Walnut flour (WF) is a nutrient-rich source that can be used as an alternative for individuals on a gluten-free diet. This study aimed to assess the physical, chemical, and sensory changes in low-carbohydrate bread when supplemented with WF. Molecular-level changes were also examined using ATR-FTIR spectra. The bread recipe, containing buckwheat and flaxseed, was enriched with WF at levels ranging from 5% to 20%. The addition of WF resulted in increased loaf volume and decreased baking loss. Enriched bread samples showed higher protein content, while fat and available carbohydrate content decreased. Additionally, WF incorporation led to a decrease in crumb brightness and an increase in redness (from 23.1 to 25.4) and yellowness (from 23.8 to 26.7). WF also increased crumb hardness and chewiness. Moreover, the tested additives primarily influenced the intensity of FTIR spectra, indicating changes in protein, carbohydrate, and fat content, with increased band intensity observed in the protein region. We particularly recommend bread with a WF content of 15%. This type of bread is characterized by high consumer acceptance. Furthermore, compared to bread without the addition of WF, it has a higher content of phenolic compounds, protein, and fat by approximately 40%, 8%, and 4%, respectively. The antioxidant activity of this bread, determined using the ABTS and DPPH methods, is also significantly higher compared to the control bread. Among the population, there is an increasing intolerance to gluten—a protein present in wheat, rye, or barley—so scientists are looking for products that will be made from alternative cereals . The litOur previous research allowed us to develop a recipe for gluten-free bread with reduced carbohydrate content and increased protein content based on flaxseed and buckwheat flour as well as psyllium and potato fiber additives . The choIn recent years, there has been growing interest in both plant and high-protein products, with an emphasis on bread as a carrier of the necessary ingredients of plant origin ,12,13. Tα, β, γ, and δ), sterols, and carotenoids [Walnuts are among the best sources of polyunsaturated fatty acids with the most preferred ratio of n-6 and n-3 fatty acids . The comotenoids ,20.Walnut flour (WF) is gaining more and more interest from consumers, for example, due to the fact that its regular consumption in the daily diet reduces the risk of some diseases . WF is fMoreover, it is a valuable source of phenolic compounds . WF is wThe following agents  were used for this study: sodium salicylate, gallic acid, ferrozine (3-(2-pyridyl)-5,6-bis-(4-phenyl-sulfonic acid)-1,2,4-triazone), and ABTS ) in analytical purity.White buckwheat flour , flaxseed flour , and partially defatted walnut flour from manufacturer OlVita, Poland , and psyllium , potato fiber , guar gum , dried yeast , and Himalayan salt were used in the bread recipe.Samples of a mixture of flour (buckwheat and flaxseed 1:1) with an appropriate percentage of WF  were sieved, weighed, and combined with other technological ingredients such as psyllium, dried yeast, guar gum, and salt. To all samples, 130 mL of water 20 °C was added directly from the tap at room temperature. The control bread dough was made of 50 g of white buckwheat flour, 50 g of flaxseed flour, 4 g psyllium, 2 g of salt, 3 g of guar gum, 1 g of dried yeast, and 130 mL of water. Subsequent trials with the appropriate addition of WF  had a reduced amount of base flours (white buckwheat and flaxseed flour), so thatthe weight of all flours used was 100 g. The dough mixing process took 5 min, where the dough was initially mixed at 100 rpm (approx. 2 min) and then increased to 200 rpm and mixed for another 3 min, and then 120 g pieces of dough were formed by hand and put into the loaf tins . The dough fermentation process was successively carried out at 30 °C and 75–88% humidity for 1 h. After this time, the samples were moistened with water and placed in a baking oven  heated to a temperature of 210 °C. The baking process took 20 min. The obtained bread samples, after cooling down to room temperature, were further analyzed.The basic composition of bread was evaluated according to AACC standards available online . The conThe volume of obtained loaves of bread was determined using the millet seeds displacement method  convertiThe measurement of the color of the bread crumb was carried out using a CR 30-16 colorimeter . This device works based on the CIE L*a*b* color system, where the L* value indicates lightness of color (0–100), a* denotes the red/green value (range of −150 to +100), and b* defines the blue/yellow (range of −100 to +150). The total color difference (ΔE) was calculated between the control low-carbohydrate bread and the WF-enriched bread . The heaThe sensory evaluation was carried out 24 h after baking by forty untrained panelists (25–60 years old). Round bread crumb samples with a diameter of 28 mm were cut from a slice of bread of 10 mm thickness, coded, and submitted for evaluation in a closed odor-free room. The sensory attributes such as texture, color, taste, smell, and overall acceptability of bread were assessed. The degrees of liking bread with varying amounts of WF were based on a nine-point hedonic scale [−1, using a mandrel with a diameter of 25 mm. The head was used with a load force range of up to 100 N. The following parameters were determined [The texture parameters of the bread crumb were tested using the TPA test after 24 h and 48 h of baking. Using a cutter with a diameter of 28 mm, samples were cut from the center of a slice (15 mm thick). Later, the samples prepared in this way were placed on the bottom plate of the ZWICK Z020/TN2S  testing machine and were compressed twice to 60% of its thickness, at a speed of 20 mm·stermined  using thThe methanolic extract was prepared for the determination of total phenolics and antioxidant activity of flour and bread samples according to the procedure described by Różyło et al. . Total p−1 at a resolution of 2 cm−1. The measurements were conducted at the Laboratories of the Department of Biophysics, University of Life Sciences in Lublin. Prior to analysis, all the spectra were processed using GramsAI software (ver. 93) from ThermoGalactic Industries . The spectra were measured at room temperature.FTIR spectra were measured for the analyzed samples of flour and flour with the addition of WF using an IRSprit spectrometer by Shimatzu . The measurements were conducted with the use of an ATR  attachment comprising a ZnSe crystal with suitable geometry (in our case—truncated at 45°). This facilitated 20-fold internal reflection of the laser beam in the spectrometer. A total of 24 scans were registered during every measurement. The software automatically averaged the obtained results. Prior to and after each measurement, the crystal was thoroughly cleaned with ultrapure solvents purchased from Sigma-Aldrich . For exactly 1 h prior to and during each measurement, neutral N2 atmosphere was maintained inside the measurement chamber. The spectra were registered within the spectral range from 400 to 3800 cmThe experiments were conducted in triplicate unless mentioned otherwise. The obtained results were subjected to statistical analysis using Statistica 12.0 program . One-way analysis of variance (ANOVA) was performed, and Tukey’s test was utilized to compare the average values at a significance level of α = 0.05.The addition of walnut flour (WF) had an impact on the chemical composition of the bread loaves produced. As anticipated, the protein content significantly increased from 22.6 g/100 g DM (dry mass) in the sample without WF to 34.7 g/100 g DM in the bread with 20% WF, as shown in E is between 2.0 and 3.5, it would be possible for an inexperienced observer to recognize it, and with a value above 3.5, the difference in color would become clear [The addition of walnut flour (WF) resulted in a change in the color of the bread crumb, making it browner and consequently reducing the lightness values. The most significant decrease in crumb lightness was observed in bread prepared with 20% WF, where the lightness value decreased from 39.5 to 32.3. Additionally, the redness and yellowness of the crumb also increased with the addition of WF. As a result, the total color difference (∆E) increased from 4.2 to 9.8 . If ∆E ime clear . Taking me clear , who alsIn all the bread samples examined, the attributes that received the highest ratings were color and overall acceptability, indicating that bread supplemented with walnut flour (WF) was visually appealing . The texIn the case of wheat bread, enriching it with various plant additives often leads to a decrease in consumer acceptability, mainly due to the weakening of gluten structure, resulting in a decrease in volume and deterioration of texture characteristics, leading to a decline in consumer acceptability of the bread ,38,39. THowever, in this study, the addition of WF resulted in an increase in bread