Type 2 diabetes mellitus (T2DM) has become a major health problem, threatening the quality of life of nearly 500 million patients worldwide. As a typical multifactorial metabolic disease, T2DM involves the changes and interactions of various metabolic pathways such as carbohydrates, amino acid, and lipids. It has been suggested that metabolites are not only the endpoints of upstream biochemical processes, but also play a critical role as regulators of disease progression. For example, excess free fatty acids can lead to reduced glucose utilization in skeletal muscle and induce insulin resistance; metabolism disorder of branched-chain amino acids contributes to the accumulation of toxic metabolic intermediates, and promotes the dysfunction of β-cell mitochondria, stress signal transduction, and apoptosis. In this paper, we discuss the role of metabolites in the pathogenesis of T2DM and their potential as biomarkers. Finally, we list the effects of anti-hyperglycemic drugs on serum/plasma metabolic profiles. This may partly explain the impaired oxidative phosphorylation of mitochondria. More importantly, glucose stimulation at 8mM appears to be sufficient to initiate this cycle, suggesting that cumulative impairment of pancreatic function may have already begun in patients with early impaired glucose tolerance  , 9, and nd liver , while cde novo synthesis of diacylglycerol (DAG), the activator of protein kinase C (PKC)  . In the  C (PKC) –28. In te stress , but alse stress , 31. As e stress . This poe stress , and is e stress .2.2Since Felig et al. found in the 1970s that the increased concentration of circulating amino acids in obese people is associated with decreased insulin sensitivity , a largeBCAA is most closely related to homeostasis model assessment for insulin resistance (HOMA-IR) and blood glucose , 57, 58.Paradoxically, Leucine has also been suggested to increase GLUT4-mediated glucose uptake, stimulate insulin-dependent PI3K and protein kinase C (PKC) signaling cascades, and increase mitochondrial biogenesis and substrate oxidation capacity , 68. LeuDifferent organs also show differences in amino acid profiles, liver but not muscle IR was associated with increased levels of leucine and tyrosine, leucine deprivation enhances insulin sensitivity by increasing AMPK phosphorylation and inhibiting the mTOR/S6K pathway in the mouse liver , while bStudies have suggested that increased IR leads to increased levels of circulating fasting BCAA and inflammation , 79. ObeIn addition to BCAA and AAA, multiple additional amino acids and derivatives are associated with diabetes progression , 90, 91.Studies have shown that alanine, tryptophan, and trytophan-related metabolic intermediates are associated with a higher risk of T2DM and prediabetes , 44, 93,Elevated levels of lysine and its metabolic intermediate 2-aminoadipic acid were associated with an increased risk of T2DM. 2-aminoadipic acid metabolism occurs primarily in mitochondria and is broken down into acetyl-CoA before entering the  cycle. Plasma 2-Aminoadipic acid level increases by 47% in obesity and is positively correlated with IR . As a no2.3Elevated blood levels of triacylglycerols (TAGs) are traditional risk indicators for T2DM , 103. FrIncreased FFA release and oxidation rate can antagonize glucose oxidation, resulting in the disturbance of pyruvate metabolism and impaired insulin sensitivity , 119, anThe inflammatory response mediated by SFA is an essential cause of IR and β-cell damage Figure 3In specific lipids and derivatives, baseline lysophosphatidylcholine (LPC), phosphatidylcholine (PC), sphingomyelin, and cholesterol esters were inversely associated with T2DM risk . LPC werCeramide is a relatively minor component of the total cellular lipidome with a particularly low abundance, and increased ceramide content has been shown to be positively correlated with HOMA-IR, fasting glucose, and cardiovascular diseases , 137. ElAcylcarnitines, metabolites of FA, play crucial roles in cellular energy metabolism and are gradually considered as influential biomarkers of metabolic disorders in metabolic syndrome, diabetes, cardiovascular diseases and other diseases. For example, C2, C3DC-CH3, C4, C5, C7 and C26 have been observed to be associated with HOMA-IR . In crosRecently, FFA ligand-specific G-protein-coupled receptor (GPR) including GPR40 , GPR43 (FFA2), GPR41 (FFA3), GPR120 (FFA4) has been extensively studied. In HFD-fed mice, FFA2 function is more mediated by Gi/o and activated by short-chain FA, which inhibit insulin signaling in adipose tissue, increasing energy expenditure and improving insulin sensitivity in different tissues, including liver and muscle , 148. Pr2+ signaling and increasing intracellular Ca2+ concentration  is an organic acid derived from α-ketobutyrate, a by-product of amino acids such as methionine and threonine catabolism and glutathione synthesis . Increas3-Hydroxyisobutyrate (3-HIB), an intermediate product of valine decomposition, is considered a higher sensitive biomarker of T2DM than valine. Impaired valine catabolism increases 3-HIB production, leading to increased lipid oxidation and acylcarnitine accumulation. Currently, 3-HIB is believed to mediate the occurrence of IR by affecting FA uptake in endothelial cells, and 3-HIB treatment has a regulatory effect on mitochondrial metabolism in white and brown adipocytes . Recent Serum concentrations of 3-carboxy-4-methyl-5-propyl-2-furan propionic acid (CMPF), the main endogenous metabolite of furan FA, are elevated in patients with impaired glucose tolerance and T2DM, and can directly act on pancreatic β-cells to lead to impaired insulin secretion –177. HowBile acids (BAs) can act as signaling regulators for lipids and glucose metabolism, and the concentration of BAs changed has been linked to metabolic disorders such as IR. Studies have shown that after adjusting for age, sex, BMI, waist circumference, and fasting blood glucose, increased circulating 12α-hydroxylated BAs concentration is significantly associated with increased HOMA-IR and fasting blood glucose , 181, bu3Many of commonly used anti-hyperglycemic medications can also have pleiotropic effects on the metabolite profile, which may positively affect T2DM and complications. In the following, we summarize the therapeutic effects of current mainstream hypoglycemic drugs on metabolites associated with different diabetes risks.3.1Metformin is a common drug for T2DM management. In randomized controlled trials, taking metformin was associated with increased levels of betaine, alanine, histidine, leucine/isoleucine and decreased levels of carnitine, phenylalanine, tyrosine and valine , 187. In3.2Compared with metformin, pioglitazone increased myocardial glucose uptake and decreased hepatic TAG content , 192, bu3.3Treatment with liraglutide can significantly reduce serum tyrosine, valine and isoleucine levels in obese people, but has no significant effect on T2DM patients , 196. Af3.4Studies have shown that 6 months of vildagliptin treatment can reduce the level of asymmetric dimethylarginine in T2DM patients , but has3.5The present investigation shows that 3 months of metformin plus pioglitazone can significantly reduce the levels of phenylalanine/tyrosine, citrulline/arginine, and lysine/α-aminoadipic acid in T2DM and obese adults . CompareAt present, the effects of hypoglycemic drugs on serum metabolites in patients with T2DM are more focused on the effects of lipids and lipoproteins, but the number of studies on amino acids and metabolic derivatives are limited. GLP-1 agonists have shown relatively better effects on lipid and amino acid metabolites, and improvements in metabolites associated with cardiovascular risk have been observed in short-term trials, but long-term follow-up evidence is still lacking. An anti-hyperglycemic drug’s effect on blood metabolites needs more prospective, intervention and randomized clinical trial studies to confirm the molecular mechanism of further metabolites.4As a typical metabolic disease, exploring changes in metabolites and their regulatory mechanisms is closer to the essence of T2DM. Among the promising metabolites, blood concentrations of hexose, BCAA, AAA, TAG, phospholipids and sphingomyelins were significantly and positively associated with T2DM incidence, while glycine and glutamine were negatively associated with T2DM risk. However, using only one metabolite type as a biomarker has many limitations in terms of disease duration, race, or diet, so a comprehensive judgment of multiple metabolite prediction models is necessary. Understanding the metabolism of metabolites in specific tissues and the influence of the regulation of corresponding receptors on immune response and biological efficacy, as well as verifying causality through mechanism studies, is key to metabolite research. Finally, while we have an initial understanding of the functions of metabolites as regulators, the results of dietary interventions do not completely match our expectations. How dietary nutrients cause changes in metabolic pathways and certain protein signaling pathways, as well as the role of gut flora in metabolite synthesis and downstream regulation, will be attractive topics.YL mainly wrote the manuscript. DW ccollects data, corrects and provides fund support. Y-PL reviews, editing and provides fund support. All authors have read and agreed to the published version of the manuscript.


