Bacillus pseudomycoides is able to promote growth and drought stress tolerance in wheat by suppressing the MYB gene, which synthesizes Myb protein (TaMpc1-D4) through secreted volatile compounds. In the present study, Triticum aestivum seeds were inoculated with five distinct bacterial strains. The growth, germination rate, root-shoot length, RWC, and chlorophyll content of seedlings were investigated. Furthermore, the levels of soluble sugars, proteins, H2O2, NO, cell death, and antioxidant enzymes  were observed throughout the growth stage. All of the results showed that B. pseudomycoides had a substantially higher ability to form biofilm and promote these traits than the other strains. In terms of molecular gene expression, B. pseudomycoides inoculation strongly expressed the Dreb1 gene by silencing the expression of MYB gene through secreted volatile compounds. For identifying the specific volatile compound that silenced the MYB gene, molecular docking with Myb protein was performed. Out of 45 volatile compounds found, 2,6-ditert-butylcyclohexa-2,5-diene-1,4-dione and 3,5-ditert-butylphenol had a binding free energy of − 6.2 and − 6.5, Kcal/mol, respectively, which predicted that these compounds could suppress this protein's expression. In molecular dynamics simulations, the RMSD, SASA, Rg, RMSF, and hydrogen bonding values found assured the docked complexes' binding stability. These findings suggest that these targeted compounds may be suppressing Myb protein expression as well as the expression of Dreb1 and other drought response genes in wheat. More research  into plant growth and drought stress is needed to support the findings of this study.The plant growth-boosting biofilm-forming bacteria  Triticum aestivum is the principal source of food for most of the world’s population including many American, European, Australian, African, and some Asian countries2. It is the main component of foods such as bread, biscuits, pasta, noodles, cakes, pizza, beer, and vodka, and contains carbohydrates (71%), protein (13%), water (13%), fat (1.5%), and small amounts of phosphorus, niacin, and dietary fiber3. It is estimated that the world population will exceed 8 billion in 2030, and ensuring food security for such a large population will be a major issue for the agricultural sector due to water scarcity4. Future drought mitigation gains are critical if food demand for the vast population is to be met5. Drought intensity is growing as a result of climate change and rising temperatures around the world, causing agricultural crops to suffer6. In Bangladesh, for instance, most of the farmers in the 13,000 sq. km Barind Tract land in the Districts of Rajshahi, Dinajpur, and Bogura face water scarcity.7. Bacterial species in genera such as Bacillus, Enterobacter, Azospirillum, Klebsiella, Pseudomonas, Arthobacter, and Serratia, among others, enrich growth by directly or indirectly supplying nutrients10, while secretion of indole acetic acid and cytokinin-like phytohormones promotes root area and surface proliferation11. Apart from this, cytokinins are also involved in the management of environmental stresses by transfer signaling from root to shoot12. Drought causes the production of reactive oxygen species (ROS), which can harm numerous plant structures13, such as proteins and lipids, and cause nucleic acid breakdown14. Plant cells include many antioxidant enzymes such as APX (ascorbate peroxidase), CAT , SOD (superoxide dismutase), POD (peroxides), and GPX (glutathione peroxidase), which reduce free radical H2O2 and superoxide O-2 production during drought conditions and improve plant survival16. Under extreme drought conditions, Plant-Growth Promoting Rhizobacteria (PGPR) improved an antioxidant CAT, implying that it can reduce oxidative damage17.Some beneficial microorganisms influence plant metabolism to help plants survive from abiotic stress conditions such as drought and salt stress20. Therefore, we chose five bacterial strains , although only B. pseudomycoides has been shown to have potentially to produce biofilms in dry conditions. Volatile compounds (VOCs) are organic molecules released by bacteria that mediate increased biomass in plant roots, disease resistance, and abiotic stress tolerance19 and do not include any known plant growth hormones21, but they do mediate plant endogenous auxin homeostasis and iron uptake22.Researchers have recently reported that bacterial biofilm formation on root hairs under drought conditions may have a substantial positive impact on plants during high levels of drought stress23. As a result, silencing of this gene may result in increased drought tolerance. Molecular docking is a powerful method for identifying putative inhibitors based on protein–ligand interactions, with molecular simulation used to validate the binding poses and orientations. To identify the best binding compounds regarding targeted protein active regions, homology modeling of myb protein and volatile compounds were used for molecular docking. This technique is both cost- and time-effective for understanding the relationship between proteins and ligands25.Previous studies have found that over expression of the R2R3 MYB gene reduces drought tolerance and has detrimental consequences in wheat under drought stress26. The current study aimed to find environmentally friendly bacterial strains that can form biofilms that aid in plant growth promotion. By possibly silencing the R2R3 MYB gene and increasing the expression of Dreb1 genes in wheat, specific volatile compounds that might help with drought stress tolerance could be identified.Many researchers are attempting to develop drought-tolerant wheat varieties that are both low-cost and ecofriendly in response to global climate concernsThe Bp treatment had a germination rate of 88.8%, while Bm had an 82.22% germination rate, Sm had a 74.44% germination rate, As had a 75.55% germination rate, Bt had a 71.11% germination rate, and the Control group a 71.11% germination rate Fig. . Bp inoc−1 and 5.08 mg g−1, respectively, in both Bp and Bm inoculation compared to 3.81 mg g-1 in the control   Fig. a. As a rAntioxidant activity was considerably higher in the Bp treatment but was only modestly increased in the other inoculations over the control. The highest levels of APX, SOD, and CAT activity were observed in the Bp inoculation Fig. . As a reThe formation of biofilms is demonstrated in Fig. B. pseudomycoides, 45 volatile compounds were found in the GC–MS analysis. IUPAC name, PubChem CID, canolic smile, molecular weight and formula, retention time, area and height, and the GC–MS chromatogram are shown in Table For In the pots, plant survival rates were 83.33%, 66.67%, 58.33%, and 16.67% for Bp, Bm, Bt, and Sm, respectively, however, neither the control nor As had any surviving plants after 15 days Fig. . In the The extracted RNA showed two strong bands of 28S and 18S ribosomal RNA in 1% agarose gel and no significant RNA degradation was observed Fig. . The ReaT. aestivum was modeled and run for short dynamics for energy minimization processes. The structural stability and stiffness of the complex were investigated using the root mean square deviations of the C-alpha atoms from simulation trajectories over time. Because of their flexibility, the complexes exhibited a greater initial RMSD, as seen in Fig. Using Phyre2 tools, the MYB gene from B. pseudomycoides were investigated in this study based on their lower binding energies. These ligand molecules, 2,6-ditert-butylcyclohexa-2,5-diene-1,4-dione and 3,5-ditert-butylphenol, exhibited a higher binding affinity than other compounds. The binding energies of these compounds were −6.2 and −6.5 kcal/mol, respectively, indicating that 3,5-ditert-butylphenol and the MYB protein complex had higher binding energies than other compounds. The cartoon view, 3D view, and the surface view of these docking complexes are shown in Fig. Two volatile compounds from The goal of the molecular dynamics simulation was to understand the stable profile of docked complexes as well as the flexible behavior of ligand–protein systems. The 2,6-ditert-butylcyclohexa-2,5-diene-1,4-dione, and 3,5-ditert-butylphenol complexes showed reduced degrees of fluctuations over the simulation trajectories and hence demonstrated a stable mode during the simulation duration, as shown in Figs. The lesser degree of the complexes' packing systems may be due to increased deviations at the initial points of the complexes with MYB and 2, 6-ditert-butylcyclohexa-2,5-diene-1,4-dione and 3,5-ditert-butylphenol, as seen in Fig. T. aestivum seeds germinate and grow faster after receiving beneficial bacterial inoculation27.Global population expansions and global climate change are both concerns that impact cultivated crops. As a result, agricultural crops that can survive abiotic stresses are required around the world. Drought is a major abiotic phenomenon that reduces plant development and growth, affecting agricultural demands. Although many scientists believe that artificial gene transfer can be used to solve abiotic problems, there are many concerns regarding this methodology. PGPR, on the other hand, can spontaneously transmit genes. As a result, many scientists are attempting to use PGPR to further adapt drought-tolerant crop varieties. While this is not a novel method, researchers have recently applied this technique to the global climate change dilemma under abiotic stress circumstances. PGPR also reduces the use of chemical fertilizer by engaging in growth simulation. B. pseudomycoides inoculation was more effective than B. massilioanrexius, S. marcescens, B. turingiensis, and Acinetobacter sp. in terms of morphological growth, as measured by germination rate, potential, vigor index, potential, root-shoot length and number, and dry weight. Due to bacterial inoculation, changes in biochemical levels, functions, and morphology of T. aestivum could be observed. The substantial functional differences between the bacterial inoculation and control seeds were obvious throughout the entire experiment.In our experiment, B. pseudomycoides-inoculated seeds accumulated considerably more dry weight, RWC, and chlorophyll