volume, indicating that this additive strengthens the dough structure and leads to an increase in crumb hardness. Interestingly, the results found by Pycia and Ivanišová  showed tp < 0.05). The increased phenolic content in the bread recipe resulted in higher antioxidant activity (AA) in the enriched bread. In both the ABTS and DPPH assays, WF decreased the EC50 index, indicating an increase in the antioxidant capacity of the enriched samples. However, in the case of ABTS antiradical activity, WF only slightly increased the AA of the bread, and the value of this index decreased with the addition of WF from 116.73 mg DM/mL (control bread) to 103.71 mg DM/mL (bread with 20% WF). On the other hand, in the case of the DPPH assay, the values changed within a wider range, from 139.91 mg DM/mL to 106.17 mg DM/mL, respectively. The correlation coefficients between WF content in bread and ABTS and DPPH were statistically significant (p < 0.05) and resulted in r = 0.991 and 0.955, respectively. Pycia and Ivanišová [Enriching cereal products with various plant raw materials is a current trend. Such activities increase the nutritional and nutraceutical value of food. According to the data presented in vanišová , the conIn order to analyze the samples of flour and flour with the addition of WF at the molecular level, FTIR spectroscopy measurements were performed. The processed spectra are presented in −1 are associated with the stretching vibrations of –OH groups characteristic of starch molecules, the additives used, as well as water. With the increasing content of WF, a significant decrease in the intensity of the vibrations in this range was observed. This may evidence a strong influence of vibrations originating from intermolecular hydrogen bonds with the increasing presence of the additive. The region with the maxima at ~3005, 2920, and 2852 cm−1 corresponded to stretching vibrations characteristic of -C–H groups in -CH2 groupings [−1. However, in the case of this type of food sample, the band with the maximum at approx. 1630 cm−1 corresponds to the very characteristic vibrations of the Amide I structure, i.e., mainly stretching vibrations of the C=O and C-N groups and deformation vibrations of the N-H group. In the relevant samples, it constitutes the primary indication of the second-tier protein structure [−1 reflects the stretching vibrations of the carbonyl group [−1 belong to the vibrations of the Amide II structure [−1 are also characteristic vibrations of protein structures [−1, also characteristic of proteins, which correspond to vibrations of the Amide III group, i.e., mainly stretching vibrations of the C-N, deformation of the N-H, and stretching of the C-C groups [−1 correspond to the characteristic C-O stretching vibrations and stretching of the C-C groups and, above all, the ν(C-O-C) structure. It is also noteworthy that the ratio intensity for bands located at 1037 and 991 cm−1, which indicates the degree of crystallinity, did not change significantly in our samples.As follows from the data available in the literature ,51,52,53roupings ,49. Withtructure . The chayl group . Changestructure ,53, i.e.ructures ,53. NextC groups . The max−1 also correspond to vibrations in the bonds of the sugar fractions present in the structure of starch [Vibrations in this spectral range are characteristic of the crystalline and amorphous region of starch ,53 as wef starch . MeanwhiReplacing buckwheat and linseed flour with WF resulted in low-carbohydrate bread with a higher volume and lower baking loss, while still maintaining the desired sensory quality for consumers. Additionally, the protein content increased, while the fat and available carbohydrates content decreased in the enriched bread samples. The bread enriched with WF exhibited a darker, redder, and more yellow crumb compared to the control bread. Furthermore, the incorporation of WF notably increased the antioxidant activity of the bread. However, it also led to an increase in crumb hardness and chewiness. The analysis of FTIR spectra clearly indicated that the primary structural skeleton of starch, consisting of amylase and/or amylopectin, was preserved in the end products. We specifically suggest opting for bread containing 15% WF content. This bread variety is recognized for its high sensory acceptance by consumers. Moreover, in comparison to bread lacking WF incorporation, it possesses elevated levels of phenolic compounds, protein, and fat. Future studies on this type of bread should focus on determining the shelf life and optimal storage conditions of such kinds of products.


ATP citrate lyase (ACLY) is the predominant nucleocytosolic source of acetyl-CoA and is aberrantly regulated in many diseases making it an attractive therapeutic target. Structural studies of ACLY reveal a central homotetrameric core citrate synthase homology (CSH) module flanked by acyl-CoA synthetase homology (ASH) domains, with ATP and citrate binding the ASH domain and CoA binding the ASH-CSH interface to produce acetyl-CoA and oxaloacetate products. The specific catalytic role of the CSH module and an essential D1026A residue contained within it has been a matter of debate. Here, we report biochemical and structural analysis of an ACLY-D1026A mutant demonstrating that this mutant traps a (3S)-citryl-CoA intermediate in the ASH domain in a configuration that is incompatible with the formation of acetyl-CoA, is able to convert acetyl-CoA and OAA to (3S)-citryl-CoA in the ASH domain, and can load CoA and unload acetyl-CoA in the CSH module. Together, this data support an allosteric role for the CSH module in ACLY catalysis. ATP citrate lyase (ACLY) is the main nucleocytosolic source of acetyl-CoA and the enzyme contains citrate synthase homology (CSH) and acyl-CoA synthetase homology (ASH) domains. Here, the authors report data on an ACLY mutant that supports an allosteric role for the CSH domain in ACLY catalysis. Aberrant activity of ACLY is found in many disorders2, including nonalcoholic fatty liver disease4, atherosclerotic cardiovascular disease6, and cancers of the breast, prostate, cervix, lung, liver, and brain10. The importance of ACLY in health and disease has prompted a renewed interest in a detailed understanding of its molecular mechanism and the development of ACLY inhibitors for therapeutic application13, including the identification of bempedoic acid, recently approved by the FDA to reduce LDL cholesterol for patients with atherosclerotic cardiovascular risk15.ATP citrate lyase (ACLY) is a major source of nucleocytosolic acetyl-CoA, an essential building block for the biosynthesis of fatty acids, isoprenoids, and cholesterol, as well as a cofactor for acetylation of histones to regulate gene expression and other proteins and RNA to regulate diverse biological activities16 and a C-terminal citrate synthase homology (CSH) domain18. Together, these domains use mitochondrial-derived citrate with Mg-ATP and Coenzyme-A (CoA) co-substrates to produce the acetyl-CoA and oxaloacetate (OAA) coproducts. Structural studies have been instrumental in providing important details underlying the molecular mechanism of ACLY activity. Pioneering X-ray crystallographic work from the Fraser laboratory revealed that the isolated ASH domain binds citrate and ATP to form a covalent citryl-phosphate adduct20, but the ASH is unable to bind CoA on its own, thus implicating a role for CSH domain in CoA binding and subsequent ACLY catalysis. More recent biochemical and structural studies revealed that the CSH domain mediates homotetramerization of ACLY, whereby the CSH domains form a centrally located tetramerization module, with two each of the ASH domains on opposite sides of the CSH module24. The structures also show that different subunits of the tetramer participate in binding each of four CoA substrates, with the adenine base and ribose ring interacting with the CSH domain of one subunit and the pantothenic arm and β-mercapto group pointing into the active site of the ASH domain of another subunit. Paradoxically, the cryo-EM structure of ACLY with CoA revealed a second mutually exclusive CoA binding site where the ADP moiety is shifted about 8 Å towards the CSH module and the pantothenic arm is bent over to interact with the CSH module24. The significance of this alternative binding site was reinforced by a crystal structure of the isolated CSH module bound to CoA in an analogous fashion22.ACLY contains an N-terminal acyl-CoA synthetase homology (ASH) superdomain23. In model 2, CoA first binds to the ACLY CSH domain in a non-productive conformation, which induces a structural rearrangement of ACLY, resulting in the translocation of CoA to the ASH domain in a productive conformation, where CoA reacts with ASH-bound citrate and ATP to form OAA and acetyl-CoA products24. Once the acetyl-CoA product is formed in the ASH domain, another structural rearrangement of ACLY unloads acetyl-CoA through binding to the CSH module before acetyl-CoA leaves ACLY24. Consistent