CDH1 gene codes for the epithelial-cadherin (E-cad) protein, which is embedded in the plasma membrane of epithelial cells to form adherens junctions. E-cad is known to be essential for maintaining the integrity of epithelial tissues, and the loss of E-cad has been widely considered a hallmark of metastatic cancers enabling carcinoma cells to acquire the ability to migrate and invade nearby tissues. However, this conclusion has come under scrutiny.The CDH1 and E-cad expression changes during cancer progression, we analyzed multiple large transcriptomics, proteomics, and immunohistochemistry datasets on clinical cancer samples and cancer cell lines to determine the CDH1 mRNA and E-cad protein expression profiles in tumor and normal cells.To assess how CDH1 mRNA and E-cad protein are either upregulated or remain unchanged in most carcinoma cells compared to normal cells. In addition, the CDH1 mRNA upregulation occurs in the early stages of tumor development and the levels remain elevated as tumors progress to later stages across most carcinoma types. Furthermore, E-cad protein levels are not downregulated in most metastatic tumor cells compared to primary tumor cells. The CDH1 mRNA and E-cad protein levels are positively correlated, and the CDH1 mRNA levels are positively correlated to cancer patient’s survival. We have discussed potential mechanisms underlying the observed expression changes in CDH1 and E-cad during tumor progression.In contrast to the textbook knowledge of the loss of E-cad during tumor progression and metastasis, the levels of CDH1 mRNA and E-cadherin protein are not downregulated in most tumor tissues and cell lines derived from commonly occurring carcinomas. The role of E-cad in tumor progression and metastasis may have previously been oversimplified. CDH1 mRNA levels may serve as a reliable biomarker for the diagnosis of some tumors  due to the marked upregulation of CDH1 mRNA in the early stages of tumor development of these carcinomas.The online version contains supplementary material available at 10.1186/s12885-023-10916-0. CDH1 is a gene that codes for the epithelial-cadherin (E-cad) protein, which is embedded in the plasma membrane of epithelial cells forming the tissues that cover the body surfaces and line the walls of cavities, channels, and glands [d glands . E-cad id glands , 4. Thisd glands , 6. In id glands , 8. E-cad glands . In the d glands , 11.CDH1 locus, nonsense mutations [The loss of E-cad expression has been considered a hallmark of cancer progression and metastasis , 13 via utations , or promutations . E-cad autations . E-cad autations , 18, andutations . The losutations –22. Asseutations –25 as mautations –29.CDH1 mRNA and E-cad protein in different carcinomas during tumor progression. Strikingly, the levels of CDH1 mRNA and E-cad protein were not reduced in most of the examined tumors, even in the later stages of cancer compared to respective healthy tissues. The only exception to this trend was kidney cancer, which exhibited significantly lower levels of CDH1 mRNA and E-cad protein, the pattern normally described in textbooks. The observations presented in this study demonstrate that the changes in E-cad expression during tumor progression and metastasis are more complex than widely believed.In this study, we analyzed multiple large transcriptomics, proteomics, and immunohistochemistry datasets on clinical cancer samples and cancer cell lines to determine the levels of http://gepia2.cancer-pku.cn/#index) [CDH1 mRNA in the tumors of interest. GEPIA2 is a resource for gene expression analysis compiling tumor and normal samples from The Cancer Genome Atlas (TCGA), a database containing samples from 11,000 patients, and the Genome-Typing Expression (GTEx), a database containing 948 post-mortem donors and approximately 17,382 RNA sequencing (RNA-seq) samples across 54 tissue sites and 2 cell lines [CDH1 mRNA levels between tumor and normal samples for breast, colon, lung, ovarian, pancreatic, endometrial, kidney, liver, and head/neck tissues were analyzed using one-way analysis of variance (ANOVA). A change was considered significantly different if i) the log2 fold change was larger than 1 , ii) the q-value was smaller than 0.01 or the p-value was smaller than 0.05, and iii) the samples were available in both the TCGA and GTEx databases. The CDH1 mRNA expression in tumor subtypes relative to corresponding normal tissues was analyzed with the same parameter as discussed above. CDH1 mRNA expression across specific tumor stages was performed using a one-way ANOVA, in which expression was compared among the major stages of specific carcinomas. Only the major stages containing enough samples for statistical analysis were analyzed.The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) web server (/#index) , 31 was ll lines . CDH1 mRhttps://depmap.org/portal/), which systematically identifies genetic and pharmacologic dependencies and biomarkers for 1,072 cancer cell lines from various lineages [CDH1 mRNA profile in cell lines derived from breast, colorectal, endometrial, head/neck, kidney, lung, liver, pancreatic, and ovarian carcinomas. The CDH1 mRNA levels in the kidney, breast, liver, colorectal, ovarian, pancreatic, endometrial, and lung carcinomas cell lines were analyzed using the Cancer Cell Line Encyclopedia (CCLE) RNA-seq data available on the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI) database (https://www.ebi.ac.uk/gxa/experiments/E-MTAB-2770/) with the expression value set at 0.5 and the data reported as transcripts per million (TPM) [The Cancer Dependency Map  . The reshttp://ualcan.path.uab.edu/analysis-prot.html), an interactive web source for analyzing cancer OMICS data with a focus on transcriptomics and proteomics, and the TCGA methylation datasets  were used to assess promoter methylation in kidney carcinomas compared to normal samples, and among the major stages of kidney carcinoma [CDH1 gene’s start site were plotted. Beta values range from 0 (unmethylated) to 1 (fully methylated).The University of Alabama at Birmingham Cancer Data Analysis Portal  [Z-values, the standard deviation from the median across samples for any given carcinoma compared to normal tissues. Differential expression of E-cad with p-values smaller than 0.05 was considered significant.The expression levels of E-cad protein in cancer and normal tissues were determined using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery dataset available on the UALCAN web-server (ot.html) . The loghttps://depmap.org/portal/interactive/) produced from the quantitative analyses of protein expression in 375 cancer cell lines in the CCLE [The expression of E-cad in cancer cell lines was determined using the proteomics data available in the DepMap portal  , then cahttps://www.proteinatlas.org/) was assessed [E-cad expression in immunohistochemically stained with CAB072856 antibody, and pathologist-certified/annotated images for breast, colon, head/neck, kidney, lung, liver, pancreatic, ovarian, and endometrial carcinomas, and corresponding normal tissue microarrays stored on the Human Protein Atlas (HPA)  to compare the CDH1 mRNA and E-cad protein levels in 9 individual carcinoma cell lineages, including endometrial, head/neck, pancreatic, ovarian, liver, kidney, colorectal, breast, and lung tissues [CDH1 mRNA levels and E-cad protein levels in all the carcinoma cell lines was also determined.Pearson Correlation Analysis and Spearman Correlation Analysis were carried out using the DepMap portal  was used to assess the relationship between CDH1 mRNA levels in carcinoma tissues and patient’s overall survival (OS) and disease-free survival (DFS) [CDH1 mRNA: the high CDH1 mRNA group represented half of the patients with tumors expressing higher levels of CDH1 mRNA, and the low CDH1 mRNA group represented half of the patients with tumors expressing lower levels of CDH1 mRNA relative to the median CDH1 mRNA level in the group. A p-value less than 0.05 was considered significant, meaning that the null hypothesis was rejected and that there was a significant difference in the survival between the two groups.GEPIA2  , 31. GEPal (DFS) , 31. CanCDH1 mRNA expression between carcinoma and normal tissues, between carcinoma subtypes and normal tissues, across major cancer stages, and between metastatic and primary cancer cell lines were analyzed with one-way ANOVA. Survival analyses were performed using a Log-rank test (Mantel-Cox test) [CDH1 mRNA and E-cad protein expression was analyzed using both Pearson correlation analysis and Spearman correlation analysis [The difference in ox test) , 31. Theox test) –37, 40. analysis , 38.CDH1 transcription in clinical tissue samples, we assessed mRNA levels in carcinoma and normal tissues in various parts of the body from the TCGA and GTEx databases using the GEPIA2 web server (http://gepia2.cancer-pku.cn/#index). Surprisingly, most carcinoma tissues did not show significant reductions in CDH1 mRNA levels compared to normal tissues  were upregulated  and endometrial carcinoma (148x), compared to corresponding normal tissues . Consistent with the data on tumor tissues  exhibited medium levels, and 17 (31%) cell lines showed low or no detection of CDH1 mRNA  compared to corresponding normal tissues  [CDH1 mRNA expression profile  . All theile Fig. A-B and wile Fig. .Fig. 5ImSince the E-cad protein is postulated to play a critical role during the transition from primary tumors to metastatic tumors in carcinomas , 53, 54,CDH1 gene expression in cancer cells has been widely reported [CDH1 mRNA and E-cad protein were downregulated in certain carcinomas, particularly kidney carcinoma , cluster 3 (m3), and cluster 4 (m4), but remained unchanged in cluster 1 (m1) compared to normal tissues  kidney carcinoma cell lines exhibited moderate to high levels of CDH1 mRNA . CDH1 promoter methylation was significantly higher in kidney primary tumors than in normal tissues  fared significantly better than those with tumors that expressed lower levels of CDH1 mRNA  with better OS and DFS     or 3D scaffold cell culture with a focus on single-cell invasion/migration [CDH1/E-cad expression is not significantly downregulated when primary tumors progress into metastatic tumors  and basal (T=65) compared to the normal tissues (N=171) (A) and in the three subtypes of lung carcinoma: proximal inflammatory (T=78), proximal proliferative (T=58), and terminal respiratory unit (T=68) compared to the normal tissues (N=347) (B). Grey, normal; red, carcinoma in (A) and (B). The levels of CDH1 mRNA remain unchanged across the major stages of colon (C), pancreatic (D), lung (E), and endometrial (F) carcinomas. **, p < 0.01. SupplementaryFigure 2. E-cad protein is either upregulated or remains unchanged in most major tumor stages. E-cad levels are either significantly upregulated or remain unchanged in distinct stages of endometrial carcinoma (A), lung carcinoma (B), ovarian carcinoma (C), head/neck carcinoma (D), and colon carcinoma (E and F) except for stage 1 of head/neck carcinoma (D) compared to the corresponding normal tissues. Numbers of tissues used in the analysis are: endometrial carcinoma: normal, n=31, stage 1,n=74, stage 2, n=8, stage 3, n=15; lung cancer: normal, n=11, stage 1, n=59, stage 2, n=30, stage 3, n=21; ovarian cancer: normal, n=25, stage 1, n=2, stage 3, n=75, stage 4, n=16; head/neck carcinoma: normal, n=71, stage 1, n=7, stage 2, n=25, stage 3, n=75, stage 4, n=4; colon carcinoma: normal, n=100, mucinous, n=19, non-mucinous, n=77, stage 1, n=10, stage 2, n=39, stage 3, n=40, stage 4, n=8. *, p < 0.05, **, p < 0.01.Additional file 2: Supplementary Table 1. List of carcinoma cell lines used for CDH1 mRNA expression analysis in Table Supplementary Table 2. List of carcinoma cell lines used for E-cad expression analysis in Table Supplementary Table 3. List of carcinoma cell lines used for Pearson analysis and Spearman analysis in Table Supplementary Table 4. List of cancer cell lines used for CDH1 mRNA expression analysis across different types of cancers in Fig. Supplementary Table 5. List of carcinoma cell lines used for E-cad proteomics analysis comparing metastic vs primary tumor derived cells lines in Fig. Supplementary Table 6. List of carcinoma cell lines used for the E-cad and CDH1 mRNA correlation analysis in Fig.


