Investigating the Impact of Beneficial Microorganisms Inoculated Cotton Plants on Spodoptera exigua

Investigating the Impact of Beneficial Microorganisms Inoculated Cotton Plants on Spodoptera exigua | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the Impact of Beneficial Microorganisms Inoculated Cotton Plants on Spodoptera exigua Nurhan Didem Kızılkan, Metin Konuş, Mehmet Ramazan Rişvanlı, Can Yılmaz, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4170111/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study discusses the knowledge obtained about the plant-mediated effects of beneficial soil-borne microorganisms on survival rates, development, reproduction, and population growth parameters of Spodoptera exigua , a major cotton pest. Specifically, we examined how beneficial microorganisms impact the oxidative stress, chlorophyll content, sugar and protein levels within the cotton plant and subsequently influence the pest's population growth performance. We also evaluated the reciprocal effects of plant-insect-microorganisms interaction on total glutathione levels, glutathione S-transferase and esterase enzyme activities in S. exigua . The findings of this study revealed that there was no evidence of oxidative stress in the treated plants. However, the treated plants exhibited enhanced chlorophyll content while demonstrating reduced levels of Protein/Carbohydrate, consequently leading to a discernible decrease in the population growth performance of S. exigua . These results highlight the intricate interplay between oxidative stress, chlorophyll content, and nutritional composition, which collectively influence the population dynamics and performance of the treated plants. The concept of "host plant quality" which encompasses characteristics like nitrogen and carbon content, trace elements, and defence chemicals, played a crucial role in shaping the success of S. exigua . Changes in plant characteristics and nutrient balance can affect the performance of insects, even if they consume more leaves, leading to an increase in midgut tissue weight in S. exigua larvae. Consequently, this decrease in pest numbers is expected to enhance the plant's ability to withstand damage. Additionally, the findings from this study provided valuable insights and pertinent information regarding the intricate three-way interactions among plants, microorganisms, and pests, thereby offering potential strategies for implementing these interactions in pest management practices. Three-way interactions beneficial microorganisms Spodoptera exigua cotton biological control pest management Figures Figure 1 Figure 2 Figure 3 Figure 4 1 INTRODUCTION Cotton, also referred to as "white gold," is the crop that contributes the most to the global production of fibre, oil and protein. One of the main challenges in achieving high yields in cotton is the damage caused by herbivores. Cotton pests cause about 15% of product loss per year (Tokel et al. 2022 ). About 200 insect species feeding on cotton plants have been described (Dhaliwal et al. 2010 ). Spodoptera exigua (Hübner) (Lepidoptera, Noctuidae), also known as the beet armyworm, feed on more than 130 plant species from at least 30 families, including many economically important crops, such as cotton (Fu et al. 2017 ; Mehrkhou et al. 2012 ; Robinson et al. 2010 ). To control S. exigua , chemical pesticides are frequently used. A pest management strategy focused on chemical pesticides is unsustainable due to its detrimental effects. Therefore, obtaining high efficiency from plants with environmentally friendly techniques is a necessity in today's world. Plants face many environmental challenges. They are constantly under biotic stress due to herbivores that cause severe damage to crops worldwide. Plants have evolved a wide variety of physical and chemical mechanisms to defend themselves against these herbivores (Sharma et al. 2009 ; War et al. 2012 ; War et al. 2011 ). Plant growth-promoting rhizobacteria (PGPR) and plant growth-promoting fungus (PGPF) have been demonstrated to promote plant growth and health (Lugtenberg and Kamilova 2009 ). Recent studies have shown that microbial biota in soil also affects herbivores. Microorganisms can change the fitness and quality of plants and thus indirectly affect herbivores (Contreras-Cornejo et al. 2021 ; Friesen et al. 2011 ; Grabka et al. 2022 ; Lugtenberg and Kamilova 2009 ; Noman et al. 2020 ; Ortiz and Sansinenea 2022 ; Pappas et al. 2021 ; Risvanli 2022 ; Sheridan et al. 2023 ). Evaluating the effects of microorganisms on the plant preference or performance of insects still has many questions to be uncovered. Moreover, how these interactions depend on the insect population, the microorganism lifestyle, and the environmental effects continues to be extensively investigated (Bakker et al. 2018 ; Fernandez-Conradi et al. 2018 ; Verma et al. 2019 ). Molecules such as hydrogen peroxide and superoxide anion, which are reactive oxygen species (ROS), can be formed in the mitochondria of herbivorous insects by intrinsic pathways such as oxidative phosphorylation. Besides, herbivorous insects can be exposed to these molecules exogenously through the plants they consume (Krishnan and Sehnal 2006 ; Orozco-Cardenas and Ryan 1999 ; Sytykiewicz 2011 ). Insects have esterase and glutathione S-transferase enzymes that metabolise xenobiotic molecules and render them harmless. These detoxification enzymes are also involved in the prevention of cellular and tissue damage that may result from oxidative stress (Konuş 2014 ). In addition to these enzymes, insects also possess non-enzymatic reactive oxygen species scavenging molecules such as ascorbate and glutathione (Kazek et al. 2020 ). Besides, it has been reported that increased xenobiotic metabolism in insects leads to decreases in total thiol levels in insect cells, so it causes a weakening of the antioxidant defence system in those insects (Vontas et al. 2001 ). Consequently, examining markers related to thiol level is important in determining the level of oxidative stress. Recently, interest in using beneficial microorganisms to grow robust-structure plants and yield more has increased. These organisms support plant growth, encourage resistance to biotic and abiotic difficulties, enhance nutrient uptake, lessen stress, and shield plants from pests and diseases, among many other functions (Lazcano et al. 2021 ; Santoyo et al. 2021 ; Zhang et al. 2022 ). Choosing suitable methods is extremely important to understand exactly how the effects of beneficial microorganisms on plants are reflected in the pest population dynamics. One notable approach in this regard is the use of life tables. The life table is a mathematical tool used to quantitatively measure the population dynamics of various organisms, mainly insects, and is very useful in understanding the impact of various factors on the population's dynamics. The age-stage, two-sex life table, in contrast to the traditional female age-specific life tables, is capable of explaining stage differentiation (i.e., metamorphosis) and the variations in developmental rates among individuals and takes males into account, thus providing a more detailed understanding of population dynamics (Chi 1988 ; Chi et al. 2022 ; Chi and Su 2006 ; Chi et al. 2020 ). This study, firstly, aimed to determine the plant-mediated effects of a beneficial microorganism complex on the development, survival, reproduction, and consequently, population growth parameters of S. exigua . Secondly, the effects of the changes led by the beneficial microorganisms on oxidative stress, chlorophyll content, sugar and protein levels were measured in cotton leaves. Finally, the reciprocal effects of plant-insect-microorganisms on total glutathione levels and glutathione S-transferase and esterase enzyme activities in S. exigua midguts were determined to investigate the effect of beneficial microorganisms inoculated cotton plants on S. exigua . Our knowledge of the beneficial interactions between beneficial soil-borne microorganisms, plants and pests will be furthered by recording this communication, which is one of the exciting alternatives to pest management. 2 MATERIALS AND METHODS 2.1 Plant and insect. Cotton plants ( Gossypium hirsutum L., cultivar Beyazaltın BA-440) were grown in growth chambers under controlled conditions (temperature of 25 ± 2°C, relative humidity of 60 ± 10%, photoperiod of 16 h light/8 h dark). The potting medium was composed of peat: perlite in a ratio of 2:1. Plants were watered and fertilised twice a week with Hoagland's nutrient solution. Cotton plants of approximately six to eight fully expanded leaves were selected for the experiments. The Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae) was reared at the Laboratory of Entomology, Department of Plant Protection, Van Yüzüncü Yıl University, Van, Türkiye. The insects were kept under controlled climate room conditions. 2.2 Microorganisms and their inoculation. Each cotton seed was lightly watered in, and then a microorganism complex including ectomycorrhiza spp., endomycorrhiza spp., and other beneficial fungal and bacterial spp. [Great White Premium Mycorrhizae (GW), Plant Success, https://www.plant-success.com ] was applied to the seedbeds according to the label.1.25 grams of GW was added to 7.57 litres of water at a rate of 300 millilitres per pot. This application was repeated in 3-week periods. GW ingredient list is given below in Table 1 . Sampled and re-isolated soil and plant tissues to confirm the presence of microorganisms. Table 1 Great White Premium Ingredient List Ectomycorrhiza spp. Propagules/gram Pisolithus tinctorius 187,875 Rhizopagan luteolus 5,219 Rhizopagan fulvigleba 5,219 Rhizopagan villosullus 5,219 Rhizopagan amylopogon 5,219 Scleroderma citrinum 5,219 Scleroderma cepa 5,219 Endomycorrhiza spp. Propagules/gram Glomus aggregatrum 83 Glomus intraradices 83 Glomus mosseae 83 Glomus etunicatum 83 Glomus clarum 11 Glomus monosporum 11 Glomus deserticola 11 Paraglomus brasilianum 11 Gigaspora margarita 11 Trichoderma spp. Propagules/gram Trichoderma koningii 187,875 Trichoderma harzianum 125,250 Other Fungal and Bacterial spp. CFU’s/gram Azobacter chroococcum 525,000 Bacillus subtilis 525,000 Bacillus licheniformis 525,000 Bacillus azotoformans 525,000 Bacillus megaterium 525,000 Bacillus coagulens 525,000 Bacillus pumilis 525,000 Bacillus amyloliquefaciens 525,000 Paenibacillus polymyxa 525,000 Paenibacillus durum 525,000 Saccharomyces cerevisiae 525,000 Pseudomonas aureofaciens 525,000 Pseudomonas fluorescens 525,000 2.3 Life Table Study The eggs of S. exigua collected from laboratory-mated adults were used to maintain the colony and perform the experiments. Sixty to eighty newly hatched S. exigua larvae (3 per plant) were placed on cotton plant leaves of GW-treated control plants. S. exigua larvae were placed on leaves using a paintbrush and placed into plastic bags. Plastic bags were covered with mesh to prevent larval escape. The developmental stage and survival of S. exigua were observed and recorded daily. The sexes in the pupal stage were paired, and each pair was placed in a separate cylindrical plastic container (15 cm diameter and 19 cm height). Then, emerged adults were released in plastic containers for laying eggs. During the reproduction period, cotton wool soaked in 25% of honey–water solution was placed in the containers for adult moths feeding. A piece of wax paper was set around the containers for the adults to lay eggs. The survival and fecundity of the S. exigua were recorded daily until the death of all individuals. 2.4 Midgut tissue isolation from Spodoptera exigua larvae All measurements were conducted on early fourth-instar S. exigua larvae for enzymatic antioxidant assays and non-enzymatic antioxidant level determination. To isolate midgut tissue from 30 fourth-instar S. exigua larvae, the larvae were embedded in ice and left to paralyse for a while. Then, larvae were cut along their length with the help of a razor blade. After that, the removed larva midguts were immediately cleaned with 1.15 M potassium chloride dried on filter paper. Finally, larva midgut weights of insects were recorded by analytical balance and stored in the deep freezer (-80 ˚C). 2.5 Preparation of Spodoptera exigua midgut homogenates Spodoptera exigua fourth-instar larvae midgut samples were homogenised with ultra-turrax in 1 ml homogenisation solution (0.1 M phosphate buffer (pH 7.2), 1 mM dithiothreitol (DTT), 1 mM phenyl methyl sulfonyl fluoride (PMSF) and 1 mM ethylene diamine tetra-acetic acid (EDTA). During the homogenisation process, each sample was homogenised five times for 20 seconds and kept on ice for 20 seconds after each 20 seconds of homogenisation. The homogenised samples were centrifuged at + 4 ℃ and 10000 x g for 30 minutes. After centrifugation, while the pellet was discarded; the supernatant was placed in a deep freezer at − 80°C to be used as an enzyme source for further analysis such as esterase, glutathione S-transferase activity assays, and total thiol level. The protein concentration was determined according to the Bradford method (Bradford 1976 ). 