Morpho-physiological and biochemical responses of cotton (Gossypium hirsutum L.) genotypes upon sucking insect-pest infestations

Abstract

The effects of sucking insect-pests on the morpho-physiological and biochemical changes in the leaves of four cotton genotypes—Bio 100 BG-II and GCH-3 (highly tolerant); KDCHH-9810 BG-II and HS-6 (highly susceptible)—were examined. Compared to tolerant genotypes, susceptible genotypes showed a decrease in relative water content, specific leaf weight, leaf area, photosynthetic rate, and total chlorophyll content, with an increase in electrolyte leakage. Hydrogen peroxide and total soluble sugar content were higher in susceptible plants. In contrast, resistant plants had higher levels of total soluble protein, total phenolic content, gossypol content, tannin content, peroxidase activity, and polyphenol oxidase. The findings demonstrated that the Bio 100 BG-II and GCH-3 genotypes effectively offset the impact of sucking insect-pests by modifying the factors mentioned above. The KDCHH-9810 BG-II and HS-6 genotypes could not completely negate the effects of sucking insect-pests. Customized metabolites and total soluble protein are more efficient in protecting cotton plants from damage brought on by infestations of sucking insects and pests.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01253-w.

Introduction

Cotton, also referred to as "white gold," is a significant economic crop in India. Gossypium, the genus that includes cotton, has the allotetraploids G. hirsutum and G. barbadense and the diploids G. herbaceum and G. arboreum, all of which are widely grown for textile fibre, edible cottonseed oil, and cottonseed meal for the dairy industry (Paterson et al. 2012). G. hirsutum contributes most of the total yield of lint cotton, and its productivity is considerably impacted by biotic stress (Lee et al. 2007; Mehanathan et al. 2018). Several arthropod species have been classified as cotton-pests, but less than 40 are considered severe crop pests (Luttrell et al. 2015). Sucking and chewing insect-pests are two categories within the cotton insect-pest complex.

Under field and laboratory conditions, the transgenic cotton displays outstanding resistance to Helicoverpa armigera, Pectinophora gossypiella, Earias vittella, and Earias insulana (Kranthi and Kranthi 2004). Bt toxins in transgenic cotton can efficiently control specific lepidopterous species but lack resistance against sucking insect-pests (Sharma and Pampapathy 2006). Little consideration has been paid to the shifting dynamics of sucking-pests and other non-target organisms in the impact evaluation of transgenic cotton. It has been observed that in Bt cotton, less insecticide use increases the population of sucking insect-pests (Men et al. 2005). As a result, sucking pests are getting worse in the Bt cotton period, calling for indiscriminate pesticides.

Aphids, thrips, leafhoppers, and whiteflies are the leading cotton pests among these sucking insect-pests and can cause up to 22.85% drop in cotton yield (Lawo et al. 2009). As they suck the plant's sap, weaken it, and, in cases of severe infestation, cause leaf shedding and wilting, these pests are particularly harmful during cotton seedling and vegetative growth stages. The whitefly Bemisia tabaci poses a grave threat to cotton because it harms the plant by sucking cell sap, which can result in a 50% reduction in the production of bolls (Ahmad et al. 2002). In addition, it serves as a vector for the cotton leaf curl virus (CLCuV), which causes cotton leaf curl disease (CLCuD), which is a threat to our cotton-based economy (Nelson et al. 1998). Numerous biochemical and morpho-physiological constituents in cotton genotypes impact whitefly populations and vice versa (Rizwan et al. 2021). Among all the processes, components of the biochemical pathway are crucial in protecting plants against sap-sucking insects.

When there is no stress, plants grow and develop at their best levels by utilizing the available oxygen. However, reactive oxygen species (ROS) are produced when plant tissues are under stress, such as during the attack of sap-sucking insects (Singla et al. 2019). ROS induce photo-oxidative damage to biomolecules and internal cellular structures (Xie et al. 2016; Mittler 2017). In response to such interactions with pests, the plants undergo various biochemical alterations linked to stress signalling, which activates their defence mechanisms. The induced defensive system consists of a variety of non-enzymatic elements, including phenolic substances like gossypol, enzymes like polyphenol oxidase (PPO) for phenol metabolism, antioxidant enzymes such as peroxidases (POX), and tannin build-up (Debona et al. 2012; Akter et al. 2015). As a result, plants have great natural biochemical defences against plant diseases. Researchers have recently sparked a strong interest in deciphering the biochemical and molecular interactions between sap-sucking insects and plants which result in significant yield losses.

In a single study effect of whitefly on tolerant and susceptible genotypes of both Bt and non-Bt cotton was not studied before this study for both morpho-physiological and biochemical parameters. The present study describes the effects of whitefly infestation on morpho-physiological and biochemical parameters, which can be utilized as stress markers for biotic conditions. The stress resistance mechanism in cotton and other plants can be studied using these criteria, and future defence tactics against the whitefly population could be developed using the investigated characteristics.

Materials and methods

The present investigation was conducted at Cotton Research Area, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar. The field area is situated at 29.148315° N, and 75.696434° E, and 215.2 m above mean sea level in India.

Layout and design of the experiment

Seeds of four cotton genotypes (G), i.e. Bio-100 BG II and GCH 3 (highly tolerant to sucking insect-pests) and KDCHH-9810 BG II and HS 6 (highly susceptible to sucking insect pests), were sown on 07–05-2016. Parent genotypes of HS-6 are Bikaneri Narma and K3199, and the parent genotype of GCH 3 is Bio-100 BG II. After sowing, genotypes were divided into two groups, i.e. controlled and stressed. Each treatment contained four replications, and each replication had 11 plants. Plants that were net-covered to prevent the attack of sucking insect-pests served as control, and plants that were net-covered and contained whitefly served as stressed. The design of the experiment was Randomized Blocks Design (RBD), the number of replication was four, and the number of treatments (T) was two, i.e. controlled and stressed (Fig. 1 and 2). From top to down, the plant's 3rd leaf was taken for further analysis.

Fig. 1.

Control cotton plants used for experiments grown in pots under screen house conditions

Fig. 2.

Cotton plants artificially inoculated with whitefly under screen house conditions

Chemicals and reagents

All chemicals used in the present study were purchased from Loba chemicals, Merck and Sigma Chemical Co. (USA) and were of high purity and analytical grade.

Experimental layout and sampling

Whitefly population was recorded in stressed plants. The leaves of uniform age and size were collected at 45, 60, 75 and 90 days after sowing (DAS) to study the biochemical and morpho-physiological parameters from both controlled and whitefly-infested plants.

Morpho-physiological parameters

The relative water content (RWC) of leaves was estimated following the method of Barrs and Weatherley (1962). The following formula calculated the RWC of leaves:

$$\text{RWC}(\%)=\frac{\text{Fresh}\text{weight}\left(\text{FW}\right)-\text{Dry}\text{weight}(\text{DW})}{\text{Fully}\text{turgid}\text{weight}\left(\text{FTW}\right)-\text{Dry}\text{weight}(\text{DW})}\times 100$$

The specific leaf weight (SLW) was estimated by Bondada and Oosterhuis (2001) method. It was calculated with the given formula:

$$\text{SLW}\left({\text{mg.cm}}^{-2}\right)=\frac{\text{Total}\text{dry}\text{weight}(\text{mg})}{\text{Total}\text{leaf}\text{area}({\text{cm}}^2)}$$

The total chlorophyll content of leaves was estimated by following the method of Hiscox and Israelstam (1979). Small pieces of leaf samples (100 mg) were dissolved in 10 ml dimethyl sulphoxide (DMSO) and kept for 12 h in the dark. Absorbance was read at 645 and 663 nm against a reagent blank. Total chlorophyll content was calculated following the equation used by Arnon (1949), also recommended by Paul et al. (2017):

$$\text{Total}\text{chlorophyll}\text{content}\left({\text{mg.g}}^{-1}\text{FW}\right)=\left[\left(20.2\times {\text{A}}_{645}\right)+\left(8.02\times {\text{A}}_{663}\right)\right]\times \frac{\text{V}}{1000\times \text{W}}$$

where, A₆₄₅ = Absorbance at 645 nm, A₆₆₃ = Absorbance at 645 nm, V = Total volume of supernatant in ml, W = Weight of sample taken in grams (g).