E mode,was applied to investigate the metabolome of durum wheat in responseto sustainable treatments, i.e., the addition of biochar, commercialplant growth promoting microbes, and their combination. Partial leastsquares-discriminant analysis provided a good discrimination amongtreatments with sensitivity, specificity, and a non-error rate closeto 1. A total of 88 and 45 discriminant compounds having biological,nutritional, and technological implications were tentatively identifiedin samples grown in 2020 and 2021. The addition of biochar-biostimulantsproduced the highest up-regulation of lipids and flavonoids, withthe glycolipid desaturation being the most impacted pathway, whereascarbohydrates were mostly down-regulated. The findings achieved suggestthe safe use of the combined biochar-biostimulant treatment for sustainablewheat cultivation.Sustainable agriculture aims at achieving a healthy foodproductionwhile reducing the use of fertilizers and greenhouse gas emissionsusing biostimulants and soil amendments. Untargeted metabolomics byultra-high performance liquid chromatography–ion mobility–high-resolutionmass spectrometry, operating in a high-definition MS In contrast to targeted metabolomics, which isfocused on the identification and quantitation of a limited numberof defined metabolites, the untargeted approach aims at acquiringdata related to all ions within a certain mass range, thus providinga snapshot of most of the small molecules present in complex biologicalsamples. Advances in high-resolution mass spectrometry (HRMS) permita comprehensive profiling of food samples, representing a valuabletechnique for studying the metabolic changes developed by organismsin the presence of variable environmental factors. Ultra-high performanceliquid chromatography coupled to HRMS (UHPLC-HRMS) currently representsthe best tool to face challenges related to the complexity of metabolome.2 UHPLC provides efficient chromatographic separationof compounds, whereas HRMS is characterized by unparalleled specificity,sensitivity, and availability of large spectral databases. In addition,ion mobility spectrometry (IMS) offers great potential for improvingdepth of coverage in metabolomic studies, separating ions accordingto their collisional cross-section (CCS) and providing unique featuresfor metabolite identification.3Metabolomics is basedon cutting-edge analytical techniques providinga snapshot of small molecules present in complex biological samples.5 Dueto its nutritional relevance with 12–16% protein content, 70%carbohydrate, 1.9% fat, 1.6% fiber, 1.6% minerals, and other essentialnutrients including antioxidants,6 togetherwith the ease of handling, cooking, long shelf life, and high digestibility,durum wheat is suitable for pasta making and human consumption. Althoughit is a principal staple food for human nutrition and one of the mostimportant and extensive crop species in the Mediterranean region,the studies involving the metabolomics of durum wheat are less than10% of those related to wheat, which are mainly focused on bread wheator other Triticum species.9Metabolomicshas increasingly been applied to detect alterationsin plants exposed to different treatments.5Recently, there has been growing awarenessof the potential ofmetabolomics in environmental sciences and its management. In fact,environmental metabolomics is a rapidly maturing bioanalytical techniquethat can be applied for sustainable agriculture to understand cropresponse to stress by producing plant metabolites.12 Improved soil health is recognized as a contribution to make agriculturemore sustainable and the use of biostimulants and soil amendmentsparticipate in the development of the so-called climate smart agriculture.13 Among biostimulants, the plant growth promotingmicrobes (PGPM) can increase the bioavailability of nutrients in soil,14 while protecting crops from contaminants andpathogens.18 To improve the performance of biostimulants, it is possible to combinePGPM with soil amendments, which can affect soil fertility in termsof nutrient retention capacity and water filtration.19 Biochar, obtained by pyrolysis or pyro-gasification ofrenewable resources, is a good amendment especially when inoculatedwith PGPM, improving plant growth, yield, stress tolerance, adsorptionof nutrients, and acting as a CO2 sink.21Environmental sustainability aims to a healthy foodproductionwhile reducing the use of fertilizers and greenhouse gas emissions.25An importantissue concerns the effects of biostimulants and amendmentson food quality. The role of PGPM in modifying crop quality in termsof nutritional content and technological properties by triggeringthe synthesis of beneficial metabolites has not yet been elucidated.In fact, up until now, most of the investigations have been focusedon the role of PGPM in improving environmental stress response andcrop yield.In this study, UHPLC-IMS-HRMS was applied for the first timetoassess metabolomic response to different sustainable treatments infield trials: the addition of biochar, commercial PGPM (Micosat_F1)and a combination of biochar/Micosat_F1. Multivariate data analysiswas applied to identify biomarkers able to differentiate among treatments,highlighting the triggered metabolic pathways, and supporting thedecision-making process for a more sustainable agri-food environment.22.1LC-MS gradewater, acetonitrile, methanol, and formic acid were purchased fromHoneywell Burdick & Jackson . Leucine enkephalinstandard was from the Waters TOF G2-S Sample Kit-1 .2.2Triticum durum Desf., cultivar Svevo)was released by Produttori Sementi Bologna PSB S.p.A.  in 1996.The wheat field trials were performed at the experimental farm Stuard, closeto the city of Parma . Meteorological parameters were collecteddaily by an automatic weather station installed in the neighboringexperimental field during both growing seasons.Durum wheat  always consideringthree replicated treatments per year. The conditionstested were: (i) CTR, control condition, having the soil treatedwith only 50 kg ha–1 of N (urea) instead of theusual fertilization ha–1. All parcels were treated in the same way,with no supplement of organic fertilization ;(ii) CHAR, biochar obtained by slow pyrolysis of wood pellet,20 was applied before sowing at a rate of 0.25kg m–2 (2.5 ton ha–1) and buriedat a depth of 10 cm; (iii) Micosat_F1, granular microbial mix includingboth bacteria and fungi, provided by CCS Aosta , wasapplied at the ratio 1:1 w/w pergr of seeds (which corresponds to 20 g m–2) (compositionof Micosat_F1 is given in Table S1); (iv)CHAR_Micosat_F1, biochar with the addition of Micosat_F1 in the sameamount described for the previous conditions. The experiments wereperformed during two harvesting years, namely, 2020 and 2021.The Svevo cultivarwas sown at a density of 400 seeds m2.32.3.1v/v) werevortexed for 10 min at 2000 rpm, then the samples were extracted byultrasonic assisted solvent extraction ,Italy) at a power of 4 au at 4 °C for 30 min. After extraction,the samples were centrifuged at 14,000g for 10 minat 4 °C, and the supernatant was collected, filtered throughnylon filtering membrane 0.2 μm  andsubmitted to UHPLC- IMS-HRMS analysis. For each year, a sample obtainedby pooling the extracts of all the treatments was used as the qualitycontrol (QC) sample.About 100g of Svevo grains were randomly sampled from each condition, thenthe samples were freeze-dried, milled using Knifetec 1095  for 60 s at 4 °C, passed through a 0.5 mm sieve, andstored at −80 °C until analysis. Fifty mg of wheat flourand 2 mL of methanol 70% (2.3.22) column , maintained at 40 °C. The operating conditionswere as follows: solvent A, water with 0.1% (v/v) formic acid; and solvent B, acetonitrile with 0.1% (v/v) formic acid. The flow rate was 0.4mL min–1 and the injection volume was 2 μL.The multistep linear gradient elution started with solvent B set at2% for 2 min, followed by a linear gradient to 50% within 9 min, thento 85% in 13 min, to 95% in 19.5 min maintained for 0.5 min beforecolumn re-equilibration (4 min). Electrospray conditions were as follows:capillary voltage, 0.80 and 0.50 kV in ESI+ and ESI– respectively; cone voltage, 50 V; source temperature,150 °C; source offset, 80 V; desolvation temperature, 600 °C;cone gas, 50 L h–1; desolvation gas, 800 L h–1; nebulizer pressure, 6.5 bar. IMS-HRMS analyses wereperformed at a mass resolution of 20000 fwhm and using a traveling wave (TWIM) as a drift cell. Ion mobility resolutionwas ∼45 (Ω/ΔΩ) as reported in the literaturefor TWIMS-based instruments.26 Nitrogenwas used as the drift gas at a flow rate of 90 mL min–1, transfer wave velocity was set at 215 m s–1,wave velocity at 650 m s–1, and wave height at 40V. The Major Mix IMS/Tof Calibration Kit (Waters) - mass range: 151.1–1966.9Da; CCS: 130.4–372.6 Å2 was used for the CCScalibration in both positive and negative ion modes (Table S1). CCS calibration27 wasautomatically performed by the IntelliStart software using the MassLynxplatform. A detailed description of the calibration procedure is reportedin Table S1. The calibration settings areused throughout the analysis. A leucine enkephalin solution  with 0.1% formic acid) was used as the lockmass. Spectra were acquired operating in the data-independent High-DefinitionMSE acquisition mode using dynamic range enhancement anda collision energy ramp from 25 to 45 V for the high energy profile.Untargetedmetabolomics was performed using a binary Acquity UPLC I-Class system(Waters) coupled to a Waters Synapt G2-Si HDMS QTOF mass spectrometerequipped with an electrospray ionization (ESI) ZsprayTM (Waters) byoperating both in positive and negative ion modes. Reversed phasechromatographic separation was carried out using a Kinetex 2.6 μmPS C18 100 Å  software (Waters). Data analysis was performed byprocessing the raw data using the Progenesis QI software  as follows: auto- and manual alignment of signals, peakpeaking, deconvolution, and normalization. The following adducts wereconsidered: [M + H]28 mdatools,29 caret,30 ggpubr,31 ggfortify,32 MetabolAnalyze,33 devtools,34 and rstatix.35 PCA was performed to explore the data set and to obtainthe features able to differentiate samples belonging to the differentagro-treatments. PLS-DA followed by k-fold cross-validation was performed.Variable importance in projection  was used to rankmetabolites according to their discrimination potential (VIP score≥2) with the final aim of reducing the number of discriminatingcompounds. A fold change >1.2 and <0.8 was used for the assessmentof up- and down-regulation, respectively.Multivariate data analysis, namely, principalcomponent analysis(PCA) and Partial least squares-discriminant analysis (PLS-DA), wereperformed on Pareto scaled data using open-source software Rstudioversion 2022.12.0 Build 353 using the following packages: tidyverse,36 and DeepCCS.37 Whenpossible, a comparison of experimental CCS values was performed withthose obtained by the analysis of standards. Discriminant compoundswere analyzed in PlantCyc38 to highlightenriched metabolic pathways. The LIPID MAPS glycerophospholipid abbreviations are used here to refer to the annotated analytes.Compound identificationwas performed by comparing the spectrawith those stored in online libraries, namely, Human Metabolome Database,Food Metabolome Database, PlantCyc, Carotenoid Database, LIPID MAPS,Phenol Explorer Database, and KEGG Database, using a mass error toleranceof 5 ppm for precursor ions and 10 ppm for fragment ions. An isotopesimilarity threshold of 80% was set. To increase the annotation reliability,the CCS values obtained in this study were compared, using a 5% thresholdvalue, with those stored in homemade databases or predicted by machinelearning algorithms, also present in HMDB among which are AllCCS39Finally, compound heatmaps were computed using MATLAB release R2023a.340 In fact, this parameter can decrease the number of false positiveannotations and resolve ambiguity among isomers and isobaric species,especially by considering the matching of the CCS of the differentadduct species for the same feature (if present).41 In addition, a third separation dimension is added to thesystem, thus allowing a more effective separation of metabolites,especially for such a complex biological mixture. This additionalseparation decreases the background noise, and it is not affectedby the matrix effect,41 thus improvingthe reliability of the analysis.In this study,a metabolomic analysis on grains collected fromdurum wheat (cv Svevo) grown by applying different sustainable treatmentswas carried out to provide more insights into the effects of biostimulantsand biochar addition both on the pattern and on the amount of metabolitesin durum wheat flours. Being commercially available, with a worldwidegross market, both Micosat_F1 and biochar were selected as biostimulantand amendment, respectively. Most applications in the last years wereon potato, tomato, and other horticultural crops; however, there wasa growing interest in testing their effects also on commodity cropslike wheat, performing field trials, like in our case. To study themetabolome, the use of IMS combined with HRMS provided significantimprovements compared to the use of the sole HRMS since it allowedthe determination of the drift for each detected feature, allowingthe calculation of CCS values, which is a compound-specific parameterthat could be used as an additional identification parameter for featureannotation and compound identification.3.1E acquisitionmode, based on alternating scanning