2.6 Determination of Total Thiol Content The modified method of Sedlak and Lindsay was used to determine the total thiol groups of S. exigua midgut homogenates (Sedlak and Lindsay 1968 ). The contents of the reaction mixture used in this method included 20 mM EDTA, 2 mM DTNB, methanol, 10 µL homogenate, and 0.2 M Tris buffer (pH: 8.2) in a final volume of 200 µL. After the last addition of the enzyme source, the plate was incubated at 25°C for 30 minutes. It was then measured at each 405 nm wavelength. Standard curves prepared using reduced glutathione were used to calculate the total thiol group level. The total thiol group level was determined as nmol/mg protein. 2.7 Determination of Glutathione S-Transferase (GST) Enzyme Activity GST activity in S. exigua midgut tissue homogenate was measured using the Habig method (Habig et al. 1974 ). The final volume of the reaction mixture prepared for GST activity was 250 µL (0.1 M phosphate buffer (pH 7.4), 1 mM GSH, and 1 mM with 1-chloro-2,4 dinitrobenzene (CDNB). Activity measurements were performed at 37°C at a wavelength of 340 nm. 2.8 Determination of Esterase (EST) Activity Esterase enzyme activities of S. exigua 30 midgut samples were determined with p-nitrophenyl acetate substrate according to the Van Asperen method (Van Asperen 1962 ). The final volume of the reaction mixture prepared for esterase activity was 200 µL(0.1 M phosphate buffer (pH 7.0), Triton X-100, and 3.8 mM p-nitrophenyl acetate (PNPA) substrate). Esterase enzyme activity of S. exigua samples was described as nmol/min/mg protein (Konuş 2014 ). 2.9 Cotton Plant Leaves Homogenisation 50 mg cotton leaves were homogenised with Ultra-turrax homogeniser on ice in 1 ml of homogenisation solution, containing 100 mM TrisHCl, pH:7,2; 0,07% (v/v) 2-Mercaptoethanol; 1mM PMSF; 0,5% Nonidet P40; 2 mM EDTA. Each sample was homogenised four times for 15 seconds during the homogenisation process. The homogenised samples were centrifuged at + 4 ℃ and 12000 x g for 30 minutes. After that, the pellet was discarded; the supernatant was aliquoted (200 µL)and stored at -80°C. It was used for the determination of the total soluble sugar, protein, and in vitro antioxidant capacities of cotton plant leaves. 2.10 Determination of Total Soluble Sugar and Protein Amount in Cotton Leaves The carbohydrate analysis, that is, the total amount of soluble glucose in cotton leaves where S. exigua samples were grown, was made as described (Dubois 1956 ). In addition, protein amounts of cotton plant leaves were determined by the Bradford method (Bradford 1976 ). 2.11 DPPH and ABTS Assays The assay of DPPH radical scavenging activities of cotton leaves (control and GW-treated) and Trolox standard were determined as described by Konuş et al. (Konuş et al. 2019 ). Absorbance values of analysed samples and standard were read at 517 nm wavelength. A dose-responsive curve of cotton leaves homogenates and trolox was plotted to calculate the EC 50 value (concentration providing 50% inhibition). Antioxidant capacities of cotton leaves (control and GW-treated) and Trolox standard were determined as described previously (Re et al. 1999 ). Firstly, in ABTS assay, the standard or cotton leave homogenates were mixed with the ABTS solution and then incubated in the dark for 30 min. Finally, the absorbancies were measured at a wavelength of 734 nm. 2.12 Chlorophyll Content Determination A SPAD chlorophyll meter (Minolta Camera Co., Osaka, Japan) was used to estimate the chlorophyll content of cotton leaves. For this purpose, three different parts of six leaves were used in each treatment, and SPAD readings of each cotton leaf were measured three times. Data were analysed by SPSS 26.0 (IBM, Armonk, New York, USA), and differences among means were determined by using Duncan’s multiple range test with the significance level at p < 0 .05. For observation 1, samples were taken from insect-free 4–6 leaf stage cotton plants. For observation 2, they were taken from the same plants on which the insects were fed at the end of the experiment, four weeks after the initial sampling. 2.13 Statistical Analysis Total Thiol, GST, EST, Total soluble sugar, Protein amount, DPPH, and ABTS were performed in 30 samples while Chlorophyll Content was performed in triplicate and six samples. For statistical analysis, the MINITAB 14.0 software and SPSS 26.0 (IBM, Armonk, New York, USA), and differences among means were determined by using Duncan’s multiple range test and Student’s test was used. 2.13.1 Life Table Analysis The biological data (developmental time, survival, and reproduction) of all individuals were included in the demographic analysis using the TWOSEX-MSChart program (Chi 2023b ) based on the age-stage, two-sex life table theory (Chi 1988 ; Chi and Liu 1985 ). The population parameters that were calculated, definitions, equations and references are listed in Table 2 . The bootstrap technique (Efron and Tibshirani 1994 ) was used to estimate the standard errors of biological and population parameters with 100,000 resamples. The difference between treatments was compared using the paired bootstrap test (Tibshirani and Efron 1993 ). The computer program TWOSEX-MS Chart incorporates the bootstrap method and paired bootstrap test. Sigma Plot 14.0 software was used to create graphs. The population growth projection was calculated using Timing-MSChart (Chi 2023a ). Simulated population growth and age-stage structure for an initial population of 10 newly hatched larvae over 60 days was estimated. Table 2 Population parameters, their definitions, and equations used in their calculations Parameter Definition and equations s xj The probability that a newly hatched larvae will survive to age x and stage j (Chi and Liu 1985 ). It can be calculated as \({s_{xj}}=\frac{{{n_{xj}}}}{{{n_{01}}}}\) where n xj is the number of individuals survive to age x and stage j , n 01 is the number of newly hatched larvae used at the beginning of life table study (Chang et al. 2016 ). m x The mean number of eggs produced by individuals at age x . It is calculated as \({m_x}={{\left( {\sum\limits_{{j=1}}^{m} {{s_{xj}}{f_{xj}}} } \right)} \mathord{\left/ {\vphantom {{\left( {\sum\limits_{{j=1}}^{m} {{s_{xj}}{f_{xj}}} } \right)} {\left( {\sum\limits_{{j=1}}^{m} {{s_{xj}}} } \right)}}} \right. \kern-0pt} {\left( {\sum\limits_{{j=1}}^{m} {{s_{xj}}} } \right)}}\) , where m is the number of stages (Chi and Liu 1985 ). r The population growth rate as time approaches infinity and the population reaches the stable age-stage distribution (SASD). The population size will increase at the rate of e r per time unit. It is calculated by using the Euler-Lotka equation with age indexed from 0 (Chi and Liu 1985 ; Goodman 1982 ): \(\sum\limits_{{x=0}}^{\infty } {\left( {{e^{ - r\left( {x+1} \right)}}\sum\limits_{{j=1}}^{m} {{s_{xj}}{f_{xj}}} } \right)=\sum\limits_{{x=0}}^{\infty } {{e^{ - r\left( {x+1} \right)}}} {l_x}{m_x}} =1\) λ Finite rate of increase ( λ ): The population growth rate as time approaches infinity and the population reaches the stable age-stage distribution. The population size will increase at the rate of λ per time unit: R 0 The total number of offspring that an average individual (incl. females, males and immature mortalities) can produce during its lifetime (Chi and Liu 1985 ). It is calculated as \({R_{\text{0}}}=\sum\limits_{{x=0}}^{\infty } {\sum\limits_{{j=1}}^{m} {{s_{xj}}{f_{xj}}} } =\sum\limits_{{x=0}}^{\infty } {{l_x}{m_x}}\) T It is the period that a population requires to increase to R 0 -fold of its size as time approaches infinity and the population settles down to a stable age-stage distribution. \(T=\frac{{\ln {R_0}}}{r}=\frac{{\ln {R_0}}}{{\ln \lambda }}\) 3 RESULTS 3.1 The Spodoptera exigua 's life history and population parameters The results indicated that GW treatment affected the biological parameters of S. exigua . An extension of the total development period was observed with GW treatment ( P < 0.05) (Table 3 ). The total preoviposition period (TPOP) of S. exigua was significantly prolonged when reared on cotton plants treated with GW compared to untreated (control) plants ( P < 0.05) (Table 3 ). However, there were no differences between the treatments in total longevity, adult preoviposition period (APOP), fecundity (eggs/female), oviposition days, and preadult survival rate (%). The age-stage specific survival rate ( s xj ) indicates the probability that a newly laid S. exigua egg will survive to age x and stage j GW treatment had a significant effect on this probability; this probability in the cohort fed on the GW-treated plants was lower than in the cohort of the control treatment. On the other hand, stage overlaps can be observed in the s xj curves (Fig. 1 ). The pest's age-specific survival rate ( l x ) curve showed a rapid decline compared to the control, due to the high level of mortality observed in individuals fed on plants treated with the GW. The peaks formed by the age-specific reproduction ( m x ) and maternity ( l x m x ) curves of the pests were also lower on the GW-treated plants (Fig. 2 ). Compared to the control group, the reproductive peak of the pest's cohort fed with GW-treated plants occurred later and the peak values were lower (Fig. 2 ). In control and GW treatment, the reproduction peak occurred on days 24 and 31, respectively, with peak values of 51.60 and 42.29 eggs. The effect of GW treatment was also seen in the life table parameters derived from the biological characteristics (development time, survival rate and reproduction, etc.) of the pest. The intrinsic rate of increase ( r ), finite rate of increase ( λ ), and net reproductive rate ( R 0 ) values obtained in the individuals fed on the GW-treated plants were significantly lower than those obtained on the control plants. On the contrary, the value of the mean generation time (T) was higher in the GW treatment, this refers to the prolongation of this period ( P < 0.05) (Table 4 ). 3.2 Population projection The population projection results indicated a significant difference between the populations formed by the pest on the control and the GW-treated plants at the end of the period of 60 days. The number of individuals reached by the tested cohorts was 406,908 and 15,356 on control and GW-treated plants, respectively (Fig. 3 ). Table 3 Developmental times, oviposition days, fecundity, and survival rate (mean ± SE) of Spodoptera exigua reared on GW-treated and untreated (control) cotton plants. Life history parameters n Control n GW 1st Larvae (days) 31 2.61 ± 0.12a 38 2.39 ± 0.08a 2nd Larvae (days) 30 2.87 ± 0.12a 34 2.97 ± 0.10a 3rd Larvae (days) 30 3.07 ± 0.18a 31 2.81 ± 0.17a 4th Larvae (days) 29 2.83 ± 0.15a 30 2.83 ± 0.15a 5th Larvae (days) 26 2.73 ± 0.11a 29 3.03 ± 0.08b Pre-pupa (days) 25 1.20 ± 0.08a 29 1.41 ± 0.09a Pupa (days) 25 9.68 ± 0.42a 27 11.7 ± 0.30b Total preadult duration (days) 25 24.88 ± 0.64a 27 27.22 ± 0.44b Total longevity (days) 31 10.52 ± 0.46a 43 9.26 ± 0.54a Adult preoviposition period (APOP) (days) 15 2.60 ± 0.19a 11 3.09 ± 0.25a Total preoviposition period (TPOP) (days) 15 27.4 ± 0.94a 11 30.36 ± 0.86b Fecundity ( F ) (eggs/female) 15 519.53 ± 62.72a 11 415 ± 52.54a Oviposition days 15 5.73 ± 0.59a 11 5.09 ± 0.58a Preadult survival rate (%) 31 80.65 ± 7.08a 43 62.79 ± 7.4a Note: The standard errors were estimated by using 100,000 bootstraps. Means followed by the same letter in a row are not significantly different using the paired bootstrap test based on the confidence interval of 100,000 differences ( P < 0.05). Table 4 Population parameters (mean ± SE) of Spodoptera exigua reared on GW-treated and untreated (control) cotton plants. Population parameters n Control n GW Intrinsic rate of increase ( r ) (day − 1 ) 31 0.1968 ± 0.012b 43 0.1463 ± 0.011a Finite rate of increase ( λ ) (day − 1 ) 31 1.2175 ± 0.014b 43 1.1576 ± 0.013a Net reproduction rate ( R 0 ) (offspring) 31 251.39 ± 55.13b 43 106.16 ± 30.48a Mean generation time ( T ) (days) 31 28.08 ± 0.89a 43 31.88 ± 1.15b Note: The standard errors were estimated by using 100,000 bootstraps. Means followed by the same letter in a row are not significantly different using the paired bootstrap test based on the confidence interval of 100,000 differences ( P < 0.05). 3.3 Determination of Total Thiol Groups The total thiol amounts of S. exigua insects were shown in Table 5 . While it was determined that the total thiol groups of the untreated control group midguts of S. exigua (2.962 ± 0.32 nmol/mg protein) were higher than the ones with GW-treated group (2.613 ± 0.32 nmol/mg protein), this difference was not significant ( P > 0.05 ). 3.4 Determination of GST enzyme activity The GST enzyme activity of S. exigua midgut samples was determined using CDNB as a substrate. GST enzyme activities of S. exigua from the untreated control group and GW-treated group of cotton leaves were calculated as 134.81 ± 8.4 and 133.93 ± 9.5 nmol/min/mg protein, respectively (Table 5 ). According to these results, there was no statistical difference (P > 0.05) in GST activity between the GW-treated and untreated control groups. 