with model 2, the cryo-EM structure of ACLY in the presence of acetyl-CoA and OAA products reveal that they are bound in the ASH domain24. The difference between the two models of ACLY catalysis centers around the role of the CSH domain in ACLY catalysis. In model 1, residues within the CSH domain are proposed to play a direct role in cleavage of (3S)-citryl-CoA to acetyl-CoA and OAA products with residue Asp1026, shown to be essential for ACLY catalysis, acting as a general base for the reaction23. In model 2, the CSH module plays an allosteric role in binding the CoA substrate prior to its translocation to the ASH domain and binding the acetyl-CoA product prior to release from the enzyme24. Consistent with the importance of Asp1026 in ACLY function, mutation of this residue to alanine renders the enzyme inactive25.The presence of a “non-productive” CoA binding site against the CSH module has led to two alternative models for catalysis by ACLY Fig. . In modeTo help resolve the alternative models on the role of the CSH module on ACLY catalysis, we report here a biochemical and structural analysis of the ACLY-D1026A mutant. Our data is most consistent with an allosteric role (model 2) of the CSH module in ACLY catalysis.24  in an attempt to capture a reacted state of the mutant enzyme for cryo-EM structure determination. The cryo-EM structure (ACLY-D1026A—substrates) was determined at an overall resolution of 2.2 Å with imposing D2 symmetry -citryl-CoA reaction intermediate ate Fig. . Unexpecate Fig. . We hypoled as a  S-citryl26. Prior crystallographic studies and our recent cryo-EM studies reveal that the loop containing His760 is only ordered when phospho-histidine or a mimic is present27. Interestingly, the loop containing His760 is disordered in the ACLY-D1026A—substrates structures, even though phosphate in the form of ATP was added in the ACLY-D1026A—substrates structure. These observations are consistent with the ACLY-WT-like hydrolysis of ATP in the ACLY-D1026A mutant, but the non-typical conformation of the ACLY active site that might contribute to the trapping of (3S)-citryl-CoA in the active site.In the ACLY reaction mechanism, His760 transiently accepts the phosphate group from ATP prior to transfer to citrate to form a phosphocitrate intermediateNotably, a superposition of ASH domains from the ACLY-D1026A—substrates and ACLY—OAA—acetyl—CoA product structure (6UV5) reveals that the (3S)-citryl-CoA bound to the ASH domain of the ACLY-D1026A—substrates structure is incompatible with cleavage to the acetyl-CoA and OAA products. Specifically, Phe347 in the ASH appears to take on two distinct conformations when the ASH domain is bound to (3S)-citryl-CoA or acetyl-CoA + OAA Fig. . When bo24. A superposition of the ASH-bound (3 S)-citryl-CoA/CSH-bound non-productive CoA pair with the CSH-bound non-canonical CoA reveals the mutually exclusive nature of these two binding states  reveals three symmetric and one asymmetric conformation Table . The symGiven the findings above that the ACLY-D1026A mutant is not configured to form acetyl-CoA and OAA products in the ASH domain, we reasoned that if we saturated the mutant with acetyl-CoA and OAA products, that we would either find acetyl-CoA bound to the CSH domain (in its unloaded position) or converted to citryl-CoA within the ASH domain, since the retro-aldol cleavage reaction is reversible. We proceeded to incubate the ACLY-D1026A mutant with saturating levels of acetyl-CoA, OAA, and ATP (to more closely mimic an active conformation of the H760-containing loop), incubated at room temperature for 15 min, and determined the structure by cryo-EM. The ACLY-D1026A—products structure was determined to have an overall resolution of 2.4 Å imposing D2 symmetry and to 2.8 Å without symmetry imposed. After building and refining the symmetric structure, we observed unambiguous density in the ASH domain, which could best be modeled as (3S)-citryl-CoA as well as a non-canonical acetyl-CoA bound to the CSH domain  Fig. . In partLike the asymmetric ACLY-D1026A—substrates-asym structure, the ACLY-D1026A—products-asym structure reveals three symmetric and one asymmetric and more flexible ASH domain with more varied conformation Table , 5A, B. 2 at room temperature for 15 min, quenched the sample with 10% trichloroacetic acid in water, and analyzed the acyl-CoAs in the reaction by liquid chromatography-quadrupole/Orbitrap high-resolution mass spectrometry (LC-HRMS and LC-MS/MS)28. We identified citryl-CoA within the extracts from the enzymatic reaction with the ACLY-D1026 A mutant, while analogous reactions without OAA and ATP or without ATP did not result in detectable levels of citryl-CoA -citryl-CoA intermediate by the ACLY-D1026A mutant in solution, we incubated the mutant protein with acetyl-CoA, OAA, ATP (to more closely mimic an active conformation of the H760-containing loop) and 5 mM MgClCoA Fig. . In an aCoA Fig. . InteresCoA Fig. , suggest2 at room temperature and monitored citryl-CoA formation over time by quenching the reaction and semi-quantitation of citryl-CoA formation by LC-HRMS and LC-MS/MS using 13C315N1-HMG-CoA as a surrogate internal standard to derive a relative abundance of citryl-CoA.  -citryl-CoA from reaction products, we prepared an additional ACLY mutant that is predicted to destabilize non-productive CoA binding but not affect productive CoA binding and incubated this mutant with products and carried out similar LC-MS studies as described above to probe for production of (3 S)-citryl-CoA intermediate. The mutant that we prepared was ACLY-R976E within the CSH domain, as R976 was observed to interact with non-productive CoA (but not productive CoA) in the ACLY-WT bound to CoA refined without symmetry in the closed conformation, and an ACLY-R976E was shown to have background levels of activityThe homotetrameric ACLY enzyme is composed of a central homotetrameric CSH module with two ASH modules on opposite sides of the CSH module. It is well established that citrate and ATP bind to the ASH domain with CoA binding at the ASH-CSH domain interface to generate acetyl-CoA and OAA products. However, there have been contradictory reports on the fate of the (3 S)-citryl-CoA reaction intermediate and the role of the CSH domain in processing this intermediate Fig. . In modeThis study was carried out to help resolve these alternative models underlying the role of the CSH module of ACLY in catalysis, by focusing on an inactive D1026A mutant within the ACLY CSH domain. In model 1, D1026 is proposed to act as a general base for cleavage of the 3 S)-citryl-CoA intermediate to products in the CSH domain, while in model 2, this mutant is proposed to disrupt the allosteric regulation required for ACLY activity, which involves CoA loading through the CSH domain, acetyl-CoA and OAA production in the ASH domain and acetyl-CoA unloading through the CSH domain. The cryo-EM structure ACLY-D1026A in complex with substrates (ACLY-D1026A—substrates) reported here demonstrates that the mutant alters the ASH active site such that the (3 S)-citryl-CoA intermediate cannot be cleaved into acetyl-CoA and OAA products, most likely due to the improper loading of non-productive CoA to the CSH domain Fig. . In part S-citryl24, shows a second OAA molecule bound in a ‘non-productive’ OAA conformation to the CSH domain, which is proposed to play a regulatory role, potentially functioning as an inhibitor to inactivate ACLY at high OAA product concentrations24. This non-productive OAA was proposed to function by sequestering the pantothenic arm of CoA or acetyl-CoA to prevent proper loading and unloading, respectively24. Consistent with the functional importance of this non-productive OAA site, the ACLY-D1026A—product structure also contains cryo-EM density at the same site on the CSH domain, which we have also modeled as an OAA molecule While this study does not exclude the possibility that the CSH domain of ACLY could directly participate in catalysis in some cellular context or in ACLY enzymes from other species, this study demonstrates that human ACLY can mediate acetyl-CoA production with allosteric instead of direct catalytic participation of the CSH domain. Given the interest in developing ACLY inhibitors to treat many disorders, including hypercholesterolemia and cardiovascular disease, targeting the CSH module of ACLY may provide another viable site for drugging ACLY.21 was used to prepare the ACLY-D1026A and ACLY-R576A mutants, via quick change mutagenesis29. Proteins were overexpressed in BL21(DE3) cells and purified to homogeneity using a combination of Cobalt Chelating Resin and gel filtration chromatography essentially as described for ACLY-WT24. The purified protein in a buffer containing 25 mM Hepes, pH7.5, 150 mM NaCl, 5 mM MgCl2, and 10 mM β-mercaptoehanol was concentrated to 6–10 mg/mL, aliquoted into 20 uL portions and flash frozen in liquid nitrogen for storage in −80 freezer until use.A gene encoding human ACLY-WT with a