Electrolyte leakage (EL) was estimated by following the method of Dionisio-Sese and Tobita (1998). Leaf disks of 1 cm diameter were taken from fresh leaves, and these disks were kept in vials containing 10 ml distilled water for 5 h. Now, the surrounding water's electrical conductivity (ECa) was measured. These vials with leaf disks were then heated in a boiling water bath for 50 minutes (min). After cooling, the electrical conductivity (EC_b) of surrounding water was again measured, and Relative Stress Injury (RSI) was calculated as follows:

$$\text{RSI}(\%)=\frac{{\text{EC}}_{\text{a}}}{{\text{EC}}_{\text{b}}}\times 100$$

The leaf area was recorded by the leaf area meter (LI 3000 Area meter, LICOR Ltd. Nebraska, USA). Photosynthetic rate (μmol CO₂ /m²/s) of leaves from 4 replications was recorded in bright sunlight from 10:45 AM to 12:00 Noon using an open system LCA-4 ADC portable Infrared Gas Analyser (IRGA) (Analytical Development Company, Hoddeson, England).

Biochemical parameters

Total soluble sugar & Total phenolic content

Leaf samples (100 mg) with 80% ethanol (30 ml) were kept in a water bath. These were homogenized, centrifuged, and the supernatants were collected. The supernatant was evaporated, and residues were dissolved in five ml of distilled water.

Total soluble sugar was estimated by the method of Dubois et al. (1956). Estimation of total phenolic content was done following the method of Bray and Thorpe (1954).

Total soluble protein

All the steps of extraction were carried out at 0–4 °C. Leaf tissue (500 mg) was macerated with 3 ml 0.1 M phosphate buffer (pH 7.0). The homogenate was centrifuged for 20 min, and the supernatant was collected. The total soluble protein in the supernatant was precipitated by adding 20% trichloroacetic acid (TCA) overnight. The content was centrifuged; residues were washed twice with cold acetone and re-dissolved in 0.5 ml of 0.1 N NaOH solution. Total soluble protein was estimated by the method of Lowry et al. (1951).

Gossypol content

Tubes containing 500 mg leaf samples were filled with 15 ml ethanol (80%) and kept at 80 °C in a water bath. The supernatant was collected, and the pH of the extract was adjusted to 3.0 with 0.1 N HCl. It was followed by mixing the contents with 15 ml diethyl ether. The ether phase was then evaporated, and the residue was re-dissolved in 3 ml of 95% ethanol. Gossypol content was estimated by the method of Bell (1967).

Tannin content

Leaf samples (500 mg) were taken in 30 ml of 70% acetone and kept in a water bath for 10 min at 70 °C. It was homogenized; the supernatant was collected and evaporated until dry. The residue was re-dissolved in 5 ml of 70% acetone, and tannin content was estimated using a standard method of Porter et al. (1985).

Hydrogen peroxide (H₂O₂) content

Leaf samples (500 mg) were homogenized in 4 ml of chilled 5% trichloroacetic acid (TCA), and 100 mg of activated charcoal was added. Homogenate was centrifuged at 7,000 rpm for 15 min. The clear supernatant was collected, filtered carefully, and used to estimate H₂O₂ by following the method of Sinha (1972).

Peroxidase (POX) activity (EC 1.11.1.7)

Leaf tissue (500 mg) was ground with 4 ml of ice-cold 0.1 M Tris–HCl buffer (pH 7.6) and 1 ml of 0.1% EDTA. The homogenate was centrifuged at 10,000 × g for 15 min at 4 °C to get the supernatant. POX activity was estimated in the supernatant following the method of Shannon et al. (1966), and enzyme activity was expressed as units/g FW/min (U/g FW/min).

Polyphenol oxidase (PPO) activity (EC 1.10.3.1)

The leaf tissue (500 mg) was homogenized in 4 ml of 0.1 M sodium phosphate buffer (pH 7.0) and 1 ml of 0.1% EDTA. The homogenate was centrifuged for 15 min at 10,000 × g at 4 °C, and the supernatant was used for PPO assay by following the modified method of Taneja and Sachar (1974). For each sample, the change in absorbance was read at 420 nm in the spectrophotometer. The enzyme activity was expressed as U/g FW/min.

Data analysis

Two factorial analysis was done for the whitefly population in a stressed environment, on different days after sowing and in different genotypes. Three factorial analysis was done for biochemical and morpho-physiological data analysis. The correlation matrix between the whitefly population and biochemical and morpho-physiological parameters under stress conditions was obtained by using Pearson Correlation. R version 4.0.3 (2020–10–10) was used for all of these analyses.

Results

Morpho-physiological Parameters

Leaf area

Leaf area significantly differed in all the genotypes, in treatments and at each stage of observation, and two factorial interactions for DAS, treatments and genotypes were significant for leaf area (Table 1). It was significantly declined with an increase in sucking insect-pests infestation levels in all genotypes, and the reduction was still higher in highly susceptible genotypes (Table 2). An increase in leaf area was observed in controlled conditions from 45 to 90 DAS in highly tolerant (11.70–13.74%) and highly susceptible (7.74–9.85%) genotypes. Still, in a stressed condition, a decrease in leaf area was observed in all genotypes from 60 to 90 DAS. The highest leaf area was observed in the control plants of highly tolerant genotype, namely Bio-100 BG II at 90DAS and the lowest in stressed plants of highly susceptible genotype, namely HS-6 at 90 DAS with 52.04 and 30.80 cm², respectively. The highly tolerant genotypes showed higher leaf area than highly susceptible genotypes in both treatments and at all the DAS (Table 2). The highly tolerant genotypes showed a higher decrease in leaf area from control to the stressed environment than highly susceptible genotypes (Table 2; Fig. 3a).

Table 1.

ANOVA of biochemical and morpho-physiological parameters

Source of Variations	Df	P values
		LA	SLW	PS Rate	RWC	T Chl	EL	Phenol	H2O2	Tannin	PPO	POX	Gossypol	Protein	Sugar
DAS	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
DAS X T	3	0	0	0	0	0	0.003	0.65	0	0	0	0	0	0.19	0
G	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0
DAS X G	9	0	0	0	0.99	0.57	0	0.62	0	0	0	0	0	0.83	0
T X G	3	0	0	0	0.31	0.22	0	0.93	0	0	0	0	0	0.002	0.019
DS X T X G	9	0.75	0.99	0	1	0.99	0.92	1	0	0.99	0.004	0.20	0.86	1	0.74

Df degrees of freedom, 0:- highly significant, 0.001–0.009:- significant, 0.01–0.09:- significant, > 0.09:- non-significant. DAS Days after sowing, T Treatment, G Genotype

Table 2.

