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Harnessing multivariate insights coupled with susceptibility indices to reveal morpho-physiological and biochemical traits in heat tolerance of cotton

BMC Plant Biology volume 25, Article number: 126 (2025) Cite this article

Abstract

Cotton is essential for the global textile industry however, climate change, especially extreme temperatures, threatens sustainable cotton production. This research aims to identify breeding strategies to improve heat tolerance and utilize stress-resistant traits in cotton cultivars. This study investigated heat tolerance for 50 cotton genotypes at the seedling stage by examining various traits at three temperatures (32 °C, 45 °C and 48 °C) in a randomized plot experiment. Analysis of variance revealed significant differences among the genotypes for all the studied traits. Morphological traits, including root and shoot length, fresh and dry root, and shoot weights, were adversely affected by heat stress. Chlorophyll contents declined significantly, indicating impaired and compromised photosynthetic efficiency. Biochemical assays underlined the elevated activities of antioxidant enzymes superoxide dismutase (SOD), peroxidase (POD), total free amino acids (TFA), total soluble sugars (TSS), proline content and declined production of total soluble proteins (TSP), which is indicative of oxidative stress. Physiological traits such as photosynthetic rate and cell membrane stability% decreased severely under stress conditions. The first five PCs under control and the first six PCs under stresses depicted eigenvalues > 1 and presented 72.96%, 76.11%, and 77.93% of total cumulative variability under control, T1 and T2, respectively. Cell membrane stability, a potential marker for heat tolerance, showed a strong positive correlation with total soluble sugars (TSS) and root length (RL) under extreme stress. Based on clustering, the genotypes were classified into four groups. Stress susceptibility indices indicated that NIAB-545 and FH-142 are promising genotypes for developing heat tolerance breeding strategies in cotton.

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Introduction

Cotton serves a key role in the national economy of Pakistan by providing essential basic raw materials to the textile industries. Pakistan ranks among the highest cotton producing countries in the world, and it generates a huge amount of foreign exchange by its exports [1]. The cotton-producing belt of Pakistan is negatively impacted by climate change and due to the occurrence of more frequent heat waves as a result of global warming, and it’s the prime natural disaster that affects the productivity of the cotton crop [2]. During its entire growth period, cotton is frequently subjected to heat stress, especially during its seedling, flowering, and boll-forming stages. Heat stress during the seedling stage of cotton severely restricts the seedling growth and development and ultimately leads to death, which raises the cost of sowing [3,4,5]. Moreover, certain cotton-producing areas in Pakistan have practiced a progressively increased heat stress period in summers, which results in a reduction in the productivity of cotton [6]. Hence, it is of prime importance to establish a system to classify and select heat-tolerant cotton genotypes for high yield and quality under high temperature stress. Crop physiology, morphology, yield, metabolism, and quality traits are all negatively influenced by the heat stress [7]. Under heat stress, the reactive oxygen species (ROS) increase rapidly, activating the natural defense system (antioxidant metabolism), leading to increased activity of enzymes such as POD, SOD, etc [8, 9]. The prolonged heat stress disrupts the proper functioning of the production-scavenging mechanism of ROS species in the leaves, resulting in damage to chloroplasts and reduced photosynthesis [9,10,11,12]. High temperature surpassing 35–40 °C decreases cotton’s photosynthetic ability which in turn limits the production of sucrose which is responsible for fiber elongation by turgor pressure [13,14,15]. Under heat stress conditions, inhibition of carbohydrate assimilation occurs, leading to reduction in the number of fibers per seed [16]. Additionally, ROS also promotes the development of secondary cell wall, which halts the elongation process of fiber resulting into shorter and thinner fibers [14]. Heat stress promotes transpiration which reduces carbohydrate supply leading to lower fiber weight [17]. At the seedling stage, HT has a detrimental effect on phenotypic indices of cotton roots. Certain characteristics of roots, such as their volume, length, and surface area can be used as potential indicators of heat tolerance in cotton [9]. Furthermore, earlier researchers have identified heat-tolerant cotton cultivars by assessing variations in physio-agronomic traits/indices under heat stress. These include stomatal conductance [18], cell membrane stability [19,20,21], and chlorophyll content [22]. Many previous research studies used multiple indices systems to evaluate the heat tolerance in cotton cultivars, thereby inhibiting effective assessment of varietal variations in heat tolerance [23, 24]. Multiple traits/indicators are employed in multivariate analysis, which integrates statistical methods (agglomerative cluster analysis) to assess statistical trends/patterns of diverse test objects [25]. Currently, multiple statistical analyses are being performed to assess the heat tolerance of multiple crops such as maize [26], wheat [27], and soybean [28]. Multiple trait sets (yield and male/female spike) were used previously for evaluation and characterization of heat tolerance indices to categorize heat-tolerant material [26]. Multiple comprehensive indices are generated from several indices by using multivariate analysis, simplifying the assessment procedure and enabling the fast, easy, and efficient characterization of heat-tolerant and susceptible cotton genotypes. HT has substantial negative impact on cotton development and growth. To address this problem, efficient strategies are needed to establish indices for the classification of heat-tolerant or susceptible genotypes. In field experiments, there are many limitations in screening methods because of temperature regulation, time constraints, and labor consumption. These inadequacies can be covered up by conducting seedling experiments. Prior research studies have reported that genotypes having substantial differences in heat tolerance during the seedling stage have produced similar results in heat resistance verification conducted in the field [9, 24, 29]. Hence, integrating field study with seedling experiments could efficiently confirm the differential heat tolerance of cotton cultivars. In addition to impairing cell membrane stability and a reduction in the photosynthetic capability of the plant, high temperature (HT) stress has a detrimental influence on root growth. However, a comprehensive assessment of heat tolerance that incorporates physiological markers both below and above ground during the cotton seedling stage has not been done. Hence, this study was conducted to explore the physiological, morphological, and biochemical differences under high temperature stress. This research attempts (1) to identify heat-tolerant cotton cultivars by integrating multivariate analysis with seedling experiments. (2) To evaluate the differential behavior of biochemical and morpho-physiological traits of cotton cultivars under heat stress and to narrow down the ideal cotton cultivars for studying heat stress. This study will provide the base for the screening and identification of heat-tolerant/susceptible germplasm of cotton and will improve the index system for evaluating their heat tolerance.

Materials and methods

Experimental material

A total of 50 cotton genotypes were collected from the Plant Breeding and Genetics Division of NIAB, Central Cotton Research Institute Multan, Ayub Agriculture Research Institute, and Cotton Research Station Bahawalpur to evaluate their potential heat tolerance under greenhouse conditions. The name of the genotypes used in this study is given in Table 1.

Table 1 List of genotypes used in the study
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Experimental design and agronomic management

The trial was conducted in a completely randomized design (CRD) in triplicate. Firstly, soil analysis was done to determine the field capacity (FC). Several traits of soil were examined and recorded, such as its elemental composition, conductivity, PH, texture, and organic matter. All the values of recorded parameters are given in Table 2. Cotton seeds were sown in polythene bags (30 × 14 cm) in November 2021. The polythene bags were filled with 2 kg of silt. Before sowing, cotton seeds were soaked overnight. Seeds were sown at a depth of 3–4 cm the next morning. Proceeding successful germination thinning was done to maintain one healthy seedling per polythene bag. Three sets (450 bags/set) of each genotype were prepared and allowed to grow. To ensure uniform and healthy growth of the cotton seedlings, agronomic practices were strictly followed, such as fertilizer application, manual weed removal, herbicide treatment, hoeing, and watering. To circumvent the occurrence of sucking pests/insects (whitefly thrips and aphids) and to avoid attacks of pink and army bollworms, sprays of pesticides and insecticides were applied occasionally when needed. The cotton seedlings of 50 days were subjected to heat treatment. One set of genotypes was maintained at control conditions of 32/28°C, while the other two sets were subjected to two elevated heat stresses of 45/30°C (referred to as T1) and 48/32°C (referred to as T2) by cooling and heating systems for consecutive five days. After application of heat stress, physiological parameters were recorded, and leaves of cotton seedlings were collected for biochemical tests. Young plants of cotton were uprooted to measure morphological traits.