content. Similar agronomic improvements have been reported in the past with several bacterial inoculations30. Chlorophyll, a component of photosynthesis, has been shown to contribute to sorghum yield (in drought conditions) or improve sorghum yield and transpiration efficiency (in water-limited conditions)33. It has been observed that applying N2 to the seed surface improves germination, which is consistent with our findings. B. pseudomycoides inoculation was found to be more effective in boosting germination than other strains, showing that B. pseudomycoides could be an efficient in seed germination promoter. B. pseudomycoides inoculation in seeds resulted in an increase in total soluble protein and sugar in the root and shoot, demonstrating that B. pseudomycoides is effective in boosting cell metabolism and hence wheat plant growth and development. This observation is in line with prior wheat findings34. However, NO is an important signaling molecule that is involved in plant germination, growth, pollen tube growth, root organogenesis, flowering, physiology, biotic, abiotic, and developmental processes39, and in our study, NO levels were not significantly affected in the root but significantly increased after B. pseudomycoides inoculation.In the current study, we found that plants grown from 2O2 scavenging to prevent ROS formation41. According to our findings, CAT, SOD, and APX activities increased in plants treated with B. pseudomycoides, but were lower in other bacterial treatments and non-treated plants. Iron uptake by electrons is reduced when Fe 3 + is reduced to Fe 2 + at the cell surface40. We also noticed that when B. pseudomycoides was inoculated, the concentrations of Fe and Zn in the root and shoot increased dramatically. Taran, Nataliya, et al. 2017 showed that Cu, Zn-nanoparticles reduced the detrimental effects of drought in wheat41. According to some studies, an adequate supply of Zn can modulate drought tolerance in crops like wheat, sunflower, tomato, and red cabbage44. So, inoculation with B. pseudomycoides may also affect electron discharge, facilitating iron uptake in wheat plants.In drought conditions, plant cells develop functional reactive oxygen species (ROS), whereas antioxidant enzymes inhibit ROS generation and reactivity to environmental stresses. Antioxidant enzymes such as SOD, CAT, and APX play a key role in reducing oxidative damage and ROS generation in plants, with CAT being the most essential in H45. This biofilm formation encourages root-to-soil contact, which allows plants to absorb more nutrients from the soil. In response to certain PGPR strains, wheat produces hygroscopic polysaccharide-type alginates that improve water status, while the bacterial alginates' water-holding capacity slowly depletes48. This cellular metabolic adjustment caused by B. pseudomycoides wheat treatment adds to biofilm development, which reduces water loss. Flagella are required for the biofilm community to approach and travel across the surface, according to the widely accepted theory of biofilm formation, which holds that environmental cues start the process. Pili or LPSs, which are components of the outer membrane, mediate the initial stages of attachment. The development of quorum-sensing signals is conducive to the formation of a mature biofilm after the formation of micro colonies49. Surfactin was created during B. subtilis colonization50. This Surfactin contributes to the development of biofilms in plant root hair. It is known that Surfactin and other lipopeptides generated by Bacillus spp. cause plants to develop systemic resistance. As a result, B. pseudomycoides’ alginate pathway may be responsible for drought tolerance. According to a recent study, several genes, including DREB151, AtWRKY3052, TaASR153, and TaMYB30-B54, considerably improve wheat drought stress tolerance. We chose DREB1 and MYB3R-1 genes expression after induced drought to illustrate wheat drought stress-related genes expression. B. pseudomycoides has a significant ability to naturally express the DREB1 gene but a lessened ability to express the MYB3R gene.Under drought conditions, bacterial biofilm formation in plant root hairs is significant because it prevents the dispersion of bacterial biologically active substance secretion, the products of which then accumulate on the surface of the root to help plants absorb nutrients55, induce biofilm, and improve disease resistance56, iron nutrition, and abiotic stress tolerance20. For instance, Pseudomonas chlororaphis also produces 2,3-butanediol, a VOC that induces drought stress57. Homology modeling of myb protein and volatile compounds were used for molecular docking to discover the best-binding compounds to active regions of the targeted protein, MYB. Overexpression of myb reduces drought tolerance in transgenic Arabidopsis, as demonstrated by a decreased germination rate, shorter root length, lower proline content, and impaired POD, SOD, and CAT activities. Silencing myb expression in wheat enhanced antioxidant enzyme activity, proline content, and RWC in addition to activating DREB1 and DREB31 stress-related genes. The myb gene was found to have a negative effect on drought tolerance genes58. Here, the VOC 2, 6-ditert-butylcyclohexa-2, 5-diene-1, 4-dione, and 3, 5-ditert-butylphenol were shown to suppress MYB gene expression in wheat, possibly enhancing drought stress.VOC released by PGPR bacteria have been shown to boost plant growthB. pseudomycoides, showed significant morphological improvement and chlorophyll content, leading to increased photosynthesis under normal and drought conditions. This inoculation also revealed drought morphological adaptation, PEG treatment, biofilm formation, and Dreb1 gene expression under drought conditions. B. pseudomycoides may also secrete several volatile compounds, with the best results coming from in silico study on 2, 6-ditert-butylcyclohexa-2, 5-diene-1, 4-dione, and 3, 5-ditert-butylphenol. Binding stability was demonstrated in a molecular simulation of protein and two volatile compounds.Wheat seeds inoculated with five distinct bacterial strains, including T. aestivum (BARI gom-33) were collected from the Regional Wheat Research Center  and transported to the Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh, for further analysis. Samples were treated with bacterial strains B. pseudomycoides (Bp), B. massilioanrexius (Bm), B. thuringiensis (Bt), Serratia marcescens (Sm), and Acinetobacter sp. (As), which were collected from this laboratory. We confirmed that all experiments related to plants (Seed samples of T. aestivum (BARI gom-33)) were in accordance with the relevant guidelines and regulations.Seed samples of 27. Briefly, seeds were soaked and washed with distilled water for 20 min and then were sterilized with 70% (v/v) ethanol for 2.5 min. After washing thoroughly with distilled water, seeds were placed in a conical flask and inoculated with 100 mL of concentrated fresh bacterial culture. Seeds treated with the cultured medium alone were used as a control. After this, the conical flasks were placed on an orbital shaker and shaken for 6 h at ambient temperature at 160 rpm.Seeds were inoculated with freshly cultured bacterium stains according to the method described by Paul, et al. 202059. After treatment, 30 seeds were placed in 90 mm petri dishes that had four layers of wet tissue papers at the bottom, and the dishes were placed in a growth chamber set at 23 ± 2 °C and 50% humidity to monitor germination. After germination, the GR (Germination Rate), GP , IG (Index of Germination), and LI (Length index) were also calculated by the above reported method. After 10 days in petri dishes, plant growth was measured. Plants were carefully removed from the tissue paper, and the roots were cleaned with tap water. The lengths of the roots and shoots were measured and the number of roots tallied.Germination parameters were screened following the method described by Zhou et al. 201660. The dry ratio was determined using the methods of Xu et al., 201561. Five plants were randomly selected from each replication and rinsed with tap water to measure both shoot and root dry weights. Then shoots were then cut at the base and dried for 4 days at 70 °C in an incubator. The sample weights were determined using an electronic scale, and RWC (Relative Water Content) was measured according to62.The root and shoot dry weight measurements were carried out according to Arshadullar and Zaidi 200763. In this case, young fresh leaves were weighed and ground using a mortar and pestle in 90% methanol. Following grinding, the sample mixtures were centrifuged at 12,000 rpm for 10 min and the supernatants transferred. Finally, the optical density was recorded at 662 nm for chlorophyll a and 653 nm for chlorophyll b using a spectrophotometer , and the total chlorophyll content was calculated as mg/g.The total chlorophyll content in leaves was measured according to Lichtenthaler and Wellburn 198364, Sun and Zigman, 197865, Yordanova et al. 199966, and Verma and Dubey, 200364, respectively. In the case of all antioxidant activities, 0.3 g leaf and roots samples were crushed separately in phosphate buffer . Then, the homogenate samples were centrifuged at 12,000 rpm for 12 min to separate the supernatant. For CAT activity, 2 mL of reaction mixtures containing potassium phosphate buffer , H2O2 (400 μl of 6%), and 100 μl leaf extract were prepared. Then, the decrease in absorbance was recorded at 240 nm by using a UV–Vis spectrophotometer . For SOD, reaction components were prepared by mixing sodium bicarbonate or carbonate buffer , EDTA , and Epinephrine . Then, adrenochrome formation was measured at 475 nm . In the case of APX, reaction mixtures were prepared by using potassium phosphate buffer , ascorbic acid (500 μl of 0.2 mM), EDTA (100 μl of 0.2 mM), H2O2 (300 μl of 6%), and 100 μl of plant extract, and the decrease of absorbance was recorded at 290 nm using a UV–VIS spectrophotometer  at 10 s intervals up to 1 min. The specific activity of the enzyme was expressed as μmol min-1 (mg protein)-1.CAT, SOD, POD, and APX activities were measured according to Verma and Dubey, 200367. For this, fresh plant