, and longitude. These trends indicate adaptation to arid, nutrient-poor, alkaline soils, typically found in higher elevation and interior continental regions. In contrast, group II, including A. diezianum, A. sordidum, A. glandulosum, and A. korshinskyi, exhibited significantly higher values of EC, OM, precipitation, and latitude, coupled with lower TN, CaCO₃, P, K, temperature, and height. This pattern reflects a tendency toward humid, lowland ecosystems with higher soil fertility, greater organic input, and mesic climatic conditions (Fig. 6).

Fig. 6.

Principal component analysis for classification of environmental factors in different species of Acanthophyllum based on dimensions 1 and 2. In the PCA plot, each point represents a sample or ecotype. The horizontal axis (Dim1) and vertical axis (Dim2) correspond to the first and second principal components (PC), respectively, which explain the largest portions of variance in the dataset. Abbreviations: Rain: Rainfall, Height: elevation, K: potassium, P: phosphorus, TN: total nitrogen, OM: organic matter, CaCo3: calcium carbonate, EC: electrical conductivity

Heat map analysis

A heat map analysis was performed to explain environmental clustering patterns among Acanthophyllum species based on eleven key edaphic and climatic variables (Fig. 7). The analysis revealed two distinct groups for Acanthophyllum species including I (A. diezianum, A. sordidum, and A. glandulosum, A. korshinskyi and II (A. laxiusculum and A. squarrosum, A. heratense and A. spinosum, A. bracteatum, A. microcephalum) based on environmental and edaphic features. Notably, A. sordidum was associated with geographical locations with higher values for longitude, OM, and EC, while A. diezianum showed more consistency with locations by elevated longitude and latitude. A. heratense was associated with locations presenting higher elevation, temperature and CaCO₃ content in soil, whereas A. glandulosum corresponded to higher annual rainfall. A. spinosum species corresponded with higher annual temperature, and P content in soil. A. squarrosum grew in higher temperature and higher potassium content in soil. A. laxiusculum characterized by higher temperature and elevated total nitrogen content.

Fig. 7.

Heat-map graph showing the clustering of ten Acanthophyllum species based on climatic data and soil properties. Abbreviations: Rain: Rainfall, TEMP: temperature; Height: elevation, K: potassium, P: phosphorus, TN: total nitrogen, OM: organic matter, CaCo3: calcium carbonate, EC: electrical conductivity. The color scale ( -2 to +2 )represents the strength and direction of the correlation coefficients. Red shades indicate strong positive correlations, blue shades indicate strong negative correlations, while yellow to light- green colors represents weak or moderate associations

Discussion

Phytochemical traits variation

Genetic diversity serves as a basis for the conservation and sustainable utilization of medicinal plant genetic resources, forming the basis for their adaptive capacity and phytochemical enhancement programs [36, 37]. The genetic diversity of domesticated medicinal plants has been stabilized due to the use of limited genetic bases in breeding programs, and the diversity of native medicinal plants presents a decreasing trend [38, 39]. Interspecific variation for phytochemical traits proposes profound insights into plant phylogenetic relationships and ecological adaptations [40]. The genetic diversity evaluation and broadening the genetic base of Acanthophyllum is necessitated for exploiting available genetic resources, which are needed for breeding programs and germplasm conservation. Despite different applications of Acanthophyllum species no comprehensive study has yet been conducted to evaluate the genetic diversity of Acanthophyllum species based on phytochemical traits. This study revealed significant interspecific variations based on key phytochemical traits in Acanthophyllum genus that reflect different adaptive responses of different Acanthophyllum species to various environmental conditions similar to other plant species as Himantoglossum Spreng [40] Secropia [41] and willow herb [42].

The highest levels of total phenolics, total flavonoids, total flavonols, total tannin of leaves, total tannin content of roots, total polysaccharides, total saponins and DPPH scavenging activity were observed in A. bracteatum, A. diezianum, A. glandulosum, A. heratense, A. korshinskyi, A. laxiusculum, A. microcephalum, A. sordidum, A. spinosum, and A. squarrosum, respectively (Table 3). This species variation could be attributed to several factors, including different genomic backgrounds of the studied species and the significant effects of environmental stimuli such as ecological differences, which have a strong influence on the variation of SMs between different species within a genus [39, 40, 43].