Table 2 Physiochemical properties of the soil used for field experiments
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Data collection

Morphological traits

Data regarding morphological traits were recorded on root length (cm) (RL), shoot length (cm) (SL), fresh shoot weight (g) (FSW), fresh root weight (g) (FRW), dry root weight (g) (DRW) and dry shoot weight (g) (DSW). Most of the traits used in this study proved potentially useful for assessing heat tolerance among diverse cotton genotypes.

Physiological traits

Physiological traits such as stomatal conductance (SC), transpiration rate (E) and photosynthetic rate (Pn) were recorded by utilizing a porometer (LI-COR, Inc., Model: LI-600). Data was recorded from 11:00 a.m. to 1:00 p.m. in the presence of proper sunlight conditions. Data was recorded from the third leaf of every genotype and further processed by applying different formulas to calculate stomatal conductance (SC) and photosynthetic rate (Pn). Transpiration rate (E) was calculated in µg cm− 2 s-1 by division of the transpiration value of the porometer with 10,000 and multiplying with 1000.

$$\:SC=\frac{1}{DR}\times\:CF$$

Where DR is diffusion rate and CF is correction factor.

Pn = SC (mmol m− 2s− 1)/E (µg cm− 2 s− 1) × CF.

Cell membrane thermostability (CMS) was calculated by assessing relative cell injury% following the method described by [30].

CMS (%) = 1 − (T1/ T2) /1 − (C1 /C2) * 100

Where T1 and T2 denote the EC of heat-stressed samples before and after autoclave, respectively, C1 and C2 represent the values of the control sample before and after autoclave, respectively. The water potential of the fully expanded fresh leaf was measured by pressure chamber.

Heat susceptibility index

The measurement of heat resistance on the basis of least yield loss under heat stress in comparison to control conditions is referred to as the heat susceptibility index. It is utilized to depict the relative tolerance level of different genotypes towards stress by adopting the method explained by Fischer and Maurer [31].

Biochemical assays

To investigate the heat stress-mediated changes in the biochemical profiles of the genotypes, different biochemical parameters were estimated. All the biochemical assays were carried out in the stress physiology lab, Nuclear Institute for Agriculture and Biology, Faisalabad.

Total soluble proteins (TSP)

To estimate the total soluble proteins, Lowry’s method was utilized [32]. 0.5 g of fresh leaf for each genotype was chopped and finely ground in 10 ml of 0.2 M phosphate buffer (PH = 7). The ground mixture was centrifuged for about 5 min at a speed of 5000 x g. The clear supernatant was taken and used for the determination of proteins. 1 ml of separated supernatant (leaf extract) was taken in the test tube. One ml of phosphate buffer with the PH = 7 was used as a blank. Fresh alkaline solution (1 mL) was prepared, added to each test tube, and left at room temperature for about 10 min. The diluted Folin-phenol reagent (0.5 ml) was added and incubated at room temperature for half an hour. The OD (optical density) was measured at 620 nm by using a spectrophotometer (Hitachi, 220, Japan).

Total free amino acids (TFA)

Total free amino acids were estimated by following the method of Hamilton and Van Slyke [33]. Leaf extract was prepared by chopping the 0.5 g leaf sample in 0.2 M phosphate buffer with PH 7. One ml of the extract was taken in a volumetric flask (50 ml), and one ml of ninhydrin (2%) and pyridine (10%) were added in the flask. The flask containing the mixture was heated in a water bath for about 30 min. The volume of flasks was maintained at 50 mL by the addition of distilled water. The optical density (OD) of the colored solution was measured at 570 nm by spectrophotometer (Hitatchi, 220, Japan).

Total soluble sugars (TSS)

Total soluble sugars were measured by following the method of Yemm and Willis [34]. Plant extract was prepared by using 5 ml of ethanol solution (80%) for 0.5 g of plant sample. This prepared extract was used for estimation of total soluble sugars. Test tubes of 25 ml were taken for each sample with 0.1 ml of plant extract and 6 ml of anthrone; the whole mixture was heated for 10 min in a water bath. Following heat treatment, test tubes were ice-cooled for about 10 min and incubated at room temperature for almost 20 min. The optical density of this reaction mixture was measured at 625 nm for sugar estimation by using a spectrophotometer (Hitatchi, 220, Japan).

Proline

The proline content was measured by following the method explained by [35]. Fresh leaf 0.5 g was ground in 10 ml of sulfo-salicylic acid (3%). Test tubes of 25 ml were taken for each sample with 2 ml of sample extract and 2 ml of acid ninhydrin solution, followed by 2 ml of glacial acetic acid, and test tubes containing this reaction mixture were heated at 100 oC for an hour and terminated in an ice bath. This mixture was extracted with toluene (10 ml), which results in the formation of chromophore. Continuous air was supplied to separate the aqueous phase from the chromophore. Separated colored phase was left at room temperature for almost 3 min, and OD was measured at 520 nm by spectrophotometer (Hitatchi, 220, Japan).

Peroxidase (POD)

The POD activity was estimated by assessing the peroxidation of hydrogen per oxide (H2O2) by guaiacol [36]. The reaction mixture for estimation of POD was composed of 50 mM phosphate buffer (PH = 5), 40 mM H2O2, 0.1 mL enzyme extract, and guaiacol (20 mM). The gradual upturn of absorbance was due to the production of tetraguaiacol; optical density was recorded after every 20 s. One unit of enzyme was depicted to be responsible for the 0.01 rise in optical density in just sixty seconds.

Superoxide dismutase (SOD)

Initially, cotton leaves were ground in the medium containing potassium phosphate buffer (50 mM, pH 7.0), 0.1 mM EDTA, and 1 mM dithiothreitol (DTT) by following the method [37]. SOD activity was evaluated by quantifying its capacity to stop nitroblue tetrazolium (NBT) from being reduced photochemically. The photochemical degradation of NBT is 50% inhibited for each unit of SOD activity [38].

Chlorophyll (a, b, and total chl)

Chlorophyll contents were measured according to the method described by Arnon [39] and calculated by utilizing the formula provided by Davies [40]. Fresh cotton leaves weighing 0.5 g were extracted overnight at 100C by using 5 mL of acetone (80%). The reaction mixture was centrifuged at 14,000 x g for five minutes, and a spectrophotometer (Hitatchi, 220, Japan) was used to measure the supernatant’s absorbance at 645 and 663 nm. Formulas for calculating chlorophyll a, b and total chlorophyll are given below.

Chla = [12.7 (OD 663) -2.69 (OD 645)] x V/1000x W.

Chlb = [22.9 (OD 645) -4.68 (OD 663)] x V/1000x W.

Total Chl (Chla+b) = [20.2 (OD 645) + 8.02 (OD 663)] x V/100x W.

Where V represents the volume of the respective sample and W is denoting the weight of the sample.

Data analysis

Descriptive analysis was carried out by using Microsoft Excel. Cluster analysis, principal component analysis (PCA), and correlation were performed by XLSTAT 2014 software, while two-way ANOVA was applied by the usage of Statistix 8.1 version. Analysis of variance showed significant differences between genotypes for all the morpho-physiological and biochemical traits. The contribution of each trait towards variation was assessed by PCA, which simply reduces the complexity of multiple traits in a data set to major components/traits that presented maximum variation among the genotypes. Cluster analysis was done to explore the differences among cotton genotypes. Correlation analysis was performed to identify the positive/negative associations between the traits under study, which helps in targeting the desired traits to get genetic gain.