3.5 Determination of Non-Specific Esterase (EST-PNPA) Activity EST-PNPA activities of S. exigua control (untreated) and GW-treated samples were calculated as 76.45 ± 5.1 and71.34 ± 3.8 nmol/min/mg protein, respectively (Table 5 ). Similar to GST enzyme activity results of S. exigua midguts, EST-PNPA activity results also didn’t indicate a statistically significant difference ( P > 0.05 ). Table 5 Summary of overall enzyme activities and total thiol content results of S. exigua midguts n Control (Mean ± S.E.M) GW (Mean ± S.E.M) P* GST-CDNB (nmol/min/mg protein) 30 134.81 ± 8.40 133.93 ± 9.50 0.945 EST-PNPA (nmol/min/mg protein) 30 76.45 ± 5.10 71.34 ± 3.80 0.404 Total Thiol Content (nmol/mg protein) 30 2.962 ± 0.32 2.613 ± 0.32 0.404 *There are no significant differences between the means in the same line (Student’s t -test, P > 0.05) SEM: Standard Error of Mean In addition, the weights of midgut tissue were measured as 78.37 ± 6.03 mg in the control cotton plants and 156.56 ± 1.69 mg in the GW groups. According to the weighing results, it was determined that the midgut tissues of those fed with GW-treated cotton plants had a statistically significant 2-fold increase in weight compared to those fed with control cotton plants (P < 0.05). 3.6 Total Soluble Sugar and Protein Results of Cotton Leaves In this work, protein contents and total soluble sugar amounts were determined in the leaves of the control and GW-treated cotton plants, in which S. exigua samples were fed (Fig. 4 ). According to the results, while no statistically significant difference could be determined in terms of protein content in the leaves, it was determined that the total amount of soluble sugar showed a statistically significant increase in the cotton plant with GW treatment ( P < 0.05). 3.7 Antioxidant Assays of Cotton Leaves DPPH and ABTS methods were used to determine the antioxidant activities in the untreated control and GW-treated cotton plant leaves on which S. exigua larvae were fed. As a result of the analyses, the EC 50 values of the cotton extract, which inhibited half of the tested DPPH and ABTS radicals, were calculated. In addition, the EC 50 value of the trolox standard was calculated and given in the table below (Table 6 ). According to the results obtained, the numerical value of EC 50 was found to be lower than the leaves of plants treated with GW, according to the results obtained by both methods. In other words, although GW-treated cotton leaves showed more antioxidant activity, these results were not statistically significant. Table 6 DPPH and ABTS assay results of GW-treated and untreated (control) cotton plants. Control EC 50 (mg/ml) GW EC 50 (mg/ml) DPPH Assay 0.968 ± 0.15* 0.822 ± 0.15* ABTS Assay 0.176 ± 0.02* 0.159 ± 0.01* The results are mean ± SEM, n = 4, Student’s t-test. *There are no significant differences between the means in the same line (Student’s t -test, P > 0.05) SEM: Standard Error of Mean 3.8 Chlorophyll Content Determination The table provided shows the chlorophyll content results of two observations, comparing a control group with a group treated with GW (Table 7 ). The chlorophyll content of the GW-treated cotton plants increased by 8.33% compared to the control plants for observation 1. In Observation 2, the chlorophyll content of the GW-treated cotton plants increased by 19.18% compared to the control plants. Based on these observations, it can be concluded that the treatment with GW has resulted in a noticeable increase in chlorophyll content compared to the control group. Table 7 Chlorophyll content results of GW-treated and untreated (control) cotton plants. Control GW P-value Observation 1 38.86 ± 0.60aA 42.10 ± 0.77bA P = 0.008, df = 10 Observation 2 39.10 ± 0.70aA 46.60 ± 1.93bA P = 0.04, df = 10 P-value P = 0.80, df = 10 P = 0.06, df = 10 The results are mean ± SEM, n = 18. The lowercase different letters in the same row indicate a statistically significant difference between the means (p < 0.05). The uppercase different letters in the same column indicate a statistically significant difference between the means (Duncan, p < 0.05). 4 DISCUSSION Our results indicate that the treatment of cotton plants by GW triggered plant metabolic changes leading to reduced population growth performance of S. exigua . Compared to those fed on untreated control plants, S. exigua individuals fed on GW-treated cotton plants had a longer preadult development time, lower survival rate, and significantly lower reproduction. The effect of GW treatment on the biological traits of the pest was also reflected in the life table parameters calculated by using these traits. The intrinsic rate of increase ( r ), the finite rate of increase ( λ ), and the net reproductive rate ( R 0 ) values of the pest obtained on GW-treated plants were significantly lower than those obtained on control plants, while the mean generation time ( T ) value was significantly higher. It is speculated that these results are due to the presence of beneficial microorganisms in host plants that induced systemic resistance. Through these metabolic changes in plants, microorganisms can negatively affect insects. Negative effects of insects such as avoidance of laying eggs on plants inoculated with microorganisms, larval development, decreased performance, and increased mortality in larvae and pupae can be observed (Agbessenou et al. 2022 ; Contreras-Cornejo et al. 2018 ; Contreras-Cornejo et al. 2020 ; Coppola et al. 2019 ; Papadopoulou and van Dam 2017 ; Papantoniou et al. 2021 ; Pappas et al. 2021 ). Studies on this topic show that beneficial microorganisms tend to reduce the performance of herbivorous insects by resulting in prolongation in the preadult development period, and a decrease in the survival rate and reproduction via plant-mediated effects (Fernandez-Conradi et al. 2018 ; Jaber and Vidal 2010 ; Jafarbeigi et al. 2020 ; Silva et al. 2019 ; Verma et al. 2019 ). In addition to all of this that it has been stated that some beneficial microorganism species may have some biocontrol effect against insects in the order Lepidoptera, as they can attack the insect cuticle under suitable conditions and adversely affect the peritrophic matrix of insects (Berini et al. 2016 ). This result shows that microorganisms can have extra negative effects against insects. No result showed that any oxidative stress occurred in cotton leaves treated with GW by DPPH and ABTS methods. By this result, no statistically significant difference was detected in the level of non-enzymatic glutathione levels as well as GST and esterase enzyme activities in S. exigua , feeding on the GW-treated cotton plant. In this study, a 2-fold increase was found in the weight of fourth instar S. exigua larvae stomach tissues fed the cotton plant treated with GW. Guo et al. ( 2014 ) stated that avoidance or attraction between insects and microorganisms was positively associated with feeding behaviour and weight gain in insect larvae (Noman et al. 2020 ). Therefore, insects appear to have different abilities to detect and avoid toxic compounds to survive. It can be said that avoidance and attraction due to modulated plant metabolism directly correspond to the survival of insects (Noman et al. 2020 ). Based on these results, it turns out that the appropriate concentration of dietary components is crucial to the successful completion of an individual's life cycle. In addition to all these changes in the plant, the chlorophyll content of the plants treated with GW was found to be higher than the control plants in our study. Similarly, studies on beneficial microorganisms and plants have noted that the leaves of treated plants are markedly greener (Harman 2000 ). This situation is explained by the increased development of the shoots and roots of the plants, along with some increase in the photosynthetic ability and rate of the plants (Harman et al. 2021 ). A greener plant indicates that its leaves have higher levels of chlorophyll pigment. As in several studies on this subject, beneficial microorganisms have documented that species help the plant to have a high chlorophyll capacity (Azarmi et al. 2011 ; Doni et al. 2017 ; Harman 2000 ; Liu et al. 2018 ; Vitti et al. 2016 ). The nutritional quality of plants can have an impact on the preference and performance of herbivorous insects (Awmack and Leather 2002 ). One component that can be selected by insect herbivores is the ratio of dietary protein to digestible carbohydrate (P:C) (Deans et al. 2022 ). They need dietary proteins to get the nitrogen they need for growth and reproduction, and S. exigua prefers a diet relatively high in proteins (Merkx-Jacques et al. 2008 ). Mating outcomes may be affected by different diets that promote behavioural seclusion. The duration of copulation, the length of copulation, and the number of eggs laid varied dramatically between the diets. Females on the artificial diet rarely mated with males of their choice (Di et al. 2021 ). The second instar S. exigua exhibited high mortality and delayed development on the C-rich, and P-poor diets, indicating the possible negative consequences of excess carbohydrates and the significance of protein for growth and development (Al-Zubaidi and Capinera 1983 ; Merkx-Jacques et al. 2008 ) let larvae of this species were reared on sugar beet leaves from plants fertilised with below-normal, normal and above-normal levels of nitrogen and allowed to cannibalise beet armyworm pupae to assess the effect of dietary nitrogen levels on the cannibalistic behaviour of this species. They reported that the foliar nitrogen content and the percentage of cannibalism showed a substantial inverse correlation and female larvae's cannibalistic behaviour dramatically enhanced fecundity. It was proposed that increased cannibalism by larvae might serve as a compensatory mechanism for their poor nutrition and play a significant role in the dynamics of herbivore populations. In the present study, we have obtained results in this regard. The age-stage two-sex life table, a crucial tool for examining the effects of environmental factors on insect development, reproduction, survival, and population dynamics, demonstrated that there were decreases in the survival rate, reproductive potential as the fecundity of females, and the calculated possibility of reaching the adult stage in individuals fed on GW-treated plants (P < 0.05). The statistically significant decrease in the protein/carbohydrate ratio measured in cotton plant samples is inferred to have a negative effect on the population growth performance of S. exigua . It was found that there was a decrease of about 30.7% in this ratio in the plants treated with microorganisms compared to that calculated in the control group. We report that such a change leads to a reduction in both the reproduction of S. exigua and the survival of its larvae. Beet armyworms may have overcompensated by feeding more than necessary to make up for the lack of protein in their diets, and over-compensatory feeding probably should have increased as the quantity of soluble carbohydrates in diets increased (Wang et al. 2018 ). Many insect species larvae use elevated lipid deposition as one strategy to deal with dietary C excesses against limited P. A C-biased diet was said to increase pupal lipid stores (Babic et al. 2008 ). Some Lepidopteran species showed delayed development and increased pupal mass with more soluble carbohydrates (Raubenheimer et al. 2005 ). A similar situation was observed in the present study; in fact, a statistically significant increase in the weight of larvae was observed. The midgut tissues of S. exigua larvae fed with GW-treated cotton plants weigh twice as much, which means that organisms in this group do not pass through the developmental stages in the ordinary course but instead tend to accumulate fat at this stage. This evidence supports the observation of a lower increase in the total population of the host organism on cotton plants. Research on the potential effects of these changes in the plant on herbivores is limited, but studies on this subject will provide important information on the use of these microorganisms in pest management. An accurate estimate of its potential for plant protection may be made using the interaction between plants, microorganisms, and S. exigua , including both changes in insect phenotype and plant transcriptome. The use of beneficial microorganism species has significantly improved plant performance by enhancing plant protection against abiotic and biotic stressors. Plant defence and health, as well as host choice, feeding behaviour, and arthropod fitness, have all been documented to be affected by three-way interactions between plants, microbes, and arthropods. 