Electrolyte leakage, Leaf area, SLW, Total chlorophyll content, RWC and Photosynthetic rate in different genotypes under controlled and stressed environment on different days after sowing

DAS	Treatment	Genotype	LA	SLW	PS Rate	RWC	T Chl	EL
45	Control	B (HT)	45.75 h ± 0.67	6.43 m ± 0.37	18.34 g ± 0.81	82.17b ± 0.88	2.85j ± 0.42	9.51x ± 0.69
		G (HT)	35.84t ± 0.64	6.71d ± 0.49	15.66 k ± 0.70	80.73c ± 1.12	2.76ij ± 0.43	9.66v ± 0.70
		K (HS)	41.66 l ± 0.70	6.13 m ± 0.45	14.64n ± 0.70	79.39 g ± 0.80	2.71j ± 0.43	9.95u ± 0.61
		H (HS)	33.74w ± 0.67	6.35ij ± 0.29	13.46 s ± 0.77	78.19 k ± 0.78	2.70j ± 0.44	10.56r ± 0.74
	Stress	B (HT)	43.18j ± 0.88	6.17e−g ± 0.45	17.19 g ± 0.88	81.88a ± 1.16	2.59ij ± 0.46	9.74t ± 0.67
		G (HT)	33.77w ± 0.67	6.51f−h ± 0.33	14.34° ± 0.81	79.61f ± 1.31	2.35 mn ± 0.50	12.18 l ± 0.88
		K (HS)	39.97n ± 0.61	5.65p ± 0.41	13.66r ± 0.70	78.34j ± 0.93	2.26no ± 0.52	13.81i ± 0.64
		H (HS)	32.23A ± 0.84	5.95° ± 0.21	12.64v ± 0.64	77.75q ± 1.23	2.22p ± 0.48	15.72e ± 0.36
60	Control	B (HT)	49.75c ± 0.67	6.85de ± 0.41	20.04c ± 0.74	81.45c ± 0.89	3.16c−e ± 0.55	9.65w ± 0.70
		G (HT)	38.54q ± 0.74	7.27a ± 0.33	16.76i ± 0.67	79.93e ± 1.22	3.01d−f ± 0.46	9.86u ± 0.64
		K (HS)	44.56i ± 0.74	6.34ij ± 0.29	15.64 k ± 0.70	78.51i ± 0.92	2.92f−h ± 0.41	10.41 s ± 0.77
		H (HS)	35.74t ± 0.67	6.69d ± 0.45	14.16p ± 0.88	77.17p ± 0.88	2.86 g−i ± 0.42	11.13p ± 0.88
	Stress	B (HT)	45.98d ± 0.61	6.42d−f ± 0.25	18.09d ± 0.64	79.81d ± 1.15	2.85f−h ± 0.42	10.02r ± 0.74
		G (HT)	35.51u ± 0.74	6.85c ± 0.41	15.04 m ± 0.74	77.54no ± 0.90	2.55 k ± 0.46	12.74 k ± 0.67
		K (HS)	41.78 k ± 0.67	5.71op ± 0.45	14.16p ± 0.88	76.11t ± 0.88	2.40 lm ± 0.50	14.52 g ± 0.74
		H (HS)	33.51x ± 0.74	6.12 k−m ± 0.37	13.02t ± 0.72	75.42r ± 0.77	2.33 lm ± 0.50	16.92d ± 0.04
75	Control	B (HT)	50.96b ± 0.61	6.61j−l ± 0.29	22.13b ± 0.88	80.56 g ± 0.90	3.52a ± 0.46	10.04u ± 0.62
		G (HT)	39.34° ± 0.81	7.08b ± 0.45	18.36e ± 0.81	79.01 h ± 0.78	3.31a ± 0.50	10.35 s ± 0.75
		K (HS)	45.36 g ± 0.81	5.91n ± 0.37	16.83i ± 0.64	77.55n ± 0.87	3.16bc ± 0.50	11.08p ± 0.70
		H (HS)	36.07 s ± 0.74	6.42hi ± 0.33	15.31 l ± 0.85	76.14t ± 0.90	3.05c−e ± 0.46	12.03 m ± 0.51
	Stress	B (HT)	45.02f ± 0.74	5.96jk ± 0.41	17.58f ± 0.74	76.69° ± 1.14	2.79f−h ± 0.43	10.69op ± 0.68
		G (HT)	34.46v ± 0.48	6.47gh ± 0.49	14.44° ± 0.77	74.31v ± 0.82	2.48kl ± 0.48	13.72i ± 0.70
		K (HS)	40.51 m ± 0.35	5.04 s ± 0.45	13.41 s ± 0.77	72.86x ± 0.64	2.33 mn ± 0.50	15.83f ± 0.89
		H (HS)	32.11z ± 0.67	5.51q ± 0.29	12.25v ± 0.84	72.18y ± 0.75	2.24no ± 0.52	18.91b ± 0.47
90	Control	B (HT)	52.04a ± 0.70	6.46 m ± 0.33	23.89a ± 0.64	79.58 l ± 0.90	3.45ab ± 0.48	10.28t ± 0.50
		G (HT)	40.04n ± 0.58	6.98b ± 0.41	19.48c ± 0.77	77.84 m ± 0.91	3.23ab ± 0.52	10.82q ± 0.55
		K (HS)	45.76e ± 0.67	5.64p ± 0.49	17.81 g ± 0.64	76.26 s ± 0.85	3.08 cd ± 0.46	11.78n ± 0.51
		H (HS)	36.35r ± 0.61	6.17 lm ± 0.37	15.88j ± 0.50	74.79u ± 0.89	2.95e−g ± 0.41	12.96j ± 0.43
	Stress	B (HT)	44.15 h ± 0.88	5.76 m ± 0.45	16.97 h ± 0.61	72.98w ± 0.62	2.72 h−j ± 0.43	10.91° ± 0.45
		G (HT)	33.50y ± 0.77	6.31ij ± 0.37	13.79q ± 0.67	70.51z ± 0.75	2.39 lm ± 0.50	14.21 h ± 0.18
		K (HS)	39.06p ± 0.74	4.71t ± 0.31	12.41u ± 0.77	69.01A ± 0.71	2.17° ± 0.43	16.75c ± 0.39
		H (HS)	30.80B ± 0.64	5.19r ± 0.41	11.16w ± 0.88	68.34B ± 0.80	2.01p ± 0.42	20.01a ± 0.37

Treatment means in the same columns with different letters differ significantly (P < 0.05). The design consists of four sampling times [45, 60, 75 and 90 days after sowing (DAS)], two treatments [T] (controlled and stressed), and four cotton genotypes with contrasting behaviour to sucking insect-pests (2 highly tolerant [B (HT) & G (HT)] and 2 highly susceptible [K (HS) & H (HS)]) [B (HT)-Bio-100 BG II, G (HT)- GCH-3, K (HS)- KDCHH-9810 BG II, H (HS)- HS-6]; LA- Leaf area (cm²), SLW Specific leaf weight (mg/cm²), PS Rate- Photosynthetic rate (μmol CO₂ fixed/m²/ s), RWC Relative water content (%), T Chl- Total chlorophyll content (mg/g FW), EL Electrolyte leakage (%RSI). Sign of ± shows standard error of mean value

Fig. 3.

Percent change in a Leaf area, b SLW, c Photosynthetic rate, d RWC, e Total chlorophyll content and f Electrolyte leakage from controlled to the stressed environment among different cotton genotypes at 45, 60, 75 and 90 days after sowing (DAS). [B (HT)-Bio-100 BG II, G (HT)- GCH-3, K (HS)- KDCHH-9810 BG II, H (HS)- HS-6]. $\mathrm{Percent}\mathrm{change}=\frac{\mathrm{Mean}\mathrm{value}\mathrm{in}\mathrm{stress}-\mathrm{Mean}\mathrm{value}\mathrm{in}\mathrm{control}}{\mathrm{Mean}\mathrm{value}\mathrm{in}\mathrm{control}}\times 100$

Specific leaf weight (SLW)

SLW significantly differed between all the genotypes, treatments and each stage of observation, and two factorial interactions for DAS, treatments and genotypes were significant for SLW (Table 1). SLW significantly decreased in the leaves of highly tolerant and highly susceptible genotypes under stressed conditions from 60 to 90 DAS. In contrast, from 45 to 60 DAS, it increased in both conditions (Table 2). The highest decrease in SLW was observed from 60 to 90 DAS in highly susceptible genotypes under stress conditions, and the lowest reduction in SLW was found in highly tolerant genotypes. The highest SLW was observed in the controlled plants of highly tolerant genotype, namely GCH-3 at 60 DAS and the lowest in stressed plants of highly susceptible genotype, namely KDCHH-9810 BG II at 90 DAS, with 7.27 and 4.71 mg/cm², respectively. The highly tolerant genotypes showed higher SLW than highly susceptible genotypes in both treatments and all the DAS (Table 2). SLW decreased from control to stressed environments, which was higher in highly susceptible genotypes than highly tolerant ones (Table 2; Fig. 3b).