tissues were washed with CaSO4 (1 mM) for 5 min and then thoroughly washed with distilled water. After this, clean samples were dried in an incubator at 80 °C for 6 days. A total of 3 mL of HNO3 and 1 mL of H2O2 were added to each sample in the test tube and heated at 75 °C for 12 min. Zn and Fe concentrations were evaluated by Flame Atomic Absorption Spectroscopy, using a ASC-6100 auto sampler and air-acetylene atomization gas mixture system , where standard solutions of Zn and Fe were separately prepared from their respective concentrations.The concentrations of Zn and Fe were determined according to Kabir et al. 20172O2 was measured according to Alexieva et al. 200168, cell death was determined according to Jacyn Baker and Mock, 199469, and NO was estimated according to Orozco-Cárdenas et al., 200270. In case of H2O2, fresh plant samples were separately homogenized in 0.1% Trichloroacetic acid (TCA) and centrifuged at 10,000 rpm for 14 min. The supernatants were mixed with KI (1 M) and phosphate buffer  then kept in the dark for 1 h, and the optical density (OD) of extracts were taken at 390 nm by spectrophotometer . For cell death determination, fresh samples were incubated at 25 °C in 0.25% Evans blue solution for 16 min, then in 1.0 mL of 80% ethyl alcohol for 10 min, and finally incubated in a water bath at 50 °C for 15 min. After this, the mixtures were centrifuged at 12,000 rpm for 10 min. Then, the absorbance was measured at 600 nm  and the amount of cell death calculated based on fresh weight. For NO determination, samples were homogenized in cooled NO buffer (1 mL) and centrifuged at 10,000 rpm for 10 min. The supernatants were transferred to a 5 mM oxyhemoglobin (HbO2) solution and incubated for 7 min at room temperature. Optical density was measured at 401 nm  to determine the conversion rate of oxyhemoglobin (HbO2) to methemoglobin (metHb), which is an indicator of the presence of NO.H71, and soluble protein content was determined using the Bradford protein assay according to He, 201172. For soluble sugar, the fresh samples were homogenized in 90 °C hot aqueous ethanol (v/v 80%) and centrifuged at 12,000 rpm for 10 min. Following centrifugation, the supernatants were mixed with 0.2% anthrone reagent and incubated in a boiling water bath for 10 min. After cooling on ice, the optical density OD was recorded at 620 nm . In the case of soluble protein, homogenized samples with 2 mM EDTA (Ethylenediamine Tetraacetic Acid), 50 mM Tris–HCl, pH 7.5 and 0.04% (v/v) 2-mercaptoethanol added were centrifuged at 12,000 rpm for 15 min at 25 °C. The supernatant was mixed with 1 mL CBB (Coomassie Brilliant Blue), and the optical density was determined at 595 nm using spectrophotometer .The total soluble sugar content of the plant samples was determined using the method of M. Dubois, et al., 195673. For this, 20 mL of each bacterial culture was inoculated into 30 mL of LB liquid medium in a vial and incubated at 30 °C for 6 days with constant shaking. The SPME fiber was then inserted into the headspace of the vial and heated to 50 °C for 30 min. SPME fibers were desorbed at 220 °C for 5 min, and GC–MS was run for 25 min using GC–MS, Shimadzu, with helium gas as the carrier. The column temperature was set to 35 °C for 3 min before gradually increasing to 180 °C at a rate of 10 °C/min. After this, the temperature was increased to 240 °C for 5 min, and the mass spectrometer was run in electron ionization mode at 70 Ev with a temperature of 220 °C and a continuous scan from 50 to 500 m/z. The results were compared to the Mass Spectrum Library of the NIST/EPA/NIH.Volatile compounds were analyzed according to P. Fincheira et al., 2017For drought stress analysis, eight plants were selected in each treatment with three replications, and water was discontinued for 15 days to assess vulnerability in pot conditions. After stress induction, discussed below, the plants were watered and given four days to recover. The plants that were rescued were counted as survivors.PEG (MW: 6000) was used to induce drought stress in wheat, with three treatments: control (0% PEG), moderate drought stress (10% PEG), and severe drought stress (15% PEG). Every day, each petri dish received a total of 10 doses of 350 mL PEG solution. The drought stress period lasted 12 days for each treatment.After 15 days of artificial drought stress, plants were removed from the soil, and roots were safely rinsed with tap water to remove adhering dirt. The roots were then split with scissors and placed on a carbon tape before being coated by gold using a Sputter Coater 108 auto Cressington. Finally, the samples were examined via a scanning electron microscope (JCM-6000 Plus (JEOL Japan) at various wavelengths for scanning biofilm.74. Briefly, two genes, Dreb1 and MYB3R, were chosen for gene expression analysis in leaves using quantitative reverse transcription PCR techniques. A total of 30 mg of leaves were ground in liquid nitrogen using a mortar and pestle, and total RNA was extracted using Promega SV total RNA isolation system. After DNase treatment, 1 μl extracted RNA of some samples was run in 1% agarose gel to observe the level of RNA degradation. First-strand cDNA was produced using Promega, Reverse Transcription systems, and the RNA extraction was confirmed by MoF buffer in agarose gel electrophoresis and quantified by Nano drop. After this, RNase was used to clean the cDNA, and real-time PCR analysis was performed using an Eco™ real-time PCR system controlled by Eco Software v 4.7.0 (Illumina. USA), with a program of 3 min at 95 °C, 40 cycles of 30 s at 94 °C, 15 s at 56 °C and 30 s at 72 °C. Expression was normalized using actin as an internal control.RNA isolation and gene expression were analyzed by following the method described by Rahman et al. 2018T. aestivum had not yet explored in the protein database (RCSBD). So, for further protein modeling and molecular docking study, we used sequences from the UniPort database, MYB (UniProt ID: A0A3B6RJY9). The protein sequences’ fasta format was extracted and entered into the Phyre2 web tools. The C chain of a specific DNA complex of the MYB DNA-2 binding domain with cooperative recognition helices (PDB ID: 1MSE) was chosen as a template, with confidence and percent identity of 100, and 56 for the MYB gene, respectively. The MYB model protein was exposed to short molecular dynamics simulations (10 ns) for refinements and minimizations. Further validations were performed using the last snapshots from the simulation trajectories. ERRAT and Ramachandran Plot analysis was used to further assess the reduced structures.The crystal structure of the MYB-like domain-containing protein of B. pseudomycoides were derived through GC–MS analysis, and the sdf format of 3D structures was retrieved from the PubChem database (http://www.pubchem.ncbi.nlm.nih.gov/)75. The mmff94 force field was used to construct and optimize the structure of the ligands with the aid of Avogadro software76.The volatile compounds of 79. Docking was performed using the Lamarckian algorithm, with the parameters set to 250 runs and 25,000,000 energy assessments for each cycle. The level of thoroughness was set to 8. Finally, docking calculations were run in AutoDock Vina. The Discovery Studio (version 3.0)80, and Pymol software was used (version 2.3)81 for binding interactions analysis.Molecular docking is an important tool for identifying the best compounds to fit receptor molecules based on docking energy. The Auto Dock Vina software package was used for performing molecular docking. The ligands were converted to PDBQT format, and the grid box center was set to  and , respectively83. The docked complexes were cleaned, optimized, and a hydrogen bond networking system was used with a pH of 7.4, a temperature of 310 K, and a 0.9% NaCl concentration. A TIP3P solvation model was used with periodic boundary conditions to produce a cubic simulations cell. Simulated annealing methods were used to do fundamental energy minimization in steepest-gradient algorithms (5000 cycles). Long-range electrostatic interactions were calculated by the Particle Mesh Ewalds (PME), with a cut-off radius of 8.0 Å. The simulation cell was extended by 20 Å in each case from the complexes. The simulations were run using a time step of 2.0 fs. The final simulation was run for 100 ns84, while adhering to constant pressure and a Berendsen thermostat. The root mean square deviation, solvent accessible surface area, radius of gyration, and hydrogen bonding were all calculated using simulated trajectories stored at 100 ps interval92.The molecular dynamics simulation was performed by the YASARA dynamics package using the AMBER14 force fieldP ≤ 0.05 level of significance in a one-way ANOVA in SPSS Statistics 26 software. Graph Pad Prism 8.0.2.263 was used for preparing all figures.The experiment was performed using a CRBD (Complete Randomized Block Design) with three replication of each biological sample. The DMRT (Duncan’s Multiple Range Test) was used to analyze the significance of each group’s data at a B. pseudomycoides in a normal environment, with increased of total soluble sugar, protein, chlorophyll concentrations, RWC, and root-shoot length. This inoculation can form biofilm in drought stress, which supports morphological drought stress and expresses drought response genes. Molecular evidence revealed that B. pseudomycoides has a strong ability to spontaneously express the DREB1 gene but a weak ability to express the MYB gene. The overexpression of MYB gene suppresses the expression of DREB1 gene. B. pseudomycoides secreted volatile compounds such as 2, 6-ditert-butyl cyclohex-2, 5-diene-1, 4-dione, and 3, 5-ditert-butylphenol, which had binding affinity and stability in molecular docking and simulation. B. pseudomycoides, as well as 2, 6-ditert-butyl cyclohex-2, 5-diene-1, 4-dione, and 3, 5-ditert-butylphenol, may enhance DREB1 gene expression by suppressing MYB gene under drought conditions. Microorganisms may be able to help mitigate the detrimental effects of climate change on crop growth, according to the study. However, more investigation into the subject is required, as well as clarification of the protective method.Wheat growth is enhanced by seed inoculation with Supplementary Information.