5 CONCLUSION Modern agriculture has a significant challenge in the search for novel pathogen or pest control methods that can lessen the need for chemical pesticides. Among the different biological control options, the use of beneficial microorganisms to reduce plant losses and increase plant growth is promising. This has significant ramifications for the interactions between plants, microbes, and insects. Various research advances in the field of plant-microbe-insect interactions can heavily affect agricultural productivity. Due to the significant difference between laboratory/field and real-world conditions, further research is required to integrate insects, plants, and microorganisms for specific and multiple interactions, often under different climatic and ecological variables. Also, it will be an important future research target to uncover how the genetic pathways regulating plant and insect resistance coordinate the selection of microbial traits. In order to encourage researchers to take on these present and future problems in this field, we hope that this issue may spark some interest and passion for the study of microbial mediation of plant-insect interactions. This study illuminates novel insights into the plant-mediated influence of beneficial soil-borne microorganisms on S. exigua , a significant cotton pest. The information presented here on the effects of beneficial microorganisms' treatment on S. exigua , where a significant reduction of fecundity, population number, survival rate etc. occurred is crucial in improving the controlling strategies of this species. The use of beneficial soil-borne microorganisms for controlling S. exigua is an essential advantage over insecticides due to their low cost and environmental safety. These microorganisms can be used as a sustainable alternative to chemical insecticides, which can have negative effects on the environment and human health. Moreover, using microorganisms can help reduce the development of insecticide resistance in S. exigua populations, which is a major concern in pest management. Declarations Author contributions Nurhan Didem Kızılkan, Metin Konuş, and Mehmet Ramazan Rişvanlı designed the research and conducted experiments. Hilmi Kara analysed life table data. Doğan Çetin analysed plant and enzyme experiments. Can Yılmaz, Remzi Atlihan, and Mehmet Salih Özgökçe reviewed and edited the manuscript. Acknowledgements This work was supported by BAPB, University of YüzüncüYıl, Project No.FYL-2020-8856 (Türkiye). Conflict of Interest. The authors have no conflicts of interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4170111","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287169270,"identity":"97cbd884-a13a-4016-b64b-93b3252f7fe5","order_by":0,"name":"Nurhan Didem Kızılkan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nurhan","middleName":"Didem","lastName":"Kızılkan","suffix":""},{"id":287169271,"identity":"b4517ac6-886e-4d91-8a69-6a129ffb60ea","order_by":1,"name":"Metin 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Çetin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Doğan","middleName":"","lastName":"Çetin","suffix":""},{"id":287169277,"identity":"710647ed-b4ad-4478-bfe3-94f2bddf3530","order_by":7,"name":"Remzi Atlıhan","email":"","orcid":"","institution":"Van Yüzüncü Yıl University","correspondingAuthor":false,"prefix":"","firstName":"Remzi","middleName":"","lastName":"Atlıhan","suffix":""}],"badges":[],"createdAt":"2024-03-26 13:15:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4170111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4170111/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54292956,"identity":"4977d65c-de7b-41a8-9f9f-94030a19c7d2","added_by":"auto","created_at":"2024-04-08 12:12:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64237,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific survival rate (\u003cem\u003es\u003c/em\u003e\u003csub\u003e\u003cem\u003exj\u003c/em\u003e\u003c/sub\u003e) of \u003cem\u003eSpodoptera exigua\u003c/em\u003e feeding on GW treated and untreated (control) cotton plants.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4170111/v1/e617484399554441e7c5c899.jpg"},{"id":54292959,"identity":"9256515d-ce26-4bde-951b-337dbacf7c3b","added_by":"auto","created_at":"2024-04-08 12:12:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47860,"visible":true,"origin":"","legend":"\u003cp\u003eAge specific survival rate (\u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e), fecundity (\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e) curves of \u003cem\u003eSpodoptera exigua\u003c/em\u003e fed on GW treated and untreated (control) cotton plants.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4170111/v1/e55b8960b913b3a76798bac8.jpg"},{"id":54293557,"identity":"1e422312-f305-407d-8cb8-acf3f75aff09","added_by":"auto","created_at":"2024-04-08 12:20:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87288,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation development simulation of \u003cem\u003eSpodoptera exigua\u003c/em\u003efeeding on GW treated and untreated (control) cotton plants.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4170111/v1/b7cd290db74ddf2e1805c9c7.jpg"},{"id":54293810,"identity":"c3c81266-9a76-433d-8755-1c3b7e69ce0c","added_by":"auto","created_at":"2024-04-08 12:28:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50819,"visible":true,"origin":"","legend":"\u003cp\u003eTotal soluble sugar and protein amount of cotton leaves\u003c/p\u003e\n\u003cp\u003eThe results are mean ± Standart Deviation, n=4, Student’s t-test. An asterisk above the bars indicates a significant difference at \u003cem\u003eP \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4170111/v1/5905a1cd26a88657956772e1.jpg"},{"id":54572378,"identity":"dd3b971f-36c9-43c1-a569-d04fbd79cf49","added_by":"auto","created_at":"2024-04-12 13:02:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":812138,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4170111/v1/4e0f3073-baa3-47a2-90a3-db11d4c643dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Impact of Beneficial Microorganisms Inoculated Cotton Plants on Spodoptera exigua","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eCotton, also referred to as \"white gold,\" is the crop that contributes the most to the global production of fibre, oil and protein. One of the main challenges in achieving high yields in cotton is the damage caused by herbivores. Cotton pests cause about 15% of product loss per year (Tokel et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). About 200 insect species feeding on cotton plants have been described (Dhaliwal et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). \u003cem\u003eSpodoptera exigua\u003c/em\u003e (H\u0026uuml;bner) (Lepidoptera, Noctuidae), also known as the beet armyworm, feed on more than 130 plant species from at least 30 families, including many economically important crops, such as cotton (Fu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mehrkhou et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Robinson et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To control \u003cem\u003eS. exigua\u003c/em\u003e, chemical pesticides are frequently used. A pest management strategy focused on chemical pesticides is unsustainable due to its detrimental effects. Therefore, obtaining high efficiency from plants with environmentally friendly techniques is a necessity in today's world.\u003c/p\u003e \u003cp\u003ePlants face many environmental challenges. They are constantly under biotic stress due to herbivores that cause severe damage to crops worldwide. Plants have evolved a wide variety of physical and chemical mechanisms to defend themselves against these herbivores (Sharma et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; War et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; War et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Plant growth-promoting rhizobacteria (PGPR) and plant growth-promoting fungus (PGPF) have been demonstrated to promote plant growth and health (Lugtenberg and Kamilova \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Recent studies have shown that microbial biota in soil also affects herbivores. Microorganisms can change the fitness and quality of plants and thus indirectly affect herbivores (Contreras-Cornejo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Friesen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Grabka et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lugtenberg and Kamilova \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Noman et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ortiz and Sansinenea \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pappas et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Risvanli \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sheridan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Evaluating the effects of microorganisms on the plant preference or performance of insects still has many questions to be uncovered. Moreover, how these interactions depend on the insect population, the microorganism lifestyle, and the environmental effects continues to be extensively investigated (Bakker et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fernandez-Conradi et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Verma et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMolecules such as hydrogen peroxide and superoxide anion, which are reactive oxygen species (ROS), can be formed in the mitochondria of herbivorous insects by intrinsic pathways such as oxidative phosphorylation. Besides, herbivorous insects can be exposed to these molecules exogenously through the plants they consume (Krishnan and Sehnal \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Orozco-Cardenas and Ryan \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Sytykiewicz \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Insects have esterase and glutathione S-transferase enzymes that metabolise xenobiotic molecules and render them harmless. These detoxification enzymes are also involved in the prevention of cellular and tissue damage that may result from oxidative stress (Konuş \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition to these enzymes, insects also possess non-enzymatic reactive oxygen species scavenging molecules such as ascorbate and glutathione (Kazek et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Besides, it has been reported that increased xenobiotic metabolism in insects leads to decreases in total thiol levels in insect cells, so it causes a weakening of the antioxidant defence system in those insects (Vontas et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Consequently, examining markers related to thiol level is important in determining the level of oxidative stress.\u003c/p\u003e \u003cp\u003eRecently, interest in using beneficial microorganisms to grow robust-structure plants and yield more has increased. These organisms support plant growth, encourage resistance to biotic and abiotic difficulties, enhance nutrient uptake, lessen stress, and shield plants from pests and diseases, among many other functions (Lazcano et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Santoyo et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Choosing suitable methods is extremely important to understand exactly how the effects of beneficial microorganisms on plants are reflected in the pest population dynamics. One notable approach in this regard is the use of life tables. The life table is a mathematical tool used to quantitatively measure the population dynamics of various organisms, mainly insects, and is very useful in understanding the impact of various factors on the population's dynamics. The age-stage, two-sex life table, in contrast to the traditional female age-specific life tables, is capable of explaining stage differentiation (i.e., metamorphosis) and the variations in developmental rates among individuals and takes males into account, thus providing a more detailed understanding of population dynamics (Chi \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Chi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chi and Su \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chi et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study, firstly, aimed to determine the plant-mediated effects of a beneficial microorganism complex on the development, survival, reproduction, and consequently, population growth parameters of \u003cem\u003eS. exigua\u003c/em\u003e. Secondly, the effects of the changes led by the beneficial microorganisms on oxidative stress, chlorophyll content, sugar and protein levels were measured in cotton leaves. Finally, the reciprocal effects of plant-insect-microorganisms on total glutathione levels and glutathione S-transferase and esterase enzyme activities in \u003cem\u003eS. exigua\u003c/em\u003e midguts were determined to investigate the effect of beneficial microorganisms inoculated cotton plants on \u003cem\u003eS. exigua\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eOur knowledge of the beneficial interactions between beneficial soil-borne microorganisms, plants and pests will be furthered by recording this communication, which is one of the exciting alternatives to pest management.\u003c/p\u003e"},{"header":"2 MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1 Plant and insect.