C-terminal 6xHis tag, codon optimized for expression in bacteria (Genscript)2, and 10 mM β-mercaptoethanol. Reactions were allowed to proceed for 15 min for single time point experiments or between 0 and 60 min for time course experiments, before quenching reactions with 10% trichloroacetic acid in water and flash freezing to halt the reaction until LC/MS analysis. Acyl-CoA extractions and analysis to resolve acyl-CoA thioesters were performed as described previously in ref. 28. Briefly, extracts of enzymatic reactions were resuspended in 1000 µl of ice-cold 10% trichloroacetic acid and pulse-sonicated using a sonic dismembrator. The samples were centrifuged at 17,000 × g for 10 min and the supernatants were purified by solid-phase extraction. Waters  Oasis HLB 1 mL (30 mg) solid-phase extraction columns were conditioned with 1 mL methanol, followed by 1 mL of H2O. The supernatants were applied to the column and washed with 1 mL of H2O. The analytes were eluted in methanol containing 25 mM ammonium acetate. The eluates were evaporated to dryness under N2 gas and resuspended in 50 µl of 5% 5-sulfosalicylic acid. About 10 µl injections were injected onto a Waters HSS T3 2.1 × 150 mm 3.5 µm Column on an Ultimate 3000 UHPLC coupled to a QExactive Plus  operating in positive ion mode. Mass spectrometer settings were a resolution setting of 280,000, AGC target of 5e6, isolation window of 20 m/z with an offset of 6 m/z, and an insource-CID of 5 eV with data acquisition using XCalibur 4.3 and analysis with TraceFinder 5.1. The LC gradient was buffer A (5 mM ammonium acetate in water) in buffer B (5 mM ammonium acetate in 95:5 acetonitrile: water (v/v)) starting at 0.2 mL/min 100% A until 3 min, then 20% B at 5 min, then 100% B at 12 min, followed by washing with buffer C ) for 3.5 min at 0.275 mL/min then re-equilibration for 5 min at starting conditions. For semi-quantitation of citryl-CoA and acetyl-CoA during time course experiments, internal standards prepared biosynthetically from 13C3,15N1-pantothenate were generated as previously described in ref. 30, spiked into each sample before extraction, and then 13C3,15N1-HMG-CoA and 13C3,15N1-acetyl-CoA, respectively, were used as internal standards to express relative abundance. Data were tabulated using Microsoft Excel 16.30 and time course data were plotted using GraphPad Prism 9.5.1.ACLY samples for LC/MS analysis were assembled with 10 µM protein supplemented with 5 mM substrates , or products , as indicated, in 25 mM Hepes, pH7.5, 150 mM NaCl, 5 mM MgCln-dodecyl β-d-maltoside or 1 μL 0.05%(v/v) NP-40 was added to 20 μL protein sample and 3 μL of the protein/detergent mixture was applied to glow discharged Quantifoil R1.2/1.3 300 mesh copper grids with carbon foil and blotted for 10 s (blot force = 2) under 100% humidity at 16 °C and plunged into liquid ethane using a FEVitribot Mark IV. These grids were screened using an FEI TF20 microscope equipped with Falcon 3 detector. Data collection was performed using a Titan Krios equipped with a K3 direct detector (Gatan). Defocus range of 1.0–2.0 µm was applied in image acquisition with EPU 2.10.For the ACLY-D1026A—substrates sample, the protein was incubated with 5 mM CoA, 5 mM citrate, and 5 mM ATP at room temperature for 15 min.; and for the ACLY-D1026A—products sample, the protein was incubated with 5 mM acetyl-CoA, 5 mM OAA, and 5 mM ATP at room temperature for 15 min. About 1 μL 0.1%(m/v) 31. After motion correction, all micrographs were imported into Cryosparc2 and subsequent data processing was performed in Cryosparc232. Ctffind4 was used to determine the defocus value of each micrograph33. Micrographs, which contained CTF information worse than 5 Å, were discarded.All cryo-EM data were processed using the following workflow. First, beam-induced motions were corrected using MotionCor2For 3D reconstruction of the ACLY-D1026A—substrates structure, 2,994,064 particles were picked from 5674 micrographs. These particles were classified into 200 classes. After 2D classification, 1,874,207 particles were kept. Four initial models were generated from these particles, and multiple rounds of heterogeneous refinement resulted in one symmetric tetramer with D2 symmetry and three asymmetric tetramers with distinct conformations. There were 378,979 particles in the class of the symmetric tetramer. A map with an overall resolution of 2.4 Å was generated from these particles after applying D2 symmetry. The resolution was further improved to 2.2 Å after global CTF refinement, local CTF refinement, and non-uniform refinement. Due to its flexibility, the local resolution of the ASH domain is lower than the CSH domain. Particle symmetry expansion with D2 symmetry was performed by rotating all four ASH monomers to put them in the same position. Particle subtraction was done to remove the duplicates of ASH monomers. By applying a mask covering the CSH tetramer and ASH monomer, 3D classification without alignment was performed. Particles showing poor density of (3 S)-citryl-CoA were discarded. About 1,122,938 particles were kept, and a 2.2 Å resolution map with an improved density of the ASH domain was obtained after local refinement. For the asymmetric particles, two maps with different conformations , DeLano Scientific, 2002, respectively.For 3D reconstruction of the ACLY-D1026A—products structure, 2,126,238 particles were picked from 6057 micrographs. After 2D classification, 939,097 particles were kept. Four initial models were generated from these particles, and multiple rounds of heterogeneous refinement resulted in one symmetric tetramer with D2 symmetry, one asymmetric tetramer, and bad particles. There were 289,796 particles in the class of the symmetric tetramer and 183,036 in the asymmetric class. A map with an overall resolution of 2.7 Å was generated from these symmetric particles after applying D2 symmetry. The resolution was further improved to 2.4 Å after global CTF refinement, local CTF refinement, and non-uniform refinement. Due to its flexibility, the local resolution of the ASH domain is lower than the CSH domain. Particle symmetry expansion, particle subtraction, and 3D classification without alignment was performed to further sort these particles and 613,268 particles that showed a better density of (3 S)-citryl-CoA were kept. A 2.5 Å resolution map with an improved density of the ASH domain was obtained after local refinement. For the asymmetric particles, a map at a resolution of 2.8 Å was generated. Image processing statistics for all cryo-EM data are provided in Table Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Emotion recognition constitutes a pivotal research topic within affective computing, owing to its potential applications across various domains. Currently, emotion recognition methods based on deep learning frameworks utilizing electroencephalogram (EEG) signals have demonstrated effective application and achieved impressive performance. However, in EEG-based emotion recognition, there exists a significant performance drop in cross-subject EEG Emotion recognition due to inter-individual differences among subjects. In order to address this challenge, a hybrid transfer learning strategy is proposed, and the Domain Adaptation with a Few-shot Fine-tuning Network (DFF-Net) is designed for cross-subject EEG emotion recognition. The first step involves the design of a domain adaptive learning module specialized for EEG emotion recognition, known as the Emo-DA module. Following this, the Emo-DA module is utilized to pre-train a model on both the source and target domains. Subsequently, fine-tuning is performed on the target domain specifically for the purpose of cross-subject EEG emotion recognition testing. This comprehensive approach effectively harnesses the attributes of domain adaptation and fine-tuning, resulting in a noteworthy improvement in the accuracy of the model for the challenging task of cross-subject EEG emotion recognition. The proposed DFF-Net surpasses the state-of-the-art methods in the cross-subject EEG emotion recognition task, achieving an average recognition accuracy of 93.37% on the SEED dataset and 82.32% on the SEED-IV dataset. Emotion recognition has emerged as a crucial research task within the field of affective computing  is employed for cross-subject EEG emotion recognition. Firstly, the original EEG signals are divided into segments, each lasting 4 seconds. For each segment, Differential Entropy (DE) features in the δ, θ, α, β, and γ frequency bands are extracted. These features are spatially mapped based on electrode positions to generate EEG feature representations. To enhance the extraction of EEG features, a Vision Transformer (ViT) is employed as the Feature Extractor. Subsequently, building upon the original Domain-Adversarial Neural Network (DANN) model, a domain adaptive learning module named the Emo-DA module is devised for EEG emotion recognition. The module addresses domain discrepancies among different subjects. The Emo-DA