Withania somnifera (Ashwagandha). It has several biological activities including anti-cancer, anti-diabetic, neuroprotective, hepatoprotective and immune-modulatory properties. The acute and sub-acute toxicity of oral WA was investigated in mice. In the acute toxicity study, up to 2000 mg/kg of WA was well tolerated without any signs of toxicity or death. In the sub-acute toxicity study, mice were orally administered 10, 70 and 500 mg/kg of WA respectively, daily for 28 days. Upon physiological, serum chemistry, hematology and histopathogical examination, no features suggestive of drug-induced toxicity were observed at any dose levels, thereby confirming the No-Observed Adverse Effect Level (NOAEL) to be at least 500 mg/kg. Furthermore, the oral bioavailability of WA was evaluated using single intravenous and oral doses of 10 mg/kg and 70 mg/kg respectively using sparse sampling strategy. Bioanalysis was carried out using a validated LC-MS/MS method. The AUC of WA was found to be 3996.9 ± 557.6 ng/mL*h and 141.7 ± 16.8 ng/mL*h for the intravenous and oral routes of administration respectively. The oral bioavailability was determined to be 1.8%. To conclude, WA was found to be extremely safe even at high doses, with a low oral bioavailability.Withaferin-A (WA) is the principle component of   •In acute toxicity study Withaferin-A was found to be safe up to 2000 mg/kg.•50 of Withaferin-A was > 2000 mg/kg body weight.The observed LD•Based on repeat dose toxicity 500 mg/kg of Withaferin-A was considered as NOAEL dose.•The oral bioavailability of Withaferin-A was found to be 1.8%. Different parts of this plant have been used in many Ayurvedic formulations, most common being the root of Ashwagandha which contains many biologically active components such as alkaloids , saponins and steroidal lactones (withanolides and withaferins) Plants have served as a source of medicine over 4000 years Withania somnifera, was first isolated from the leaves by Lavie and Yarden ex-vivo treatment of donor cells with WA could ameliorate the onset of Graft versus Host Disease (GvHD) in mouse model of allogeneic bone marrow transplantation by modulating Akt-mTOR signaling pathway + T cells could be dose limiting. Gambhir et al., demonstrated that WA reduced the secretion of Th1 and Th2 cytokines and inhibited the mitogen-induced T-cell and B-cell proliferation. This immunosuppressive effect of WA was mediated through its binding with cysteine-62 residue of p50 and subsequent inactivation of the NF-κB pathway in T lymphocytes Withaferin-A (WA), the principle active element of Withania somnifera extract Withania somnifera. With an aim to bring WA a step closer to the clinics, the current study evaluated the oral safety, toxicity and pharmacokinetics of WA in mice.Despite its pharmacological actions, the translation of WA to the bedside has not been accomplished. Establishing the non-clinical toxicity and pharmacokinetics (PK) of a molecule is an important aspect of drug development, and provides vital insights about its safety and posology in humans. Several groups have worked extensively on understanding the toxicity of 22.1Withaferin-A and fluoximesterone was generous gift from Pharmanza herbal Pvt. Ltd, Gujrat, India. LC-MS grade ethyl acetate, ammonium acetate and acetonitrile, sulfosalicylic acid were used in the study. Milli-Q water was filtered additionally with a 0.22 µ filter paper using filter assembly.2.2. A 12 h light/dark cycle was maintained at 22–25° C temperature and humidity of approximately 50%. Mice were acclimatized for one week prior to experimentation. At the end of experiments all animals were humanely euthanized. All procedures were carried out as per the committee for the purpose of control and supervision of experiments on animals (CPCSEA) guidelines. The study was performed in accordance with the ARRIVE guidelines.The study was initiated after approval from the Institutional Animal Ethics Committee of Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial centre (TMC), Mumbai, India (project no. 17/2020). Female BALB/c mice of 8–10 weeks old, weighing 20 ± 2 g were used for the study. Mice were housed in the small animal facility of ACTREC. Standard chow and water was given ad libitum2.3https://admetmesh.scbdd.com/). Briefly, the 2D structure of WA was retrieved from PubChem database and the SMILES format was generated by submitting the structure of WA to SwissADME tool (http://www.swissadme.ch/index.php). This SMILES was then acquiesced to ADMETlab2.0 for ADMET analysis.ADMET properties, as well as some physicochemical properties and medicinal chemistry friendliness of WA can be predicted by ADMETlab2.0 online tool (2.4Toxtree (v3.1.0) was used to assess the toxic hazards of WA by applying decision tree approach. Toxtree decision tree was employed for pertaining the Verhaar scheme, skin irritation and corrosion prediction, eye irritation and corrosion prediction, Benigni/Bossa rule base (for mutagenicity and carcinogenicity), skin sensitization alerts, START biodegradability, cytochrome P450-mediated drug metabolism, DNA binding alerts, protein binding alerts, etc. It can estimate the threshold of toxicological concern (TTC) of the compound or their possible toxicity. The molecule site(s) labile to metabolism by cytochrome P450 isoform 3A4 was predicted by SMARTCyp reactivity model.2.5The acute oral toxicity study was conducted as per the Organisation for Economic Cooperation and Development (OECD) test guidelines 423 adopted on 17th December 2001. Animals were kept on 4 h of fasting before and after administering the dose. A single oral dose of 50 mg/kg, 300 mg/kg and 2000 mg/kg of WA suspended in 100 µl of 0.5% carboxymethylcellulose (CMC) in 1X phosphate buffer saline was administered to mice using oral gavage. Vehicle control group was administered only 100 µl of 0.5% CMC in 1X phosphate buffer saline. Animals were monitored individually in the initial 4 h at 30 min interval post dosing, intermittently during the first 24 h, and once a day thereafter up to 14 days for any sign of clinical toxicity. Mice weight were recorded on day 0, 7 and 14. Mice were sacrificed after 14 days and blood samples was collected for clinical biochemistry and hematological investigation. Vital organs were harvested for histopathology examination.2.6Sub-acute toxicity or 28 days repeat dose toxicity study were performed in accordance with OECD guideline 407 adopted on 3 October 2008. Thirty female mice were divided into six groups (five mice per group); 1. Vehicle control (CMC), 2. 10 mg/kg treatment (low dose), 3. 70 mg/kg treatment (medium dose) 4. 500 mg/kg treatment (high dose), 5. Vehicle control (satellite/recovery groups) and 6. 500 mg/kg treatment (satellite/recovery groups). WA was administered in 100 µl of 0.5% carboxymethylcellulose (CMC) in 1X phosphate buffer saline daily for 28 days through oral gavage. Two satellite groups, after 28 days of dosing, were kept under observation for 14 more days to check the reversal or delayed onset of toxicity. All mice were observed every day for any clinical sign of toxicity. Mice weight were recorded on day 0,7,14 and 28. For the satellite groups weight were recorded again on day 42. At the end of the experiment, mice were sacrificed, blood samples were collected for biochemical, hematological investigation and vital organs were harvested for histopathology.2.7Following 400–50 μl of blood collection in clot activator tube, serum was harvested by centrifugation at 3000 rpm for 10 min. Liver function test (LFT) and renal function test (RFT) were done using Dimension EXL 200-Siemens (Germany) autoanalyzer.2.8Blood samples (100 μl) collected in EDTA tubes were subjected to complete blood count (CBC) analysis in ADVIA 2120i (USA) autoanalyser. Hemoglobin, RBC, total count, platelets and other blood parameters were analysed.2.9Manually, slides were prepared for counting the lymphocytes and neutrophils as per the protocol described by Hoppe et al., 2.10Immediately after sacrificing the mice, tissues for histopathology were collected in 10% formalin and sent to histopathology facility of ACTREC for the preparation and staining of sides. Further the slides were evaluated by two independent pathologist from our centre to investigate any possible signs of tissue toxicity.33.1Female BALB/c mice were used for the PK study. Mice were given single oral dose of 70 mg/kg of WA and their blood samples was collected in EDTA tubes at 0.083, 0.25, 0.5, 1, 2, 4, 6, 8, 12, 24 h (5 mice per time points). To another set of female BALB/c mice, intravenous injection (IV) of WA was given at dose of 10 mg/kg, blood samples were collected in EDTA tubes at 0.03, 0.17, 0.5, 1, 2, 4, 6, 8, 12, 24 h (5 mice per time points). Blood from both the groups  were centrifuged at 3000 rpm for 10 min at 4 °C. Plasma samples were collected and kept in − 80 °C until further analysis. The level of plasma WA were quantified by using LC-MS/MS as per the protocol mentioned in 3.2LC-MS/MS method was performed using the AB SCIEX QTRAP-4500 LC-MS/MS instrument, LC SHIMADZU Nexera X2 Micro LC. A Kinetex® 1.7 µm C18 100 Å  column was used for chromatographic separation. The mobile phase consisted of acetonitrile and 10 mM ammonium acetate in milli-Q water (60:40 v/v). Mass spectrometer was operated in the positive ion mode. The aqueous phase was eluted at a flow rate of 0.2 mL/min and the analytes were quantified in MRM mode using the following mass transitions i.e., WA - Q1/Q3 471.400/ 281.200 and fluoxymesterone, the internal standard (IS), -Q1/Q3 337.200/91.100. Quantitation was achieved by measurement of the peak area ratios of the drug to the internal standard. Data acquisition was performed with Analyst version 1.6.1 software.3.3p value < 0.05 was considered as statistically significant. All PK parameters were expressed as Mean ± SEM except Tmax which is presented as median (range).All biochemistry and haematological parameters were analysed using GraphPad Prism 8.0 software and expressed as mean ± SEM. One-way ANOVA followed by Tukey’s multiple comparison tests was done. 44.1ADMET properties