Photosynthetic rate

Photosynthetic rate significantly differed with the duration of crop growth in all the genotypes and under both types of conditions; all types of interactions were significant for it (Table 1). The highest photosynthetic rate was observed in control plants of highly tolerant genotype, namely Bio-100 BG II at 90 DAS and the lowest in stressed plants of highly susceptible genotype, namely HS-6 at 90 DAS with 23.89 and 11.16 μmol CO₂ fixed/m²/s, respectively. There was a significant increase in photosynthetic rate in the leaves of both highly tolerant and highly susceptible genotypes under the control condition from 45 to 90 DAS. In contrast, in a stressed condition, sucking insect-pests infestation led to an increase in photosynthetic rate from 45 to 60 DAS, after then declined. The highly tolerant genotypes had a higher photosynthetic rate than highly susceptible genotypes in both treatments and at all the DAS (Table 2). An increase in photosynthetic rate from control to the stressed environment was at par in highly susceptible and highly tolerant genotypes (Table 2; Fig. 3c).

Relative water content (RWC)

The RWC significantly differed in all the genotypes, in treatments, and at different DAS, and interaction between DAS and treatment (T) was significant for it (Table 1). Sucking insect-pests infestation led to a significant decrease in RWC in the leaves of both highly tolerant and highly susceptible genotypes from 45 to 90 DAS. However, highly tolerant genotypes showed a lower decline in RWC as compared to highly susceptible genotypes. The highest RWC was observed in the controlled plants of highly tolerant genotype, namely Bio-100 BG II at 45 DAS and the lowest in stressed plants of highly susceptible genotype, namely HS-6 at 90 DAS with 82.2 and 68.3%, respectively. The highly tolerant genotypes had higher RWC than highly susceptible genotypes in both treatments and all the days after sowing (Table 2). The overall percent deviation (decrease in RWC from control to stressed environment) was at par in highly susceptible and highly tolerant genotypes (Table 2; Fig. 3d).

Total chlorophyll content

ANOVA shows that total chlorophyll content differed between DAS, treatment (T) and genotypes (G), and interaction between DAS and T was significant for total chlorophyll content (Table 1). Total chlorophyll content was higher in highly tolerant genotypes than in highly susceptible genotypes in control and stressed conditions (Table 2). Its content decreased from 60 DAS onwards in stressed plants and 75 DAS onwards in control plants of all the genotypes. However, the decrease was maximum in the HS-6 genotype under a stressed condition. The highest total chlorophyll content was observed in the control plants of highly tolerant genotype Bio-100 BG II at 75 DAS. The lowest content was in stressed plants of highly susceptible genotype HS-6 at 90 DAS with 3.52 2.01 mg/g FW, respectively. The highly susceptible genotypes showed a far higher decrease in total chlorophyll content from controlled to the stressed environment than highly tolerant genotypes (Table 2; Fig. 3e).

Electrolyte leakage

Electrolyte leakage significantly differed between all the genotypes, treatments and at each stage of observation; two factorial interactions for DAS, treatments and genotypes were significant for electrolyte leakage (Table 1). It significantly increased from 45 to 90 DAS in all the genotypes under stressed conditions. However, the increase was lower in highly tolerant genotypes, especially in Bio 100 BG-II, compared to highly susceptible genotypes. The highest electrolyte leakage was observed in the stressed plants of highly susceptible genotype, namely HS-6 at 90 DAS and the lowest in controlled plants of highly tolerant genotype, namely Bio-100 BG II at 45 DAS with 20.01 and 9.51% RSI, respectively. The highly susceptible genotypes showed higher electrolyte leakage than highly tolerant genotypes in both treatments and all days after sowing (Table 2). The increase in electrolyte leakage from the control to the stressed environment was far higher in highly susceptible genotypes than in highly tolerant genotypes (Table 2; Fig. 3f).

Biochemical parameters

Total phenolic content

Analysis of variance (ANOVA) showed that total phenolic content significantly differed between all the genotypes, treatments and DAS, but the interactions were non-significant (Table 1). Sucking insect-pests infestation led to a significant increase in total phenolic content in the leaves of highly tolerant (Bio 100 BG-II and GCH-3) and highly susceptible (KDCHH-9810 BG-II and HS-6) genotypes from 45 to 90 DAS (Table 3). The highest total phenolic content was observed in stressed plants of highly tolerant genotype Bio-100 BG II at 90 DAS and the lowest in controlled plants of highly susceptible genotype viz. HS-6 at 45 DAS with 0.61 and 0.23 mg catechol equivalent/g FW (mg CLE/g FW), respectively. Highly tolerant genotypes had higher phenolic content than highly susceptible genotypes in treatments and DAS. The overall percent deviation (increase in phenolic content from controlled to stressed environment) was far higher in highly susceptible genotypes than in highly tolerant genotypes (Table 3; Fig. 4a).

Table 3.

Total phenolic content, H₂O₂ content, Tannin content and PPO activity in different genotypes under controlled and stressed environment on different days after sowing

DAS	Treatment	Genotype	Phenol	H2O2	Tannin	PPO
45	Control	B (HT)	0.35d−j ± 0.055	11.07y ± 0.84	0.25 g−m ± 0.048	0.28 l−o ± 0.051
		G (HT)	0.27 h−j ± 0.050	14.90t ± 0.20	0.22 h−m ± 0.058	0.32j−o ± 0.054
		K (HS)	0.24ij ± 0.044	18.60 m ± 0.46	0.19j−m ± 0.054	0.21° ± 0.051
		H (HS)	0.23j ± 0.039	16.90p ± 0.51	0.14 m ± 0.039	0.26no ± 0.037
	Stress	B (HT)	0.38d−j ± 0.054	11.20x ± 0.42	0.28d−k ± 0.045	0.36 g−l ± 0.049
		G (HT)	0.29 g−j ± 0.049	15.70 s ± 0.39	0.25f−m ± 0.058	0.45d−i ± 0.043
		K (HS)	0.27 h−j ± 0.047	20.50 k ± 0.35	0.24 g−m ± 0.051	0.26 m−o ± 0.045
		H (HS)	0.26ij ± 0.041	18.00° ± 0.49	0.23i−m ± 0.054	0.32 k−o ± 0.046
60	Control	B (HT)	0.40c−g ± 0.039	13.80v ± 0.35	0.30d−k ± 0.058	0.35i−n ± 0.043
		G (HT)	0.30 g−j ± 0.025	19.30 l ± 0.29	0.25f−m ± 0.054	0.41f−k ± 0.048
		K (HS)	0.25ij ± 0.043	26.10c ± 0.57	0.21i−m ± 0.051	0.26no ± 0.046
		H (HS)	0.24ij ± 0.046	22.80 g ± 0.42	0.15 lm ± 0.046	0.31 k−o ± 0.049
	Stress	B (HT)	0.44c−f ± 0.039	14.50u ± 0.32	0.34b−f ± 0.054	0.46d−h ± 0.045
		G (HT)	0.33d−j ± 0.034	21.10i ± 0.46	0.31d−j ± 0.058	0.58b−d ± 0.045
		K (HS)	0.30f−j ± 0.031	30.10a ± 0.53	0.28e−l ± 0.051	0.32j−o ± 0.039
		H (HS)	0.29 g−j ± 0.041	25.10e ± 0.42	0.27d−k ± 0.045	0.40f−l ± 0.051
75	Control	B (HT)	0.46b−d ± 0.025	11.60x ± 0.53	0.37b−g ± 0.054	0.45d−i ± 0.048
		G (HT)	0.35d−j ± 0.032	16.71q ± 0.57	0.30d−k ± 0.051	0.54b−e ± 0.049
		K (HS)	0.29 g−j ± 0.025	24.10d ± 0.42	0.25f−m ± 0.048	0.31 k−o ± 0.043
		H (HS)	0.27 h−j ± 0.035	20.60 k ± 0.35	0.18 k−m ± 0.045	0.39 g−l ± 0.054
	Stress	B (HT)	0.53ab ± 0.042	12.10w ± 0.49	0.44ab ± 0.051	0.62b ± 0.042
		G (HT)	0.39d−h ± 0.036	18.50 m ± 0.35	0.39b−e ± 0.055	0.79a ± 0.045
		K (HS)	0.36d−j ± 0.025	28.60b ± 0.46	0.34c−h ± 0.039	0.41f−l ± 0.048
		H (HS)	0.34d−j ± 0.025	23.10f ± 0.61	0.32d−i ± 0.046	0.52c−f ± 0.041
90	Control	B (HT)	0.52a−c ± 0.037	9.19A ± 0.42	0.40b−d ± 0.048	0.43e−j ± 0.037
		G (HT)	0.39d−h ± 0.037	13.80v ± 0.32	0.32d−i ± 0.045	0.52c−f ± 0.045
		K (HS)	0.32e−j ± 0.034	21.50 h ± 0.29	0.27e−m ± 0.051	0.29 k−o ± 0.051
		H (HS)	0.29 g−j ± 0.035	18.10n ± 0.46	0.19j−m ± 0.058	0.37 h−n ± 0.039
	Stress	B (HT)	0.61a ± 0.040	9.90z ± 0.21	0.51a ± 0.045	0.60bc ± 0.051
		G (HT)	0.44c−e ± 0.031	15.81r ± 0.46	0.45a−c ± 0.042	0.77a ± 0.048
		K (HS)	0.39d−h ± 0.025	26.10c ± 0.49	0.39b−e ± 0.048	0.39 g−m ± 0.043
		H (HS)	0.37d−i ± 0.021	20.90j ± 0.35	0.36b−g ± 0.051	0.50d−g ± 0.045