Results

Descriptive statistics of morpho-physiological and biochemical traits

Descriptive statistics were performed for all 50 cotton genotypes for all the morpho-physiological and biochemical traits, including TSP, TFA, TSS, Proline, SOD, POD, Chl a, Chl b, Total Chl, SC, E, Pn, WUE, WP, RL, SL, FSW, FRW, DRW, DSW, and CMS. The mean of TSP under control was 32.2 ± 5.8, which decreased to 30.5 ± 4.4 and 27.9 ± 5.7 in T1 and T2, respectively. The TFA in control had a mean of 22.0 ± 5.0, which increased to 25.6 ± 4.8 and 24.9 ± 5.5 for T1 and T2, respectively. TSS under control had a mean 9.3 ± 4.6, which increased to 10.1 ± 4.1 (T1) and 10.3 ± 4.1 (T2).The proline under the control condition had a mean 52.3 ± 8.3, which upgraded to 58.3 ± 6.5 and 55.9 ± 6.2 for T1 and T2, respectively. The SOD under control had an average of 279.6 ± 158.8, which increased to 382.9 ± 124.7 (T1) and 331 ± 151.3 (T2). Under control, POD had a mean 402.3 ± 72.2, which increased to 446.3 ± 67.0 and 439.4 ± 64.3 in T1 and T2, respectively. Chl a in the control condition had an average 1.7 ± 0.2, which decreased to 1.5 ± 0.3 and 1.6 ± 0.2 in T1 and T2, respectively. In control conditions, the average of Chl b was 0.8 ± 0.24, which turns to 1.1 ± 0.3 and 0.7 ± 0.2 in T1 and T2, respectively. Total Chl under control had a mean 2.6 ± 0.37, which remained the same in T1 (2.6 ± 0.4) but decreased to 2.3 ± 0.2 in T2. SC had an average of 184.8 ± 24.2 under control, but it increased to 232.9 ± 34.0 (T1) and 345.2 ± 76.2 (T2).E (transpiration rate) had a mean of 6.0 ± 0.69 under control, which decreased to 5.5 ± 0.3 under T1 but remained the same 6.0 ± 0.6 under T2. Pn under control had an average 33.0 ± 3.86, which decreased to 24.0 ± 3.2 and 18.1 ± 3.4 under T1 and T2, respectively. WUE had an average 5.4 ± 0.69 (control), which decreased to 4.3 ± 0.6 (T1) and 3.0 ± 0.6 (T2). The WP under control conditions had a mean 0.4 ± 0.05, which increased slightly to 0.5 ± 0.03 and 0.5 ± 0.03 under T1 and T2, respectively. The RL (root length) under control had a mean 14.5 ± 2.84, which reduced to 11.9 ± 3.1 and 9.5 ± 3.5 under T1 and T2, respectively. SL had an average 20.7 ± 4.28, which reduced to 15.8 ± 3.6 and 13.4 ± 3.5 under T1 and T2 correspondingly. The FSW had a mean 1.6 ± 0.47 (control), which decreased to 1.1 ± 0.3 (T1) and 1.2 ± 0.3 (T2). FRW had an average of 0.7 ± 0.21 under control, which reduced to 0.6 ± 0.1 and 0.6 ± 0.1 under T1 and T2, respectively. DRW in the control condition had a mean 0.06 ± 0.04, which decreased to 0.04 ± 0.02 under T1 and 0.03 ± 0.02 under T2. DSW had a mean 0.1 ± 0.07 under control, while it remained the same under T1 (0.1 ± 0.07) but decreased to 0.07 ± 0.04 under T2. CMS had an average of 54.6 ± 5.40 under control, which decreased drastically to 31.0 ± 7.5 and 26.5 ± 6.8 under T1 and T2 conditions, respectively. However the relative injury % had an average of 45.4 which increased to 69 and 73.5% under T1 and T2. The ranges for all the parameters under control, T1 and T2, are provided in Table 3; Fig. 1.

Table 3 Descriptive statistics (Range, mean ± SD) and ANOVA for morpho-physiological and biochemical traits under control and heat stress conditions
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Fig. 1
figure 1figure 1

Overlaid histograms showing frequency distributions of TSP, TFA, TSS, Proline, SOD, POD, Chl a, Chl b, Total chl, SC, E, Pn, WUE, WP, RL, SL, FSW, FRW, DRW, DSW and CMS across control (green), T1 (orange) and T2 (blue). The vertical dotted lines indicate the mean values of each trait

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Analysis of variance

ANOVA was applied to all the morpho-physiological and biochemical traits to explore the differences among genotypes. Highly significant variations were observed among all the genotypes for all the studied traits and treatments, as given in Table 3.

Correlation analysis

Correlation under control

Under control conditions, TSP showed maximum positive correlation with total cholesterol (r = 0.421), chl b (r = 0.373), and chl a (r = 0.295). TFA exhibited a slight positive correlation with proline (r = 0.265). TSS depicted a strong positive correlation with SOD (r = 0.871**), E (r = 0.838**), and DRW (r = 0.730*). TSS showed maximum positive correlation with SOD (r = 0.870**) and E (r = 0.838**). Proline has the maximum positive relation with FRW (r = 0.357). SOD has a significant positive correlation with TSS (r = 0.871**) and E (r = 0.817**). POD has a positive maximum correlation with TSS (r = 0.652*) and SOD (r = 0.638*). Chl a exhibited strong positive correlation with total chl (r = 0.782*). Chl b depicted a significant positive correlation with total chl (r = 0.810**). SC has a positive correlation with E (r = 0.632*). Pn have the maximum relation with WUE (r = 0.541). WUE exhibits the maximum negative correlation with SC (r = -0.990***). WP has the maximum positive relation with E (r = 0.562). RL has shown a slight positive correlation with TSS (r = 0.341). SL has the maximum negative correlation with SOD (r = -0.466). FSW expressed maximum correlation with FRW (r = 0.717*). FRW exhibited maximum correlation with DRW (r = 0.798*) and vice versa. DSW showed maximum positive relation to DRW (r = 0.674*). CMS showed a slight relation to WUE (r = 0.234), as shown (supplementary Table 1).

Correlation under T1

Under stress level 1 (T1), TSP showed maximum negative correlation with WP (r = -0.442). TFA exhibited the highest negative correlation with TSS (r = -0.486). TSS depicted the highest positive correlation with SOD (r = 0.745*) and DSW (r = 0.744*). Proline showed maximum negative relation with SOD (r = -0.524). SOD had a strong positive correlation with TSS (r = 0.745*). POD depicted maximum positive correlation to SOD (r = 0.693*). Chl a showed significant positive correlation with total chl (r = 0.760*). Chl b exhibited the maximum positive correlation with Total chl (r = 0.688*). Total chl showed the highest positive linkage with chl a (r = 0.760*). SC depicted a highly significant negative relation with WUE (r = -0.990***). E showed maximum positive correlation with SC (r = 0.565). Pn exhibited the highest positive relation with WUE (r = 0.864**) and vice versa. WP depicted a negative correlation with TSP (r = -0.442). RL had shown maximum positive correlation with DSW (r = 0.603*). SL had a slight positive correlation with TFA (r = 0.335). FSW exhibited the highest positive correlation with CMS (r = 0.485). FRW showed a significant positive relation with DRW (r = 0.729*) and DSW (r = 0.728*). DSW depicted significant linkage with TSS (r = 0.744*). CMS showed maximum positive correlation with TSS (r = 0.630), as shown in supplementary Table 2.