module is then applied to pre-train a model on both the source and target domains. Fine-tuning is subsequently employed on the target domain to further test cross-subject EEG emotion recognition. Lastly, a series of comparative and ablation experiments are conducted using the DFF-Net framework. These experiments not only demonstrate the superiority of DFF-Net overall state-of-the-art models but also explore the contributions of key components within DFF-Net to the recognition performance in the cross-subject EEG emotion recognition task.The primary contributions of this paper can be outlined as follows:With the aim of reducing the disparities between the source and target domains, a domain adaptive learning module for EEG emotion recognition was crafted, named the Emo-DA module. This module facilitates the model in achieving improved generalization on the target domain.In order to enhance the adaptability of the model to the target domain and make use of a limited amount of target domain data, the integration of domain adaptation and fine-tuning techniques is designed, leading to the creation of the domain adaptation with a few-shot fine-tuning network (DFF-Net). This approach is devised to better accommodate the specific features of the target domain, thereby enhancing the accuracy of the cross-subject EEG emotion recognition task.The proposed DFF-Net model achieves accuracy rates of 93.37 and 82.32% on the SEED and SEED-IV datasets, respectively, for cross-subject EEG emotion recognition. These rates surpass those of all state-of-the-art models. Furthermore, a series of ablation experiments were conducted to investigate the contributions of key components within DFF-Net to the recognition performance of cross-subject EEG emotion recognition tasks.This section offers a thorough overview of relevant research in EEG-based emotion recognition, focusing on the application of transfer learning strategies in the context of cross-subject emotion recognition.In recent years, there has been a notable proliferation of applications for electroencephalogram signals in the realm of emotion recognition. This heightened interest is primarily attributable to their inherent capacity to accurately and faithfully capture authentic emotional states within individuals. Prior investigations have predominantly concentrated on the enhancement of EEG-based emotion recognition methodologies through processes such as signal denoising, feature engineering, and classifier training. For instance, Wang et al.  introducIn order to tackle the stated constraints, deep learning methodologies have been employed .F* represents the optimized model parameters, Ns denotes the number of samples in the source domain, F on the labeled samples from the source domain, F on the unlabeled samples from the target domain, where B belongs to the set B∈{δ, θ, α, β, γ}, indicating the frequency band. Ne pertains to the electrodes, and is represented by FP1, FPZ, ..., CB2. The collection of EEG signals from all Ne electrodes on frequency band B is denoted as b∈1, 2, ..., B represents the frequency band, H signifies the height of the matrix, and W indicates the width of the matrix. This transformation is performed based on the spatial arrangement of the electrodes on the brain. Finally, the EEG feature representation is crafted by combining frequency-domain brain electrode position matrices across various frequencies, resulting in The Vision Transformer (ViT) has achieved state-of-the-art performance in various computer vision applications, such as image classification and segmentation. This motivates us to use ViT as the feature extractor for feature extraction from EEG feature representations. The architecture of the ViT as the feature extractor for feature extraction from EEG feature representations is illustrated in H × W × B, is partitioned into B EEG representation patches H×W. The representation patches are subsequently inputted into the Linear Embedding layer, which maps them to a fixed size denoted as Ed. According to Equation (2), WA can be deduced as the input for the Transformer Encoder. In this context, the class token, denoted by NTB∈T, B signifies the count of EEG representation patches, and The ViT model takes the EEG feature representation denoted as As shown in A Domain-Adversarial Neural Network (DANN) is utilized for achieving transfer learning. This framework was originally proposed by Ganin and Lempitsky  for imagThe Feature Extractor is used to extract shared EEG emotion representations from both the source and target domain input data. For the Emo-DA module, the Vision Transformer model is selected as the Feature Extractor. The formula for the Feature Extractor in the Emo-DA module can be represented as Equation (3):xi represents the input sample, and Hi represents the output feature representation from the Feature Extractor. The Feature Extractor maps the input sample xi to a high-level feature space using the parameter θf, which contains abstract features useful for the emotion recognition task. These features will be passed to the Emotion Classifier and Domain Classifier for subsequent emotion recognition and domain adaptive learning.where The Emotion Classifier is a classifier used for emotion classification. It takes the shared features extracted by the Feature Extractor as input and performs emotion classification on the source domain data. In this case, a fully connected layer is chosen as the classifier for emotion classification. The formula for the Emotion Classifier in the Emo-DA module can be represented as Equation (4):Hi represents the output feature representation from the Feature Extractor, and Yi represents the emotion prediction results of the model for the input sample xi. The Emotion Classifier maps the feature representation Hi to a predicted probability distribution over emotion labels using the parameter ϕy.where The Domain Classifier is used to differentiate the features between the source and target domains. It takes the shared features extracted by the Feature Extractor as input and attempts to correctly classify them as belonging to either the source or target domain. The objective of the Domain Classifier is achieved through adversarial training, which aims to make the extracted features indistinguishable with respect to the domain. The formula for the Domain Classifier in the Emo-DA module can be represented as Equation (5):Hi represents the output feature representation from the Feature Extractor, and Di represents the prediction results of the domain label for the input sample xi. The Domain Classifier maps the feature representation Hi to a predicted probability distribution over domain labels using the parameter ψd.where The Emo-DA module is capable of learning generalizable feature representations from the emotion data of different subjects, resulting in improved emotion recognition performance on both the source and target domains. Through domain adaptive training, the Emo-DA module aligns the feature representations of the source and target domains, thereby enhancing the generalization ability and adaptability of the model to the target domain. The overall training objective of the Emo-DA module can be expressed as Equation (6).f, ϕy, and ψd represent the parameters of the feature extractor Fθ, the emotion classifier Gϕ, and the domain classifier Dψ, respectively. xi, and xi comes from the source domain, and xi comes from the target domain. The Emo-DA module first optimizes the parameters θf and ϕy of the feature extractor Fθ and emotion classifier Gϕ by minimizing the classification loss and the feature extractor loss. This is achieved through the following formula, as shown in Equation (7):where θd of the Domain Classifier Dψ by maximizing its loss. This is achieved through the following formula, as shown in Equation (8):Then, The Emo-DA module optimizes the parameters ψThe two steps mentioned above are alternated until the network converges. During the domain adaptive learning process, a Gradient Reversal Layer is employed to induce the feature extractor to learn adversarial feature representations, as shown in Equation (9):xi represents the input feature representation, Rλ is the function of the Gradient Reversal Layer, and λ is a hyper-parameter that controls the strength of gradient reversal. During the forward, the Gradient Reversal Layer behaves as an identity map, making xi equal. However, during the backward, the gradient of where s to learn a function represented by the parameters θ, as illustrated in Equation (10).Pre-training and Fine-tuning are model-based transfer learning methods. The primary objective of this approach is to identify shared parameter information between the source and target domains, facilitating knowledge transfer. When provided with a target domain dataset * represents the optimized model parameters, and s. The optimization aims to find the best parameters θ that minimize the loss function and facilitate effective knowledge transfer