Treatment means in the same columns with different letters differ significantly (P < 0.05). The design consists of four sampling times [45, 60, 75 and 90 days after sowing (DAS)], two treatments (controlled and stressed), and four cotton genotypes with contrasting behaviour to sucking insect-pests (2 highly tolerant [B (HT) & G (HT)] and 2 highly susceptible [K (HS) & H (HS)]); [B (HT)-Bio-100 BG II, G (HT)- GCH-3, K (HS)- KDCHH-9810 BG II, H (HS)- HS-6]; Phenol- Total phenolic content (mg CLE/g FW), H₂O₂-H₂O₂ content (μmole/g FW), Tannin- Tannin content (mg CNE/g FW), PPO- PPO activity (U/g FW/min). Sign of ± shows standard error of mean value. $\mathrm{Total}\mathrm{phenolic}\mathrm{content}(\mathrm{mg}\mathrm{CLE}/\mathrm{g}\mathrm{FW})=\frac{{\mathrm{A}}_{650}\times {\mathrm{V}}_1\times 1000}{0.0054\times {\mathrm{V}}_2\times 1000\times \mathrm{W}}$; ${\mathrm{H}}_2{\mathrm{O}}_2\mathrm{content}\left(\mu \mathrm{moles},/,\mathrm{g}\mathrm{FW}\right)=\frac{{\mathrm{A}}_{570}\times {\mathrm{V}}_1\times 1000}{0.092\times {\mathrm{V}}_2\times \mathrm{W}}$;$\mathrm{Tannin}\mathrm{content}(\mathrm{mg}\mathrm{CNE}/\mathrm{g}\mathrm{FW})=\frac{{\mathrm{A}}_{550}\times {\mathrm{V}}_1\times 1000}{0.016\times {\mathrm{V}}_2\times 1000\times \mathrm{W}}$;$\mathrm{PPO}\mathrm{activity}(\mathrm{U}/\mathrm{g}\mathrm{FW}/\min )=\frac{({\mathrm{A}}_{420}\mathrm{at}\mathrm{T}-{\mathrm{A}}_{420}\mathrm{at}{\mathrm{T}}_0)\times 60\times {\mathrm{V}}_1\times 1000}{\mathrm{T}\times {\mathrm{V}}_2\times \mathrm{W}}$. One unit is expressed as a 1.0 change in absorbance per g FW per min. V₁ = Total volume of sample/supernatant (ml); V₂ = Volume of sample/supernatant taken in assay (ml); W = Weight of sample (mg); A = Absorbance of sample, T = Time at a particular absorbance reading (s); T₀ = Time at zero second immediately after adding enzyme extract(s)

Fig. 4.

Percent change in a Phenolic content, b H₂O₂ content, c Tannin content and d PPO activity from controlled to stressed environment among different cotton genotypes at 45, 60, 75 and 90 days after sowing (DAS). [B (HT)-Bio-100 BG II, G (HT)- GCH-3, K (HS)- KDCHH-9810 BG II, H (HS)- HS-6]

H₂O₂ content

H₂O₂ content significantly differed with the duration of crop growth, in all the genotypes and under both types of conditions; all interactions were significant for H₂O₂ content (Table 1). Sucking insect-pests infestation led to a significant increase in H₂O₂ content in the leaves of highly tolerant and highly susceptible genotypes from 45 to 60 DAS (Table 3). The highly susceptible genotypes showed a higher increase in H₂O₂ content as compared to highly tolerant genotypes. In both highly tolerant and highly susceptible genotypes, a decrease in H₂O₂ content was recorded in controlled and stressed conditions from 60 to 90 DAS. The highest H₂O₂ content was observed in the stressed plants of highly susceptible genotype KDCHH-9810 BG II at 60 DAS and the lowest in controlled plants of highly tolerant genotype Bio-100 BG II at 90 DAS with values of 30.10 and 9.19 μmole/g FW, respectively. In both the treatments and DAS, the highly susceptible genotypes had higher H₂O₂ content than highly tolerant genotypes (Table 3). Highly susceptible genotypes showed a far higher increase in H₂O₂ content (overall percent deviation) from controlled to stressed environments than highly tolerant genotypes (Table 3; Fig. 4b).

Tannin content

Tannin content significantly differed between DAS, genotypes and both types of conditions. Two factorial interactions for DAS, treatments and genotypes were significant for tannin content (Table 1). Sucking insect-pests infestation led to a significant increase in tannin content in the leaves of highly tolerant and highly susceptible genotypes from 45 to 90 DAS (Table 3). However, highly tolerant genotypes showed a higher increase than highly susceptible genotypes under stressed conditions. The highest tannin content was observed in the stressed plants of highly tolerant genotype. Bio-100 BG II at 90 DAS and the lowest in controlled plants of highly susceptible genotype viz. HS-6 at 45 DAS with 0.51 and 0.14 mg Catechin equivalent/g FW (mg CNE/g FW), respectively. In both the treatments and all DAS, the highly tolerant genotypes had higher tannin content than highly susceptible genotypes (Table 3). The increased tannin content from controlled to stressed environments was far higher in highly susceptible genotypes than in highly tolerant genotypes (Table 3; Fig. 4c).