Wild species growing in natural habitats often synthesize SMs more effectively under environmental stress conditions, reflecting ecological adaptation [36, 39]. Here, identified characterization of soil properties, combined with geographic and climatic data, provides a fundamental understanding of the edaphic factors shaping the ecological distribution and phytochemical profiles of Acanthophyllum species. The findings offer valuable visions into the adaptive strategies of Acanthophyllum species and their potential for SMs accumulation in response to habitat conditions.

Phenolic compounds as the most abundant SMs of the medicinal plants, serve as crucial antioxidants, which are responsible for scavenging reactive oxygen species (ROS) generated by various environmental stressors [44]. Here, the highest TPC was reported in A. bracteatum (16.80 mg GAE g^− 1 DW) species. Accordingly, different values for TPC are reported as 4.5 mg GAE g^− 1 DW in A. laxiusculum [5] and 63.85 mg GAE g^− 1 DW in A. glandulosum [45]. In this regard, the highest level for TPC in A. bracteatum species could be due to the enhancing effects of soil (organic matter and phosphorus availability) and edaphic condition (longitude, latitude and height) on the biosynthesis pathways of phenolic compounds as phenylalanine ammonia lyase pathway [46] similar to findings in Echinacea purpurea L [47]., Sage [48] and some Caryophyllaceae family [46].

Flavonoids, as the most abundant polyphenols, exhibit antioxidant, hepatoprotective, antimicrobial, anti-inflammatory, and anti-carcinogenic effects [49]. Despite a few investigations on Acanthophyllum species as A. glandulosum, the evaluation of total flavonoids and flavonols within this genus remains incomplete. The highest TFD (7.22 mg QE g^− 1 DW) and TFL (3.75 mg QE g^− 1 DW) was reported in species of A. glandulosum and A. sordidum, respectively. The highest content of total flavonoids in A. glandulosum appears to result from geographical growing locations with greater latitudinal position and increased rainfall, which may enhance flavonoid biosynthesis through the increase in biosynthesis pathways of flavonoids as chalcone synthetase. In consistence with this finding, these habitats (increased UV exposure and cooler growing seasons) have been shown to enhance TFD production in some medicinal plants [50–52]. The increased content of soil organic matter could act as an elicitor that increases the enzymes involved in flavonols biosynthesis as flavonol synthase (FLS), which results in enhanced value for TFL [53], similar to A. glandulosum and A. sordidum in this study.

Tannins as a high molecular weight group in SMs, exhibit astringent properties as wound healing, antifungal, antibacterial, and antiviral activities due to their antioxidant potential [54]. Notably, a comprehensive review revealed a lack of quantifying total tannin content in Acanthophyllum. The highest TTCR (108.43 mg CE g^− 1 DW) and TTCL (57.5 mg TAE g^− 1 DW) was recorded in A. glandolusum and A. squarrosum species, respectively. Consistent with previous reports in Drypis spinosa L [55]., the content of tannin was higher in roots compared to leaves in Acanthophyllum species. According to this finding, lower temperatures, lower nitrogen content of soil and greater latitudinal position are considered as potent inducer of tannin-related compounds leaves in A. glandulosum species similar to findings in other species as Casuarina equisetifolia L [56]. and Cymbopogon citratus (DC.) Stapf [52]. But it has been noted the tannin content is roots and roots are affected by different climatic and soil factors [52].

Extracted polysaccharides from Acanthophyllum roots contribute to therapeutic effects as anti-tumor, immunomodulatory, and antioxidant functions [9]. Different values for TPS were reported as 110 mg GE g^− 1 DW in A. bracteatum roots [57] and 74 mg GE g^− 1 DW in A. acerosum roots [58]. Here, the highest TPS was reported in species of A. laxiusculum (9.8 mg GE g^− 1 DW). Augmented TPS in A. laxiusculum could be raised due to higher soil, total nitrogen and temperature which enhancing the biosynthesis of TPS by upregulating the pentose phosphate pathway and key enzymatic pathways, such as UDP-glucose synthesis [59, 60].