from the source domain to the target domain.where θIn this section, the introduction of two widely used datasets and the description of the experiment settings are presented. Subsequently, a comparison is made between our proposed method and the baseline method. Finally, ablation experiments are conducted, and the results are reported and discussed.The study was carried out using the SEED , θ (4–8 Hz), α (8–14 Hz), β (14–31 Hz), and γ (31–50 Hz). The differential entropy (DE) features stand out as the most pertinent EEG characteristics for emotion recognition . To simplify the computation of the DE feature, Formula (12) is utilized. Formula (12) is given as follows.where DE indicates the value of differential entropy, serving as a measure of the unpredictability associated with continuous random variables. For EEG signal analysis, it is assumed that the signal follows a normal distribution, specifically In the experiments, the training and testing were performed on a Tesla V100-SXM2-32GB GPU, with the implementation carried out using the PyTorch framework. The main focus of the experiments was on cross-subject transfer. The EEG data of one subject was taken as the target domain, and the EEG data of all the remaining subjects served as the source domain. For training in the source domain, the source model was obtained. The samples in the source domain were randomly shuffled, and the data were divided into training and testing sets with a ratio of 7:3. Similarly, for training in the target domain, the target model was obtained. However, in this case, only 0.1% of the target domain samples were used for fine-tuning training, while the remaining samples were used for testing. During the training in both the source and target domains, the cross-entropy loss function was used. The summary of hyper-parameter settings for the experiments on the SEED and SEED-IV datasets is provided in In order to evaluate the effectiveness of the proposed model, a comparative analysis was conducted with several baseline methods using the SEED and SEED IV datasets. Brief introductions to each of these methods are provided below.DDC  is proposed, creating a novel hybrid transfer learning strategy. The results of DFF-Net on the SEED and SEED IV datasets demonstrate its superior performance in cross-subject EEG emotion recognition compared to other state-of-the-art methods. This introduces a fresh strategy for future EEG-based emotion recognition systems. Several noteworthy discussion points arise from the proposed DFF-Net model.The performance disparity of our proposed DFF-Net model between the SEED dataset and the SEED IV dataset is quite noticeable. Under identical experimental settings, the accuracy achieved in SEED IV dataset experiments is lower than that in SEED dataset experiments. The reasons for this discrepancy can be delineated as follows: Firstly, the SEED-IV dataset comprises four emotion classes: Happy, Sad, Fear, and Neutral, while the SEED dataset has three classes: Positive, Neutral, and Negative. Consequently, the experimental complexity of the SEED IV dataset is significantly higher compared to the SEED dataset. Secondly, the feature extractor of the DFF-Net model employs a Transformer architecture, a type of model that typically performs better with larger sample sizes. Given that the SEED IV dataset contains fewer samples than the SEED dataset, the performance of the model on the SEED IV dataset is comparatively inferior. Lastly, variations in sample quality between the SEED dataset and the SEED IV dataset contribute to the divergent performance of the DFF-Net model on these two datasets.To validate the significance of the Emo-DA module and fine-tuning components within our proposed DFF-Net model, we conducted ablation experiments on the DFF-Net model. The experiments were carried out under the same experimental settings. From the experimental results, it is evident that the strategy of disregarding any form of transfer learning yields the poorest outcomes. This strategy involves testing the transformer model directly on the target domain after training on the source domain. This further underscores the limitations of deep learning models in cross-subject EEG emotion recognition.Another observation from the experimental results is that employing the fine-tuning strategy alone surpasses the performance of using domain adaptation alone. This can be attributed to several factors. Firstly, fine-tuning enables the model to adjust its parameters according to the specifics of the target domain. This flexibility aids the model in maintaining alignment with subtle variations in target domain data, thereby enhancing its performance in cross-subject EEG emotion recognition. Secondly, domain adaptation cannot completely mitigate all domain discrepancies, such as variations in electrode placement or signal noise, which significantly impact model performance. Fine-tuning assists the model in more accurately learning these domain-specific features.In the final experimental results, it becomes evident that the performance of our proposed DFF-Net model surpasses that of using the fine-tuning strategy alone. This is because DFF-Net, as a hybrid transfer learning strategy, benefits from the Emo-DA module, which facilitates more effective alignment between the source and target domain distributions. In contrast to the standalone fine-tuning strategy, DFF-Net better leverages valuable information from the source domain while adjusting according to specific features of the target domain. In conclusion, the Hybrid Transfer Learning Strategy of DFF-Net effectively addresses the limitations of using domain adaptation or fine-tuning in isolation. This comprehensive approach ensures that DFF-Net can harness the advantages of domain adaptation and fine-tuning, consequently enhancing the accuracy of cross-subject EEG emotion recognition tasks.In this paper, we introduce a hybrid transfer learning strategy, specifically referred to as the domain adaptation with a few-Shot fine-tuning Network (DFF-Net), for the task of cross-subject EEG emotion recognition. First, we extract Differential Entropy (DE) features and map them spatially based on the electrode positions to generate the EEG feature representation, which serves as the input for our proposed model. Then, we employ the Vision Transformer (ViT) as the Feature Extractor, and building upon the original Domain-Adversarial Neural Network (DANN) model, we develop a domain adaptive learning module for EEG emotion recognition, named the Emo-DA module. Finally, we apply the Emo-DA module to pre-train a model on both the source and target domains, and then use fine-tuning on the target domain for cross-subject EEG emotion recognition testing. This approach is designed to better adapt to the specific features of the target domain, thereby enhancing the accuracy of the cross-subject EEG emotion recognition task. The proposed DFF-Net achieved average recognition accuracies of 93.37% on the SEED dataset and 82.32% on the SEED-IV dataset, surpassing the state-of-the-art methods. To assess the impact of different components in DFF-Net on EEG emotion recognition tasks, we conducted ablation experiments on both the SEED and SEED-IV datasets. The experimental results demonstrate that the integration of domain adaptation and fine-tuning effectively enhances the adaptability of the model to the target domain, mitigates the influence of domain discrepancies, and minimizes the reliance on large annotated datasets in the target domain. Ultimately, this approach significantly improves the accuracy of cross-subject EEG emotion recognition. The proposed DFF-Net introduces a novel approach for cross-subject EEG emotion recognition tasks. This method can also be easily applied to other cross-subject EEG classification tasks, such as motor imagery and sleep stage classification. However, the current model still has some limitations in practical applications. For instance, the model lacks the capability for real-time online processing, and it requires a small number of samples from the target domain during training. In future work, we will investigate the real-time online capability and domain generalization of DFF-Net for cross-subject EEG emotion recognition, aiming to further enhance its model generalization and practicality.https://bcmi.sjtu.edu.cn/home/seed/seed.html.Publicly available datasets were analyzed in this study. This data can be found here: WL: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Visualization, Writing – original draft, Writing – review & editing. HL: Formal analysis, Funding acquisition, Project administration, Writing – review & editing. HM: Conceptualization, Formal analysis, Funding acquisition, Project administration, Resources, Software, Supervision, Writing – review & editing. T–PT: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – review & editing. LX: Project administration, Funding acquisition, Software, Writing – review & editing.