Correlation under T2

Under stress level 2 (T2), TSP showed maximum positive correlation with RL (r = 0.390) and Total chl (r = 0.328). TFA exhibited the highest negative correlation with SOD (r = -0.478). TSS depicted maximum positive correlation with FRW (r = 0.830**) and SOD (r = 0.807**). Proline expressed maximum negative correlation with SOD (r = -0.432) and Total chl (r = -0.432). SOD showed the highest correlation with TSS (r = 0.807**). POD exhibited maximum linkage with DSW (r = 0.710*). Chl a expressed the highest positive linkage with total chl (r = 0.744*). Chl b depicted the maximum positive correlation with total chl (r = 0.670*). SC had highly significant negative correlation with WUE (r = -0.982***). E showed maximum positive correlation with SC (r = 0.514). Pn had highly significant positive association with WUE (r = 0.860**) and vice versa. WP has the highest positive linkage with DSW (r = 0.450). RL showed a highly positive correlation with SOD (r = 0.621). SL had the maximum positive correlation with RL (r = 0.423). FSW depicted the highest positive correlation with SL (r = 0.421). FRW showed a highly significant correlation with TSS (r = 0.830**). DRW expressed maximum positive association with RL (r = 0.412). DSW had highly significant positive correlation with SOD (r = 0.793*). CMS showed highly positive correlation with TSS (r = 0.607) and RL (r = 0.604), as shown in supplementary Table 3.

Principal component analysis

Principal component analysis under control

Out of 20 PCs, the first five PCs (PC-I, PC-II, PC-III, PC-IV, and PC-V) depicted eigenvalues greater than 1 and presented 72.96% of total cumulative variability among the diverse cotton genotypes. PC-I exhibited the highest variability of 33.57%, followed by 15, 11.62, 7.37, and 5.38% by PC-II, PC-III, PC-IV, and PC-V correspondingly (Fig. 2).

Fig. 2
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Scree plot of morpho-physiological and biochemical traits under control representing variability

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The traits that showed maximum positive factor loadings towards PC-I include E, TSS, DRW, FRW, and SOD. Total chl followed by chl a showed the highest contribution towards PC-II. Pn and WUE showed the highest contribution to PC-III. CMS, RL, and SL had the highest contribution towards PC-IV. TFA displayed the highest contribution towards PC-V. PCA revealed that PC-I and PC-II contributed about 48.57% of total variation. This study exposed the traits that presented maximum variation among the cotton genotypes and presented a valuable resource for future breeding programs to enhance crop potential, as shown in Fig. 3.

Fig. 3
figure 3

Distribution of cotton genotypes in biplot for first two principal components under control conditions

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Principal component analysis under T1

The first six components having eigenvalue > 1 presented maximum variance (76.11%) among cotton genotypes. PC-I depicted maximum variability of 28.75%, followed by 16.29, 10.21, 8.99, 6.50, and 5.36% by PC-II, PC-III, PC-IV, PC-V, and PC-VI, respectively (Fig. 4).

Fig. 4
figure 4

Scree plot displaying the variability of morpho-physiological and biochemical traits under T1

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The characters that exhibited the highest values of factor loadings towards PC-I include TSS, DSW, SOD, CMS, FRW, DRW, POD, and RL. WUE and Pn contributed the most towards PC-II. Total chl followed by chl a showed maximum contribution towards PC-III. Chl b, SL, and E had the highest contribution towards PC-IV. WP and POD exhibited maximum variation towards PC-V. E, SOD, Pn, and POD presented the highest factor loading values for PC-VI. PCA exposed that PC-I and PC-II had contributions of 45.03% of the whole variation. The biplot analysis between PC-I and PC-II revealed the traits of interest under heat stress, which showed maximum variation among cotton genotypes and offered base material for future crop improvement programs as depicted (Fig. 5).

Fig. 5
figure 5

Distribution of cotton genotypes in the biplot based on the first two principal components under T1

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Principal component analysis under T2

The first 6 PCs out of twenty PCs had eigenvalues > 1 and showed maximum cumulative variability of 77.93% between the genotypes under study. PCA analysis revealed that PC-I exhibited maximum variation of 36.31%, followed by 13.54, 9.65, 7.89, 5.39, and 5.14% by PC-II, PC-III, PC-IV, PC-V, and PC-VI (Fig. 6).

Fig. 6
figure 6

Scree plot illustrating the variability of morpho-physiological and biochemical traits under T2

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PC-I had the highest values of factor loadings of traits such as SOD, TSS, FRW, WUE, DSW, Pn, POD, CMS, RL, and chl a. Traits that contributed majorly towards PC-II include total cholesterol, chl b, and chl a. SL, FSW, and TFA contributed majorly towards PC-III. WP and SC contributed mainly towards PC-IV. The traits, i.e., DRW TSP and E, contributed maximally towards PC-V. PC-VI had the maximum contribution from traits such as E and Pn. The bi-plot study exposed the traits of interest under extreme heat stress, which contributed maximum variability among genotypes used in this research and provided the basic knowledge of potential heat-tolerant germplasm to start future breeding programs to enhance the crop potential under abiotic stress as shown (Fig. 7).

Fig. 7
figure 7

Biplot showing the distribution of cotton genotypes for the first two principal components under T2

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Cluster analysis

Cluster analysis under control

Cluster analysis generated four distinct groups, with cluster-I having 13 genotypes, followed by 11, 17, and 9 in clusters II, III, and IV, respectively (Fig. 8). Cluster-IV had a minimum number of genotypes (9) which is attributed to the minimum number of characters used in this research. Cluster-I diverged from all others on the basis of TSP, Chl b, and Total Chl. Cluster-II differed from all others on the basis of RL. Cluster-III showed differences due to the traits such as WUE, SL, and CMS. Cluster-IV showed variation due to TSS, SOD, POD, chl a, SC, E, Pn, WP, FSW, FRW, DRW, and DSW.

Fig. 8
figure 8

Dendrogram depicting relationships among cotton genotypes based on morpho-physiological and biochemical traits under control

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Cluster analysis under T1

Clustering produced four distinguished groups, with cluster I having 19 genotypes, followed by 24, 6, and 1 in cluster II, III, and IV correspondingly (Fig. 9). Cluster IV had the lowest number of genotypes (1), which could be due to the limited number of traits used. Cluster-I varied due to its distinguished performance for the traits such as TFA, proline, WP, and SL. Cluster-II had genotypes that outperformed for the traits, i.e., chl b, SC, and E. Cluster-III differed from the left by outperforming for the traits, i.e., TSP, TSS, SOD, POD, Pn, RL, FSW, FRW, DSW, DRW, and CMS. Cluster-IV separated due to chl a, total chl, and WUE.

Fig. 9
figure 9

Dendrogram illustrating relationships among cotton genotypes based on agro-morphological and biochemical traits under T1

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Cluster analysis under T2

Clustering created four discrete groups, with cluster-I containing 25 genotypes, followed by 9, 13, and 3 in clusters II, III, and IV, respectively (Fig. 10). Cluster-I contained all the genotypes that had higher values for TSP, chl b, and total chl. Cluster-II is composed of traits that have higher performance for the traits, including TSS, SOD, POD, chl a, Pn, WUE, WP, RL, FSW, FRW, DRW, DSW, and CMS. All the genotypes that had higher values for TFA and proline were placed in cluster III. The genotypes that outperformed for SC, E, and SL were placed in cluster IV.