Saponins, high molecular weight of glycosides compounds, are utilized in the manufacture of soaps, detergents, fire extinguishers, shampoos, beer, and cosmetics [54]. To date, no comprehensive investigation has been conducted on saponin content of Acanthophyllum species. Our study demonstrated a broad variation in TSC from 49.55 mg SE g^− 1 DW in A. microcephalum to 124.8 mg SE g^− 1 DW in A. diezianum. However, the saponin content is very variable within and between species and it depends on local geoclimatic conditions such as light, temperature, humidity and soil fertility [61, 62). Previously, different values were reported for TSC as 159.28 mg SE g^− 1 DW in A. laxiusculum [5] and 90 mg SE g^− 1 DW in A. bracteatum [8]. Here, higher saponin was observed in Acanthophyllum species which were grown under higher under higher latitude, longitude and more rainfall. This finding was similar to previous reports [37, 62] which may be due to upregulation of enzymes involving triterpenoid/steroidal saponin biosynthesis and mevalonate pathway under more latitude and longitude [37]. Different with this finding, higher elevations have been resulted to more saponin production in some species as Polygonatum L [63].

The differences observed in phytochemical traits among the studied species and ecotypes are biologically meaningful because these metabolites represent key adaptive and defensive traits shaped by local environmental pressures [64]. Identifying species or ecotypes with the highest levels is therefore important, as it reveals distinct species and ecotypes with superior stress-tolerance potential and highlights valuable germplasm for pharmaceutical, nutraceutical, and breeding applications in Acanthophyllum genus.

Phytochemical traits correlation

The observed network of significant correlations among key phytochemical constituents in Acanthophyllum reveals a coordinated metabolic architecture. The significant positive correlations between TPC with TFD (r = 0.53^**) and TFL (r = 0.42^**) unequivocally indicate that flavonoids and flavonols comprise a major portion of TPC in Acanthophyllum, with the predominant role of flavonoids groups [65]. The higher positive correlation between TPC and TFD suggests that flavonoids are the predominant polyphenolic group in Acanthophyllum species.

Additionally, correlations of TPC with TTCR (r = 0.34^**) and TTCL (r = 0.30^*) indicate that tannins, as a class of polyphenolic compounds, substantially contribute to the TPC in both the roots and leaves of Acanthophyllum species similar to findings in Salvia officinalis L [66]. and in Myrtus communis L [67]. The positive correlations of TFD with TTCL (r = 0.32^**) and TFL with TTCL (r = 0.36^**) proposed the complex interplay and potential overlap between different polyphenol classes within the plant’s metabolism, collectively contributing to its overall bioactive profile in Acanthophyllum species. This co-accumulation is primarily attributed to their shared biosynthetic origins within the phenylpropanoid pathway, as both compound classes derive from common precursors (phenylalanine, malonyl-CoA) and critical early enzymatic steps [68]. The strong positive and significant correlation between TSC and TTCR (r = 0.52^**). Although saponins and tannins are synthesized through distinct biosynthetic routes (terpenoid and phenylpropanoid pathways, respectively), the strong positive correlation between their root contents (r = 0.52^**), in Acanthophyllum species may results from shared regulatory mechanisms [62]. Another reason for this correlation may be due to their coordinated accumulation under adaptive defense responses due to competing for common carbon precursors and cytosolic acetyl-CoA pools [69]. A significant and positive correlation between antioxidant activity (DPPH) with TPC (r = 0.37^**) and TTCL (r = 0.37^**) confirms the predominant role of phenolic compounds and tannins in leaves as major SMs with antioxidant activity in Acanthophyllum species. A positive correlation between TTCR and antioxidant activity was consistent with previous reports in other species [68, 70]. Overall, the significant correlations among the studied traits identify phytochemical traits that can be synergistically improved in breeding programs to enhance desirable traits in Acanthophyllum.

The strong concordance of phytochemical traits with the PCA and cluster analyses further confirms that the variation is structured and reflects real biochemical differentiation rather than random fluctuation. Overall, the observed metabolite diversity has clear ecological, functional, justifying the emphasis on species and ecotypes with the highest concentrations.