Results: Positive BIRC6 staining was found in 91.7% of the subjects of OLP, 88.1% of HK, 86.1% of ED, 93% of OLPD, and 94.7% of OSCC. OSCC showed the highest BIRC6 expression (p = 0.00). The average total staining score was remarkably greater in OSCC and dysplastic lesions compared with HK . Conclusions: While the current study suggested that BIRC6 may play a role in the tumorigenesis of OSCC, its role in the malignant transformation of OLP has yet to be definitively established.Background: BIRC6, regarded as the pivotal member of the inhibitor of the apoptosis (IAP) family, has been linked to the development of different types of cancer in humans. The objective of this study was to examine the expression of BIRC6 in various oral conditions, including OLP with dysplasia (OLPD), hyperkeratosis (HK), OLP, epithelial dysplasia (ED), and oral squamous cell carcinoma (OSCC), to investigate its potential involvement in the development of OSCC and the pathogenesis and malignant transformation of OLP, which is known as a precancerous condition. Methods: In this retrospective cross-sectional study, 99 cases, consisting of 19 cases of OSCC, 21 cases of ED, 23 cases of OLP, 20 cases of OLPD, and 16 cases of HK as the control group, were investigated regarding BIRC6 expression by immunohistochemical staining. After that, the immunohistochemical expression of BIRC6 in the epithelial compartment was analyzed. Statistical analysis was performed to investigate the relationship between the expression of BIRC6 and clinicopathological variables. The statistical analysis of the data involved the use of one-way ANOVA, post hoc Tukey, Kruskal–Wallis, Chi-square, Spearman’s correlation, and Mann–Whitney tests. The significance level was set at  Oral health plays a significant role in maintaining general health ,2. Oral Oral lichen planus (OLP) stands among the chronic inflammatory diseases of the oral mucosa ,13. The Dysplasia refers to abnormal growth, and the grade of oral epithelial dysplasia (ED) helps assess the likelihood of a lesion becoming malignant. The presence of oral epithelial dysplasia is considered the gold standard for managing oral potentially malignant disorders ,17. The BIRC6 expression has been suggested by many researchers to be correlated with tumor stage and differentiation, angiogenesis, chemotherapeutic response, and other clinical properties in the context of cancer . PreviouIn this cross-sectional study, 99 histological samples were selected from the archive of the Oral and Maxillofacial Pathology Department  between 2011 and 2018, among which, 19 cases were diagnosed with OSCC, 21 cases with ED, 23 patients with OLP, 20 cases with OLPD, and finally 16 cases with HK. Subsequently, the hematoxylin-eosin (H&E) slides of the selected pathological lesions were re-evaluated to confirm the result of the previous diagnosis. In this study, specimens lacking a definitive diagnosis, displaying systemic diseases, featuring tumors in other body sites, or lacking adequate epithelial components were excluded from consideration. Erosive and ulcerative OLPs were also excluded. We gathered patients’ demographic information and clinical details, including age, gender, and lesion location, from their medical records. Subsequently, we conducted histopathologic grading for dysplastic and tumoral samples, collecting the resulting data for future reference.This study design was approved by the Ethics in Human Research Committee of Shiraz University of Medical Sciences (IR.SUMS.DENTAL.REC.1398.135). It was performed in full accordance with ethical principles, including the World Medical Association Declaration of Helsinki (version 2008). Informed consent was obtained from all participants in the present study.To perform immunohistochemical evaluations, sections of sample tissue were first prepared using a microtome with a thickness of 4 µm, and then the prepared tissue sections were mounted and fixed on glass slides. Paraffin-embedded sections of all the samples were deparaffinized in xylene and rehydrated in a decreasing serial dilution of ethanol in distilled water. Routine H&E staining was conducted on the rehydrated sections for histopathologic analysis. Further analyses were performed using immunohistochemistry (IHC) staining by labeling the sections with immunohistochemistry streptavidin–biotin and subsequent incubation with polyclonal anti-BIRC6 antibody . A brown-stained cytoplasm was considered positive for the staining, and a sample with no primary antibody coating was considered negative. Of note, healthy brain tissue was used as a positive control. Typically, the stained samples were evaluated using a light microscope with ×400 magnification for each slide, through which the amount of positively stained area was utilized to allocate intensity and percentage of BIRC6 gene expression. We observed more stained areas in the slides with heterogenous staining. The percentage of the positive cells was assessed in 1000 cells of each lesion at ×400 magnification. The intensity of immunohistochemical staining was also scored from 1 to 3 , and the number of the positively stained cells was scaled from 0 to 3  by two evaluators. The final score of each sample was achieved by multiplying the scores of staining intensity and percentage of positive cells, which was then classified as a low expression of BIRC6 when the final scores were between 1 and 3 and high when they were from 4 to 9 . p-values less than 0.05 were considered significant.Using the SPSS statistical software (version 18.0), the data were statistically analyzed using one-way ANOVA, post hoc Tukey test, and Kruskal–Wallis tests to compare the mean values of BIRC6 expression in the study’s groups. The intensity of staining and the final scores of each specimen were further analyzed using the Chi-square test. Spearman’s correlation and Mann–Whitney tests were also utilized to compare BIRC6 expression with demographic variables such as age and gender, respectively. p = 0.422, p = 0.854), or other clinicopathologic characteristics of the patients.In this study, 99 participants were included, consisting of 46 men and 53 women, with a mean age of 50.8 years old . An effective monitoring and evaluation required collecting baseline data of all the study samples, summarized in Positive cytoplasmic BIRC6 expression, indicated by a brown stain, was observed in epithelial cell components in all the study samples. In this regard, In the samples categorized in the ED group, positive BIRC6 staining was observed, and the mean percentage of BIRC6 expression attained in the suprabasal and basal layers was 86.19% and 99.04%, respectively . GeneralThe second highest value for BIRC6 expression was observed from the OLPD samples, with a mean percentage of 96.5% . The oveIn the OLP specimens, the mean value of BIRC6 expression was 91.7% in the suprabasal layer and 100% in the basal layer . In mostIn the HK group, the suprabasal layer had 88.12% BIRC6 expression . Only twAs mentioned earlier, the highest mean BIRC6 expression in the central layer was seen in the OSCC group. In all the samples, staining was more evident in basal/peripheral cells compared with suprabasal/central layers.p < 0.05). Moreover, the staining intensity and the final score of BIRC6 expression in the suprabasal layer differed significantly among these groups (p < 0.05). In the light of analysis via Dunn test, it was found that the suprabasal percentage in OSCC was higher than that of the HK and ED groups (p = 0.012). The intensity of suprabasal staining and the final score also appeared to be significantly different among the groups, which were remarkably greater in the samples of OSCC compared with the achieved values in HK . In addition, they were higher in the ED groups (ED and OLPD) than in the HK group (p = 0.01). The final scores in different grades of the dysplasia group are listed in p < 0.001, p < 0.001). In other words, as the intensity and final score of BIRC6 expression in the suprabasal layer elevated, the grade of dysplasia significantly increased (p > 0.05). Utilization of the Chi-square test and one-way ANOVA highlighted no difference between the clinicopathologic manifestations and the two variables of gender and age (p > 0.05). The degree of BIRC6 expression in basal and suprabasal layers among different groups was evaluated using the Kruskal–Wallis test. The data elucidated that the mean BIRC6 expression in the suprabasal layer significantly differed among groups (ncreased . More prThe modified expression of BIRC6 protein has been implicated in the pathogenesis of several human malignancies, making its expression a critical factor to study. BIRC6, also known as Apollon or Bruce, is a crucial member of the IAP family that performs its function by binding to caspases-3, -6, -7, -9, and SMAC and inhibiting their activities through its BIR domain . In thisThe upregulation and overexpression of BIRC6 have been detected in various types of tumors, including gastric carcinoma, colorectal cancer, and primary breast cancer, compared with normal tissues ,21,33. HOur study found that BIRC6 was overexpressed in OSCC cases, and to the best of our knowledge, this is the first report to clarify BIRC6 expression in OSCC. However, Banakar et al. assessed the serum level of BIRC6 in OSCC and found no significant difference in the serum levels of BIRC6 between patients with OSCC and healthy individuals . Other sStudies have shown that BIRC6 can regulate the expression of cyclin D1, a vital regulator of the G1 phase of the cell cycle, in cancer cells . BIRC6 hAs stated earlier, we detected that the average percentage of BIRC6 expression was significantly higher in OSCC than in HK cases, which was the control group. Similarly, Salehi et al. detected a higher BIRC6 expression in gastric carcinoma tissues compared with the normal margin of gastric tissues . GharabaThe elevated BIRC6 expression in the basal or peripheral cells of the specimens, especially the OSCC samples, agreed with the findings of previous studies in gastric carcinoma and childhood acute myeloid leukemia ,21. In tThe expression and function of apoptotic proteins in OLP are still unknown ,42. Our We also observed that BIRC6 expression was significantly higher in the dysplastic groups than in HK. As we know, Bcl-2 typically blocks the activation of caspase-3. After that, when Bax blocks Bcl-2 activity, caspase-3 activity is unchecked, and apoptotic cell death proceeds. Similarly, S Juneja et al. studied Bcl-2, an oncoprotein restraining apoptosis-like BIRC6 in ED, and showed that along with increasing the grade of dysplasia, the expression of the Bcl-2 protein elevated . These fConsistent with our findings, Muzio et al. stated that a high expression of one of the IAP members, named Survivin, was detected in 97% of oral epithelial dysplasia samples. As BIRC6 and Survivin both play roles in suppressing caspases, including caspase-3, -7, and initiator caspase-9, we can attribute this relationship to caspase activity between our finding and that of Muzio et al.’s study ,50. FurtSome studies suggested that the number of apoptotic cells, such as caspase-3 and Survivin, decreases in oral lesions due to the increased molecular abnormalities in epithelial cells and tumors .In the present study, no correlation was observed between different grades of tumor and BIRC6 expression. Some studies have shown that BIRC6 expression could be related to the grade of tumors, for instance, in non-small-cell lung carcinoma and prostate cancer ,32,40. AIn normal oral tissue, BI has rarely been described in humans, and the four reports available are similar to what we reported herein. We can describe BIRC6 expression with a broader range of functional alterations, and all benign cases were positive, which is not in line with Salehi et al. and Tang et al.’s studies ,25.The current study also detected BIRC6 expression in the cytoplasm of tumor cells, confirming previous studies showing that it is localized to the Golgi compartment and the vesicular system . SimilarYoshihara et al. reported the idea of basket clinical trials, wherein patients are assigned experimental treatments according to their genomic profile instead of the tumor’s tissue of origin. Consequently, the results of the current study may pave the way for novel avenues in the diagnosis and management of tumors .To the authors’ knowledge, this is the first study elucidating BIRC6 expression in OED, OLP, OLPD, and HK. These findings can be attributed to the known functions of the BIRC6 protein. However, further studies are needed to investigate different aspects of BIRC6 function in these lesions.Overall, the present study provides important insights into the expression of BIRC6 in various oral lesions and suggests that BIRC6 may play a role in the tumorigenesis of OSCC. However, additional studies involving larger sample sizes are required to validate these results and gain a more comprehensive understanding of the underlying mechanisms through which BIRC6 contributes to the development of oral cancer.One of the limitations of this current research is the limited number of cases that were studied. Therefore, further investigations with larger sample sizes are needed.The present study found a significant increase in BIRC6 protein expression in OSCC, ED, OLP, and OLPD compared with HK. These findings suggest that BIRC6 may serve as a novel and effective marker for predicting and diagnosing these diseases, and the early detection of BIRC6 expression may improve patient outcomes. This study confirmed the role of BIRC6 in the carcinogenesis of OSCC, but its function in the malignant transformation of OLP was not established. Further investigations are needed to determine whether BIRC6 can be used as an independent prognostic factor to guide treatment and improve survival rates for these diseases. These findings provide important insights into the potential clinical utility of BIRC6 as a biomarker for oral lesions and highlight the need for further research to fully elucidate its role in oral carcinogenesis.