describes the therapeutic action of a molecule that depends on it reaching the target site in the body at an adequate amount. ADMETlab2.0 is a freely accessible that provides fast yet robust predictive data about the physicochemical properties, pharmacokinetics and drug-likeness of a compound. We utilized it to compute the physicochemical properties and bioavailability of WA. Detailed physicochemical and ADMET parameters are tabulated in 4.2Cytotoxicity by CYP450-mediated drug metabolism for WA predicted 4 sites of metabolism viz. epoxidation and aliphatic hydroxylation. START biodegradability and Verhaar scheme explained WA to be a persistent chemical and possess unspecific reactivity based on its structural annotation. The presence of α,β-unsaturated carbonyls in WA explained the structural alerts for genotoxic carcinogenicity and S. typhimurium mutagenicity computed by Benigni/Bossa rule base (for mutagenicity and carcinogenicity). The protein binding and DNA binding alerts for Michael acceptor and SN2-nucleoplilic aliphatic substitution were also identified.4.350 >2000 mg/kg body weight) compound as per OECD Guideline No. 423, December 2001.WA treated group did not show any clinical symptoms of toxicity compared to the vehicle control group. There was no visible sign of toxicity in any animal in the WA group, and none of the animals died. Based on these findings, WA may be classified as GHS  category 5  group. None of the animals in the test or recovery groups showed any visible signs of toxicity. These results suggest that oral administration of WA to mice every day for 28 days is safe and well tolerated. Therefore, 500 mg/kg oral WA shall be considered as No-Observed Adverse Effect Level (NOAEL) dose as per OECD Guideline No. 407, October 2008.4.5Upon oral WA administration, mice in both acute toxicity as well as in sub-acute toxicity studies did not show any changes in body weight compared to their respective controls a-b.Fig. 4.6Serum RFT and LFT parameters were investigated. RFT parameters included blood urea nitrogen (BUN), uric acid (URCA) and creatinine (CREA). LFT parameters included total protein (TP), albumin (ALB), alkaline phosphatase (ALP), total bilirubin (TB), aspartate aminotransferase (AST) and alanine aminotransferase (ALT). In acute and sub-acute toxicity, no changes were observed in LFT and RFT parameters in WA treated groups compared to their respective control groups .Table 1a4.7We analyzed the CBC and differential counts in acute and sub-acute toxicity animals following oral administration of WA. At any given acute toxicity dose none of the CBC parameters were altered, barring a non-significant dose dependent decrease in total WBC count . Hematol4.8Upon histopathological observation of vital organs such as brain, heart, lungs, liver, kidney, spleen, bone, small and large intestine, none of them showed any sign of drug induced toxicity in acute as well as in sub-acute toxicity studies a and b.F4.9max) were found to be 3996.9 ± 557.6 ng/mL and 141.7 ± 16.8 ng/mL for IV and oral doses respectively. The median Tmax following oral administration was 0.5 h. The mean plasma concentration-time curves of WA are illustrated in PK samples were analyzed for WA levels using a validated LC-MS/MS technique. The validation parameters of the method are shown in 4.10Phytochemicals have shown benefit in prevention of diseases and their progression Withania somnifera extract having a mixture of withanosides and withanolides, which may not truly reflect the toxicity of its individual components. Therefore profiling the toxicity and pharmacokinetics of WA, one of the most promising phytomedicine, is desirable.In the current study, for the first time we carried out a comprehensive toxicity evaluation of pure WA. Earlier, toxicity studies have been carried out on in silico studies to predict the ADMET properties of WA. The parameters obtained from the ADMET analysis based on the structural chemistry of the drug predicted good safety and tolerability profile in mice which were corroborated in the in vivo toxicity studies. The current study was carried out using female mice, because females are generally more sensitive to drug related toxicity Initially, we carried out 50 of WA at sub-100 mg/kg dose in acute toxicity studies following intraperitoneal (IP) administration (80 mg/kg and 54 mg/kg respectively) 50 between the two studies is perhaps attributable to differences in environment, method of isolation and species variation between herbs grown in India and Israel While to the best of our knowledge this is the first study to conclusively demonstrate the toxicity profile of WA in mice, Prabhu et al., reported the safety of a hydro alcoholic extract of Withania somnifera root (WSR) up to 2000 mg/kg in acute and sub-acute toxicity studies in rats. No significant hematological, biochemical and histological changes were observed in Wister rats at these doses in silico analysis. In silico analysis predicted a high first-pass effect due to extensive metabolism by CYP3A4 in the liver, while absorption across the GI tract itself was not expected to be low given a high Caco-2 and MDCK-permeability. Dai et al., demonstrated rapid metabolism of WA in liver microsomes which explains the first-pass effect max=20 min) but low Cmax of 16.69 ng/mL following oral administration of 1000 mg/kg of an aqueous root extract of Withania somnifera[35]. Surprisingly, Dai et al., reported an exceptionally high average Cmax of 619 ng/mL with a 10 mg/kg oral dose of WA. Further, they reported oral bioavailability of approximately 32%. The difference in PK parameters could be possibly explained by the vehicles used in the two studies. We administered oral WA in CMC which forms drug suspension while Dai et al. formulated WA in ethanol–solutol HS 15–distilled water . They observed an extremely rapid oral absorption (Tmax= 6 min) which could have possibly saturated the CYP enzymes, leading to high Cmax, and consequently a higher AUC. Recently, Modi et. al., carried out the PK study of withanosides  and withanolides  by administering 500 mg/kg of a Withania somnifera extract in rats. They reported the Tmax of 13, 124, 157 and 7 ng/mL for withanoside IV, WA, 12-deoxy-withastramonolide and withanolide A respectively. Other constituents such as withanone and withanolide B were reported below lower limit of quantification. The PK pattern of WA in the extract reported in this study corroborates our finding Pharmacokinetic study showed low oral bioavailability of WA which also corroborated the outcome of With the increasing use of herbal medicine in modern era and considering their poor bioavailability, there is surge in research on bioenhancers. Bioenhancers have the potential to increase the bioavailability of drugs by modulating their first pass metabolism or membrane permeation Withania somnifera extract, our group demonstrated the safety of WA up to 216 mg/day in patients with osteosarcoma Earlier in a phase I clinical trial using standardised 4.11Our results suggest that WA is safe at doses of 2000 mg/kg and 500 mg/kg in acute and repeat dose toxicity respectively, albeit with low oral bioavailability. In future, development of newer, better bioavailable formulations of WA, perhaps in combination with bioenhancers, should be considered to exploit its therapeutic potential.The study was funded through the Intramural Annual Scientific Funding of Tata Memorial Centre, India.Saurabh Kumar Gupta: Writing – original draft, Conceptualization, Data curation, Formal analysis, Methodology, Investigation, Validation, Visualization. Shraddha Jadhav: Data curation, Formal analysis, Validation. Dievya Gohil: Data curation, Investigation. Girish Ch. Panigrahi: Data curation, Investigation. Rajiv Kumar Kaushal: Data curative, Investigation, Validation. Khushboo Gandhi: Validation, Visualization. Anand Patil: Formal analysis, Software. Preeti Chavan: Resources, Validation. Vikram Gota: Conceptualization, Methodology, Investigation, Formal analysis, Resources, Supervision, Project administration, Funding acquisition, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Pomegranate tree is cultivated since ancient times in Morocco, where a high genetic diversity is hosted mainly in traditional agroecosystems. Over the past decade, it regained importance through extension of cultivated area, but remains thus far little valued. To date, its genetic variability and chemodiversity have gone unheeded for many reasons, some of which are related to previous agricultural strategies. In this context, the present study aimed to screen an ex-situ collection of seven local cultivars and seven exotic varieties with regard to 50 fruit morphometric and biochemical descriptors. The results showed statistically significant variability within accessions (p < 0.01), based on all aforementioned traits, except for seed weight, with coefficients of variation greater than 49%. This indicated a high level of phenotypic diversity among the studied genetic pool. The 3D scatter plot built based on the principal component analysis displayed an interesting discrimination with regard to the genotypes' geographic origins with a total variance of about 50%. According to morphometric based-heatmap, four main clusters were identified distinguishing the typicality of some local cultivars compared to exotic varieties, mainly ‘Sefri’, ‘Bzou’, ‘Chioukhi’ and ‘Djeibi’. Traits having the highest impact on discrimination between accessions were, by order of importance, fruit weight and its dimensions, juice yield, aril yield, single aril diameter, soluble sugars (glucose and fructose) along with contents in some organic acids, including citric, palmitic, linoleic and malic acids. Potential statistically significant correlations were spotted through bi-dimensional heatmap analysis, particularly between the fruit size, shape and peel traits along with some biochemical attributes. As many areas of the species chemodiversity and functional properties are still needed to be investigated further, the results of the present study are of great interest for the species valorization and for breeding programs. Punica granatum L., phenotypic diversity, fruit