Multivariate analysis based on phytochemical and environmental factors

In conclusion, the application of cluster analysis, heat map, and correlation provides valuable information into the relationship between phytochemical traits in the studied species and ecotypes. Here, the multivariate analyses (PCA and heatmap) provide trait-based grouping that could guide selection of suitable parents and hybridization strategies. Cluster analysis of Acanthophyllum ecotypes revealed significant diversity for phytochemical traits, identified suitable ecotypes and species for breeding programs aims specially at developing superior hybrids by selecting most distance ecotypes from different subclusters. In this regard, the ecotypes of A. microcephalum and A. sordidum from different provinces formed a single cluster, suggesting genetic similarity for their phytochemical traits despite geographic separation. In contrast, ecotypes of A. bracteatum, A. glandulosum, A. squarrosum, A. korshinskyi, and A. spinosum were distributed across multiple clusters, even within the same province, indicating high genetic diversity and regional divergence.

Clustering patterns exhibit different adaptive strategies in response to environmental factors (soil and climatic variables) for both studied species and ecotypes. In Group I species (A. squarrosum, A. spinosum, A. laxiusculum, A. diezianum) and ecotypes (e.g., A.gl21, A.gl13) with higher TPS, TSC, antioxidant activity (DPPH assay), and TTCR may reflect adaptations prioritizing carbohydrate and saponin accumulation. Conversely, Group II comprising species like A. microcephalum and A. sordidum and their ecotypes (e.g., A.mi1, A.mi5, A.so4), exhibited lower values for TPC, TFD, TFL, TTCL, TPS, TSC, TTCR, and antioxidant activity, possibly indicating less stressful habitats or genetic restrictions on SMs production. Group III, species (A. bracteatum, A. glandulosum, A. korshinskyi, A. heratense) and ecotypes (e.g., A.gl19, A.br2) elevated TPC, TFD, TFL, and TTCL was observed that likely confirms the presence of these samples in environments favoring phenolic compound synthesis, possibly due to higher oxidative stress. Here a high confirmation between PCA and cluster analysis validated the differentiation of studies of ecotypes based on their phytochemical traits [41, 71]. According to the results, a good concordance was observed between the grouping of cluster analysis and PCA for both species and ecotypes. This similarity indicates that the phytochemical variables are influenced by consistent underlying genetic factors. When the biosynthesis of major SMs is governed by shared gene networks, co-regulated genes, or tightly linked loci, species with similar genetic backgrounds tend to express comparable SMs patterns [72].

Environmental data provide critical context for understanding the conditions influencing plant development and SMs accumulation [59]. The classification species based on phytochemical and environmental variables revealed distinct and divergent patterns among the studied Acanthophyllum species. Notably, A. squarrosum, A. spinosum, and A. laxiusculum consistently grouped together in both phytochemical (Group I: high TPS, TSC, DPPH, and TTCR; Fig. 4a) and environmental (Group I: high temperature, elevation, CaCO₃, and TN; Fig. 6) groups. These findings suggest that elevated temperature and soil nitrogen significantly shape their SMs profiles. In contrast, A. diezianum clustered with Group I in the phytochemical analysis but was assigned to environmental Group II. Similarly, A. glandulosum aligned with A. korshinskyi, A. bracteatum, and A. heratense in the phytochemical analysis (Group III: high TPC, TFD, and TFL) but occupied a distinct environmental niche. Specifically, A. glandulosum and A. korshinskyi were classified in environmental Group II (high rainfall, latitude, EC, OM), whereas A. bracteatum and A. heratense were placed in environmental Group I. Likewise, A. sordidum and A. microcephalum clustered together in the phytochemical analysis (Group II: low levels of studied traits) but diverged in environmental groups (A. sordidum in Group II, A. microcephalum in Group I). These results indicate that genetic factors may predominantly govern SMs content, potentially overriding environmental influences. The interplay between genetic and environmental factors shapes different SMs profiles [73].

Genetic regulation of biosynthetic pathways drives species-specific SMs profiles, which are modulated by environmental stressors like temperature, TN, and CaCO₃ [74]. Consequently, the predominance of genetic factors explains the occurrence of species with distinct phytochemical profiles within the same environmental group, as genetic regulation primarily utters SMs biosynthesis [42]. For instance, A. diezianum clustered with A. sordidum, A. glandulosum, and A. korshinskyi in environmental Group II (high rainfall, latitude, EC, and OM) but diverged in phytochemical groups (A. diezianum in Group I, A. sordidum in Group II, and A. glandulosum and A. korshinskyi in Group III). Conversely, environmental factors can determine similar phytochemical profiles across different environmental groups, as adaptive responses to abiotic stressors which influence on SMs production [75]. These findings suggest that environmental factors strongly influence on the range of diversity for each SMs profiles among different Acanthophyllum species that grown in different geographical locations. The partial overlap for Acanthophyllum species for classification in clustering and heatmap patterns reinforces the notion that both environmental conditions and genetic regulation synergistically shape phytochemical diversity within this genus similar to previous reports in other plant species [76].