acquisitions at both low and highenergies, provided useful information on both precursor and fragmentions within a single run.42 Additionally,the capability of IMS was exploited to add an extra dimension to theseparation of complex samples, also increasing the confidence in analyteidentification via calculation of the CCS values.UHPLC-IMS-HRMSanalyses were performed to identify metabolites able to differentiateamong the applied treatments. The use of the MSTo reducethe number of features produced by IMS-HRMS and identify only thoseable to differentiate among the treatments, filtering and data reductionstrategies in terms of intra-group variability, minimum fold change,and statistical power were applied, thus obtaining a total of 3587and 4686 features for Svevo 2020 and Svevo 2021, respectively.First, an explorative PCA was carried out to detect possible clusterswithin samples: PC1 and PC2 explained 62 and 65% of the total variancefor Svevo 2020 and Svevo 2021, respectively 1.Since only a partial differentiation among thetreatments for bothharvesting years was observed, a supervised PLS-DA pattern recognitionapproach was applied. Selecting six latent variables, a good discriminationamong the treatments was feasible, as shown in 43 All calculated parameterswere close to 1, proving the good performance of the model in boththe fitting and cross-validation.VIP analysis was applied to reduce the number of discriminatorymetabolites to be identified, according to VIP score criteria >2,265, and 285 metabolites for the Svevo 2020 and Svevo 2021 sampleswere obtained, respectively. The corresponding features were submittedto identification considering the information derived from accuratemass measurements of both parent and fragment ions, fragmentationand isotopic patterns, library matching, score fit, and CCS values.As for CCS values, the match between the experimental values calculatedin this study with those contained in public databases or predictedby machine learning algorithms was used to obtain the proper levelof identification (level 2). When the injection of standards was feasible,the obtained values allowed for a level 1 identification.3.2Table S1). Glycerophospholipids, carbohydrates,and carbohydrate conjugates were the most abundant chemical classesin Svevo 2020, accounting for 18 compounds, whereas glycerophospholipids,glycerolipids, flavones and flavonoids, carbohydrates and carbohydrateconjugates, fatty acyls, carboxylic acids, and derivatives were themost abundant chemical classes in Svevo grain cultivated in 2021,accounting for 77 compounds. The annotated VIP compounds were submittedto fold change analysis to describe direction and intensity of regulation.In fact, most of the metabolites play a key role in the metabolomicprocesses; as shown in 46 As reported in previous studies, drought conditionsinduced an increase in gluten strength, whereas heat stress exertedan opposite effect.48 Our findings demonstrated a slightup-regulation for 14 out of 46 lipids (30%) when the combined treatmentCHAR_Micosat_F1 was applied to Svevo 2021 samples. A similar effectwas observed for Svevo 2020 as 53% (10 compounds out of 19) of thelipid compounds were over-expressed when the CHAR_Micosat_F1 treatmentwas applied.A total of45 and 88 metabolites belonging to different classes were tentativelyidentified in Svevo grains cultivated in 2020 and 2021, respectively.When the CHAR_Micosat_F1 treatment was used, moreup-regulatedlipids  ascompared to the Micosat_F1 treatment (37 and 9%) were observed. Dueits highly porous structure, CHAR provides a favorable habitat formicroorganisms permitting their growth in the soil environment,46 showed thatmycorrhization can enhance the expression of genes related to thelipid metabolism to maintain the integrity of cellular membranes,demonstrating that under stress conditions, plants are able to promotelipid biosynthesis, thus increasing the cell membrane thickness asa defense mechanism. Conversely, Bernardo et al.49 observed a down-accumulation of lipids in mycorrhizal rootsunder water stress conditions, ascribing this behavior to the consumptionof lipids as the primary source of C by mycorrhizal fungi. Therefore,the most impacted pathway was glycolipid desaturation  obtainedby using the combined CHAR_Micosat_F1 treatment could be consideredas a first step to produce higher quality flour for the productionof bakery products. As already observed by Bernardo et al.,49 carbohydrates were generally down-regulatedaccounting for 45 and 50% when CHAR_Micosat_F1 was applied, whereasthe use of Micosat_F1 resulted in a decrease of 27 and 38% for 2020and 2021, respectively. In particular, α-d-Xylopyranosyl-(1-6)-β-d-glucopyranosyl-(1-4)-d-glucose, istamycin B3, andmethyl-2-O-α-l-fucopyranosyl-β-d-galactoside were the down-regulated carbohydrates when bothMicosat_F1 and CHAR_Micosat_F1 treatments were applied in Svevo 2020samples. Similarly, amylopectin, methyl-2-alfa-l-fucopyranosyl-β-d-galactoside, and neokestose were expressed at low levels inSvevo 2021 samples. This could be ascribed to the use of these carbohydratesas an additional supply of C to mycorrhizal fungi. Similar findingswere also observed by Wang and co-workers,54 who evaluated the regulation of nutrient exchange between planthosts and arbuscular mycorrhizal symbiosis.It should be noted that lipids can also affectthe breadmakingquality of flour, thus playing a valuable role from a technologicalpoint of view. As demonstrated by Min and co-workers,55 Dietary fiber is important for human health, since it hasimpact on the utilization of grain and on the end-use quality, beingused for the production of different functional foods.56 It is known that small fermentable carbohydrates,among which are raffinose and neokestose, are poorly absorbed in thesmall intestine, thus arriving at the colonic lumen, where they canexert a prebiotic effect but also trigger gastrointestinal symptomsin susceptible individuals. A diet characterized by a low intake ofthese metabolites can reduce fermentation in the colon, being recommendedfor people affected by irritable bowel syndrome and inflammatory boweldiseases.57 Finally, as shown in 58 The number of compounds annotated as VIPs in the two harvestingyears was very different, with only 3 compounds, namely, apimaysin,torosaflavone C, and vitexin 2-O-α-d-arabinofuranoside belonging to Svevo 2020 samples as compared tothe 14 compounds annotated in Svevo 2021 samples. Apimaysin and torosoflavoneC  of flavones and flavonoids, with the exceptionof torosoflavone C which was up-regulated. As for Svevo 2020 samples,the up-regulation for torosoflavone C was still observed for bothCHAR_Micosat_F1 and Micosat_F1, whereas vitexin 2-As a general remark, it can be stated that the identificationofspecific pattern of metabolites for each treatment can be of paramountimportance in assessing the quality of durum wheat, including itsbeneficial effects on health, thus allowing the agri-food systemsto move toward green and climate resilient practices. Preliminaryinvestigations (data not shown) in the rhizospheric microbial populationdid not reveal any significant reduction in biodiversity when CHAR_Micosat_F1was used, thus ensuring its safe application in sustainable wheatcultivation. Further analyses dealing with soil variations, plantgrowth, and metagenomic analyses of roots are in progress to obtaindeeper insights into the plant response after interactions with microbes.Additional expected benefits based on increased use of natural amendmentsand biofertilizers will rely on (i) the reduction in both water consumptionand greenhouse gas emissions, (ii) a considerable decrease in thecarbon footprint of food production, and (iii) a reduction of soilpollution due to a lower use of fertilizers. In this context, theachieved results could pave the way for assessing harmonized conditionsbased on the use of biofertilizers combined with biochar derivingfrom agricultural and/or food processing wastes to close the gap betweenthe production and their consumption.


Type 2 diabetes mellitus (T2DM) is a global burden, with an increasing number of people affected and increasing treatment costs. The advances in research and guidelines improve the management of blood glucose and related diseases, but T2DM and its complications are still a big challenge in clinical practice. T2DM is a metabolic disorder in which insulin signaling is impaired from reaching its effectors. Mitochondria are the “powerhouses” that not only generate the energy as adenosine triphosphate (ATP) using pyruvate supplied from glucose, free fatty acid (FFA), and amino acids (AA) but also regulate multiple cellular processes such as calcium homeostasis, redox balance, and apoptosis. Mitochondrial dysfunction leads to various diseases, including cardiovascular diseases, metabolic disorders, and cancer. The mitochondria are highly dynamic in adjusting their functions according to cellular conditions. The shape, morphology, distribution, and number of mitochondria reflect their function through various processes, collectively known as mitochondrial dynamics, including mitochondrial fusion, fission, biogenesis, transport, and mitophagy. These processes determine the overall mitochondrial health and vitality. More evidence supports the idea that dysregulated mitochondrial dynamics play essential roles in the pathophysiology of insulin resistance, obesity, and T2DM, as well as imbalanced mitochondrial dynamics found in T2DM. This review updates and discusses mitochondrial dynamics and the complex interactions between it and metabolic disorders. Diabetes mellitus, known as “diabetes”, is a critical challenge for public health and a therapeutic conundrum. Diabetes comprises a collection of metabolic conditions characterized and identified by hyperglycemia due to the deficiency of insulin secretion, impaired insulin action, or both . There aThe “powerhouse of the cell,” mitochondria are cellular organelles. The word “mitochondria” comes from two Greek words that denote thread “mitos” and granule “chondros”. Most eukaryotic cells contain highly active mitochondria. It is hypothesized that mitochondria are the descendants of a prokaryote formed in a prehistoric era that underwent an endosymbiotic relationship with early eukaryotes . TransloThe dominant role of mitochondria is energy conversion. To sustain bioenergetics and cell energy metabolism, adenosine triphosphate (ATP), which is synthesized by aerobic respiration in the mitochondrion, is required. TCA cycle and oxidative phosphorylation (OXPHOS) are the main series of events involved in the generation of ATP. These molecular mechanisms regulate biological system functions that necessitate the consumption and production of energy, such as integrating multiple metabolic pathways, regulating cellular apoptosis, and perpetuating the calcium homeostasis mechanism . SimilarThe primary dynamic activities include biogenesis , transportation (directed movement inside a cell), fission (segregation of a single organelle into two heterogenous mitochondria with mtDNA replication), fusion (the combining of two organelles into one), and mitophagy (targeted elimination through the autophagic pathway) ,9,10,11.“Mitochondrial dysfunction” is caused by a metabolic imbalance of nutrient signal intake, energy production, and/or oxidative respiration , which iThe ultrastructure of the mitochondrion reflects its biological, cellular, and molecular functions. Mitochondrial dynamics, which regulate the functional equilibrium of the mitochondria, is essential for mitochondrial functioning, energetics, mobility, and its host cell’s fate. Mitochondrial dynamics vary across cell types and tissues, but the critical protein machinery that drives the process has been remarkably preserved through evolution . ReductiMitochondrial biogenesis (MB) occurs in healthy eukaryotic cells to generate new mitochondria and increase the number of mitochondria via dividing the pre-existing ones, which already fully grow and their DNA replication is fully completed . This pr+-dependent histone/protein deacetylase) through nicotinamide phosphoribosyl transferase which in turn increases NAD+ levels [+/NADH ratio leads to the phosphorylation of SIRT1 at thr-522 residue. Phosphorylated SIRT1 activates PGC-1α by removing the acetyl group on it. Furthermore, the MB is initiated in response to cellular stress, such as oxidative stimulus, to an increase in the energy requirements of the cells [Among the PGC-1α activators, AMPK and SIRT1, the two metabolic sensors of the cells, are the two major pathways regulating mitochondrial biogenesis . AMPK is+ levels ,42. In c+ levels . SIRT1 a+ levels ,45. A hihe cells  and extehe cells ,48,49. ATfam gene transcription. Meanwhile, TAZ promotes the translation of Tfam mRNA via Ras homolog enriched in brain (Rheb)/Rheb-like-1 (Rhebl1)- Mammalian target of rapamycin complex 1 (mTORC1) axis. TAZ also plays a vital role in the translational regulation of other mitochondrial genes [A recent study discovered a novel stimulator of MB called a transcriptional coactivator with PDZ-binding motif (TAZ) . PGC-1α al genes ,50,51. MMitochondrial transport through the cytoskeleton is crucial for normal mitochondrial morphology, network, motility, and distribution . The cytMitochondrial fusion is a complex regulatory mechanism involving several proteins’ binding from multiple mitochondrion membranes . When ceMitochondrial fission segregates a mitochondrion into two heterogenous mitochondria without mDNA  