Fig. 10
figure 10

Dendrogram depicting the relationships among cotton genotypes based on agro-morphological and biochemical traits under T2

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Differential behavior of cotton genotypes towards non-enzymatic antioxidants and some other biochemical traits under heat stress

It was detected that the amount of TSP was significantly decreased under heat-stressed conditions in all genotypes except N-135-BB/121 − 38, NS-141, CIM-602, CIM-599, BH-167, BH-184, CIM-506, Cyto-124, CIM-473, CIM-109, CIM-554, NIAB-1011/48, NIAB-1042, and FH-142 under stress level 1 (T1), while under stress level 2 (T2), all the genotypes except CIM-599, CIM-598, CIM-1100, CIM-506, CIM-109, FH-142, and NIAB-545 showed a significant decrease. Maximum and minimum decrease% for TSP were shown by N-989 (34.677) and BH-184 (-27.212), respectively, under T1, while under T2, it was CIM-448 (41.805) and CIM-506 (-41.459), respectively. It was seen that TFA was significantly increased under heat-stressed conditions for all the genotypes except IUB-13, CIM-602, CIM-598, CIM-240, CIM-554, NIAB-1011/48, NIAB-1011/64, NIAB-1042, FH lalazar, FH-142, and NIAB-1089 under T1, while under T2, the same trend of increase was shown by all except IUB-13, CIM-602, BH-167, CIM-506, CIM-482, NIAB-1011/48, NIAB-1011/64, FH-142, and NIAB-1089. Maximum and minimum decrease% for TFA were exhibited by NIAB-1089 (30.781) and N-135-36/17(YP) (-77.273) under T1, while under T2, it was CIM-482 (49.132) and NIAB-878 (-93.675), respectively. It was perceived that TSS was substantially increased under heat stress conditions for all except CIM-599, Cyto-178, BH-167, CIM-598, CIM-1100, CIM-240, NIAB-484, NIAB-1011/48, NIAB-878, NIAB-1011/64, and FH-142. While under T2, the same trend of increase was shown for all except N-135-44/BB, CIM-599, Cyto-178, CIM-598, CIM-1100, CIM-240, NIAB-484, NIAB-878, NIAB-1011/64, and FH-142. The highest and lowest decrease% was depicted by NIAB-878 (58.445), NIAB-1042 (-180.230) under T1, CIM-598 (52.066), and IUB-13 (-186.183) under T2. It was exhibited that proline content increased extensively under heat stress conditions for all the genotypes except CIM-598, CIM-1100, CIM-240, CIM-473, N-1059, NIAB-878, NIAB-1011/64, NIAB-1042, FH lalazar, FH-142, and NIAB-1089 under T1. Same trend of increase was seen under T2 from all genotypes except N-1059, NIAB-1011/64, FH lalazar, FH-142, NIAB-1089, and NIAB-545. The maximum and minimum decrease% were shown by NIAB-1042 (12.698) and N-512-33/4 (-54.500) under T1, and under T2, it was depicted by NIAB-1011/64 (19.417) and N-135-44/BB (-52.252) correspondingly. The chl significantly decreased under heat stress conditions for all the genotypes besides N-135-42/8–18, N-135-BB/71, NS-141, BH-167, AA-703, Neelum-121, CIM-1100, Cyto-124, CIM-499, N-1059, NIAB-484, and NIAB-1042 under T1. However, chl a substantially decreased for all except CIM-482, NIAB-1011/48, NIAB-1042, NIAB-1089, and NIAB-545 under T2. The maximum and minimum decrease% were shown by NIAB-1011/64 (48.182) and N-1059 (-21.221), respectively. The chl b showed considerable variation under control and heat-stressed conditions; under T1, it showed a substantial increase in chl b content for most of the genotypes except some, and the most significant increase was showed by N-135-BB/71. While under T2, chl b considerably decreased except CIM-599, Cyto-178, CIM-598, CIM-1100, CIM-240, N-1059, NIAB-1011/48, NIAB-878, and NIAB-1042. The total chlorophyll content under heat stress showed significant variation among the genotypes N-135-BB/71 and NIAB-1011/64, which showed maximum and minimum total chlorophyll content, respectively, under T1. While under T2, an overall decrease in total chlorophyll content was observed among all the genotypes except CIM-599, Cyto-178, CIM-1100, NIAB-1011/48, and NIAB-1042, as shown in S Table 6, S Table 7.

Differential behavior of cotton genotypes towards enzymatic antioxidants under stress

The activity of SOD substantially increased under heat-stressed conditions for most of the genotypes except CIM-599, Cyto-178, BH-167, CIM-1100, NIAB-878, FH-142, and NIAB-545 under T1. While under T2, the same trend was observed for all the genotypes besides CIM-599, Cyto-178, CIM-598, CIM-1100, CIM-240, NIAB-878, and FH lalazar. The maximal and minimal decrease% was shown by CIM-1100 (40.341) and NIAB-1042 (-486.585) under T1, while under T2, CIM-240 (79.508) and CIM-506 (-289.800) depicted it. The activity of POD extensively increased under heat-stressed conditions for all the genotypes except CIM-599, Cyto-178, BH-167, CIM-70, NIAB-484, NIAB-878, and FH-142 under T1. While under T2, it showed the same trend of increase for all except CIM-599, Cyto-178, CIM-598, and FH-142. The highest and lowest decrease% was depicted by CIM-599 (32.923) and FH Lalazar (-60.155) under T1, while under T2, it was shown by CIM-599 (33.784) and CIM-482 (-65.483), respectively (S Table 6, S Table 7).

Differential behavior of cotton genotypes towards morpho-physiological parameters

Heat stress impacts a plant’s morphological and physiological characteristics (RL, SL, FSW, FRW, DRW, DSW, SC, E, Pn, WUE, WP, and CMS) and severely limits its ability to grow. In general, genotypes vulnerable to heat stress normally led to lower biomass, photosynthetic rate, and CMS. The patterns of growth and decline of these morphophysiological features can be assessed for each genotype from S Table 6, S Table 7.