PPO activity

The PPO activity significantly differed with the duration of crop growth, in all the genotypes and under both types of conditions; all the types of interactions were significant for PPO activity (Table 1). Its activity significantly increased with sucking insect-pests infestation levels in both genotypes. Yet, the increase was higher in highly tolerant genotypes than in highly susceptible genotypes (Table 3). The maximum increase in PPO activity was observed in stressed plants of highly tolerant genotype viz. GCH-3 at 75 DAS and the lowest in the controlled plants of highly susceptible genotype viz. KDCHH-9810 BG II at 45 DAS with 0.79 and 0.21 U/g FW/min, respectively. The highly tolerant genotypes had higher PPO activity than highly susceptible genotypes in both the treatments and all the DAS. The PPO activity increased from 45 to 75 DAS and then decreased from 75 to 90 DAS in both treatments in a genotype-specific manner (Table 3). The increase in PPO activity from controlled to the stressed environment was far higher in highly tolerant genotypes than in highly susceptible genotypes (Table 3; Fig. 4d).

POX activity

The POX activity significantly differed between genotypes, treatments and DAS. Two factorial interactions for DAS, treatments and genotypes were significant for its activity (Tables 1 and 4). Under both conditions, POX activity significantly increased with the crop age in all the genotypes. The highest activity was observed in the stressed plants of highly tolerant genotype viz. Bio-100 BG II at 90 DAS and the lowest in controlled plants of highly susceptible genotype viz. KDCHH-9810 BG II at 45 DAS with 12.96 and 4.93 U/g FW/min, respectively. The highly tolerant genotypes showed higher POX activity than highly susceptible genotypes in both the treatments and at all DAS. The overall percent deviation (increase in POX activity from controlled to stressed environment) was far higher in highly susceptible genotypes than in highly tolerant genotypes (Table 4; Fig. 5a).

Table 4.

POX activity, Gossypol content, Total soluble protein and Total soluble sugar in different genotypes under controlled and stressed environment on different days after sowing

DAS	Treatment	Genotype	POX	Gossypol	Protein	Sugar
45	Control	B (HT)	7.50p ± 0.58	0.21j ± 0.048	2.96 g ± 0.30	2.17 lm ± 0.42
		G (HT)	7.11q ± 0.81	0.26f−j ± 0.042	3.64a ± 0.19	2.02 k−m ± 0.39
		K (HS)	4.93y ± 0.83	0.20j ± 0.051	2.69hi ± 0.11	2.67f ± 0.46
		H (HS)	5.19x ± 0.77	0.19j ± 0.037	3.39b ± 0.27	2.23hi ± 0.35
	Stress	B (HT)	8.14 l ± 0.80	0.26d−j ± 0.051	2.67 g ± 0.19	1.81kl ± 0.35
		G (HT)	8.01° ± 0.77	0.27e−j ± 0.046	3.44b ± 0.16	1.64° ± 0.42
		K (HS)	6.19u ± 0.74	0.24 g−j ± 0.049	2.21n ± 0.27	2.49 g ± 0.39
		H (HS)	6.81st ± 0.70	0.23ij ± 0.042	2.96 g ± 0.19	2.04n ± 0.32
60	Control	B (HT)	8.50 m ± 0.74	0.24 h−j ± 0.048	2.76ij ± 0.27	3.14e ± 0.39
		G (HT)	8.03° ± 0.73	0.30d−j ± 0.045	3.46b ± 0.19	2.84e ± 0.35
		K (HS)	5.41w ± 0.70	0.22 h−j ± 0.039	2.42kl ± 0.16	4.09a ± 0.46
		H (HS)	5.87v ± 0.64	0.20j ± 0.042	3.10de ± 0.13	3.31d ± 0.30
	Stress	B (HT)	9.79 h ± 0.67	0.31c−h ± 0.048	2.45ij ± 0.23	2.56e ± 0.46
		G (HT)	9.43 k ± 0.74	0.32c−j ± 0.045	3.26c ± 0.30	2.26 h ± 0.39
		K (HS)	7.06q ± 0.64	0.29e−j ± 0.058	1.93° ± 0.16	3.68b ± 0.35
		H (HS)	7.96° ± 0.61	0.26e−j ± 0.048	2.66gh ± 0.11	2.98e ± 0.43
75	Control	B (HT)	10.74f ± 0.70	0.29e−j ± 0.042	2.73j ± 0.30	2.49 h−j ± 0.42
		G (HT)	9.84i ± 0.74	0.37a−g ± 0.054	3.44b ± 0.13	2.22hi ± 0.39
		K (HS)	6.24tu ± 0.61	0.26 g−j ± 0.048	2.34 lm ± 0.23	3.43c ± 0.35
		H (HS)	6.81r ± 0.64	0.23 h−j ± 0.052	3.05ef ± 0.13	2.72f ± 0.46
	Stress	B (HT)	12.61b ± 0.67	0.38a−d ± 0.053	2.39ij ± 0.27	1.80jk ± 0.41
		G (HT)	11.59d ± 0.74	0.40a−e ± 0.048	3.20 cd ± 0.11	1.52p ± 0.45
		K (HS)	8.27n ± 0.70	0.34c−i ± 0.042	1.80p ± 0.30	2.69f ± 0.46
		H (HS)	9.51 k ± 0.74	0.31d−j ± 0.041	2.55j ± 0.19	2.12i−k ± 0.42
90	Control	B (HT)	11.31e ± 0.74	0.33c−i ± 0.042	2.65jk ± 0.19	1.74p ± 0.35
		G (HT)	10.65 g ± 0.70	0.43a−c ± 0.037	3.38b ± 0.27	1.52p ± 0.32
		K (HS)	6.36 s ± 0.67	0.29e−j ± 0.051	2.23 mn ± 0.30	2.48 g ± 0.42
		H (HS)	7.14q ± 0.64	0.25 g−j ± 0.048	2.96f ± 0.11	1.94 l−n ± 0.39
	Stress	B (HT)	12.96a ± 0.67	0.44a ± 0.051	2.24 lm ± 0.16	1.20p ± 0.45
		G (HT)	12.05c ± 0.64	0.47ab ± 0.045	3.00ef ± 0.27	0.98q ± 0.42
		K (HS)	8.26n ± 0.70	0.39a−f ± 0.042	1.60q ± 0.13	1.92 mn ± 0.39
		H (HS)	9.72j ± 0.74	0.35b−h ± 0.038	2.32 lm ± 0.30	1.47p ± 0.44

Treatment means in the same columns with different letters differ significantly (P < 0.05). The design consists of four sampling times [45, 60, 75 and 90 days after sowing (DAS)], two treatments (controlled and stressed), and four cotton genotypes with contrasting behaviour to sucking insect-pests (2 highly tolerant [B (HT) & G (HT)] and 2 highly susceptible [K (HS) & H (HS)]); [B (HT)-Bio-100 BG II, G (HT)- GCH-3, K (HS)- KDCHH-9810 BG II, H (HS)- HS-6]; POX- POX activity (U/g FW/min), Gossypol content (mg GAE/g FW), Protein- Total soluble protein (% of FW), Sugar- Total soluble sugar (% of FW). Sign of ± shows standard error of mean value. $\mathrm{POX}\mathrm{activity}(\mathrm{U}/\mathrm{g}\mathrm{FW}/\min )=\frac{\left({\mathrm{A}}_{470},,\mathrm{at}\mathrm{T},-,{\mathrm{A}}_{470},,\mathrm{at},,{\mathrm{T}}_0\right)\times 60\times {\mathrm{V}}_1\times 1000}{\mathrm{T}\times {\mathrm{V}}_2\times \mathrm{W}}$; $\mathrm{Gossypol}\mathrm{content}(\mathrm{mg}\mathrm{GAE}/\mathrm{g}\mathrm{FW})=\frac{{\mathrm{A}}_{550}\times {\mathrm{V}}_1\times 1000}{0.011\times {\mathrm{V}}_2\times 1000\times \mathrm{W}}$; $\mathrm{Total}\mathrm{soluble}\mathrm{protein}(\%\mathrm{of}\mathrm{FW})=\frac{{\mathrm{A}}_{660}\times {\mathrm{V}}_1\times 100}{0.0017\times {\mathrm{V}}_2\times 1000\times \mathrm{W}}$; $\mathrm{Total}\mathrm{soluble}\mathrm{sugar}(\%\mathrm{of}\mathrm{FW})=\frac{{\mathrm{A}}_{490}\times {\mathrm{V}}_1\times 5\times 100}{0.00673\times {\mathrm{V}}_2\times 1000\times \mathrm{W}}$; One unit is expressed as a 1.0 change in absorbance per g FW per min. V₁ = Total volume of sample/supernatant (ml); V₂ = Volume of sample/supernatant taken in assay (ml); W = Weight of sample (mg); A = Absorbance of sample, T = Time at a particular absorbance reading (s); T₀ = Time at zero second immediately after adding enzyme extract (s)

Fig. 5.