replication . FissionMitophagy , also knThe most well-known mitophagy pathway that controls mitochondrial maintenance and quality control is the PINK1/PAKN mitophagy pathway . PINK1 iT2DM and its vascular complications are associated with mitochondrial dynamics. Mitochondria acts as an important regulator of insulin secretion. In addition, Mitochondria are the primary generators of ROS and play a central role in cellular apoptosis and cell death. High glucose levels in T2DM increase ROS production ,83,84, pInsulin signaling promotes the expression of Opa-1 protein via the Akt-mTOR-NFkB-Opa-1 pathway, leading to mitochondrial fusion . In T2DMob/ob mice (obese mice model) exhibit a fusion-to-fission balance reflected by the increase in Drp-1 and the decrease in MFN2 and OPA1 protein expression, decreased mitochondrial biogenesis evidenced by a reduced content of mitochondrial DNA and PGC-1α mRNA expression. Treatment with either leptin or mitochondrial division inhibitor (mdivi-1) improved blood glucose levels, lipid oxidation, and mitochondrial function by storing mitochondrial dynamics balance [White adipose tissue derived from  balance .Moreover, T2D dyslipidemia models exhibit elevated mitochondrial fission due to mitochondrial depolarization, decreased ATP synthesis, elevated oxidative stress, and decreased insulin-stimulated glucose uptake. Insulin resistance and mitochondrial fragmentation brought on by excessive palmitate were reduced by both genetic and pharmaceutical suppression of DRP1 . In anot2+ and AMPK/SIRT1 signaling [2+ and AMPK/SIRT1 signaling  [Chronic exposure to high levels of glucose, FA, and amino acids damages β-cells. It impairs glucose-stimulated insulin secretion due to mitochondrial fragmentation, which decreases the production of ATP (increased ATP/ADP ratio close ATP-sensitive Kβ-cells) . The previous study in cybrid b4 discovered that overexpression of MFN1/2 or DRP1/FIS1 knockdown increased mitochondrial fusion, insulin signaling, and Glu4 translocation to membranes . In contA receptors, respectively [2+ overload, and decreased α-MSH release, but glucose sensing, neuronal activation, and hypothalamic ROS production are not changed [In mammals, the hypothalamus and dorsal vagal complex (DVC) have been implicated as crucial regulators of whole-body metabolism ,150,162.ectively ,178,179.ectively ,182,183 ectively ,185,186.ectively ,190,191.ectively , and mitectively . Schneeb changed ,194. DRP changed . More de changed .Recent studies also found that under excessive nutrients, Drp1 is activated, mediates mitochondrial fission in DVC neurons, and induces insulin resistance . InhibitThe activation of Drp1 and mitochondrial fission requires the functional integration between phosphorylations at S600 and at S579 sites of Drp1 . S600 ph2+/CaMK/calcineurin, AMPK/SIRT1, NO, and Akt signalings and enhance PGC-1α transcription through CaMK, calcineurin, and Akt pathways , stimulating cytokine production , leading to increased lipolysis, nonesterified fatty acid, glycerol levels, gluconeogenesis, and de novo lipogenesis, finally develop insulin resistance and diabetes. In addition, NF-κB also causes p62 protein/sequestosome 1 (p62/SQSTM1) accumulation, recruiting damaged mitochondria with polyubiquitin chains and thereby inducing excessive mitophagy. These effects of ROS are ameliorated by the administration of ROS scavenger or NF-κB inhibitor .+-dependent. Class I HDACs  are ubiquitously distributed in the nucleus. Class II is further subdivided into class IIa  and class IIb (HDAC6 and HDAC10). Class IIa HDACs shuttle between the cytoplasm and the nucleus with minimal catalytic activity. HDACs were recently identified as a potential therapeutic target for diabetes [Histone deacetylase (HDACs), one of the critical epigenetic regulators, has been shown to improve multiple metabolic disorders and regulate mitochondrial biogenesis and function. There are 18 mammalian HDAC isoforms that were identified and classified into four classes according to their sequence homology with yeast. The catalytic activity of classes I, II, and IV HDACs requires cofactor zinc ions, whereas class III HDACs are NADdiabetes ,223,224.diabetes ,226. Mordiabetes .+) in dopaminergic neurons via inhibiting histone deacetylation of the Mfn2 promoter and avoiding transcriptional repression of Mfn2 [HDACs also regulate mitochondrial dynamics via deacetylating fission and fusion machinery and biogenesis via decreased expression of the primary regulator of MB, PGC-1a. The pan HDAC inhibitor SAHA induces mitochondrial elongation via decreasing expression of mitochondrial fission protein Fis1 and reduces the translocation of Drp1 to the mitochondria in Hep3B cells . TSA als of Mfn2 . Additio of Mfn2 . Therefo of Mfn2 . AccordiMitochondrial dynamics and metabolic disorders are commonly seen in overnutrition. Targeting mitochondrial dynamics (such as HDAC inhibition) via promoting mitochondrial biogenesis, restoration of mitochondrial fusion/fission balance, and mitophagy intervention are potential strategies for improving insulin resistance and T2DM. More clinical studies are needed to evaluate the efficacy of these interventions on human beings.


These results show theoverall better performance of NRTL-SAC in a hybrid correlation/predictionapproach, while COSMO-RS can produce very satisfactory predictionseven in the absence of any experimental data.The Abraham and NRTL-SAC semipredictive models were employedtorepresent the solubility of (−)-borneol, (1R)-(+)-camphor,  From the pool of relevant monoterpenoids,four commercially important representatives were selected: (−)-borneol,(1R)-(+)-camphor, l-(−)-menthol, and thymol.Monoterpenoids constitutea structurally diverse class of compoundsabundantly found in essential oils.7 Present in Cinnamomum camphora species,camphor has been widely used in traditional medicine to treat severaldiseases, such as rheumatism, chest congestion, muscle pain, asthma,and bronchitis.6 In modern medicine, thissubstance is an ingredient of analgesics and rubefacients, althoughits overdose ingestion or skin penetration might cause severe toxiceffects or even death.7 Another bicyclicmonoterpenoid, borneol, is a monoterpene alcohol used as a fragranceingredient in personal care and cleaning products.8 This compound also presents relevant pharmacological andbiological properties, such as analgesic, antibacterial, anti-inflammatory,and antioxidant activities.9 It has beenextensively investigated as a potential permeation enhancer to facilitatedrug delivery through different biological barriers.11Camphor is one of the chief representatives of bicyclic monoterpeneketones, having several applications in the pharmaceutical field.13 It is a cyclic monoterpene alcohol produced by numerous plants fromthe Lamiaceae family. It is broadly used in medicineto relieve localized pain and respiratory disorders due to its “cool”characteristic sensation triggered by its interactions with thermoreceptorsin the human skin.14 Recently, the potential of mentholand thymol for the formulation of some innovative deep eutectic solvents17 has been demonstrated. Thymol, a phenolic monoterpenoid, presentsvarious applications in medicine, dentistry, food, and agrochemicalindustries.18 Because of its numerous bioactivities, thymolhas been extensively studied as a therapeutical agent for wound healing.21 Besides, thymol brings several nutritional and biological benefitswhen incorporated as a feed additive in fish production.22Menthol is widely employed as an aroma ingredient in cosmetics,perfumes, household cleaners, and detergents.23 and octanol-water partitioncoefficients24 of a diverse set of monoterpenoids,this workaims to investigate the solubility of (−)-borneol, (1R)-(+)-camphor, l-(−)-menthol, and thymol in seven organic solvents -limonene, and 1,2-propanediol).For (−)-borneol, (1R)-(+)-camphor, and thymol, solubility measurementswere carried out at 298.2 and 313.2 K, while for l-(−)-mentholwith a melting point of 315.6 K,25 experimentswere only performed at 298.2 K. The solubility of (−)-borneolin water, not examined in our previous works, was also obtained hereat 298.2 and 313.2 K.Following our previous studies on watersolubilities27 was applied to calculatethe monoterpenoids solubility in organic solvents at 298.2 K,30 while the Nonrandom Two-Liquid Segment Activity Coefficient (NRTL-SAC)model,32 and the Conductor-like Screening Model forReal Solvents (COSMO-RS)35 were employed to represent theavailable solubility data in water and organic solvents at differenttemperatures.37Finally, three well-established thermodynamicmodels were appliedto describe the solubility change with solvent and temperature. TheAbraham solvation model22.1Tm) and melting enthalpies (ΔmH)are also provided. The organic compounds were used as received, andthe solids were kept in the desiccator to prevent water contamination.Ultrapure water  was used in the solubility studies of (−)-borneol.All of the monoterpenoidsand organic solvents used in the solubility experiments are listedin 2.240 In brief, around 50 mL of saturated solutions of monoterpenoid andsolvent, containing small amounts of solid in excess, were preparedin all glass flasks covered with aluminum foil to prevent light degradation.The flasks were placed on magnetic stirrers  inside a heated water circulating bath equipped with a precise temperature control system (±0.1 K).Preliminary experiments demonstrated that the equilibrium was achievedafter 24 h of continuous stirring, and 16 h of settling was enoughfor most suspended solid particles to deposit.The solubilityexperiments were conducted using the isothermal shake-flask method,which was described in detail previously.3, were collected usingpreviously heated glass syringes coupled to nylon or polytetrafluoroethylenefilters (0.45 μm pore diameter). After, the samples were analyzedby UV–vis spectroscopy or gas chromatography (GC). The analyticalprocedures are presented in Section S1 inthe Supporting Information (SI). Each reported solubility value isthe average of at least three independent readings.At least threesamples, between 0.2 and 0.5 cm33.1Ss) and in water (Sw) throughthe following linear free energy relationship (LFER)27E, S, A, B, and V) and the lowercase descriptors  represent the Abraham descriptors for the solute andsolvent, respectively.The Abrahamsolvation model describes the ratio of the solubilities (in molarconcentration basis) of a solute in an organic solvent , A , and B , are frequently regressed usinga set of experimental solubility and/or partition coefficient data.Abraham’s research group has alreadyreported the model parameters for several solvents.3.250 and was described in detail by Chen and co-authors.32 In this approach, the activity coefficient of solute i is calculated as the sum of the combinatorial (γiC) and the residual (γiR) contributionsThe semipredictiveNRTL-SAC model has been extensively applied to describe the solubilityof biomolecules in water and organic solventsX), hydrophobicity (Z), polar-attractive (Y–), and polar-repulsive (Y+). These parameters are available in the literature formany solvents,50 including those addressed in this work.Therefore, only the solute descriptors are required to estimate thesolubilities with the NRTL-SAC, which will be regressed using a smallset of the experimental solubility data.In summary, the model characterizesthe solute and solvent molecules by four conceptual segments representingdifferent surface interactions: hydrophilicity (i) in a liquid mixturecan be described by51xi is the mole fraction solubility of solute i at temperature T, ΔmHi and Tim, are its melting enthalpy and temperature, respectively,ΔCip, is its heatcapacity change upon melting, γi is its activity coefficient, and R is the idealgas constant. The contribution of the ΔCip, term on the calculation of xi is often negligible,51 and The solubility of asolute -menthol and thymol, 39 and are listed in Inthis work, 3.3COSMO-RS estimatesactivity coefficients in liquid mixtures, which can be easily coupledwith 54 with the BP_TZVPD_FINE_21.ctdparametrization. This parametrization requires sigma surfaces optimizedusing the def2-TZVPD basis set, the BP-86 DFT functional, and theCOSMO solvation model with infinite permittivity. When available,these were collected from the COSMOtherm TZVPD-FINE database. Thosesigma profiles not available in the database (monoterpenoids and somesolvents) were computed using the TmoleX (version 4.5) software package,55 coupled with COSMOconfX 2021 for initial conformergeneration (BP-TZVPD-FINE-COSMO+GAS_18 template). All conformers obtainedwere considered to perform the calculations in COSMOtherm. More detailsare available in the COSMOtherm reference manual.56COSMO-RS was employedin this work through its implementation in the COSMOtherm softwarepackage (version 21.0),3.4n is the total number of data points,and i covers all of the solvent systems for a givensolute.Thedeviations between the experimental and calculated solubilities wereassessed by the average relative deviation (ARD)44.1l-(−)-menthol,and thymol in the selected organic solvents -limonene, and 1,2-propanediol)are presented in Tables S1 and S2 in the SI, respectively. At this point, itis critical to observe the impact of solid–solid transitionson the ideal solubility and, therefore, on the activity coefficients.While for (1R)-(+)-camphor the impact is almost null, which is consistentwith the very low transition enthalpy, for (−)-borneol, at298.2 K, the ideal solubility