SSI-Based categorization of genotypes: tolerant vs. susceptible under T1

SSI was computed for all the traits on the basis of percent decrease under stress as compared to normal conditions (control). The genotypes that have the least SSI value along with the minimum percentage decrease are the most tolerant ones. BH-184 was the most tolerant genotype for TSP because it had the least SSI value (SSI: -5.14), followed by NIAB-1042 (SSI: -4.51), BH-167 (SSI: -3.59), and CIM-602 (SSI: -3.05). Whereas N-989 (SSI: 6.55) followed by N-512-121 (SSI: 5.38), NIAB-1011/64 (SSI: 5.06) and NIAB-484 (SSI: 4.20) were the most susceptible genotypes with maximum percentage decrease. The SSI was estimated on the basis of TFA, and all the genotypes were categorized into two groups: tolerant and susceptible. The top tolerant genotypes include NIAB-1089 (SSI: 1.87), IUB-13 (SSI: 1.19), FH lalazar (SSI: 1.18), and CIM-602 (SSI: 0.92). However, the most susceptible genotypes encompass N-135-36/17 (YP) (SSI: 4.69), FH-941 (SSI: 4.47), BH-184 (SSI: 3.87), and N-512-33/4 (SSI: 3.57). For TSS, the SSI was calculated, and the highly tolerant genotypes were NIAB-878 (SSI: -6.85), BH-167 (SSI: -6.50), CIM-599 (SSI: -6.05), and Cyto-178 (SSI: -5.13). While the most susceptible genotypes include NIAB-1042 (SSI: 21.12), IUB-13 (SSI: 20.41), CIM-602 (SSI: 19.09), and NIAB-1089 (SSI: 16.06). On the basis of proline, SSI was computed, and extremely tolerant genotypes include NIAB-1042 (SSI: 1.12), CIM-598 (SSI: 0.78), FH lalazar (SSI: 0.77), and CIM-240 (SSI: 0.70). The highly susceptible genotypes were N-512-33/4 (SSI: 4.81), N-221 (SSI: 3.27), N-135-44/BB (SSI: -3.26) and N-135-BB/71 (SSI: 3.17). SSI was calculated on the basis of SOD, and genotypes such as CIM-1100 (SSI: 1.09), CIM-599 (SSI: 0.90), NIAB-878 (SSI: 0.87), and BH-167 (SSI: 0.86) were most tolerant, while NIAB-1042 (SSI: 13.16), FH-942 (SSI: 5.09), NIAB-484 (SSI: 4.36) and CA-12 (SSI: 3.72) were the most susceptible ones. The accessions CIM-599 (SSI: 3.01), Cyto-178 (SSI: 2.91), FH-142 (SSI: 1.58), and BH-167 (SSI: 1.30) were most tolerant, and FH lalazar (SSI: 5.50), CIM-602 (SSI: 5.47), N-512-33/4 (SSI: 4.39), and FH-118 (SSI: 4.12) were most susceptible on the basis of SSI computed for POD. The genotypes were classified on the basis of SSI for chl a. The genotypes that depicted the highest tolerance include N-1059 (SSI:-1.73), N-1011 (SSI:-1.62), CIM-499 (SSI:-1.49), and Cyto-124 (SSI:-1.42), and the accessions that showed the highest susceptibility consist of genotypes such as NIAB-1011/64 (SSI: 3.93), CIM-554 (SSI: 3.60), N-989 (SSI: 3.13), and N-512-121 (SSI: 2.68). The lowest SSI (tolerant) computed on the basis of chl b for the genotypes, i.e., N-135-36/17(YP) (SSI: 1.48), N-512-33/4 (SSI: 1.30), FH-941 (SSI: 0.82), and NIAB-545 (SSI: 0.76), and the highest SSI (susceptible) were observed for CIM-1100 (SSI: 7.60), N-135-BB/71 (SSI: 3.54), Cyto-178 (SSI: 3.52), and FH-118 (SSI: 3.47). The minimum SSI value (tolerant) was calculated on the basis of Total chl for the genotypes, i.e., NIAB-1011/64 (SSI: 12.60), N-135-36/17 (YP) (SSI: 12.07), N-512-33/4 (SSI: 10.46), and N-512-121 (SSI: 8.10), while the maximum SSI values (susceptible) were observed for CIM-1100 (SSI: 18.66), N-135-BB/71 (SSI: 15.48), FH-118 (SSI: 9.64), and CIM-473 (SSI: 9.20). The SSI computed on the basis of SC depicted that the most tolerant genotypes include NIAB-878 (SSI: -0.20), BH-167 (SSI: -0.18), Cyto-178 (SSI: -0.02), and CIM-240 (SSI: 0.27), whereas the most susceptible group consists of CIM-602 (SSI: 3.10), NIAB-1089 (SSI: 1.53), CIM-473 (SSI: 1.40), and Cyto-124 (SSI: 1.38). The SSI determined by E showed that certain genotypes such as CIM-602 (SSI: 1.45), NIAB-1089 (SSI: 0.84), IUB-13 (SSI: 0.04), and NIAB-484 (SSI: 0.36) were most tolerant, while BH-167 (SSI: 3.76), Cyto-178 (SSI: 3.35), FH-142 (SSI: 2.84), and NIAB-878 (SSI: 2.66) were most susceptible. The SSI calculated using Pn as a base has depicted the most tolerant NIAB-878 (SSI: 0.73), NIAB-484 (SSI: 0.80), NIAB-1089 (SSI: 0.84), and CIM-554 (SSI: 0.84), as well as the most susceptible genotypes, i.e., FH-142 (SSI: 1.36), CIM-602 (SSI: 1.34), CIM-599 (SSI: 1.24), and CIM-1100 (SSI: 1.23). The SSI computed using WUE as a base differentiated the tolerant genotypes NIAB-878 (SSI:-0.27), BH-167 (SSI:-0.23), Cyto-178 (SSI:-0.02), and CIM-240 (SSI:0.34) from susceptible ones CIM-602 (SSI:2.18), NIAB-1089 (SSI:1.40), CIM-473 (SSI:1.30), and Cyto-124 (SSI:1.29). SSI calculated on the basis of WP showed tolerant genotypes with the least SSI values, such as BH-167 (SSI: 3.94), FH-142 (SSI: 3.67), CIM-1100 (SSI: 2.42), and CIM-598 (SSI: 2.34), and susceptible genotypes with the highest values, such as NIAB-1089 (SSI: 5.27), N-512-33/4 (SSI: 3.94), N-135-BB/121 − 38 (SSI: 3.72), and Neelum-121 (SSI: 3.10). The genotypes explored to be tolerant, i.e., CIM-602 (SSI: 1.89), FH lalazar (SSI: 1.24), NIAB-545 (SSI: 0.97), and NIAB-1089 (SSI: 0.84), according to SSI computed on the basis of RL, whereas NIAB-1011/64 (SSI: 3.08), NIAB-878 (SSI: 2.28), CIM-598 (SSI: 2.22), and N-1011 (SSI: 2.08) were most susceptible. The genotypes identified to be most tolerant according to SSI of SL were CIM-1100 (SSI:-0.94), NIAB-545 (SSI:-0.60), BH-167 (SSI:-0.54), and FH-142 (SSI:-0.54), while genotypes such as NIAB-1042 (SSI:2.26), CIM-707 (SSI:2.14), IUB-13 (SSI:2.01), and NIAB-1011/64 (SSI:1.99) were explored to be most susceptible. According to the SSI of FSW, CIM-602 (SSI:-0.70), FH-942 (SSI:-0.61), FH-118 (SSI:-0.50), and N-1942 (SSI:-0.41) were most tolerant; however, genotypes such as CIM-707 (SSI:2.23), CIM-240 (SSI:2.06), N-1059 (SSI:2.04), and NIAB-878 (SSI:2.02) were most susceptible. The SSI computed by FRW identified the most tolerant genotypes, i.e., IUB-13 (SSI: 4.50), CIM-602 (SSI: 2.98), N-135-36/17 (YP) (SSI: 2.35), and FH lalazar (SSI: 1.92), while the most susceptible genotypes were NIAB-878 (SSI: 4.09), Cyto-178 (SSI: 3.76), CIM-1100 (SSI: 3.41), and CIM-599 (SSI: 3.10). Tolerant genotypes on the basis of DRW were CIM-602 (SSI:-23.37), NIAB-1011/64 (SSI:-7.04), FH lalazar (SSI:-6.86), and FH-118 (SSI:-2.29), while the most susceptible were NIAB-878 (SSI:2.78), N-444 (SSI:2.65), Cyto-178 (SSI:2.64), and CIM-599 (SSI:2.41). The SSI calculated by DSW showed N-135-BB/71 (SSI:-29.52), FH lalazar (SSI:-8.03), NIAB-1089 (SSI:-6.96), and IUB-13 (SSI:-5.82) were most tolerant, while NIAB-878 (3.75), N-989 (3.69), CIM-707 (3.63), and N-135-BB/121 − 38 (SSI:3.40) were highly susceptible. The best-performing genotypes according to calculated SSI on the basis of CMS were NIAB-545 (SSI: 0.26), CIM-602 (SSI: 0.31), BH-167 (SSI: 0.50), and NIAB-1089 (SSI: 0.50), while poor-performing genotypes were AA-703 (SSI: 1.58), CA-12 (SSI: 1.46), CIM-482 (SSI: 1.42), and CIM-598 (SSI: 1.34) (Supplementary Table 8).

Categorization of tolerant and susceptible genotypes based on SSI values under T2