Percent change in a POX activity, b Gossypol content, c Total soluble protein and d Total soluble sugar from controlled to stressed environment among different cotton genotypes at 45, 60, 75 and 90 days after sowing (DAS). [B (HT)-Bio-100 BG II, G (HT)- GCH-3, K (HS)- KDCHH-9810 BG II, H (HS)- HS-6]

Gossypol content

The gossypol content significantly differed between all the genotypes, under both types of conditions and with crop duration. Two factorial interactions for DAS, treatments and genotypes were significant for gossypol content. Stressed plants of highly tolerant genotypes compared to highly susceptible genotypes had higher values of gossypol content (Table 4). The highest gossypol content was observed in the stressed plants of highly tolerant genotype, namely GCH-3 at 90DAS and the lowest in controlled plants of highly susceptible genotype, namely HS-6 at 45 DAS with 0.47 and 0.19 mg gossypol acetate equivalent/g FW (mg GAE/g FW), respectively. Sucking insect-pests infestation led to a significant increase in the gossypol content from 45 to 90 DAS, both in highly tolerant and highly susceptible cotton genotypes. But, the increase was higher in highly tolerant genotypes than in highly susceptible genotypes (Table 4). The highly susceptible genotypes showed a far higher increase in gossypol content from control to stress environments than highly tolerant genotypes (Table 4; Fig. 5b).

Total soluble protein

ANOVA shows that the total soluble protein significantly differed in all the genotypes, in treatments and at each stage of observation, and interaction between treatments and genotypes was also significant (Table 1). Compared to highly susceptible genotypes, highly tolerant genotypes had a higher amount of total soluble protein at all stages of growth under controlled conditions (Table 4). In both types of genotypes, total soluble protein decreased at 45 DAS onwards in both treatments. However, the decrease was lower in highly tolerant genotypes compared to highly susceptible genotypes. The highest total soluble protein was observed in the controlled plants of highly tolerant genotype, namely GCH-3 at 45 DAS and the lowest in stressed plants of highly susceptible genotype, namely KDCHH-9810 BG II at 90 DAS with 3.64 and 1.60% of FW, respectively. The decrease in total soluble protein from controlled to stressed environments was far higher in highly susceptible genotypes than in highly tolerant ones (Table 4; Fig. 5c).

Total soluble sugar

Total soluble sugar significantly differed in all the genotypes and treatments at each observation stage. Two factorial interactions for DAS, treatments and genotypes were significant for total soluble sugar (Tables 1 and 4). The highest total soluble sugar was observed in the control plants of highly susceptible genotype KDCHH-9810 BG II at 60 DAS and the lowest in stressed plants of highly tolerant genotype GCH-3 at 90 DAS with 4.09 and 0.98% of FW, respectively. Total soluble sugar first increased from 45 to 60 DAS in all the genotypes under both controlled and stressed conditions and then decreased under both conditions. The decrease in total soluble sugar was maximum in highly tolerant genotypes compared to highly susceptible genotypes. The highly tolerant genotypes showed a far higher decrease in total soluble sugar from control to stressed environments than highly susceptible genotypes (Table 4; Fig. 5d).

Whitefly population and Correlation matrix

At 45, 60, 75, and 90 DAS whitefly populations were highest in HS-6 plants and lowest in Bio-100 BG II plants (Supplementary Table S1). The Supplementary Table S2 shows a correlation between the whitefly population and biochemical and morpho-physiological parameters under stress conditions. Total phenolic content and SLW showed significant; total chlorophyll content and photosynthetic rate showed a highly significant negative correlation with the whitefly population. Sugar content showed significant; H₂O₂ and electrolyte leakage showed a highly significant positive correlation with the whitefly population.

Discussion

Due to lower ROS production, a healthy leaf had better SLW, total chlorophyll content, leaf area, and photosynthetic rate than a pest-infected leaf. Infected leaves must produce ROS to mount a hypersensitive defence against the infection. Stress resulting from biotic or abiotic factors directly disturbs the equilibrium between creating and eliminating ROS (Mittler 2017). The generation of ROS, including hydrogen peroxide (H₂O₂), superoxide anion radicals (O₂^•−), singlet oxygen (¹O₂), and hydroxyl radicals (^•OH), is a frequent effect of stress on cells and could cause severe oxidative damage to plant tissues. Low quantities of ROS control differentiation, redox equilibrium, stress signalling, and systemic reactions, but their high levels damage cellular components by oxidizing lipids, damaging proteins, and destroying membranes (Asthir et al. 2010; Das and Roychoudhury 2014). In response to stress, plants send signals that change their metabolism and activate or synthesize defence genes in the afflicted plant parts (Gill et al. 2019). Numerous amino acids, including proline, lysine, threonine, arginine, methionine, and cysteine, are highly vulnerable to ROS assault (Petrov et al. 2015). ROS detoxification occurs when plant cells, enzymes, and redox metabolites work synergistically. The alterations in antioxidant/defensive enzymes, free radical scavenging capabilities, non-enzymatic antioxidants, osmolytes, and signalling molecules are all part of the integrated mechanism known as oxidative stress tolerance (Caverzan et al. 2016). Defence-related enzymes POX and PPO, secondary metabolites including phenolics and condensed tannins, and H₂O₂ contribute to the triggered defence (Bhaduri and Fulekar 2012).

The higher total chlorophyll content is preferred because it indicates a low photo-inhibition of the photosynthetic machinery and lowers carbohydrate losses for plant growth. Maximum light capture for photosynthesis and osmotic regulation requires a large leaf surface area. Long-term research on cotton revealed that sucking insect-pest infection decreased leaf growth and photosynthesis, most likely because of chloroplast disintegration by toxins in the insect-pests' saliva (Reddall et al. 2007). One of the most sensitive processes to stress is photosynthesis, and when it slows down under stress, plants grow slower and yield lesser (Almeselmani et al. 2006).

With Japonica rice, Nipponbare and Indica rice, Taichung Native 1, Rubia-Sanchez et al. (1999) carried out a glasshouse experiment to examine the effects of brown planthopper feeding on the physiology of the main shoot. The SLWs of these varieties were lower than that of the control treatment. Murugesan and Kavitha (2010) studied the correlation between leafhopper oviposition and the damage caused to cotton cultivars and noted that sensitive cultivars had lower chlorophyll levels. Hsu et al. (2015) investigated the photosynthetic responses of Jatropha curcas to spider mite Tetranychus urticae infestation and found decreased photosynthetic rate due to spider mite infestation.

RWC, which represents the hydrous state of plant tissue, is a marker of stress resiliency or susceptibility. According to our findings, RWC decreased when sucking insect pests were present (Table 2, Fig. 3d), but not significantly in a controlled environment. Under a stressed environment and with more sensitive genotypes, a more significant reduction in RWC was seen. Similarly, Prabhakar et al. (2013) found that cotton plants' RWC (1.93–23.49%) significantly decreased due to the infestation of Phenacoccus solenopsis Tinsley, a native of North America.