would be 0.327, 20.2% higher than thatpresented in The solubilitiesof (−)-borneol, (1R)-(+)-camphor, The good consistency of the solubility values is demonstratedbythe low coefficient of variation, lower than 2.5% for organic systemsand 3.7% for the (−)-borneol/water system.24 in all other systems studied at both temperatures,the solubility increases with the temperature. In general, the solubilitiesof the monoterpenoids in organic solvents are much higher than inwater, with differences varying from 2 orders of magnitudes for (−)-borneolto 4 orders of magnitude for l-(−)-menthol. The idealmole fraction solubility values listed in 3 for (−)-borneolat 298.2 K to 1.7 × 104 of l-(−)-mentholat 298.2 K.Excepting(1R)-(+)-camphor/water system, where the observed solubilityat 313.2 K is 3 times lower than the value obtained at 298.2 K,l-(−)-mentholis more soluble in all of the organic solvents than (−)-borneol,at 298.2 K. This behavior is in line with the ideal solubility trend. Moreover, they exhibitpositive deviations from ideality for most studied binary mixtures,except for (−)-borneol in ethanol (at 298.2 K) or 1-butanol.Among the organic solvents, the highest and the lowest solubilitiesof both solutes were observed for 1-butanol and acetonitrile, respectively.Concerning the monoterpene alcohols, 57 itcan be said that solvents of intermediate polarity and small hydrogen-bondcharacter favor camphor solubilization. Different from the other studiedsolutes, which present one HBA and one HBD site, (1R)-(+)-camphorhas only one HB acceptor site (from the carbonyl group). Therefore,solvents with high Hansen HB parameters, such as 1,2-propanediol,might not be as appropriate for solubilizing this monoterpene ketone.Excepting for the solubilities of (1R)-(+)-camphor in 1,2-propanedioland ethanol (at 298.2 K), the solubilities are higher than the idealvalue, and consequently, the activity coefficients are lower than1. The highest solubilities occur in R-(+)-limonene, ethyl acetate,and acetonitrile, whereas 1,2-propanediol is the organic solvent withthe poorest affinity to this monoterpene ketone. Taking into accountthe Hansen solubility parameters,l-(−)-menthol,the solubilities of thymol in the organic solvents are quite high,probably related to their low melting temperature and moderate meltingenthalpies, and the favorable interactions in the solution.37 However, although thymol and menthol are structurally very similar,the electron resonance of thymol enhances the HBD capability  of its hydroxyl group.58 Thisleads to better thymol–solvent interactions and, thus, smalleractivity coefficients than its menthol counterpart (with the exceptionof the fully apolar hexane and limonene). At 313.2 K, around 9 K belowthe melting point of thymol, its lowest observed solubility (in organicsolvents) is x = 0.807 (in R-(+)-limonene), whichis very close to the ideal solubility , and higher than the solubility measured for all othermonoterpenoids.Regarding the phenolic thymol, the highest solubility values areobserved in acetonitrile, ethyl acetate, and 1,2-propanediol. In contrast,the lowest values among the organics are registered in the nonpolarhexane and R-(+)-limonene. Similar to 4.260 overviewedin Table S3 in the SI. The solubilitiesof thymol in acetonitrile, 1-butanol, ethanol, and limonene are comparedin l-(−)-mentholin the studied organic solvents are reported here for the first time.Wheneverpossible, the experimental data obtained in this work were comparedto the scarce literature data30 For these systems, an averagerelative deviation  of 4.4% is found. Forthe thymol/ethanol mixture, the solubility curve reported by Bermejoet al.36 is considerably different fromthe remaining data. Regarding the thymol/R-(+)-limonene, the valueobtained in this work at 313.2 K is 14% higher than the value foundby Bermejo and co-authors.36As can be seen in 29 Other data are available,60 though the stereoisomer of thesolute was not specified. As discussed by Coquerel,61 the solubility behavior of a specific enantiomer or a racemicmixture in a given solvent system might be quite distinct, and a directcomparison of the available solubility data obtained in this workand those reported by Chen et al.29 isnot straightforward. Nonetheless, in those cases, the mole fractionwater solubility obtained in this work for (−)-borneol (9.9× 10–5) is consistent with the literature average60 of the values presented in Table S3 (8.33× 10–5). Similarly, moderate relative deviationsare observed between the solubilities of camphor measured in thiswork and those reported by Lin and Nash28 (at 298.2 K) in ethyl acetate (deviation = 7.5%), hexane (deviation= 18.2%), and 1,2-propylene glycol (deviation = 11.5%).Forborneol and camphor, a literature search showed that temperature-dependentsolubility curves were only available for their racemic mixtures inethanol.4.3E, S, A, B, and V) have been previously reported65 and are presented in Table S4 in the SI. Nevertheless, poor descriptionsof the experimental data obtained in this work were found using thedescriptors available in the literature, with ARD often superior to40% (Table S4). The isomeric form of thesolute was not specified, and the available descriptors were probablyregressed from partition coefficient data only since most solubilitydata available in the organic solvents were reported recently.36 Despite the recommendations interms of the solute concentration range adequate for this model, thedescriptors were regressed in this work for the solutes using a similarstrategy applied in a previous work of our group.40For all ofthe studied solutes, the model descriptors (xiexp) were converted to the molar basis (Siexp) using the expressionV is the molar volume(dm3·mol–1), calculated from densitydata collected from the literature.Since the Abraham solvation model was developed basedon molar solubilities, the experimental mole fraction solubilities were obtained by simultaneouslysolving a reduced set of LFERs (A) was set to 0 since no HB-donorsite is available in its chemical structure. The estimated Abrahamsolute descriptors are presented in The descriptors of LFERs 1 using t40 The best description was obtainedfor (−)-borneol (ARD = 12%), while the biggest deviations areobserved for l-(−)-menthol (ARD = 36%) and thymol(ARD = 33%). The latter solutes generally present higher solubilitiesin the studied organic solvents than the other two monoterpenoids(with only a few exceptions), while values registered for (−)-borneolare typically the lowest. This confirms that the Abraham model ismore suitable for representing moderate to low solubility data values.27A global ARD of 27% was obtained in the estimationof the modeldescriptors, which is comparable to the results found in our previouswork.E descriptors areclose to those found in the literature,65 but larger differences can beobserved for S, A, and B, particularly for thymol, where differences around 0.2–0.4units are observed.63 For the other solutes,the model descriptors obtained in this work usually deviate less than0.2 units from the values listed in Table S4. It is relevant to mention that the descriptors reported by Abrahamand co-authors63 were estimated using reducedsets of experimental partition coefficients data, which might notentirely cover the solvation behavior of the saturated conditionsaddressed here. On the other hand, the methodology is highly dependenton the available solubility values in water, which were updated withnew information in this work.In most cases, the estimated 30). A complete overview of the correlation andpredicted results is presented in The parameters presented in l-(−)-menthol and thymol, butin acetonitrile, the results are also poor. Nonetheless, the modelusually delivers very good solubility estimations in ethanol, ethylacetate, and hexane. Moreover, as shown in The highest relative deviations in the correlationstep were foundin 1,2-propanediol for 4.4X, Y–, Y+, Z) was carried out using the nonlinearleast-squares method. The objective function to minimize was definedas the global ARD , at 298.2 and 313.2K, were used. After the predictive capability of the model was alsochecked by estimating the solubilities in 1-butanol, R-(+)-limonene,and in other solvents found in the literature.36 Again, the solubility data of the racemic mixtures,29 or of stereoisomers that were not identified,28 were not included in the predicted set due tothe solubility dependency on the solute’s phase transitionproperties -menthol and thymol(ARD lower than 8%) during correlation, and even lower for the predictions,where ARD is close to 4% for both solutes. In the case of (−)-borneol,ARDs of 19 and 26% were obtained in the correlation and prediction,respectively.A very good description of the available solubilitydata for themonoterpenoids is obtained using the NRTL-SAC model, with global ARDsobtained in the correlation (15%) and prediction (14%). Excellentresults were obtained for 67 Particularly for water, the NRTL-SAC isnot capable of predicting the reduction in the solubility of camphoras the temperature rises from 298.2 to 313.2 K  than the experimental value (xexp = 1.4 × 10–4) at298.2 K.24 Notably, for such small solubilityvalues, the model delivers very reliable water solubility estimationsfor all other monoterpenoids, as illustrated in Figure S1, with a global ARD of 11% considering the four solutes.Among the four monoterpenoids, the overall weakestperformanceis for (1R)-(+)-camphor, with an ARD of 27 and 21% for correlationand prediction, respectively. Although these deviations are superiorto those obtained for all other systems, they are in line with orbetter than those reported in previous works for other biomolecules.shown in 2, provid4.537 are compared with the values obtained withCOSMO-RS. The COSMO-RS model delivers a very good representation ofthe solubility data of the monoterpenoids in organic solvents, showinga global ARD of 16%. High-quality predictions were obtained for thymol(ARD = 7%), (−)-borneol (ARD = 8%), and l-(−)-menthol(ARD = 13%). For these solutes, worse results were observed for l-(−)-menthol + acetonitrile and thymol/(−)-borneol+ hexane. In particular for thymol, a remarkably low ARD of 2.5% isobserved for the available solubility data at 313.2 K, showing thatCOSMO-RS can deliver reliable predictions considering the solubilitychange with temperature.In Concerning the solubility of (1R)-(+)-camphor inorganic solvents,the calculated global ARD is 37%. In Figure S2 in the SI. For these systems,the best solubility description is achieved for l-(−)-menthol,followed by thymol, (−)-borneol, and (1R)-(+)-camphor. Thevalues are now overestimated, and excepting (1R)-(+)-camphor, themodel provides water solubilities of the same order of magnitude asthe experimental ones. As discussed in our previous work,24 the calculation of monoterpenoids solubilityin aqueous systems, using either semiempirical or predictive models,is a challenging task. Nevertheless, the predictive COSMO-RS oftenprovides better performances than other more empirical options.The experimental and predicted solubilities in water are depictedin 37 and measured in this work. The temperaturesolubility curves are depicted in Figure S3 in the SI, while a comparison of the experimental and calculatedsolubilities is presented in Since appropriate descriptions of the solubility data at 298.2and 313.2 K were obtained with both NRTL-SAC and COSMO-RS models,they were tested to represent the temperature solubility curves ofthymol using data available in the literatureIn general, both models offer a qualitative pictureof the solubilitycurves, with NRTL-SAC (ARD = 9%) performing better than COSMO-RS (ARD= 17%). Two lines representing ARD limits of 15% were included in 5l-(−)-menthol in the same set of organic solventswere investigated at 298.2 K. The Abraham solvation and NRTL-SAC modelswere successfully applied to describe the solubility data measuredhere, and most data were retrieved from the open literature. In thecase of the Abraham model, global ARD of 27 and 8% were obtained inthe correlation and prediction steps, respectively, while the lowestglobal deviations were observed for camphor and borneol. Regardingthe NRTL-SAC model, the ARD for the solutes varied between 7 and 27%in the correlation and between 4 and 26% in the prediction, consideringthe available solubility at 298.2 and 313.2 K, being the best performancesfound for thymol and l-(−)-menthol (ARDs = 6%). Nevertheless,the use of these models depends on the availability of the soluteparameters or a small set of experimental data to estimate them. Moreover,the absence of parameters for some solvents  precludes covering all of the solubility data.New experimental solubilitydata of three monoterpenoids ((−)-borneol,(1R)-(+)-camphor, and thymol) in acetonitrile, 1-butanol, ethanol,ethyl acetate, hexane, R-(+)-limonene, and 1,2-propanediol, at 298.2and 313.2 K, are reported. For (−)-borneol, experimental watersolubilities at 298.2 and 312.2 K were also measured. The solubilitiesof On theother hand, COSMO-RS offers a good representation of thesolubility data in organic solvents at 298.2 and 313.2 K, presentingARDs between 7% (thymol) and 37% ((1R)-(+)-camphor), and a globalARD of 16%. The overall performance of COSMO-RS in describing thesolubilities of the monoterpenoids was comparable to those registeredby other hybrid approaches, which is a remarkable achievement of thisfully predictive model that requires only structural information fromthe components present in the mixture.