The genotypes that are the most tolerant have the lowest SSI value and percentage decrease. The best performing genotypes according to SSI of TSP were CIM-506 (-3.13), CIM-109 (-0.92), CIM-598 (-0.91), and CIM-599 (-0.66), while the most poor performing genotypes were CIM-448 (3.15), N-989 (3.10), N-1011 (2.56), and NIAB-484 (2.40). The most tolerant genotypes for TFA were CIM-482 (-3.65), CIM-506 (-2.86), NIAB-1089 (-2.13), and IUB-13 (-1.70), and the most susceptible were NIAB-878 (6.95), CIM-1100 (5.11), AA-703 (4.68), and FH lalazar (3.76). The most best performing genotypes on the basis of SSI calculated by TSS were CIM-598 (-4.70), CIM-599 (-4.57), NIAB-878 (-4.47), and CIM-240 (-4.41), while the most poor performing were IUB-13 (16.81), CIM-602 (13.60), NIAB-1089 (12.99), and CIM-506 (12.94). For proline, highly tolerant genotypes were NIAB-1011/64 (-2.90), CIM-506 (-1.89), NIAB-1089 (-1.66), and NIAB-545 (-0.85), while highly susceptible genotypes include N-135-44/BB (7.80), N-135-36/17(YP) (7.32), IUB-13 (5.82), and BH-184 (4.80). The most tolerant genotypes on the basis of SSI computed from SOD consist of CIM-240 (-4.32), CIM-598 (-3.54), NIAB-878 (-3.24), and CIM-1100 (-3.17), while highly susceptible genotypes were CIM-506 (15.76), CIM-482 (13.81), NIAB-484 (12.46), and NIAB-1042 (10.79). The most tolerant genotypes on the basis of POD were CIM-599 (-3.66), Cyto-178 (-3.52), FH-142 (-1.89), and NIAB-878 (-1.69). However, most susceptible were CIM-482 (7.09), N-512-33/4 (4.52), CIM-602 (4.39), and NIAB-1089 (4.37). On the basis of chl, highly tolerant genotypes were NIAB-1042 (-3.70), CIM-482 (-2.53), NIAB-1011/48 (-2.35), and NIAB-1089 (-0.80), while highly susceptible were CIM-240 (5.48), FH lalazar (3.25), CIM-598 (2.88), and NIAB-878 (1.69). Highly tolerant genotypes explored on the basis of SSI calculated by chl b consist of CIM-1100 (-6.94), Cyto-178 (-5.09), CIM-598 (-4.31), and CIM-599 (-2.40), whereas the most susceptible genotypes were BH-184 (4.69), IUB-13 (4.58), CIM-602 (4.47), and NIAB-545 (4.28). The best-performing genotypes on the basis of total chl were NIAB-1042 (-1.78), NIAB-1011/48 (-1.30), Cyto-178 (-1.11), and CIM-1100 (-1.05), while the most poor performers were IUB-13 (3.13), BH-184 (3.10), CIM-602 (3.03), and NIAB-545 (2.89). On the basis of SC, the most tolerant genotypes were BH-167 (0.29), NIAB-545 (0.38), IUB-13 (0.41), and CIM-240 (0.45), while highly susceptible were CIM-554 (2.23), N-989 (2.06), N-1011 (1.96), and NIAB-484 (1.49). The most tolerant genotypes on the basis of E were CIM-70 (-250.59), NIAB-484 (-242.28), FH-942 (-166.66), and CIM-554 (-154.09); however, the most susceptible include Cyto-178 (165.78), N-221 (163.93), CIM-240 (150.45), and CIM-598 (142.24). SSI calculated on the basis of Pn deciphered most tolerant genotypes were IUB-13 (0.52), FH lalazar (0.57), CIM-506 (0.76), and CIM-70 (0.80), while highly susceptible were CIM-598 (1.35), CIM-554 (1.28), N-989 (1.23), and N-221 (1.21). WUE showed that the highest performing genotypes were BH-167 (0.44), NIAB-545 (0.56), IUB-13 (0.59), and CIM-240 (0.62), while the lowest performing genotypes include CIM-554 (1.46), N-989 (1.42), N-1011 (1.39), and NIAB-484 (1.25). According to WP, the most tolerant genotypes explored were CIM-1100 (-0.88), BH-167 (-0.84), CIM-598 (-0.60), and FH-142 (-0.30), while the most susceptible consists of NIAB-1089 (3.18), CIM-602 (2.63), Cyto-178 (2.41), and IUB-13 (2.38). With respect to RL, the highest performing genotypes were CIM-482 (-1.09), NIAB-1089 (-0.59), FH-142 (-0.19), and NIAB-545 (-0.16), and the lowest performing genotypes included FH-941 (1.76), NIAB-1011/48 (1.72), N-135-36/17 (YP) (1.66), and CIM-707 (1.66). On the basis of SL, highly tolerant genotypes were NIAB-545, FH-142 (-0.42), CIM-1100 (-0.19), and CIM-240 (0.13), while highly susceptible genotypes consist of FH-941 (2.00), BH-184 (1.75), CIM-707 (1.74), and CIM-602 (1.59). According to the SSI of FSW, accessions that showed maximum tolerance consist of N-135-42/8–18 (-2.16), CIM-602 (-2.05), IUB-13 (-1.11), and CA-12 (-1.05), while genotypes that depict the highest susceptibility include CIM-707 (2.83), CIM-240 (2.55), BH-167 (2.51), and FH-941 (2.39). On the basis of SSI computed by FRW, the most tolerant genotypes were IUB-13 (-4.26), CIM-602 (-3.24), CIM-506 (-2.134), and NIAB-1089 (-2.04); however, the most susceptible genotypes consist of N-1059 (3.30), CIM-1100 (3.17), NIAB-878 (3.09), and CIM-240 (3.02). According to SSI of DRW, most high-performing genotypes were CIM-602 (-8.61), CIM-499 (-5.10), NIAB-484 (-3.31), and CIM-506 (-3.20), and highly poor-performing genotypes were Cyto-178 (2.54), CIM-1100 (2.42), FH-941 (2.35), and NIAB-878 (2.35). With respect to SSI of DSW, the most tolerant genotypes were N-135-BB/71 (-18.68), IUB-13 (-1.68), CIM-602 (-1.43), and CIM-506 (-1.37), while the most susceptible on the basis of susceptibility index were N-989 (1.88), N-1059 (1.85), and CIM-598 (1.77). On the basis of SSI computed from CMS, highly tolerant genotypes were NIAB-545 (0.18), FH-142 (0.37), BH-167 (0.44), and CIM-506 (0.54), whereas highly susceptible genotypes include NIAB-1011/64 (1.23), CIM-240 (1.20), CIM-534 (1.17), and N-135-42/8–18 (1.15), as shown in Supplementary Table 9.

Unveiling resilience: net score evaluation of key traits

The SSI values were computed for all the traits. The formula of RANK.AVG was applied to SSI values, which assigned the highest and lowest scores to the most tolerant and susceptible genotypes, respectively, for each trait. The genotypes were assigned scores on a scale of 0–10 on their respective performances. The average of all the scores for each genotype for all the traits classified the genotypes according to their tolerance. The highly tolerant genotype depicts maximum average score on the basis of all traits and vice versa. In our study, NIAB-545 (7.7), FH-142 (7.3), and FH-lalazar (6.40) were most heat-tolerant, while CIM-473 (3.98), cyto-124 (4.04), and FH-941 (4.14) were the most susceptible genotypes under stress level 1 (T1). However, under stress level 2 (T2), most high-performing genotypes include NIAB-545 (7.54), FH-142 (7.43), and CIM-506 (6.71), while the most poor-performing genotypes are N-989 (3.78), CIM-473 (3.90), and AA-703 (3.96), as shown in Supplementary Tables 4 and 5. The scale of grading the genotypes is given (Table 4).

Table 4 Standard scale to categorize the cotton genotypes on the basis of its scoring
Full size table