Infestations of sucking insects and pests produce H₂O₂, which causes lipid peroxidation of cell membranes, loss of vital solutes (including electrolytes) from organelles and the cell, and metabolic imbalance (Mundree et al. 2002). Kot et al. (2015) studied different infestation densities of the mealybug Pseudococcus longispinus (Targioni Tozzetti) on orchids. Electrolyte leakage in leaf tissues was positively correlated with the level of long-tailed mealybug infestation in plants. Feeding sucking insect-pest infestation increased H₂O₂ content in leaves while decreasing total soluble sugar and total soluble protein. H₂O₂ also functions as a signalling molecule in pathways connected to defence and sucking insect-pests activates signalling pathways relevant to defence via it. Mai et al. (2013) found that in pea, aphid colonization increased the O₂⁻ and H₂O₂ generation rate. Sucking insect-pest infestation reduces CO₂ assimilation and lowers the quantity of accessible carbon for growth, and resources are primarily diverted to defensive measures. The synthesis of primary and secondary metabolites, crucial to a plant's defence system against pests, uses sugars as a precursor. Amin et al. (2016) worked with five cotton cultivars and discovered that aphid and jassid infestation decreased each cultivar's sugar and protein content.

Numerous plant species produce many phytoalexins (secondary metabolites) as insect defence response. Deterrence, toxicity, and serving as a precursor to the physical defence system are some of the roles phytoalexins play in plant defence. However, the efficacy of phytoalexins as a defence against sucking insect pests depends on the type of host-pest interaction. The appropriate biosynthetic enzymes, activated by stress elicitors, regulate phytoalexin accumulation, which pierces pests' cell walls, preventing them from metabolizing food and reproduction. Over 300 compounds from more than 30 plant families have been found to resemble phytoalexins. Although phytoalexins differ chemically, most phytoalexins are formed from the same pathway as shikimic acid, which also yields most secondary metabolites.

In G. hirsutum, the total phenolics, gossypol, tannin, POX, and PPO are the most critical factors in conferring resistance to several insect pests. A high phenolic content induce an immediate fatal action through a general tanning effect, while a low quantity has a delayed effect on the parasite's cellular component. The epidermal glands of cotton produce many poisonous terpenoid aldehydes, including gossypol, which guard the plants against pathogens and pests. Plant phenolics known as tannins have an astringent, bitter taste, bind and precipitate proteins and protect plants against pests and UV radiation (Shahidi and Yeo 2016). By interacting with free radicals, chelating metal ions, and scavenging oxygen, phenolics can obstruct the oxidation of biomolecules (Masisi et al. 2016; Wang et al. 2018). A method for qualitative and quantitative analysis of 28 phenolic compounds comprising flavonoids, phenolic acids, aldehydes and alcohols via UPLC was reported in cotton (Mandhania et al. 2019).

Polyphenol oxidase is widely distributed throughout bacteria, fungi, plants, and animals, and it catalyzes the oxidation of monophenols and o-diphenols to o-quinones (Boeckx et al. 2015). The greater PPO activity in highly tolerant genotypes suggests that it prevents pest infestation by producing phenolic oxidation products that are more hazardous than phenolics (Yoruk and Marshall 2003). Shafique et al. (2014) found similar outcomes for PPO activity when they investigated the impact of mealybug herbivory in cotton plants. They discovered that PPO responded to herbivory more quickly because its activity was significantly increased right after the mealybug attack, and that increase remained persistent for 2.5 h. Additionally, there was a correlation between stress levels and total phenolic content. Our findings suggest that incompatible (tolerant) hosts accumulate phenolics more quickly than compatible hosts (susceptible).

Peroxidases frequently participate in various physiological and biochemical processes throughout the plant life cycle, most likely due to the abundance of enzymatic isoforms (isoenzymes) and their diverse enzyme-catalyzed reactions (Passardi et al. 2005). By oxidizing H₂O₂ with an electron-donating substrate, POXs detoxify H₂O₂ (Dionisio-Sese and Tobita 1998). The POX activity was increased due to the infestation of sucking insect-pests to defend against a higher concentration of H₂O₂. By accelerating lignifications, which boost resistance against infections, POX could improve cell wall fortification (Passardi et al. 2004). Exposure to plant pathogens causes the induction of POX activity in plant tissues, and highly tolerant plants show a significant rise than susceptible ones (Mydlarz and Harvell 2007). According to Heng-Moss et al. (2003), POX activity in tolerant plants changed little. It stayed greater in hemipteran-colonized plants than in control plants, with no discernible variation in POX activity.

Mandhania et al. (2016) identified a strong positive correlation between the incidence of leafhoppers and the total phenol, condensed tannin, and gossypol concentrations in 12 cotton genotypes at all growth stages (65, 80, and 95 DAS). Total phenol and gossypol levels were found to be greater in tolerant inbred cotton lines than susceptible inbred cotton lines for sucking pests in unprotected conditions, according to Harijan et al. (2017). Insect assault and phenol synthesis are directly correlated, according to Perveen et al. (2001). They discovered that the highly tolerant cotton variety Ravi had more phenolics than susceptible types at all growth stages. According to research by Shafique et al. (2014), mealybug attacks increased cotton's protective biochemicals, such as phenolics and terpenoids, by up to seven times during a few hours (0–3), inflicting injury on healthy plants. Aphid infestation increased the total phenolic content and condensed tannin of two cotton genotypes (CPD14-1 and CPD14-2), with the resistant genotype CPD14-1 recording higher phenol content (7.07 g/100 g DW) and condensed tannin (7.97 g/100 g DW) than the susceptible genotype CPD14-2. The findings indicate that increased levels of phenolics may shield cotton plants from aphid infestation. A subset of biomarkers called metabolic markers reflects the metabolites/molecules used in plant metabolism (Fernandez et al. 2016; Zaynab et al. 2019). Plants produce many metabolites under biotic stress, which are tissue- and species-specific and can serve as biomarkers for biotic stress resistance Razzaq et al. (2019). Defensive diagnostic or metabolite indicators are those metabolites that can be employed to give tolerance against biotic or abiotic stressors.

According to a correlation study, the number of leafhoppers, aphids, and thrips was inversely related to the amount of phenols and tannin in the leaves of plants treated with jasmonic and salicylic acid (Nikhath et al. 2019). Rizwan et al. (2021) found that the resistant variety NIAB-Kiran had less soluble sugars (8.54 mg g⁻¹), soluble proteins (27.11 mg g⁻¹), and more phenolics (36.56 mg g⁻¹) and flavonoids (13.10 mg g⁻¹) as compared with the susceptible check Glandless-1 upon infestation by whitefly, thrips and jassid under field conditions. All the insect populations were positively correlated with total soluble sugars and proteins; and negatively correlated with total phenolics, tannins and flavonoids. Our findings and other researchers lead us to conclude that the Bio 100 BG-II and GCH 3 genotypes are more resistant to sucking insect pests than the KDCHH-9810 BG-II and HS 6 genotypes based on morpho-physiological and biochemical criteria.

The study showed that plants strengthened themselves to sucking-pest by increasing secondary metabolites, total soluble protein, and photosynthetic efficiency. The tailored manipulation of plants' built-in defence mechanisms should be the most effective and sustainable strategy for managing plant damage. The goal of breeding programmes for pest defence should be to identify the metabolite markers that contribute to resistance against sucking insect-pests. Since they are more accurate performance indicators for plants, metabolic markers may be more valuable than molecular markers. Therefore, to effectively and sustainably manage plant infestation, it is imperative to investigate the natural metabolites in host plants.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contribution

Conceptualization: Shiwani Mandhania. Formal analysis: Vikram Singh, Taranjeet Kaur, Rashi Datten, Karmal Malik. Methodology: Shiwani Mandhania, Ajay Pal. Proofreading: Shiwani Mandhania, Prakash Banakar. Supervision, facilitation: Shiwani Mandhania, Ajay Pal. Writing—original draft: Vikram Singh. Writing—review & editing: Shiwani Mandhania, Prakash Banakar.

Funding

This research received no external funding.

Declarations

Conflicts of interest

The authors declare no conflicts of interest.