\u003c/strong\u003e Cotton plants (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L., cultivar Beyazaltın BA-440) were grown in growth chambers under controlled conditions (temperature of 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, relative humidity of 60\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, photoperiod of 16 h light/8 h dark). The potting medium was composed of peat: perlite in a ratio of 2:1. Plants were watered and fertilised twice a week with Hoagland\u0026apos;s nutrient solution. Cotton plants of approximately six to eight fully expanded leaves were selected for the experiments.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eSpodoptera exigua\u003c/em\u003e (H\u0026uuml;bner) (Lepidoptera: Noctuidae) was reared at the Laboratory of Entomology, Department of Plant Protection, Van Y\u0026uuml;z\u0026uuml;nc\u0026uuml; Yıl University, Van, T\u0026uuml;rkiye. The insects were kept under controlled climate room conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Microorganisms and their inoculation.\u003c/strong\u003e Each cotton seed was lightly watered in, and then a microorganism complex including ectomycorrhiza spp., endomycorrhiza spp., and other beneficial fungal and bacterial spp. [Great White Premium Mycorrhizae (GW), Plant Success, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.plant-success.com\u003c/span\u003e\u003c/span\u003e] was applied to the seedbeds according to the label.1.25 grams of GW was added to 7.57 litres of water at a rate of 300 millilitres per pot. This application was repeated in 3-week periods. GW ingredient list is given below in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eSampled and re-isolated soil and plant tissues to confirm the presence of microorganisms.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGreat White Premium Ingredient List\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEctomycorrhiza spp.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePropagules/gram\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePisolithus tinctorius\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187,875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRhizopagan luteolus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRhizopagan fulvigleba\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRhizopagan villosullus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRhizopagan amylopogon\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScleroderma citrinum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScleroderma cepa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndomycorrhiza spp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePropagules/gram\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGlomus aggregatrum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGlomus intraradices\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGlomus mosseae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGlomus etunicatum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGlomus clarum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGlomus monosporum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGlomus deserticola\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eParaglomus brasilianum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGigaspora margarita\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrichoderma\u003c/strong\u003e \u003cstrong\u003espp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePropagules/gram\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTrichoderma koningii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187,875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTrichoderma harzianum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125,250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Fungal and Bacterial spp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCFU\u0026rsquo;s/gram\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAzobacter chroococcum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus licheniformis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus azotoformans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus megaterium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus coagulens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus pumilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus amyloliquefaciens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePaenibacillus polymyxa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePaenibacillus durum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas aureofaciens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas fluorescens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Life Table Study\u003c/h2\u003e\n \u003cp\u003eThe eggs of \u003cem\u003eS. exigua\u003c/em\u003e collected from laboratory-mated adults were used to maintain the colony and perform the experiments. Sixty to eighty newly hatched \u003cem\u003eS. exigua\u003c/em\u003e larvae (3 per plant) were placed on cotton plant leaves of GW-treated control plants. \u003cem\u003eS. exigua\u003c/em\u003e larvae were placed on leaves using a paintbrush and placed into plastic bags. Plastic bags were covered with mesh to prevent larval escape. The developmental stage and survival of \u003cem\u003eS. exigua\u003c/em\u003e were observed and recorded daily. The sexes in the pupal stage were paired, and each pair was placed in a separate cylindrical plastic container (15 cm diameter and 19 cm height). Then, emerged adults were released in plastic containers for laying eggs. During the reproduction period, cotton wool soaked in 25% of honey\u0026ndash;water solution was placed in the containers for adult moths feeding. A piece of wax paper was set around the containers for the adults to lay eggs. The survival and fecundity of the \u003cem\u003eS. exigua\u003c/em\u003e were recorded daily until the death of all individuals.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Midgut tissue isolation from \u003cem\u003eSpodoptera exigua\u003c/em\u003e larvae\u003c/h2\u003e\n \u003cp\u003eAll measurements were conducted on early fourth-instar \u003cem\u003eS. exigua\u003c/em\u003e larvae for enzymatic antioxidant assays and non-enzymatic antioxidant level determination. To isolate midgut tissue from 30 fourth-instar \u003cem\u003eS. exigua\u003c/em\u003e larvae, the larvae were embedded in ice and left to paralyse for a while. Then, larvae were cut along their length with the help of a razor blade. After that, the removed larva midguts were immediately cleaned with 1.15 M potassium chloride dried on filter paper. Finally, larva midgut weights of insects were recorded by analytical balance and stored in the deep freezer (-80 ˚C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Preparation of \u003cem\u003eSpodoptera exigua\u003c/em\u003e midgut homogenates\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eSpodoptera exigua\u003c/em\u003e fourth-instar larvae midgut samples were homogenised with ultra-turrax in 1 ml homogenisation solution (0.1 M phosphate buffer (pH 7.2), 1 mM dithiothreitol (DTT), 1 mM phenyl methyl sulfonyl fluoride (PMSF) and 1 mM ethylene diamine tetra-acetic acid (EDTA). During the homogenisation process, each sample was homogenised five times for 20 seconds and kept on ice for 20 seconds after each 20 seconds of homogenisation. The homogenised samples were centrifuged at +\u0026thinsp;4 ℃ and 10000 x g for 30 minutes. After centrifugation, while the pellet was discarded; the supernatant was placed in a deep freezer at \u0026minus;\u0026thinsp;80\u0026deg;C to be used as an enzyme source for further analysis such as esterase, glutathione S-transferase activity assays, and total thiol level. The protein concentration was determined according to the Bradford method (Bradford \u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Determination of Total Thiol Content\u003c/h2\u003e\n \u003cp\u003eThe modified method of Sedlak and Lindsay was used to determine the total thiol groups of \u003cem\u003eS. exigua\u003c/em\u003e midgut homogenates (Sedlak and Lindsay \u003cspan class=\"CitationRef\"\u003e1968\u003c/span\u003e). The contents of the reaction mixture used in this method included 20 mM EDTA, 2 mM DTNB, methanol, 10 \u0026micro;L homogenate, and 0.2 M Tris buffer (pH: 8.2) in a final volume of 200 \u0026micro;L. After the last addition of the enzyme source, the plate was incubated at 25\u0026deg;C for 30 minutes. It was then measured at each 405 nm wavelength. Standard curves prepared using reduced glutathione were used to calculate the total thiol group level. The total thiol group level was determined as nmol/mg protein.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Determination of Glutathione S-Transferase (GST) Enzyme Activity\u003c/h2\u003e\n \u003cp\u003eGST activity in \u003cem\u003eS. exigua\u003c/em\u003e midgut tissue homogenate was measured using the Habig method (Habig et al. \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e). The final volume of the reaction mixture prepared for GST activity was 250 \u0026micro;L (0.1 M phosphate buffer (pH 7.4), 1 mM GSH, and 1 mM with 1-chloro-2,4 dinitrobenzene (CDNB). Activity measurements were performed at 37\u0026deg;C at a wavelength of 340 nm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Determination of Esterase (EST) Activity\u003c/h2\u003e\n \u003cp\u003eEsterase enzyme activities of \u003cem\u003eS. exigua\u003c/em\u003e 30 midgut samples were determined with p-nitrophenyl acetate substrate according to the Van Asperen method (Van Asperen \u003cspan class=\"CitationRef\"\u003e1962\u003c/span\u003e). The final volume of the reaction mixture prepared for esterase activity was 200 \u0026micro;L(0.1 M phosphate buffer (pH 7.0), Triton X-100, and 3.8 mM p-nitrophenyl acetate (PNPA) substrate). Esterase enzyme activity of \u003cem\u003eS. exigua\u003c/em\u003e samples was described as nmol/min/mg protein (Konuş \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9 Cotton Plant Leaves Homogenisation\u003c/h2\u003e\n \u003cp\u003e50 mg cotton leaves were homogenised with Ultra-turrax homogeniser on ice in 1 ml of homogenisation solution, containing 100 mM TrisHCl, pH:7,2; 0,07% (v/v) 2-Mercaptoethanol; 1mM PMSF; 0,5% Nonidet P40; 2 mM EDTA. Each sample was homogenised four times for 15 seconds during the homogenisation process. The homogenised samples were centrifuged at +\u0026thinsp;4 ℃ and 12000 x g for 30 minutes. After that, the pellet was discarded; the supernatant was aliquoted (200 \u0026micro;L)and stored at -80\u0026deg;C. It was used for the determination of the total soluble sugar, protein, and in vitro antioxidant capacities of cotton plant leaves.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10 Determination of Total Soluble Sugar and Protein Amount in Cotton Leaves\u003c/h2\u003e\n \u003cp\u003eThe carbohydrate analysis, that is, the total amount of soluble glucose in cotton leaves where \u003cem\u003eS. exigua\u003c/em\u003e samples were grown, was made as described (Dubois \u003cspan class=\"CitationRef\"\u003e1956\u003c/span\u003e). In addition, protein amounts of cotton plant leaves were determined by the Bradford method (Bradford \u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.11 DPPH and ABTS Assays\u003c/h2\u003e\n \u003cp\u003eThe assay of DPPH radical scavenging activities of cotton leaves (control and GW-treated) and Trolox standard were determined as described by Konuş et al. (Konuş et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Absorbance values of analysed samples and standard were read at 517 nm wavelength. A dose-responsive curve of cotton leaves homogenates and trolox was plotted to calculate the EC\u003csub\u003e50\u003c/sub\u003e value (concentration providing 50% inhibition).\u003c/p\u003e\n \u003cp\u003eAntioxidant capacities of cotton leaves (control and GW-treated) and Trolox standard were determined as described previously (Re et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). Firstly, in ABTS assay, the standard or cotton leave homogenates were mixed with the ABTS solution and then incubated in the dark for 30 min. Finally, the absorbancies were measured at a wavelength of 734 nm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.12 Chlorophyll Content Determination\u003c/h2\u003e\n \u003cp\u003eA SPAD chlorophyll meter (Minolta Camera Co., Osaka, Japan) was used to estimate the chlorophyll content of cotton leaves. For this purpose, three different parts of six leaves were used in each treatment, and SPAD readings of each cotton leaf were measured three times. Data were analysed by SPSS 26.0 (IBM, Armonk, New York, USA), and differences among means were determined by using Duncan\u0026rsquo;s multiple range test with the significance level at p\u0026thinsp;\u0026lt;\u0026thinsp;0 .05. For observation 1, samples were taken from insect-free 4\u0026ndash;6 leaf stage cotton plants. For observation 2, they were taken from the same plants on which the insects were fed at the end of the experiment, four weeks after the initial sampling.