quality traits, lipo-biochemical attributes, juice proprieties. The available studies have mainly focused on the variability within local cultivars for the physico-chemical descriptors . HoweverSeveral studies have focused on physico-chemical characterization and phenolic composition of pomegranate juice within the local populations in different countries, such as Turkey , CroatiaCollecting, preserving and characterizing local pomegranate cultivars are valid goals, especially that some local populations are subjected to genetic erosion, mainly due to the extensive selection by farmers, using narrow criteria of selection focused mainly on fruit size and color. Such works are of great interest in regions which constitute diversification centers of the species, such as the Mediterranean basin, including Morocco . In fact22.13 and rich in organic matter with an average of 2.51%. The trees were conducted in the vase shape and irrigated daily using drip system from April to October, according to reference evapotranspiration values obtained from the weather station of Ain Taoujdate, at less than 200 m of distance from the studied collection. They received the same fertilization, namely 150, 60 and 150 kg ha−1 year−1 of N, P2O5, and K2O respectively, determined based on soil analysis during dormancy period (End-November). Pest control was according to local commercial practice, including copper based pesticide application in winter and fight against aphids.This work was carried out on fourteen pomegranate genotypes including seven local cultivars and seven exotic varieties, gathered in an ex-situ collection at the experimental station of the National Institute of Agricultural Research , northern Morocco . The cul2.2In early October, fully ripe fruits were randomly collected from each cultivar, containing 20 fruit each (5 fruits from each side of the tree canopy), while avoiding sunburned and cracked fruits. The ripeness degree was detected visually when fruits lost all traces of green color and became tinged with a yellowish or reddish color, depending on the cultivar. After harvest, the weight per fruit was recorded using a precision balance, while their dimensions (length and diameter) were determined using a digital caliper. Afterward, manually extracted arils were assessed for their dimensions and juice content, while also measuring biometric traits of seeds  and bark (weight and thickness).2.32.3.1−1), using the formula: [TA = VNaOH.C.M.1000/Vjuice] where, VNaOH is the consumption of titrant at the end point, C refers to the concentration of NaOH, M is the molecular weight of citric acid (M = 192.124 g mol−1) and Vjuice is the volume of juice. The pH measurement was performed using a pH meter (Thermo Orien 3 Star). Total soluble solids content (TSS) was measured using a digital refractometer (Metteler-Toledo Gmbh. 30PX. Switzerland), calibrated by distilled water. Juice maturity index was calculated as the ratio ‘TSS/TA’. Juice dry matter was determined through freeze-drying of previously weighed juice, at -50 °C under a pressure of 0.10 mBar for 72 h.Titratable acidity (TA) was measured following the method described by 2.3.22.3.2.1−1).Total phenolics content was measured using the Folin Ciocalteu method described by 2.3.2.2−1).Total flavonoids were measured by a colorimetric assay, developed by 2.3.2.3510) and 700 nm (ABS700) and results were calculated as mg of cyanidin-3-glucoside equivalent per liter of juice (mg CGE L−1 of juice), calculated according to the 510-ABS700) pH1.0 – (ABS510-ABS700) pH4.5; MW: molecular weight (449.2); FD: dilution factor (10); CAM: molar absorption coefficient of cyanidin-3-glucoside .Total anthocyanin content was analyzed by the differential pH method using two buffers: potassium chloride buffer at pH 1.0 (25 mM) and sodium acetate buffer at pH 4.5 (0.4 M) . 0.4 mL 2.3.2.43 by stirring for 10 s. The mixture absorbance was read at 550 nm using a spectrophotometer (Safas UV-visible). Concentration in hydrolysable tannins was estimated by reference to the calibration curve using tannic acid (500–2000 mg L−1) as standard. The results were expressed as mg of tannic acid equivalent per liter of juice (mg TAE L−1).Hydrolysable tannins content was measured following the method described by 2.3.2.53Na saturated solution was prepared by dissolving 43.75 g of Na2CO3 in hot water . After chilling, the mixture was adjusted to 125 mL after being carefully filtrated. After mechanical stirring, the preparation was left for 30 min before optical density measurement at 760 nm. Concentrations of condensed tannins are estimated by reference to the calibration curve using tannic acid (0–0.1 g/L) as standard. Values were reported as mg of tannic acid per liter of juice.TCT were evaluated based on Folin Denis method as reported by 2.3.2.62 ≥ 0.999). Analyses were run in triplicate and expressed as g L−1.Sugars were extracted as being previously described by 2.3.2.7Juice protein profile was assessed according to the modified method of 2.3.2.8−1 was used as the elution buffer. Calibration curves displayed a very satisfactory linearity level (R2 ≥ 0.999) under a wavelength of 210 nm using. Concentrations were calculated as g kg−1 of dry weight.The samples were kept at -20 °C until the time of analysis. The measurements concerned contents of citric, malic and quinic acids following the method previously described by 2.3.2.9Pomegranate seeds oil was extracted using Soxhlet apparatus following NF NE ISO 659 reference method . Thus, tThe methyl esters of the pomegranate seed oil were prepared in n-heptane (0.12 g/2 mL) with a cold KOH solution (2M) according to standard NF EN ISO 5509. The obtained fatty acid esters were analyzed using an Agilent Technologies 7890A gas chromatograph (GC) equipped with a flame ionization detector (T = 250 °C). The column used was a 60 m × 0.25 mm silica capillary column. The hydrogen inlet pressure of carrier gas was 178 kPa, with 1:70 ratio. The oven temperature program was as follows: 20 min at 210 °C, 210–245 °C at 6 °C/min then 10 min at 245 °C. A volume of 1 mL was injected with a split ratio of 1:50. The fatty acids methyl ester reference standard mixture  was used for used for calibration and for the identification of the FAME by their retention times.2.4Prior to statistical analysis, data were standardized (μ = 0 and σ = 1) so they can have a comparable scale, using SPSS v22 software. Analysis of variance was performed to verify significant differences between the samples collected, except for fatty acids, as they only were determined once. The correlation heatmap was built using Pearson model by Originlab Pro v9. Principal component model was performed using the correlation coefficient and the Varimax rotation method with Kaiser normalization. In addition, a scatterplot was created according to the first two components of the principal component analysis (PCA) using SPSS v22 software. Two-dimensional clustered heatmap groups similar rows and similar columns together, with their similarity represented by a dendrogram. The data were clustered by Ward's method with the Euclidean distance. This method is of importance to achieve a better understanding of complex biological systems where one-way direction is assumed .33.13.1.1Variance analysis of fruit morphometric data are summarized in 3.1.2Results regarding biochemical characterization are summarized in The total soluble solids content (TSS) showed a significant variability with an interval of variation of 13.00–17.00 °Bx. The highest TSS contents were recorded in the cultivars ‘Grenade Jaune’, ‘Chelfi’ and ‘Mollar Osin Hueso’, of which the average levels were 17.00, 16.20 and 16.06, respectively. The lowest levels were observed in the genotypes ‘Zheri Precoce’ (13.00 °Bx) and ‘Sefri’ (14.00 °Bx). The results are comparable to those found by −1 with a mean value of 7.00 g CAE L−1. The TA was found to be higher in the local genotypes ‘Sefri2’, ‘Djeibi’ and ‘Bzou’, with average values of 25.50, 15.00 and 12.50 g CAE L−1, respectively. However, TA was substantially low in the exotic genotypes ‘Gordo de Jativa’, ‘Dwarf Semi Evergreen’ and ‘Zheri Precoce’, with respective averages of 1.80, 2.40 and 2.50 g CAE L−1. The levels herein reported for TA were generally similar to those reported by Titratable acidity (TA) also revealed significant variability within genotypes, ranging from 1.80 to 25.50 g CAE L−1 and 2.13–8.13 mg TAE L−1. Similarly, the genotypes studied were shown to have a very significant variability for the concentration of polyphenol ranging from 1.49 to 9.67 g GAE L−1 and for flavonoids ranging from 0.13 to 0.47 g QE L−1. The highest polyphenol concentrations were recorded by the genotypes ‘Grenade Jaune’, ‘Chioukhi’ and ‘Gordo de Jativa’, where the average values were respectively of 9.67, 8.80 and 8.29 g GAE L−1. Further, the lowest levels were recorded by the genotypes ‘Dwarf Semi Evergreen’ and ‘Zheri Precoce’ which were marked by the averages 1.49 and 1.56 g GAE L−1. The results herein found are comparable to those of −1 over nine Spanish pomegranate varieties. Similar intervals were also reported by −1 with an average of 108.47 mg CGE L−1. Local genotypes, ‘Djeibi’, ‘Grenade Jaune’ and ‘Sefri2’ exhibited the highest anthocyanins contents of 280.00, 161.58 and 155.16 mg CGE L−1, respectively  and ‘Bzou’  as well as by the exotic variety ‘Ruby’ . The genotype ‘Wonderful’ from the United States was characterized by the lowest concentration of glucose (29.43 g L−1) and fructose (33.38 g L−1). In general, the glucose and fructose levels in the pomegranate collection are comparable to those of previous works , ‘Wonderful’ (0.82 g kg−1) and ‘Chelfi’ (0.99 g kg−1). These results are similar to those reported by −1, with an average of around 4.60 g kg−1. The genotypes marked by high concentrations of malic acid were ‘Ruby’ (7.54 g kg−1), ‘Djeibi’ (6.83 g kg−1) and ‘Dwarf Semi Evergreen’ (6.72 g kg−1). In addition, our data showed that the two local genotypes ‘Sefri2’ and ‘Djeibi’ were identified by their high concentrations in quinic acid, with respective mean values of 16.65 and 14.10 g kg−1. The variation range for this acid was 2.33–16.65 g kg−1, with