Thinopyrum elongatum Fhb7E locus has been proven to confer outstanding resistance to Fusarium Head Blight (FHB) when transferred into wheat, minimizing yield loss and mycotoxin accumulation in grains. Despite their biological relevance and breeding implications, the molecular mechanisms underlying the resistant phenotype associated with Fhb7E have not been fully uncovered. To gain a broader understanding of processes involved in this complex plant–pathogen interaction, we analysed via untargeted metabolomics durum wheat (DW) rachises and grains upon spike inoculation with Fusarium graminearum (Fg) and water. The employment of DW near-isogenic recombinant lines carrying or lacking the Th. elongatum chromosome 7E region including Fhb7E on their 7AL arm, allowed clear-cut distinction between differentially accumulated disease-related metabolites. Besides confirming the rachis as key site of the main metabolic shift in plant response to FHB, and the upregulation of defence pathways  leading to antioxidants and lignin accumulation, novel insights were revealed. Fhb7E conferred constitutive and early-induced defence response, in which specific importance of polyamine biosynthesis, glutathione and vitamin B6 metabolisms, along with presence of multiple routes for deoxynivalenol detoxification, was highlighted. The results suggested Fhb7E to correspond to a compound locus, triggering a multi-faceted plant response to Fg, effectively limiting Fg growth and mycotoxin production.The  Triticum aestivum L., 2n = 6x = 42) and tetraploid durum wheat , covers the largest harvested area on earth of cereal species, ahead of maize and rice [Wheat, including hexaploid bread wheat , for whand rice ,15,16,17and rice ) and resand rice .Fusarium graminearum (Fg), the most prominent FHB causal agent . In wheat, either or both DON detoxification strategies turn out to be activated as part of the response to FHB , is,, is,Fusapathogen , having pathogen ,14. Fungpathogen ,22. Anthe . Ante e.g., ,34), as , as Fusae  and of their interaction with a network of background genes/QTL in addition to that with the environment. In wheat, over 600 QTL regions, distributed on all chromosomes, were found to be involved in the genetic and functional architecture of FHB resistance ,16,40. Asome arm , the Fhbo reside ,46,47,48 elusive ,50, mosttivities ,51,52,53nversion  remains on [Fhb1 ,34,51,52 profile . On the le [Fhb2 , anotherle [Fhb2 ,55. Thinopyrum genus [Thinopyrum species, one originating from the el2 accession of decaploid Th. ponticum, and the other from diploid Th. elongatum. The two QTL are likely orthologous, based on genetic mapping along their homoeologous group 7 long (L) arms, 7el2L and 7EL, respectively [2Fhb7el . Twoectively ,61,62, aas Fhb7E ,63,64,65um wheat ,62,66.2 Th. ponticum Fhb7-resistant phenotype [Th. elongatum (E genome) accession (D-3458) taken as reference was proven to have a nearly identical (98%) homolog within a 245 kb 7el2L region, while no homolog was found in a collinear region of a 7el1L arm of a susceptible Th. ponticum accession. Expression analyses showed the GST gene to be constitutively expressed in all plant tissues examined and highly induced upon Fg spike inoculation in 7el2-resistant lines and in the D-3458 Th. elongatum accession. The presence of the Fhb7 candidate, in contrast to its absence, was found to cause formation of a de-epoxidated DON–GSH adduct, which irreversibly impairs the toxin function [Fhb7 in the genomes of other Triticeae and plant species in general. Instead, homologs sharing up to 97% identity were found in the genomes of endophytic fungi of the Epichloë genus, which led to the hypothesis that the two Thinopyrum species had acquired the critical gene for trichothecene detoxification via horizontal transfer (HGT). A similar GST transcript to that of Fhb7 was subsequently identified in the distal end of the Th. elongatum 7EL arm added to bread wheat cv. Chinese Spring (CS), i.e., CS-7EL [Th. elongatum accession, distinct from the D-3458 of Wang et al. [GST sequences, a preliminary expression analysis revealed the Dvorak74-GST gene to be among the few 7EL-specific transcripts strongly upregulated by Fg infection [GST gene showing high homology or complete identity with the 7el2 Th. ponticum Fhb7 candidate was also detected in various wheat Th. ponticum translocation lines and partial amphiploids [Thinopyrum spp. GST gene did not match in all cases the FHB phenotype following Fg inoculation, with some translocation and transgenic lines for the gene in question being fully susceptible. This evidence led the authors to exclude the 7EL GST gene, and so its 7el2 homolog (Fhb7) was found to be the pivotal gene in conferring FHB resistance to wheat [Fg infection, several other transcripts with the potential to contribute to FHB resistance were detected in the 7EL distal region [A GST-encoding gene was claimed to underlie the 7elhenotype . The genfunction . Wang etfunction  reported, CS-7EL . This 7E, CS-7EL , belongsg et al. . Despitenfection . The prehiploids ,64. Unexto wheat ,64. In fl region ,65. Aparl region ,69, fortl region , defenceFhb7E-linked resistant phenotype. Yet, the picture is still incomplete. Moreover, results so far produced derive mostly from analysis of either the complete 7E chromosome or its entire long arm (carrying Fhb7E) added to the background of the moderately susceptible CS wheat [Th. elongatum 7EL  or susceptible (Fhb7E−) to Fg spike point inoculation [Fhb7E locus on the host (DW) metabolic makeup, both constitutive and induced by Fg challenge, we applied an untargeted metabolomic approach to the critical rachis tissue for FHB development and also to the mature grain of Fhb7E contrasting NIRLs. The results indicate several differentially represented metabolites and distinctively perturbed metabolic pathways in association with Fhb7E, altogether highlighting unique features of this locus besides for common elements to other major FHB resistance loci.The above reported studies provide evidence of some contributing factors and routes likely underlying the CS wheat ,69,70, c CS-7EL, ), previoculation . To haveThinopyrum ssp. homozygous recombinant line R69-9/R5+ and its control sister line R69-9/R5−, were used in the present study [Th. ponticum (7el1) and Th. elongatum (7E) chromatin (Th. ponticum 7el1 portion is known to harbour genes for leaf rust resistance (Lr19) and yellow semolina colour (Yp) and evidently no gene contributing to FHB resistance, as from the phenotype of the primary recombinant line (R5+) carrying 7el1 chromatin only [Fhb7E locus was clearly associated with the most distal Th. elongatum 7E portion introgressed into R5+, giving rise to the R69-9/R5+ secondary recombinant [3F2 progeny developed in the background of durum wheat cv. Simeto. Normal transmission of the Thinopyrum spp. alien segment was observed  lacked macroconidia. The inoculation assays were conducted in controlled conditions with 16 h light/8 h dark photoperiod at 22–24 °C day and 20 °C night temperatures, when plants were at the anthesis stage (Zadoks stage 68 [Fg- or mock-inoculated) and genotype (HOM+ and HOM−). Disease symptoms were assessed at 7, 14 and 21 days post inoculation (dpi) by calculating the percentage number of visually diseased florets out of the total number of florets per spike. Differences in disease severity between HOM+ and HOM− genotypes were estimated by means of number of diseased florets ± SE (standard error) and by application of Student’s t-test at each time point. For p-values, 0.05, 0.01 and 0.001 significance levels were considered.For plant infection with ain 3824 , freshlystage 68 ). After Fg- and mock-inoculated plants and milled into wholemeal flour, and 100 mg of flour was used for the extraction.Rachis samples for metabolomic analysis were obtained by cutting the rachis portion associated with the four spikelets included in the inoculated spike section. Rachis tissue was immediately frozen in liquid nitrogen and stored at −80 °C until metabolite extraction. Sampling was performed at 2 and 4 dpi, as they correspond to a time range when the most active phase of the infection process occurs, accompanied by dynamic changes in the metabolome . To prepg for 1 min at 4 °C before being transferred to −20 °C for 2–8 h. After thawing, liquid phases were recovered, and an equivalent volume of acetonitrile was added to precipitate any residual protein. Samples were then incubated at 4 °C for 20 min and centrifuged at 10,000× g for 10 min at 4 °C, and the collected supernatants were dried to obtain visible pellets. Finally, the dried samples were re-suspended in water containing 5% formic acid and transferred to glass autosampler vials for LC/MS analysis. Twenty microliters of extracted supernatant samples was injected into an Ultra High Performance Liquid Chromatography (UHPLC) system  and run in positive mode: samples were loaded onto a Reprosil C18 column  for metabolite separation. Chromatographic separations were achieved at a column temperature of 30 °C and flow rate of 0.2 mL/min. For positive ion mode (+) MS analyses, a 0–100% linear gradient of solvent A  to B  was employed over 20 min, returning to 100% A in 2 min and a 6 min post-time solvent A hold. Acetonitrile, formic acid and HPLC-grade water and standards  were purchased from Sigma Aldrich. The UHPLC system was coupled online with a mass spectrometer Q Exactive  scanning in full MS mode (2 μscans) at 70,000 resolution in the 67 to 1000 m/z range, with a target of 1106 ions, maximum ion injection time (IT) of 35 ms, 3.8 kV spray voltage, 40 sheath gas and 25 auxiliary gas. For DON-GSH detection, tandem mass spectrometry operated in positive ionization mode, with data dependent acquisition (DDA) mode for full-scan MS analysis. The full-scan settings were as follows: resolution 70,000; automatic gain control (AGC) target, 3 × 106; maximum injection time (IT), 100 ms; and scan range, 250–3000. The remaining settings for DDA mode were as follows: resolution, 35,000; AGC target, 1 × 105; isolation width, 1.7 Da; and collision energy (NCE), 30. Calibration was performed before each analysis against calibration mixes  to ensure sub-ppm error of the intact mass.Metabolites from both rachis and grain samples were extracted in biological and technical triplicate, as previously reported . Brieflyhttp://maven.princeton.edu/, accessed on 4 April 2023). Mass spectrometry chromatograms were elaborated for peak alignment, matching and comparison of parent and fragment ions, and tentative metabolite identification, within a 2 ppm mass-deviation range between observed and expected results against the imported KEGG database. Resistance-related constitutive (RRC) and resistance-related induced (RRI) metabolites with greater peak intensities in the resistant  genotype in comparison to the susceptible control   were calculated as in [mock HOM+/mock HOM−; (2) RRI = (Fg HOM+/mock HOM+)/(Fg HOM−/mock HOM−). To further explore the metabolic differences between Fg- and mock-inoculated rachis samples at 2 and 4 dpi and between grain extracts of FHB-resistant and susceptible plants, multivariate statistical analyses were employed on the entire metabolomics data set using the same software. The web-based tool MetPA . Global test was the selected pathway enrichment analysis method, whereas the node importance measure for topological analysis was the relative betweenness centrality. Results were graphed with GraphPad Prism 5.0 .Raw files of replicates were exported, converted into mzXML format through MassMatrix  