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.13 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eTotal Thiol, GST, EST, Total soluble sugar, Protein amount, DPPH, and ABTS were performed in 30 samples while Chlorophyll Content was performed in triplicate and six samples. For statistical analysis, the MINITAB 14.0 software and SPSS 26.0 (IBM, Armonk, New York, USA), and differences among means were determined by using Duncan\u0026rsquo;s multiple range test and Student\u0026rsquo;s test was used.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.13.1 Life Table Analysis\u003c/h2\u003e\n \u003cp\u003eThe biological data (developmental time, survival, and reproduction) of all individuals were included in the demographic analysis using the TWOSEX-MSChart program (Chi \u003cspan class=\"CitationRef\"\u003e2023b\u003c/span\u003e) based on the age-stage, two-sex life table theory (Chi \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e; Chi and Liu \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e). The population parameters that were calculated, definitions, equations and references are listed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The bootstrap technique (Efron and Tibshirani \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e) was used to estimate the standard errors of biological and population parameters with 100,000 resamples. The difference between treatments was compared using the paired bootstrap test (Tibshirani and Efron \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e). The computer program TWOSEX-MS Chart incorporates the bootstrap method and paired bootstrap test. Sigma Plot 14.0 software was used to create graphs.\u003c/p\u003e\n \u003cp\u003eThe population growth projection was calculated using Timing-MSChart (Chi \u003cspan class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Simulated population growth and age-stage structure for an initial population of 10 newly hatched larvae over 60 days was estimated.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePopulation parameters, their definitions, and equations used in their calculations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition and equations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003es\u003c/em\u003e\u003csub\u003e\u003cem\u003exj\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe probability that a newly hatched larvae will survive to age \u003cem\u003ex\u003c/em\u003e and stage \u003cem\u003ej\u003c/em\u003e (Chi and Liu \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e). It can be calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({s_{xj}}=\\frac{{{n_{xj}}}}{{{n_{01}}}}\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003exj\u003c/em\u003e\u003c/sub\u003e is the number of individuals survive to age \u003cem\u003ex\u003c/em\u003e and stage \u003cem\u003ej\u003c/em\u003e, \u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003e01\u003c/em\u003e\u003c/sub\u003e is the number of newly hatched larvae used at the beginning of life table study (Chang et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe mean number of eggs produced by individuals at age \u003cem\u003ex\u003c/em\u003e. It is calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m_x}={{\\left( {\\sum\\limits_{{j=1}}^{m} {{s_{xj}}{f_{xj}}} } \\right)} \\mathord{\\left/ {\\vphantom {{\\left( {\\sum\\limits_{{j=1}}^{m} {{s_{xj}}{f_{xj}}} } \\right)} {\\left( {\\sum\\limits_{{j=1}}^{m} {{s_{xj}}} } \\right)}}} \\right. \\kern-0pt} {\\left( {\\sum\\limits_{{j=1}}^{m} {{s_{xj}}} } \\right)}}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003em\u003c/em\u003e is the number of stages (Chi and Liu \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe population growth rate as time approaches infinity and the population reaches the stable age-stage distribution (SASD). The population size will increase at the rate of \u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003er\u003c/em\u003e\u003c/sup\u003e per time unit. It is calculated by using the Euler-Lotka equation with age indexed from 0 (Chi and Liu \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e; Goodman \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum\\limits_{{x=0}}^{\\infty } {\\left( {{e^{ - r\\left( {x+1} \\right)}}\\sum\\limits_{{j=1}}^{m} {{s_{xj}}{f_{xj}}} } \\right)=\\sum\\limits_{{x=0}}^{\\infty } {{e^{ - r\\left( {x+1} \\right)}}} {l_x}{m_x}} =1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lambda;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinite rate of increase (\u003cem\u003e\u0026lambda;\u003c/em\u003e): The population growth rate as time approaches infinity and the population reaches the stable age-stage distribution. The population size will increase at the rate of \u003cem\u003e\u0026lambda;\u003c/em\u003e per time unit:\u003cimg src=\"data:image/png;base64,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\" style=\"width: 38px; height: 21.9231px;\" width=\"38\" height=\"21.9231\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe total number of offspring that an average individual (incl. females, males and immature mortalities) can produce during its lifetime (Chi and Liu \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e). It is calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R_{\\text{0}}}=\\sum\\limits_{{x=0}}^{\\infty } {\\sum\\limits_{{j=1}}^{m} {{s_{xj}}{f_{xj}}} } =\\sum\\limits_{{x=0}}^{\\infty } {{l_x}{m_x}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt is the period that a population requires to increase to \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e-fold of its size as time approaches infinity and the population settles down to a stable age-stage distribution. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(T=\\frac{{\\ln {R_0}}}{r}=\\frac{{\\ln {R_0}}}{{\\ln \\lambda }}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The \u003cem\u003eSpodoptera exigua\u003c/em\u003e's life history and population parameters\u003c/h2\u003e \u003cp\u003eThe results indicated that GW treatment affected the biological parameters of \u003cem\u003eS. exigua\u003c/em\u003e. An extension of the total development period was observed with GW treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The total preoviposition period (TPOP) of \u003cem\u003eS. exigua\u003c/em\u003e was significantly prolonged when reared on cotton plants treated with GW compared to untreated (control) plants (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, there were no differences between the treatments in total longevity, adult preoviposition period (APOP), fecundity (eggs/female), oviposition days, and preadult survival rate (%).\u003c/p\u003e \u003cp\u003eThe age-stage specific survival rate (\u003cem\u003es\u003c/em\u003e\u003csub\u003e\u003cem\u003exj\u003c/em\u003e\u003c/sub\u003e) indicates the probability that a newly laid \u003cem\u003eS. exigua\u003c/em\u003e egg will survive to age \u003cem\u003ex\u003c/em\u003e and stage \u003cem\u003ej\u003c/em\u003e GW treatment had a significant effect on this probability; this probability in the cohort fed on the GW-treated plants was lower than in the cohort of the control treatment. On the other hand, stage overlaps can be observed in the \u003cem\u003es\u003c/em\u003e\u003csub\u003e\u003cem\u003exj\u003c/em\u003e\u003c/sub\u003e curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pest's age-specific survival rate (\u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e) curve showed a rapid decline compared to the control, due to the high level of mortality observed in individuals fed on plants treated with the GW. The peaks formed by the age-specific reproduction (\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e) and maternity (\u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e) curves of the pests were also lower on the GW-treated plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompared to the control group, the reproductive peak of the pest's cohort fed with GW-treated plants occurred later and the peak values were lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In control and GW treatment, the reproduction peak occurred on days 24 and 31, respectively, with peak values of 51.60 and 42.29 eggs.\u003c/p\u003e \u003cp\u003eThe effect of GW treatment was also seen in the life table parameters derived from the biological characteristics (development time, survival rate and reproduction, etc.) of the pest. The intrinsic rate of increase (\u003cem\u003er\u003c/em\u003e), finite rate of increase (\u003cem\u003eλ\u003c/em\u003e), and net reproductive rate (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) values obtained in the individuals fed on the GW-treated plants were significantly lower than those obtained on the control plants. On the contrary, the value of the mean generation time (T) was higher in the GW treatment, this refers to the prolongation of this period (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Population projection\u003c/h2\u003e \u003cp\u003eThe population projection results indicated a significant difference between the populations formed by the pest on the control and the GW-treated plants at the end of the period of 60 days. The number of individuals reached by the tested cohorts was 406,908 and 15,356 on control and GW-treated plants, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDevelopmental times, oviposition days, fecundity, and survival rate (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) of \u003cem\u003eSpodoptera exigua\u003c/em\u003e reared on GW-treated and untreated (control) cotton plants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife history parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Larvae (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd Larvae (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd Larvae (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th Larvae (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th Larvae (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pupa (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePupa (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal preadult duration (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal longevity (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdult preoviposition period (APOP) (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal preoviposition period (TPOP) (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFecundity (\u003cem\u003eF\u003c/em\u003e) (eggs/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e519.53\u0026thinsp;\u0026plusmn;\u0026thinsp;62.72a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e415\u0026thinsp;\u0026plusmn;\u0026thinsp;52.54a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOviposition days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreadult survival rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.65\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.79\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: The standard errors were estimated by using 100,000 bootstraps. Means followed by the same letter in a row are not significantly different using the paired bootstrap test based on the confidence interval of 100,000 differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation parameters (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) of \u003cem\u003eSpodoptera exigua\u003c/em\u003e reared on GW-treated and untreated (control) cotton plants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrinsic rate of increase (\u003cem\u003er\u003c/em\u003e) (day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1968\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1463\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinite rate of increase (\u003cem\u003eλ\u003c/em\u003e) (day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2175\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1576\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet reproduction rate (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) (offspring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251.39\u0026thinsp;\u0026plusmn;\u0026thinsp;55.13b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.16\u0026thinsp;\u0026plusmn;\u0026thinsp;30.48a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean generation time (\u003cem\u003eT\u003c/em\u003e) (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The standard errors were estimated by using 100,000 bootstraps. Means followed by the same letter in a row are not significantly different using the paired bootstrap test based on the confidence interval of 100,000 differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Determination of Total Thiol Groups\u003c/h2\u003e \u003cp\u003eThe total thiol amounts of \u003cem\u003eS. exigua\u003c/em\u003e insects were shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. While it was determined that the total thiol groups of the untreated control group midguts of \u003cem\u003eS. exigua\u003c/em\u003e (2.962\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 nmol/mg protein) were higher than the ones with GW-treated group (2.613\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 nmol/mg protein), this difference was not significant (\u003cem\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Determination of GST enzyme activity\u003c/h2\u003e \u003cp\u003eThe GST enzyme activity of \u003cem\u003eS. exigua\u003c/em\u003e midgut samples was determined using CDNB as a substrate. GST enzyme activities of \u003cem\u003eS. exigua\u003c/em\u003e from the untreated control group and GW-treated group of cotton leaves were calculated as 134.81\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4 and 133.93\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 nmol/min/mg protein, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). According to these results, there was no statistical difference (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in GST activity between the GW-treated and untreated control groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Determination of Non-Specific Esterase (EST-PNPA) Activity\u003c/h2\u003e \u003cp\u003eEST-PNPA activities of \u003cem\u003eS. exigua\u003c/em\u003e control (untreated) and GW-treated samples were calculated as 76.