an average of 9.65 (g Kg−1). The results obtained seem to present larger variation intervals than those described in prior studies ; palmitoleic acid (1.03%); oleic acid (4.39%); palmitic acid (4.18%), linolenic acid (2.7%) and gadoleic acid (0.33%). The genotypes ‘Mollar Osin Hueso’ and ‘Djeibi’ were characterized by the highest concentrations of erucic acid, the average concentrations of which were 10.83 and 11.06%, respectively. On the other hand, ‘Negro Monstrioso’ and ‘Chelfi’ were marked by the lowest concentrations, less than 0.90%. Likewise, for oleic acid, the Spanish genotypes ‘Gordo de Jativa’ and ‘Negro Monstrioso’ were characterized by a high concentration of oleic acid, with respective mean values of 8.67 and 8.44%. As for palmitoleic acid, it has been found in high concentrations in the two exotic genotypes ‘Ruby’ (2.59%) and ‘Negro Monstrioso’ (1.55%). On the other hand, the local genotypes displayed generally significant concentrations in some fatty acids, such palmitic acid fairly present in ‘Sefri2’ (3.82%), behenic acid in ‘Chioukhi’ (1.56%) and erucic acids in ‘Grenade Jaune’ (1.41%).3.2The principal component analysis (PCA) was used to identify the most significant descriptors of the data set. Only a variance greater than ǀ0.5ǀ was considered significant for each factor. Thus, total variance of 96.97% was explained by the first 11 principal components (PCs). The first four PCs included 37 variables representing more than 74% of all variables, and explained 59.79% of the total variance , which m3.3The scatter plot was prepared as a function of the first three principal components, PC1, PC2 and PC3 whose variance was respectively 21.76, 16.12 and 11.96% and which distinguish genotypes according to their phenotypic characteristics . The str3.4In order to identify the relationships between the morphometric, biochemical and lipo-chemical traits studied, all mean values were involved in the bivariate split heatmap correlation using the Pearson model. The most potential and significant correlations are summarized in Results show very strong positive and significant correlations related to fruit weight with diameter, height, fruit height without calyx, bark weight, juice yield, seed weight and seed length. These results are similar to those described in prior studies . Likewis3.5In order to obtain a simplified classification of the studied genotypes using all morphometric parameters  and lipo4This study showed a great variability within the studied pomegranate accessions, particularly among local genotypes, indicating that Morocco is an important hotspot of pomegranate germplasm diversity. Morphometric and biochemical screening of the pomegranate collection evaluated in this study revealed a wide range of variation for all parameters. The results also displayed interesting correlations linking some parameters of interest to others, making thus future screening studies of this collection more efficient. The most discriminating variables in genotypes classification were fruit weight and its dimensions, as well as juice content and some organic acids, mainly citric, palmitic, stearic and oleic acids. This multivariate screening made it possible to identify similarities and dissimilarities between some exotic varieties and local cultivars. The obtained database showed that local cultivars demonstrated a great potential and might be preconized as parents in breeding programs as well as in promising valorization pathways owing to their fruit quality. However, one of the limitations of this work is that a larger sample length is required and further validation must be performed using samples from other varieties and origins and through conducting other assays in contrasted locations, so the stability of the performances herein highlighted could be verified. Nevertheless, this study provides extensive data of great interest regarding lipo-biochemical attributes of pomegranate seeds that can be exploited for nutritional, pharmaceutical and cosmetic purposes.Assia Ejjilani: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Karim Houmanat; Lahcen Hssaini: Analyzed and interpreted the data; Wrote the paper.Hafida Hanine: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.Kaoutar Elfazazi; Francisca Hernandez; Ilham Hmid: Contributed reagents, materials, analysis tools or data.Rachid Razouk: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


Mental disorders are associated with dysregulated metabolism, but comprehensive investigations of their metabolic similarities and differences and their clinical relevance are few. Here, based on the plasma metabolome and lipidome of subcohort1, comprising 100 healthy participants, 55 cases with anxiety, 52 persons with depression, and 41 individuals with comorbidity, which are from WCHAT, a perspective cohort study of community‐dwelling older adults aged over 50, multiple metabolites as potential risk factors of mental disorders were identified. Furthermore, participants with mental illnesses were classified into three subtypes  by unsupervised classification with lipidomic data. Among them, S1 showed higher triacylglycerol and lower sphingomyelin, while S2 displayed opposite features. The metabolic profile of S3 was like that of the normal group. Compared with S3, individuals in S1 and S2 had worse quality of life, and suffered more from sleep and cognitive disorders. Notably, an assessment of 6,467 individuals from the WCHAT showed an age‐related increase in the incidence of depression. Seventeen depression‐related metabolites were significantly correlated with age, which were validated in an independent subcohort2. Collectively, this work highlights the clinical relevance of metabolic perturbation in mental disorders, and age‐related metabolic disturbances may be a bridge‐linking aging and depressive. Based on plasma metabolomics and lipidomics, we identified numerous potential risk factors for older adults with anxiety, depression, or comorbid anxiety‐depressive symptoms. Molecular classification revealed the clinical relevance of metabolic perturbations associated with these mental disorders. In addition, the incidence of depression increases with age. We revealed that the changes of depression‐related metabolites were significantly correlated with age, indicating the interplay among age, metabolic disturbances, and depression. The association of these metabolites with age was validated in an independent subcohort2 with 328 mental health participants from WCHAT cohort  cohort, a prospective cohort comprising of 7,439 participants aged over 50 years from West China,t Figure . This st22.1p < 0.05), consistent with the Hamilton Rating Scale for Depression (HRSD).Prevalence analysis of mental health assessments of 2,343 WCHAT participants over 65 showed that 11.74%, 11.35%, and 9.22% of them suffered from anxiety, depression, and comorbid anxiety‐depressive disorder, respectively. The incidence was much higher than that in the National Health Aging Trends Study (NHATS) of the United States (the participants aged over 65) . T‐SNE analysis of metabolomics data, lipidomics data, and quality controls (QCs) indicated high data quality Figure . To unco2(OR) = −1.46, 95% CI: −2.79 to −0.2, p = 0.0259), docosahexaenoic acid  = −1.63, 95% CI: −2.81 to −0.52, p = 0.0052), and ergothioneine (log2(OR) = −0.52, 95% CI: −1.01 to −0.07, p = 0.0289), are known to benefit depressive symptoms and decrease in the depression group , L‐cystine (log2(OR) = 2.33, 95% CI: 0.71–4.08, p = 0.0065), and L‐kynurenine (log2(OR) = 1.29, 95% CI: 0.02–2.61, p = 0.0487) , 1‐methylnicotinamide (log2(OR) = −1.03, 95% CI: −1.86 to −0.25, p = 0.0113), benzenebutanoic acid (log2(OR) = −0.95, 95% CI: −1.72 to −0.26, p = 0.0102), and uracil (log2(OR) = −1.47, 95% CI: −2.67 to −0.36, p = 0.0116) were significantly reduced  = −1.73, 95% CI: −3.14 to −0.43, p = 0.0119; anxiety: log2(OR) = −1.98, 95% CI: −3.42 to −0.605, p = 0.0056; comorbidity: log2(OR) = −2.26, 95% CI: −3.87 to −0.76, p = 0.0041) and linoleic acid (depression: log2(OR) = −4.21, 95% CI: −6.63 to −1.98, p = 0.0004; anxiety: log2(OR) = −2.31, 95% CI: −4.57 to −0.13, p = 0.04; comorbidity: log2(OR) = −3.07, 95% CI: −5.89 to −0.4, p = 0.0272)  Figure . In comod Figure . Overlap) Figure . Collect2.3t‐test, p = 0.0148, Figure People diagnosed with different mental disorders may have similar molecular profiles and symptoms.U‐test to screen the differential metabolites for each subtype compared with normal controls. As a result, a total of 182 differential metabolites were identified, including 125 differential metabolites for S1, 88 differential metabolites for S2, and 7 differential metabolites for S3  > 1.25 or < 0.8). Among them, 34 metabolites were differentially regulated in both S1 and S2 subtypes. Further analysis showed that 85.7% of the 182 metabolites with significant differences were lipids, of which triglycerides (TGs) and sphingomyelin (SM) dominated. SM, PC, and hexosylceramides (HexCer) decreased in S1 but dramatically increased in S2 , mental health component score (MCS), social support rating scale (SSRS), Pittsburgh sleep quality index (PSQI) score and cognitive disorder rate, as well as blood test parameters, including red blood cell count (RBC), hemoglobin (HGB), albumin–globulin (A/G), TG, high‐density lipoprotein (HDL), low‐density lipoprotein (LDL), and very low‐density lipoprotein (VLDL), showed significant differences among disease subtypes and the normal group . To confirm these results, we used another independent subcohort2 containing 328 mental health individuals from WCHAT cohort. Multiple linear regression analysis identified 188 age‐associated metabolites (p < 0.05). Overlapping analysis revealed that 17 age‐associated metabolites were identified as potential risk factors for depressive symptoms , ergothioneine , DHA , DPA , and linoleic acid  = 0.28, 95% CI: 0.01–0.58, p = 0.0484; β1 = 0.055, p < 0.0001; β2 = 0.055, p < 0.0001), L‐cystine , homogentisic acid , D‐Arabitol , and L‐kynurenine  was performed. As a result, seven metabolites including linoleic acid, Cer(d34:1), butyrylcarnitine, N‐alpha‐acetyllysine, L‐kynurenine, D‐arabitol, and deoxyribose