and then processed by MAVEN 8.1 software (ed as in : (1) RRCAnalysis ), which Fg-inoculated spikelets and the associated rachis portions of the HOM+ plants did not show any visible change with respect to the mock-inoculated HOM+ plants  and in their HOM− sibs [Fhb7E locus from Th. elongatum  and resistance-related induced (RRI), respectively, and the observed relative proportions are reported in Metabolites extracted from rachis samples at 2 and 4 dpi were analysed by liquid chromatography coupled with high-resolution accurate mass spectrometry (HRAM). Differentially expressed metabolites between HOM+ and HOM− genotypes identified in mock-inoculated condition, RRC metabolites included agmatine (FC = 96.4) and citrulline (FC = 2.26) among amino acids and amino acid derivatives; gibberellin A24 (FC = 4.18) and glabric acid (FC = 2.63) within the terpenoids cluster; and taxifolin 3-O-acetate (FC = 2.34), flavonol 3-O-β-D-glucosyl-(1->2)-β-D-glucoside (FC = 2.20) and vitexin 2-O-β-D-glucoside (FC = 1.98) in the flavonoids class. On the other hand, in the Fg-inoculated extracts, flavonol 3-O-D-xylosylglycoside (FC = 3.08) and apigenin 7-4-dimethyl ether (FC = 1.52), metabolites of the flavonoids group, as well as glutathione (FC= 2.07) and leucyl-leucine (FC = 2.37) among peptides, were the RRI metabolites significantly more accumulated in the rachis of HOM+ than in HOM− plants.At 2 dpi, considering only metabolites exhibiting a FC > 1.5, a total of 61 RRC and 16 RRI differentially expressed metabolites was identified in the comparison of HOM+ vs. HOM− rachises . The RRCIn 4 dpi rachis extracts, the same number of classes of RRC metabolites as in the 2 dpi samples was identified, most of them having the same chemical nature and relative abundance . SimilarFg inoculation (1), menaquinone , pantothenol  and vitamin B6 (VB6) vitamers (pyridoxine (FC = 2.26), pyridoxal phosphate (FC = 2.02), and pyridoxine phosphate (FC = 1.53)), were specifically detected in the resistant HOM+ and not in susceptible HOM− rachises.On the other hand, classes of RRI metabolites were markedly more numerous at 4 dpi (12) than at 2 dpi (4), indicating a wide spectrum of metabolic processes activated by culation , and remculation . Among tFg infection, a detailed analysis of altered metabolic pathways and networks was carried out on 4 dpi extracts. The web-based tool MetPA was used to analyse the following comparisons: HOM+ Fg-inoculated vs. HOM+ mock , phenylpropanoid and diterpenoid biosynthesis, glutathione metabolism and VB6 metabolism. In particular, within the induced phenylpropanoid pathway, marker metabolites such as p-coumaric acid, coumaroyl quinic acid and sinapic acid were observed in significantly higher quantity in samples of the resistant HOM+ plants, indicating that lignin biosynthesis is activated under Fg infection , which are the major building blocks of the three lignin types, i.e., G, S and H . The indnfection . In partnfection , a markenfection . Fg involved the VB6 metabolism. Accumulation of some VB6 vitamers, i.e., pyridoxine (PN) and its derivatives pyridoxal (PL) and pyridoxal 5′-phosphate (PLP), was significantly induced in HOM+ plants  were found to be upregulated in grains of resistant HOM+ vs. susceptible HOM− plants, and their increase was correlated with the downstream increase of phenylpropanoid biosynthesis  in the resistant HOM+ genotype and their complete absence in susceptible HOM− , both representing detoxified products, was investigated in HOM+ and HOM− infected rachises and grains. At 2 dpi a, DON wable HOM− , thus unc adduct . The reld plants c. The reTh. elongatum FHB resistance locus Fhb7E into durum wheat, we analysed the metabolic profiles of rachis and grain tissues of a pair of contrasting near-isogenic lines, one carrying (HOM+) and the other lacking , it was at the rachis level that pathogen diffusion was with high efficacy hindered in the resistant Fhb7E HOM+ recombinant as a consequence of activation of a strong antioxidant response, modifications of the secondary cell-wall structure and trichothecene detoxification mechanisms. Our research confirmed the significance of GST-mediated DON detoxification, apparently associated with a fungal-to-plant HGT event  derived from the Dvorak74 accession  further supported their orthologous nature, as confirmed by other authors , as well as at the level of the plant sink organ (grain).To shed light on the mechanisms of action underlying the potent resistance response against FHB associated with the introgression of the at 4 dpi . As is tT event . The 7embinants ,62,66. Ts e.g., ,61,64). ,64. Th. mock-treatment  and positive regulation of amino acid and phospholipid synthesis  under reatment  were apppersicum  and, in persicum ). Moreovnce loci . SpecifiFg infection [F. graminearum not to be stimulated by simple agmatine presence, but rather by a continuous acidification arising from agmatine catabolism. Therefore, as agmatine here was exclusively found as a constitutively accumulated metabolite in the resistant HOM+ (mock), it is probable that under Fg infection, agmatine is prevented from being metabolised to putrescine, as it commonly occurs. This is in line with the significantly lower concentration of polyamines  observed at 4 dpi in HOM+ vs. HOM− plants under Fg infection  for ROS neutralisation and xenobiotics detoxification, in addition to alkaloids and gibberellins  for antimicrobial activity. The highly significant response of the HOM+ genotype was already evident at 2 dpi, when strong antioxidant activity and a higher accumulation of peptides and flavonoids were detected  showed upregulation of pathways associated with cell wall adaptation, amino acid and sugar metabolism and flavonoid biosynthesis. Among the most significant features, there was a remarkably increased accumulation of carboxylic acids, such as shikimate and the final product of the shikimate pathway, chorismate , are among the most consistently detected induced metabolites associated with FHB resistance in wheat. Although lignin biosynthesis and deposition at the infection site is part of a typical host reaction, regardless of susceptibility , their s1 e.g., ,51,94) a,94 aFhb1., [Fhb2 . In the ion line ,70. Monoion line . All thrion line  and lignion line  more thaion line , this coion line . An additional interesting feature was represented by detection of a fungal marker metabolite, namely, chitobiose , in rachises of HOM+ plants only. This is indicative of chitinase activity, an enzyme that releases chitobiose when hydrolysing chitin, a structural component of fungal cell walls. Chitinases play a major role as defence proteins in enhancing FHB resistance in wheat ,99 and aFhb7E-mediated defence to Fg infection, as their concentrations were significantly increased in grains of the resistant vs. the susceptible recombinant , in ombinant . The accm growth ,105. Them growth ,107) andm growth . This renfection  and in pnfection . Our resFhb7E locus. They all involve direct or indirect interactions with the peptide glutathione and consist of polyamine metabolism, VB6 salvage pathway and multiple DON detoxification strategies.In addition to the metabolites and routes already known to be associated with FHB resistance in wheat, our untargeted metabolomic analysis at 4 dpi was useful for revealing the importance of at least three other, more specific metabolic pathways that significantly differed between the resistant carrier and the susceptible non-carrier of the Blumeria graminis, Puccinia hordei and Fg, was reported to induce higher PA accumulation in the resistant vs. susceptible genotypes of wheat, barley and oat [Fg  release  in plant responses to pathogen infection is debated, as both plants and pathogens produce PAs, and different mechanisms, e.g., for tissue colonisation and PA transport by the plant, are described for different plant–pathogen interactions ,112,113. and oat ,82,111, g . The laoduction . Specificulation . While Pme lapse , DON biome lapse . Hence, e . In graE. coli in the mid-1970s, is considered a superior reducing agent to GSH, thus representing a potent free radical scavenger under oxidative stress , but not spermine (Spm), conjugated with glutathione to form glutathionyl-spermidine (GSpd) in resistant HOM+ plants . GSpd, fiewed in ). Biosyniewed in ,119. To iewed in ,119. Morirulence ,116,117.irulence , the hypcus  in wheat. The upregulation of VB6 biosynthesis and metabolism was observed in plant adaptation to abiotic stress [Fusarium spp. infection is reported in a few cases, including Lilium pumilum [Vanilla planifolia [Rhizoctonia solani, three genes from the VB6 pathway and GST were upregulated and suppressed pathogen spread [VB6 vitamers , pirydoxamine and their 5′-phosphorilated derivatives piridoxal 5′-phosphate (PLP), pyridoxine 5′-phosphate (PNP), pyridoxamine 5′-phosphate) are known to have universal and specific roles in plant development, stress tolerance, antioxidant response and amino acid biosynthesis, and also to affect amino acid-dependent secondary metabolites . In the original evidence provided by Wang et al. [Thinopyrum genus. More recently, Fhb7 homologs were found in additional genera of the Triticeae tribe, such as Elymus, Leymus, Roegneria and Pseudoroegneria, which led to the hypothesised occurrence of the HGT event before Triticeae differentiation [Fhb7 carriers exhibit a perennial habit, as in other gene transfer episodes from Epichloë into grass species [Fhb7 transfer did not apparently involve typically annual Triticum species. Nonetheless, the Epichloë-to-Thinopyrum spp. HGT was made available and could benefit cultivated Triticum species thanks to finely tailored chromosome engineering interventions, which apparently did not cause negative impacts on yield performance of the recipient wheat genotypes , a potent ROS scavenger and a key molecule in DON detoxification, both as a substrate for GST activity and, possibly, as a positive regulator of UGTs. In this regard, it could be hypothesised that the HGT-acquired GST, belonging to the fungal GTE (glutathione transferase etherase-related) class of GST proteins [6 in Fhb7E-mediated resistance. It is reasonable to think that the wide array of metabolites and pathways differentiating the Fhb7E+ from the Fhb7E− response can hardly be attributed to the alien GST gene alone. Not disregarding the importance of interactions of the Thinopyrum locus with the background wheat genotype, additional cis- or trans-active genes, with structural and regulatory roles, might be expected to be involved in the makeup of the critical Thinopyrum region and hence to contribute to the Fhb7E-associated phenotype. In this view, it can be supposed that only when the fungal GST sequence “landed” into a “suitable genetic environment”, such as that of distal Thinopyrum 7EL arm portion, flanked by additional genes involved in the resistance function (see Introduction), did the composite locus become able to confer the observed outstanding resistance to FHB and also to Fusarium crown rot [Thinopyrum lines, which failed to show FHB resistance, despite having been proven to possess a highly homologous or an identical GST gene as the resistant genotypes [Fhb7E locus matches the recent description of numerous clusters in the bread wheat genome of Fg-responsive genes, containing a variety of functionally related and largely co-expressed defence genes [Fusarium diseases.Our data clearly show the importance of the proteins , not fouproteins , could irown rot ,66. Suchenotypes ,64,142. ce genes . Many of