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 and71.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 nmol/min/mg protein, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similar to GST enzyme activity results of \u003cem\u003eS. exigua\u003c/em\u003e midguts, EST-PNPA activity results also didn\u0026rsquo;t indicate a statistically significant difference (\u003cem\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of overall enzyme activities and total thiol content results of \u003cem\u003eS. exigua\u003c/em\u003e midguts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.E.M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.E.M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP*\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGST-CDNB\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(nmol/min/mg protein)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e134.81\u0026thinsp;\u0026plusmn;\u0026thinsp;8.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e133.93\u0026thinsp;\u0026plusmn;\u0026thinsp;9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEST-PNPA\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(nmol/min/mg protein)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e76.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e71.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Thiol Content\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(nmol/mg protein)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.962\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.613\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*There are no significant differences between the means in the same line (Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSEM: Standard Error of Mean\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, the weights of midgut tissue were measured as 78.37\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03 mg in the control cotton plants and 156.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69 mg in the GW groups. According to the weighing results, it was determined that the midgut tissues of those fed with GW-treated cotton plants had a statistically significant 2-fold increase in weight compared to those fed with control cotton plants\u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Total Soluble Sugar and Protein Results of Cotton Leaves\u003c/h2\u003e \u003cp\u003eIn this work, protein contents and total soluble sugar amounts were determined in the leaves of the control and GW-treated cotton plants, in which \u003cem\u003eS. exigua\u003c/em\u003e samples were fed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the results, while no statistically significant difference could be determined in terms of protein content in the leaves, it was determined that the total amount of soluble sugar showed a statistically significant increase in the cotton plant with GW treatment (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Antioxidant Assays of Cotton Leaves\u003c/h2\u003e \u003cp\u003eDPPH and ABTS methods were used to determine the antioxidant activities in the untreated control and GW-treated cotton plant leaves on which \u003cem\u003eS. exigua\u003c/em\u003e larvae were fed. As a result of the analyses, the EC\u003csub\u003e50\u003c/sub\u003e values of the cotton extract, which inhibited half of the tested DPPH and ABTS radicals, were calculated. In addition, the EC\u003csub\u003e50\u003c/sub\u003e value of the trolox standard was calculated and given in the table below (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the results obtained, the numerical value of EC\u003csub\u003e50\u003c/sub\u003e was found to be lower than the leaves of plants treated with GW, according to the results obtained by both methods. In other words, although GW-treated cotton leaves showed more antioxidant activity, these results were not statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDPPH and ABTS assay results of GW-treated and untreated (control) cotton plants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eEC\u003csub\u003e50\u003c/sub\u003e (mg/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003cp\u003eEC\u003csub\u003e50\u003c/sub\u003e (mg/ml)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPPH Assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.968\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.822\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABTS Assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.176\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.159\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM, n\u0026thinsp;=\u0026thinsp;4, Student\u0026rsquo;s t-test.\u003c/p\u003e \u003cp\u003e*There are no significant differences between the means in the same line (Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003eSEM: Standard Error of Mean\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Chlorophyll Content Determination\u003c/h2\u003e \u003cp\u003eThe table provided shows the chlorophyll content results of two observations, comparing a control group with a group treated with GW (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe chlorophyll content of the GW-treated cotton plants increased by 8.33% compared to the control plants for observation 1. In Observation 2, the chlorophyll content of the GW-treated cotton plants increased by 19.18% compared to the control plants. Based on these observations, it can be concluded that the treatment with GW has resulted in a noticeable increase in chlorophyll content compared to the control group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChlorophyll content results of GW-treated and untreated (control) cotton plants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60aA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77bA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, df\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70aA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93bA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, df\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80, df\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, df\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM, n\u0026thinsp;=\u0026thinsp;18.\u003c/p\u003e \u003cp\u003eThe lowercase different letters in the same row indicate a statistically significant difference between the means (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe uppercase different letters in the same column indicate a statistically significant difference between the means (Duncan, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eOur results indicate that the treatment of cotton plants by GW triggered plant metabolic changes leading to reduced population growth performance of \u003cem\u003eS. exigua\u003c/em\u003e. Compared to those fed on untreated control plants, \u003cem\u003eS. exigua\u003c/em\u003e individuals fed on GW-treated cotton plants had a longer preadult development time, lower survival rate, and significantly lower reproduction. The effect of GW treatment on the biological traits of the pest was also reflected in the life table parameters calculated by using these traits. The intrinsic rate of increase (\u003cem\u003er\u003c/em\u003e), the finite rate of increase (\u003cem\u003eλ\u003c/em\u003e), and the net reproductive rate (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) values of the pest obtained on GW-treated plants were significantly lower than those obtained on control plants, while the mean generation time (\u003cem\u003eT\u003c/em\u003e) value was significantly higher. It is speculated that these results are due to the presence of beneficial microorganisms in host plants that induced systemic resistance. Through these metabolic changes in plants, microorganisms can negatively affect insects. Negative effects of insects such as avoidance of laying eggs on plants inoculated with microorganisms, larval development, decreased performance, and increased mortality in larvae and pupae can be observed (Agbessenou et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Contreras-Cornejo et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Contreras-Cornejo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Coppola et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Papadopoulou and van Dam \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Papantoniou et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pappas et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies on this topic show that beneficial microorganisms tend to reduce the performance of herbivorous insects by resulting in prolongation in the preadult development period, and a decrease in the survival rate and reproduction via plant-mediated effects (Fernandez-Conradi et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jaber and Vidal \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jafarbeigi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Verma et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition to all of this that it has been stated that some beneficial microorganism species may have some biocontrol effect against insects in the order Lepidoptera, as they can attack the insect cuticle under suitable conditions and adversely affect the peritrophic matrix of insects (Berini et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This result shows that microorganisms can have extra negative effects against insects.\u003c/p\u003e \u003cp\u003eNo result showed that any oxidative stress occurred in cotton leaves treated with GW by DPPH and ABTS methods. By this result, no statistically significant difference was detected in the level of non-enzymatic glutathione levels as well as GST and esterase enzyme activities in \u003cem\u003eS. exigua\u003c/em\u003e, feeding on the GW-treated cotton plant.\u003c/p\u003e \u003cp\u003eIn this study, a 2-fold increase was found in the weight of fourth instar \u003cem\u003eS. exigua\u003c/em\u003e larvae stomach tissues fed the cotton plant treated with GW. Guo et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) stated that avoidance or attraction between insects and microorganisms was positively associated with feeding behaviour and weight gain in insect larvae (Noman et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, insects appear to have different abilities to detect and avoid toxic compounds to survive. It can be said that avoidance and attraction due to modulated plant metabolism directly correspond to the survival of insects (Noman et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Based on these results, it turns out that the appropriate concentration of dietary components is crucial to the successful completion of an individual's life cycle. In addition to all these changes in the plant, the chlorophyll content of the plants treated with GW was found to be higher than the control plants in our study. Similarly, studies on beneficial microorganisms and plants have noted that the leaves of treated plants are markedly greener (Harman \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This situation is explained by the increased development of the shoots and roots of the plants, along with some increase in the photosynthetic ability and rate of the plants (Harman et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A greener plant indicates that its leaves have higher levels of chlorophyll pigment. As in several studies on this subject, beneficial microorganisms have documented that species help the plant to have a high chlorophyll capacity (Azarmi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Doni et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Harman \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vitti et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe nutritional quality of plants can have an impact on the preference and performance of herbivorous insects (Awmack and Leather \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). One component that can be selected by insect herbivores is the ratio of dietary protein to digestible carbohydrate (P:C) (Deans et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). They need dietary proteins to get the nitrogen they need for growth and reproduction, and \u003cem\u003eS. exigua\u003c/em\u003e prefers a diet relatively high in proteins (Merkx-Jacques et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Mating outcomes may be affected by different diets that promote behavioural seclusion. The duration of copulation, the length of copulation, and the number of eggs laid varied dramatically between the diets. Females on the artificial diet rarely mated with males of their choice (Di et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The second instar \u003cem\u003eS. exigua\u003c/em\u003e exhibited high mortality and delayed development on the C-rich, and P-poor diets, indicating the possible negative consequences of excess carbohydrates and the significance of protein for growth and development (Al-Zubaidi and Capinera \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Merkx-Jacques et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) let larvae of this species were reared on sugar beet leaves from plants fertilised with below-normal, normal and above-normal levels of nitrogen and allowed to cannibalise beet armyworm pupae to assess the effect of dietary nitrogen levels on the cannibalistic behaviour of this species. They reported that the foliar nitrogen content and the percentage of cannibalism showed a substantial inverse correlation and female larvae's cannibalistic behaviour dramatically enhanced fecundity. It was proposed that increased cannibalism by larvae might serve as a compensatory mechanism for their poor nutrition and play a significant role in the dynamics of herbivore populations. In the present study, we have obtained results in this regard. The age-stage two-sex life table, a crucial tool for examining the effects of environmental factors on insect development, reproduction, survival, and population dynamics, demonstrated that there were decreases in the survival rate, reproductive potential as the fecundity of females, and the calculated possibility of reaching the adult stage in individuals fed on GW-treated plants (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The statistically significant decrease in the protein/carbohydrate ratio measured in cotton plant samples is inferred to have a negative effect on the population growth performance of \u003cem\u003eS. exigua\u003c/em\u003e. It was found that there was a decrease of about 30.7% in this ratio in the plants treated with microorganisms compared to that calculated in the control group. We report that such a change leads to a reduction in both the reproduction of \u003cem\u003eS. exigua\u003c/em\u003e and the survival of its larvae.\u003c/p\u003e \u003cp\u003eBeet armyworms may have overcompensated by feeding more than necessary to make up for the lack of protein in their diets, and over-compensatory feeding probably should have increased as the quantity of soluble carbohydrates in diets increased (Wang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Many insect species larvae use elevated lipid deposition as one strategy to deal with dietary C excesses against limited P. A C-biased diet was said to increase pupal lipid stores (Babic et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Some Lepidopteran species showed delayed development and increased pupal mass with more soluble carbohydrates (Raubenheimer et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A similar situation was observed in the present study; in fact, a statistically significant increase in the weight of larvae was observed. The midgut tissues of \u003cem\u003eS. exigua\u003c/em\u003e larvae fed with GW-treated cotton plants weigh twice as much, which means that organisms in this group do not pass through the developmental stages in the ordinary course but instead tend to accumulate fat at this stage. This evidence supports the observation of a lower increase in the total population of the host organism on cotton plants. Research on the potential effects of these changes in the plant on herbivores is limited, but studies on this subject will provide important information on the use of these microorganisms in pest management.\u003c/p\u003e \u003cp\u003eAn accurate estimate of its potential for plant protection may be made using the interaction between plants, microorganisms, and \u003cem\u003eS. exigua\u003c/em\u003e, including both changes in insect phenotype and plant transcriptome. The use of beneficial microorganism species has significantly improved plant performance by enhancing plant protection against abiotic and biotic stressors. Plant defence and health, as well as host choice, feeding behaviour, and arthropod fitness, have all been documented to be affected by three-way interactions between plants, microbes, and arthropods.\u003c/p\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eModern agriculture has a significant challenge in the search for novel pathogen or pest control methods that can lessen the need for chemical pesticides. Among the different biological control options, the use of beneficial microorganisms to reduce plant losses and increase plant growth is promising. This has significant ramifications for the interactions between plants, microbes, and insects. Various research advances in the field of plant-microbe-insect interactions can heavily affect agricultural productivity. Due to the significant difference between laboratory/field and real-world conditions, further research is required to integrate insects, plants, and microorganisms for specific and multiple interactions, often under different climatic and ecological variables. Also, it will be an important future research target to uncover how the genetic pathways regulating plant and insect resistance coordinate the selection of microbial traits. In order to encourage researchers to take on these present and future problems in this field, we hope that this issue may spark some interest and passion for the study of microbial mediation of plant-insect interactions.\u003c/p\u003e \u003cp\u003eThis study illuminates novel insights into the plant-mediated influence of beneficial soil-borne microorganisms on \u003cem\u003eS. exigua\u003c/em\u003e, a significant cotton pest.\u003c/p\u003e \u003cp\u003eThe information presented here on the effects of beneficial microorganisms' treatment on \u003cem\u003eS. exigua\u003c/em\u003e, where a significant reduction of fecundity, population number, survival rate etc. occurred is crucial in improving the controlling strategies of this species.\u003c/p\u003e \u003cp\u003eThe use of beneficial soil-borne microorganisms for controlling \u003cem\u003eS. exigua\u003c/em\u003e is an essential advantage over insecticides due to their low cost and environmental safety. These microorganisms can be used as a sustainable alternative to chemical insecticides, which can have negative effects on the environment and human health. Moreover, using microorganisms can help reduce the development of insecticide resistance in \u003cem\u003eS. exigua\u003c/em\u003e populations, which is a major concern in pest management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNurhan Didem Kızılkan, Metin Konuş, and Mehmet Ramazan Rişvanlı\u0026nbsp;\u003c/strong\u003edesigned the research and conducted experiments. \u003cstrong\u003eHilmi Kara\u0026nbsp;\u003c/strong\u003eanalysed life table data. Doğan \u0026Ccedil;etin analysed plant and enzyme experiments. \u003cstrong\u003eCan Yılmaz, Remzi Atlihan, and Mehmet Salih \u0026Ouml;zg\u0026ouml;k\u0026ccedil;e\u003c/strong\u003e reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by BAPB, University of Y\u0026uuml;z\u0026uuml;nc\u0026uuml;Yıl, Project No.FYL-2020-8856\u0026nbsp;(T\u0026uuml;rkiye).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest.\u003c/strong\u003e The authors have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgbessenou A, Akutse KS, Yusuf AA, Khamis FM (2022) The Endophyte \u003cem\u003eTrichoderma asperellum\u003c/em\u003e M2RT4 Induces the Systemic Release of Methyl Salicylate and (Z)-jasmone in Tomato Plant Affecting Host Location and Herbivory of \u003cem\u003eTuta absoluta\u003c/em\u003e Front Plant Sci 13:860309-860309 doi:10.3389/fpls.2022.860309\u003c/li\u003e\n\u003cli\u003eAl-Zubaidi F, Capinera J (1983) Application of different nitrogen levels to the host plant and cannibalistic behavior of beet armyworm, \u003cem\u003eSpodoptera exigua\u003c/em\u003e (H\u0026uuml;bner)(Lepidoptera: Noctuidae) Environ Entomol 12:1687-1689\u003c/li\u003e\n\u003cli\u003eAwmack CS, Leather SR (2002) Host plant quality and fecundity in herbivorous insects Annu Rev Entomol 47:817-844\u003c/li\u003e\n\u003cli\u003eAzarmi R, Hajieghrari B, Giglou A (2011) Effect of Trichoderma isolates on tomato seedling growth response and nutrient uptake Afr J Biotechnol 10:5850-5855\u003c/li\u003e\n\u003cli\u003eBabic B, Poisson A, Darwish S, Lacasse J, Merkx-Jacques M, Despland E, Bede JC (2008) Influence of dietary nutritional composition on caterpillar salivary enzyme activity J Insect Physiol 54:286-296\u003c/li\u003e\n\u003cli\u003eBakker PA, Pieterse CM, de Jonge R, Berendsen RL (2018) The soil-borne legacy Cell 172:1178-1180\u003c/li\u003e\n\u003cli\u003eBerini F, Caccia S, Franzetti E, Congiu T, Marinelli F, Casartelli M, Tettamanti G (2016) Effects of \u003cem\u003eTrichoderma viride \u003c/em\u003echitinases on the peritrophic matrix of Lepidoptera Pest Manag Sci 72:980-989\u003c/li\u003e\n\u003cli\u003eBradford MM (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding Anal Biochem 72:248-254\u003c/li\u003e\n\u003cli\u003eChang C, Huang C-Y, Dai S-M, Atlihan R, Chi H (2016) Genetically engineered ricin suppresses \u003cem\u003eBactrocera dorsalis \u003c/em\u003e(Diptera: Tephritidae) based on demographic analysis of group-reared life table J Econ Entomol 109:987-992\u003c/li\u003e\n\u003cli\u003eChi H (1988) Life-table analysis incorporating both sexes and variable development rates among individuals Environ Entomol 17:26-34\u003c/li\u003e\n\u003cli\u003eChi H (2023a) TIMING-MSChart: a computer program for the population projection based on age-stage, two-sex life table. 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Specifically, we examined how beneficial microorganisms impact the oxidative stress, chlorophyll content, sugar and protein levels within the cotton plant and subsequently influence the pest's population growth performance. We also evaluated the reciprocal effects of plant-insect-microorganisms interaction on total glutathione levels, glutathione S-transferase and esterase enzyme activities in\u003cem\u003e S. exigua\u003c/em\u003e. The findings of this study revealed that there was no evidence of oxidative stress in the treated plants. However, the treated plants exhibited enhanced chlorophyll content while demonstrating reduced levels of Protein/Carbohydrate, consequently leading to a discernible decrease in the population growth performance of \u003cem\u003eS. exigua\u003c/em\u003e. These results highlight the intricate interplay between oxidative stress, chlorophyll content, and nutritional composition, which collectively influence the population dynamics and performance of the treated plants. The concept of \"host plant quality\" which encompasses characteristics like nitrogen and carbon content, trace elements, and defence chemicals, played a crucial role in shaping the success of \u003cem\u003eS. exigua\u003c/em\u003e. Changes in plant characteristics and nutrient balance can affect the performance of insects, even if they consume more leaves, leading to an increase in midgut tissue weight in \u003cem\u003eS. exigua\u003c/em\u003e larvae. \u0026nbsp;Consequently, this decrease in pest numbers is expected to enhance the plant's ability to withstand damage. Additionally, the findings from this study provided valuable insights and pertinent information regarding the intricate three-way interactions among plants, microorganisms, and pests, thereby offering potential strategies for implementing these interactions in pest management practices.\u003c/p\u003e","manuscriptTitle":"Investigating the Impact of Beneficial Microorganisms Inoculated Cotton Plants on Spodoptera exigua","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 12:12:32","doi":"10.21203/rs.3.rs-4170111/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4227e697-c4c6-48aa-a966-c544bc6e9e20","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-14T19:57:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 12:12:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4170111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4170111","identity":"rs-4170111","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}