were obtained  = 1.66, 95% CI: 0.19–3.23, p = 0.0308) and is positively associated with age , was significantly increased in S2, which indicates that the increase in Cer(d34:1) may be an important signature for geriatric depression , and kynurenine, were positively associated with age and showed a higher risk for geriatric depression.In this study, based on a prospective multi‐center cohort WCHAT, we investigated the association between dysregulated metabolism and mental disorders and the connection between age and depression. Several interesting findings were gained. First, we presented the potential metabolic risk factors for anxiety, depression, and comorbid anxiety‐depressive disorder for older adults in Western China. The more severe the mental disorders were, the more dysregulated metabolites, especially lipids, were in their blood. Second, we disclosed that individuals with different mental disorders but close metabolic profiles most likely had similar clinical phenotypes, and the metabolic profile might serve as a progression indicator of mental illnesses. Third, we confirmed that patients who suffered from geriatric depression were older than normal people, and 17 age‐associated metabolites were identified as potential risk factors for depressive symptoms. Among them, five metabolites negatively correlated with age showed a lower risk for geriatric depression, including creatine, ergothioneine, DHA, DPA, and linoleic acid. Twelve metabolites, such as Inflammaging is associated with many age‐related disorders, including geriatric depression, probably induced by pronounced and prolonged immune responses.Unlike hydrophilic metabolites,TG is another kind of lipid showing distinct levels among metabolic subtypes. The levels of blood TGs in S1 participants were very high, while the situation was reversed in S2. People in the S1 subgroup had higher GDS scores, worse physical health, and suffered more from cognitive impairment, which may be associated with the abnormal increase in TGs. Elevated levels of TGs in blood are associated with unstable blood sugar levels and insulin resistance, both of which are risk factors for diabetes.People with mental disorders usually have psychosocial functioning limitations, poor physical function, and diminished quality of life.p‐cresol sulfate, homogentisic acid, and kynurenine, are proinflammatory chemicals associated with a higher risk of geriatric depression.2−, H2O2, and •OH by autoxidation,p‐Cresol sulfate is a protein‐bound uremic toxin derived from tyrosine and phenylalanine in the liver and gut. Because of the albumin‐binding capacity, the gut microbiota metabolite p‐cresol sulfate accumulates in the blood during renal function decrease and aging.p‐cresol sulfate increases oxidative stress and neuroinflammation, which may be a cause of depression.Some metabolites that increase with age, such as There are still some limitations that should be noted in this study. First, the metabolic disturbance that identified in mental disorders should be validated in another geriatric population. Second, metabolic disturbance identified in this study for mental illness should be further confirmed in brain tissues, which could promote the investigation of their functions for the development of disease. Third, more examination should be concentrated on the application of metabolic classification for the assessment of mental illness. Besides, many other metabolites also increased with age and were associated with a higher risk of depressive symptoms, such as D‐arabitol, deoxyribose, and N‐alpha‐acetyllysine, but their roles and mechanisms in the development of depression are unclear.44.1WCHAT is an ongoing population‐based cohort study of community‐dwelling older adults aged over 50 in Western China. The depressive and anxiety symptoms of participants were assessed by two doctors through the GDS‐15 and GAD‐7 questionnaire, respectively, with a threshold score of 5.A total of 6,467 participants met the inclusion criteria: (1) age > 50 years; (2) no missing data on depression or anxiety; and (3) having human plasma samples at baseline in the WCHAT study. Among them, 248 WCHAT participants, including 41 individuals with comorbid anxiety‐depressive disorder (GAD score > 5 and GDS score > 5), 52 persons with depression (only GDS score > 5), 55 cases with anxiety (only GAD score > 5), and 100 control participants (GDS score and GAD score are less than 1), without tumor and other severe disease, were randomly selected for this study. To validate age‐related metabolites, we sampled an additional 328 mentally healthy individuals, which were also without tumor and other severe disease, from WCHAT (GDS score < 5 and GAD score < 5).g for 10 min at 4°C. The supernatant was divided into equal volumes, transferred into liquid nitrogen for 3 min, and finally stored at −80°C until LC–MS/MS analysis.Fasting blood samples were collected in EDTA‐coated 10 ml tubes. Plasma was isolated by centrifugation at 1600 ×4.2Hydrophilic metabolites were extracted from plasma using four volumes of methanol, and lipids were extracted with liquid–liquid extraction by adding dichloromethane/methanol (v/v = 2:1). Untargeted metabolomics and lipidomics were carried out at the Facility Center of Metabolomics and Lipidomics of Tsinghua University.g for 20 min at 4°C, and then 90 μl of plasma was used for the extraction of hydrophilic metabolites and lipids. QC samples consisted of mixed plasma samples. 6 L‐Lysine hydrochloride powder (Silantes) and 615N4 L‐arginine hydrochloride powder (Silantes) were added as the internal standards for the hydrophilic metabolites. PE (16:0‐d31‐18:1) was added as the internal standard for the lipids.The plasma samples were centrifuged at 14,000 ×°C for 1 h and then centrifuged at 14,000 ×g for 10 min. Equal volumes of supernatant were collected and transferred to a new tube and finally dried in a vacuum centrifuge. To monitor the performance of data acquisition, Gln‐N15, inosine‐15, and Trp‐D5 were added as internal standards for the positive mode, and cholic acid‐D4, inosine‐15, stearic acid‐D35, succinate‐D4, and Trp‐D5 were added as internal standards for the negative mode before LC–MS/MS analysis.Polar metabolites were extracted using prechilled methanol (MS grade) according to the method of Yuan et al.°C. Subsequently, the lower dichloromethane layer was collected and transferred to a new tube. Then, prechilled water (MS grade) (1/5 volume of dichloromethane) was added for re‐extraction and centrifuged at 10,000 rpm for 20 min at 4°C. The lower dichloromethane layer containing lipids was collected and dried in speedvacuum. D31‐Cer(d18:1_16:0), DG(14:0_14:0), PC(14:0_14:0), PE(14:0_14:0), D31‐PE(16:0_18:1), and PS(14:0_14:0) were added as internal standards before LC–MS/MS analysis.Lipids were extracted according to the method of Bligh and Dyer.4.3Mass spectrometry analyses of polar metabolites and lipids were carried out by the Facility Center of Metabolomics and Lipidomics of Tsinghua University, according to the method described by Tang et al.4.4K‐nearest neighbor algorithms . T‐distributed stochastic neighbor embedding (t‐SNE) analysis was applied to evaluate the quality of the data. To determine the differential metabolites between normal and anxiety, depression, or comorbid anxiety‐depressive disorder, we built a multiple logistic regression model shown below:After filtering the metabolites undetected in > 20% of samples and with a coefficient of variation (CV) of QCs > 30%, we normalized the total intensities of all samples. The missing values were imputed by p < 0.05 between psychiatric patients and normal individuals as significantly changed metabolites.We defined metabolites with β1 mental disorder + β2 age + β3 sex + β4 BMI; subcohort2, metabolite level ∼ α+ β1 age + β2 sex + β3 BMI. We defined metabolites with p < 0.05 as those significantly associated with age.Then, we used multiple linear regression to explore the age‐related metabolites for the two subcohorts. The models are shown below. Subcohort1, Metabolite level ∼ α+ k‐means clustering. Up to eight clusters were tested, and the distance was measured by Euclidean distance. The consensus matrices for k = 2–8 are shown in Figure k = 3 had the lowest proportion of ambiguous clustering (PAC), and subtypes were separated clearly, which means k = 3 is the optimal cluster. In addition, the cumulative distribution function (CDF) curve showed that when k = 3, the descent gradient was the minimum .m Figure . Taken tU‐test. Significantly different metabolites in metabolic subtypes were screened with p < 0.05. FC (subtype/NC) > 1.25 or < 0.8. In addition, we applied the Kruskal–Wallis test to identify the significantly changed metabolites among the three subtypes (p < 0.05). Clinical information and blood biochemical indexes for participants were normally distributed and tested by parametric tests. Unpaired Student's t‐test and one‐way ANOVA test were used for the continuous variables of two groups and multiple groups, respectively. Classified variables were tested by the Chi‐squared test.Normality tests revealed that the metabolites were almost skewed in the distribution. Therefore, the significance of metabolites between subtypes and normal were tested by the Mann–Whitney The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.The subjects in this research are from WCHAT cohort. The Ethics Committee of Sichuan University approved this study (Permission Number: 2017‐445), and all procedures were conducted based on the principles of the Declaration of Helsinki. All the participants have signed the informed consents to conduct this study.Birong Dong, Lunzhi Dai, and Biao Dong designed the project; Yu Liu, Wanyu Zhao, Ying Lu, Yunli Zhao, Yan Zhang, and Miao Dai participated in the sample collection; Yu Liu, Wanyu Zhao, Yunli Zhao, and Ying Lu processed the sample preparation and data analysis; Lunzhi Dai, Biao Dong, Yu Liu, and Wanyu Zhao drafted and revised the manuscript; Peng Lei, Jirong Yue, Shan Hai, Ning Ge, Shuting Zhang, Mingjin Huang, Xiaohui Liu, and Shuangqing Li provided technical supports and suggestions.Supporting InformationClick here for additional data file.