Understanding the environmental influences is a critical point for optimizing cultivation practices of Acanthophyllum species to ensure consistent phytochemical quality. Comparative analysis identified that temperature and height (elevation) were considered as key factors of SMs accumulation, particularly in A. laxiusculum, A. squarrosum, and A. heratense. Soil nitrogen exhibited a moderate effect on SMs accumulation in Acanthophyllum species. Potassium and CaCO₃ had inconsistent effects, reflecting species-specific responses, whereas EC was linked to metabolite accumulation in A. sordidum. These findings align with prior studies demonstrating that abiotic stresses like temperature and elevation stimulate SMs biosynthesis as an adaptive response [74].

Implications for breeding

These findings also highlighted the potential of some Acanthophyllum ecotypes as parental lines for breeding programs targeting enhanced phytochemical content and environmental resilience. In this regards, a high diversity patterns of A. squarrosum, demonstrated by the distribution of its ecotypes (e.g., A.sq2, A.sq4, A.sq1), offers significant potential for breeding programs aimed at developing superior ecotypes. This study identifies A. squarrosum and its ecotype A.sq2 as the most promising candidates for different industries due to their high content of TPS, TSC, antioxidant activity, and TTCR, as evidenced by their placement in the high-performing group 1 and group 2 in phytochemical PCA and cluster analyses. Their adaptation to natural climates as high altitude (group I in environmental analysis), which is a key factor of SMs accumulation, makes it an ideal environment for industrial cultivation in arid and high-elevation regions for A. squarrosum. Compared to other species like A. bracteatum (high in TPC, TFD, and TFL) or A. glandulosum (limited by high-rainfall conditions), A.sq2 stands out for its diverse SMs profile and environmental adaptability.

Limitations

Despite this study 68 ecotypes belonging to 10 different species Acanthophyllum, several limitations should be acknowledged. The geographic distribution of collected ecotypes may not fully represent the entire natural range of each species in Iran, potentially affecting the generalizability of the results. The number of ecotypes per species was relatively limited, which may constrain the generalizability of trait variation patterns. Some biochemical traits are inherently variable or influenced by seasonal and temporal factors, which may introduce residual variability even after controlling for environmental conditions.

Conclusion

This study revealed a significant phytochemical variation across 68 ecotypes of ten Iranian Acanthophyllum species by the first. Principle component analysis and cluster analysis identified three distinct phytochemical groups among studied species, which were similar in grouping. Higher contents of SMs were observed in A. laxiusculum, A. squarrosum, and A. heratense. Phytochemical and environmental cluster analyses showed convergent patterns for A. squarrosum and A. laxiusculum (high TPS, TSC, DPPH, TTCR; high temperature and nitrogen) and divergent patterns for A. glandulosum and A. spinosum. Notably, high genetic diversity patterns of A. squarrosum, demonstrated by the distribution of its ecotypes offers more potential role of this species for breeding programs at future. Finally, these findings could provide a foundation for further exploration of Acanthophyllum species in pharmaceutical applications for targeted breeding or conservation strategies. These findings collectively highlight the remarkable plasticity of Acanthophyllum species, enabling them to thrive across diverse natural habitats with varying ecological pressures. Future studies should investigate the specific environmental trainers behind these phytochemical profiles and validate their functional roles in Acanthophyllum species adaptation and bioactivity.

Authors’ contributions

F.B Performed all the experimental section, data curation, writing original draft, P. G. Conceptualization, data analysis, resources preparation; writing original draft, review and final editing, H. B . supervisor, resources preparation and revising the manuscript, A. P. advisor, supporting experimental section, editing manuscript.

Funding

The author(s) declare that financial support was received from the Research Council of the Iran National Science Foundation (INSF) for funding this project under grant number [4025857].

Data availability

The findings supporting the present study, when reasonable request, are available from the corresponding author.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.