Discussion

Genetic diversity is very crucial to developing ideotypes through hybridization. It provides basic building blocks to develop advanced populations with traits of interest [41]. In order to develop superior genotypes, exploring genetic variation among the cotton genotypes is essential [42]. The goal of this study was to explore the diverse germplasm that can be utilized in future breeding programs for developing abiotic stress-tolerant ideotypes. Evaluations of genetic variation are usually biased as they depend on phenotypic data of particular traits. Heat stress is the main obstacle to attaining high yield, as it has detrimental effects on cotton growth and development because it influences multiple traits related to metabolism and yield [43]. The primary objective of plant breeders of the current era is to develop new genotypes with improved tolerance to heat stress and to expand the tolerance of existing germplasm [44]. A two-way ANOVA confirmed huge genetic variation among the cotton genotypes for all the traits under study, and this info can be utilized to develop heat-tolerant varieties of cotton by designing efficient breeding strategies. The main focus of cotton breeders is the availability of a wide genetic base of present germplasm [45, 46]. The degree to which a plant can be genetically modified depends on the heritability of its economic traits [47, 48]. Prior research has depicted that heat tolerance is a heritable phenomenon [49, 50]. Cell membrane stability (CMS) is used as a standard marker to assess the heat tolerance [21, 51]. A correlation matrix was constructed to assess the reliance of different variables upon each other for improved phenotypes to get higher yields. In our study, it was revealed that under control conditions, Cell membrane stability (CMS) showed a slight relation to WUE (r = 0.234). While under heat-stressed conditions, Cell membrane stability (CMS) depicted the highest positive correlation with TSS (r = 0.630) under T1 and with TSS (r = 0.607) and RL (r = 0.603) under T2. The percentage of cell membrane thermo stability (CMT) can be used as a potential trait for screening and identification of cotton germplasm for heat stress [52]. In our study under heat stress, susceptible genotypes in comparison to tolerant ones showed lower values of cell membrane stability index. In the current study under heat stress, TSS and RL were potential biochemical and morphological indicators for CMS. Many researchers attempted to improve the cell membrane stability of the plants under stress conditions [23, 53,54,55]. A significant reduction in RL, SL, FRW, FSW, DRW, and DSW were observed under heat stress in this study for most of the genotypes with few exceptions and are in accordance with the results reported earlier [56, 57]. These morphological traits can be used to differentiate and classify the heat-tolerant material. Water use efficiency (WUE), photosynthesis (Pn), and transpiration (E) decreased under heat stress while water potential (WP) increased. The decrease in photosynthesis was 27.2% under T1 and 45.15% under T2 and is attributed to a reduction in CO2 flow to the sites of carboxylation sites of the plants [58, 59]. Agglomerative cluster analysis was performed in this study, which grouped all the genotypes into 4 clusters under control and stressed conditions and separated the best performing genotypes from the poor ones. Many scientists used this method of classification [60]. The first six components having eigenvalue > 1 presented a maximum variance of 76.11% and 77.93% among cotton genotypes under T1 and T2, respectively. This technique was used efficiently by multiple researchers to assess genetic variation among the genotypes [51, 61]. When it comes to the results of experimental studies, multivariate analyses are a valuable source of efficiency, precision, and accuracy. An intimate view of the entire crop’s reaction to heat stress is provided by heat susceptibility indices. Heat susceptibility indices are therefore essential for screening and selection of genotypes with high yield potential under stress. Many scientists have used this indices system to improve different crop species [62,63,64]. Heat stress adversely damages leaf functioning and accelerates the production of hazardous compounds, e.g., ROS, which, by interacting with membrane, destroy its functioning. The generation of these harmful compounds (superoxide anion, hydroxyl radicals, singlet oxygen, and H2O2) was attributed to an imbalance between light uptake and its usage by the plant systems [65, 66]. These free radicals attack the cellular machinery, which results in the disturbance of proper functioning [67, 68]. Though plants have acquired their defense mechanisms to combat ROS by evolution [69, 70]. The activity of SOD extensively increased under both stressed conditions for most of the genotypes except CIM-599, Cyto-178, BH-167, CIM-1100, NIAB-878, FH-142, and NIAB-545 under T1. While under T2, the same pattern was examined for all the genotypes besides CIM-599, Cyto-178, CIM-598, CIM-1100, CIM-240, NIAB-878, and FH lalazar [71, 72]. The activity of POD noticeably increased under heat-stressed conditions for all the genotypes except CIM-599, Cyto-178, BH-167, CIM-70, NIAB-484, NIAB-878, and FH-142 under T1. While under T2, it showed the same pattern of increase for all except CIM-599, Cyto-178, CIM-598, and FH-142; similar results were reported earlier [73]. It was observed that the amount of total chl content and TSP was considerably decreased under heat-stressed conditions [74, 75]. It was exhibited that TSS, TFA, and proline content increased extensively under heat stress conditions for all the genotypes with few exceptions [76,77,78]. The overall performance of enzymes under stress increased significantly. Limited research has focused on the effects of temperature on cotton’s reproductive potential and lint quality [79]. Studies have depicted that temperature affects cotton lint yield and fiber quality parameters, such as fiber strength, length, fineness, and micronaire [80]. Early fiber elongation, from 0 to 15 days after anthesis, is more sensitive to temperature than the later stages of fiber development [81, 82]. Fiber attributes, which rely on the deposition of photosynthetic products in fiber cell walls, are sensitive to environmental fluctuations. Both low and high temperatures usually slow cellulose formation, distressing fiber maturity and elongation, leading to poorer fiber quality [83]. The screened best-performing genotypes NIAB-545, FH-142, and FH-lalazar can be used as parents for future cotton breeding programs to achieve higher heat tolerance in upcoming offspring’s. We can sustain agricultural productivity by cultivating these chosen genotypes in heat-stressed areas as these genotypes can be used as breeding material for future breeding related to heat stress further these genotypes can be developed as registered varieties for farmers. We can introgress the traits of interests by hybridization. In general, the findings of our research have exhibited significant importance in the development and characterization of heat-tolerant genotypes, which is mandatory to ensure viable cotton cultivation under diverse climate scenarios. A potential drawback of our research is that assessments were made under controlled conditions, which may present problems when applied directly to field scenarios. In order to completely understand heat stress and its effects, in addition to morpho-physiological and biochemical traits, we should incorporate the molecular aspects. To address these limitations, future long-term studies should be conducted to fully understand the environment and genetic interaction and to get a deeper knowledge of the molecular pathways under heat stress. Investigating these possibilities might advance our knowledge of cotton genotypes resistance to heat. Prospective investigations on cotton’s ability to withstand heat stress may encompass genetic and molecular characterization; Genome-wide association studies (GWAS), functional identification of putative genes, transcriptome and proteome analysis, and molecular markers for marker-assisted breeding initiatives. Heat-tolerant cotton genotypes may be validated by conducting field trials to assess the interaction with multiple environmental factors that help in the development of resilient cotton varieties.

Conclusion

The steadily rising global temperatures force plant genotypes to adjust by changing certain phenotypes. In light of increasing temperatures, it’s crucial to create cultivars that can handle sudden changes without compromising yield. The first approach is to examine the available cotton germplasm for its capacity to withstand high-temperature stress. Many plants depict increased antioxidant enzyme activity, which is crucial for their heat tolerance mechanisms. This study had identified that NIAB-545 and FH-142 were the top-performing genotypes as these genotypes were placed in group-III and showed maximum accumulation of total free amino acids and proline which could be responsible for their tolerance under control conditions while under extreme heat stress these genotypes were placed in group-II and depicted highest accretion of total soluble sugars, superoxide dismutase, peroxidase and chl a which may make them able to withstand under the adverse effects of heat stress. Reduced chlorophyll content under heat stress impairs photosynthesis, limiting the energy required for fiber development and negatively affecting fiber quality. Heat stress often causes disturbances in the typical morphological, physiological, and biochemical traits of cotton, which can ultimately reduce the yield of the cotton crop. Promising genotypes can be effectively used in future cotton breeding programs to boost yield and productivity by improving heat tolerance in response to a changing climate.

Data availability

All data generated or analyzed during this study is included in this article.

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Acknowledgements

Author wants to acknowledge NIAB-C, PIEAS management for their help to complete the research work.

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  1. Plant Breeding and Genetics Division, Nuclear Institute for Agriculture and Biology (NIAB), Faisalabad, Pakistan

    Manzoor Hussain & Muhammad Kashif Riaz Khan

  2. Nuclear Institute for Agriculture and Biology College, Pakistan Institute of Engineering and Applied Sciences (NIAB-C, PIEAS), Faisalabad, Pakistan

    Tahira Luqman, Manzoor Hussain & Muhammad Kashif Riaz Khan

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TL: Conceptualization, analytical work, data collection, data analysis, formal writing the manuscript, review & editing. MH and MKRK: contributed in designing and finalization of basic idea, supervision and finalization of the manuscript.

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Luqman, T., Hussain, M. & Khan, M.K.R. Harnessing multivariate insights coupled with susceptibility indices to reveal morpho-physiological and biochemical traits in heat tolerance of cotton. BMC Plant Biol 25, 126 (2025). https://doi.org/10.1186/s12870-025-06141-5

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