This study aims to investigate the combined effects of per- and polyfluoroalkyl substances (PFAS) on allostatic load, an index of chronic stress that is linked to several chronic diseases, including cardiovascular disease and cancer. Using data from the National Health and Nutrition Examination Survey (NHANES) 2007–2014, this study examines the relationship between six PFAS variables  and allostatic load using Bayesian Kernel Machine Regression (BKMR) analysis. The study also investigates the impact of individual and combined PFAS exposure on allostatic load using various exposure-response relationships, such as univariate, bivariate, or multivariate models. The analysis reveals that the combined exposure to PFDE, PFNA, and PFUA had the most significant positive trend with allostatic load when it was modeled as a binary variable, while PFDE, PFOS, and PFNA had the most significant positive trend with allostatic load when modeled as a continuous variable. These findings provide valuable insight into the consequences of cumulative exposure to multiple PFAS on allostatic load, which can help public health practitioners identify the dangers associated with potential combined exposure to select PFAS of interest. In summary, this study highlights the critical role of PFAS exposure in chronic stress-related diseases and emphasizes the need for effective strategies to minimize exposure to these chemicals to reduce the risk of chronic diseases. It underscores the importance of considering the combined effects of PFAS when assessing their impact on human health and offers valuable information for policymakers and regulators to develop strategies to protect public health. Exposure to multiple contaminants concurrently is a fact of life . ExposurPFAS have been utilized extensively in consumer goods for more than 70 years. The utilization of PFAS in commercial and industrial settings is due to their special thermal stability and surface activity characteristics, including hydro- and lipophobicity .PFAS have been used as lubricants in industrial processes, as additives in pesticides and pharmaceuticals, and as lubricants in food packaging, firefighting foams, and coatings to produce nonstick and stain-resistant qualities . The negMultiple per- and polyfluoroalkyl substances (PFAS) coexist at moderate to high levels in various situations that are also associated with high levels of chronic stress. Allostasis is a biological process that allows individuals to adapt their physiological traits to meet environmental demands and maintain physiological balance. Unlike homeostasis, which refers to the maintenance of physiological parameters within a set range, allostasis enables effective responses to adversities. However, if these challenges persist without resolution, the body may reset its physiological set points to higher levels in order to cope, potentially leading to long-term health consequences . When anAllostatic load combines multiple physiological markers to create a comprehensive index of biological stress, which can quantify the impact of chronic exposure to stressors on the body. Psychosocial stressors, such as poverty, inequality, and lack of resources, can increase allostatic load in communities, interacting with environmental conditions. Health outcomes are influenced by various contextual factors, including regional policies and infrastructure. Real-world exposure to stressors, such as PFAS and other environmental contaminants, varies considerably at individual and population levels ,12. GiveBobb et al. introduced a Bayesian Kernel Machine Regression (BKMR) for evaluating pollutant mixtures using the R software, version 4.1.2  . BKMR, iBKMR modeling is a powerful method that offers several advantages over traditional modeling approaches. Firstly, it enables thorough modeling of both chemical exposure and outcome, allowing for a more accurate understanding of the relationships between these variables. Additionally, BKMR modeling assesses chemical components independently of the independent—dependent function, providing a clearer picture of the individual effects of each chemical. Another key advantage is its ability to assess chemical mixture effects, which is particularly important in situations where exposure to multiple chemicals is common ,17.BKMR is a useful tool for assessing the health effects of chemical mixtures, such as multiple PFAS. It employs variable selection to estimate the importance of each exposure in the mixture through posterior inclusion probabilities (PIPs). This approach helps overcome challenges associated with collinearity and a high correlation between exposures in epidemiological and toxicological studies, making BKMR a valuable tool for researchers ,18.Through the use of BKMR, this study hypothesizes that there exists a significant association between exposure to multiple PFAS simultaneously and high levels of allostatic load. The primary objectives of this study are to determine the extent of association between exposure to multiple PFAS and allostatic load, identify the specific PFAS that contribute the most to the observed association, and investigate these factors by modeling allostatic load as a categorical and continuous variable.The findings of this study provide critical insights into the effects of multiple PFAS exposure on human health and contribute to the public health literature aimed at understanding and reducing the risk of PFAS exposure. Furthermore, identifying specific PFAS that contribute the most to the observed association can help prioritize regulatory actions and interventions aimed at reducing exposure to these harmful chemicals.This investigation utilized data from the National Health and Nutrition Examination Survey (NHANES), administered by the Centers for Disease Control and Prevention (CDC). The NHANES collected biological specimens and survey data on demographics, social, and clinical factors for the civilian, non-institutionalized population residing in any of the 50 states of the United States of America and the District of Columbia (DC) using a complex, multistage probability design. This study used data for adults aged 20 years and older.n = 23,482 individuals, consisting of 11,414 males and 12,068 females. The study aimed to investigate several variables of interest, including exposure to specific perfluoroalkyl substances (PFAS), such as Perfluorodecanoic acid (PFDE), Perfluorononanoic acid (PFNA), Perfluorooctanesulfonic acid (PFOS), Perfluoroundecanoic acid (PFUA), Perfluorooctanoic acid (PFOA), and Perfluorohexanesulfonic acid (PFHS). Additionally, the study also examined the allostatic load index, which was operationalized using the following clinical and biomarkers: Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), triglycerides, HDL cholesterol, total cholesterol, C-reactive protein (CRP), Body Mass Index (BMI), hemoglobin A1C, albumin, and creatinine clearance. Overall, this study utilized a large and diverse sample size, as well as a comprehensive set of variables of interest, to investigate the relationships between PFAS exposure and allostatic load.For this study, data were assessed over four cycles: 2007–2008, 2009–2010, 2011–2012, and 2013–2014. The final sample size for our study was As part of the NHANES, participants visited a mobile examination center (MEC) where they provided blood samples for laboratory analysis. The blood samples were used to assess a wide range of health indicators, including markers of exposure to environmental contaminants such as per- and polyfluoroalkyl substances (PFAS).To ensure that laboratory measurements were conducted under comparable circumstances, the MEC’s controlled environment was used. Blood samples were collected in polypropylene and polyethylene containers, and depending on the participant’s age, at least 0.5 mL of serum was processed. Samples were then frozen or refrigerated before being sent to labs across the country.For this study, participants who were scheduled for a morning session were required to fast for nine hours before the blood draw. After the initial blood draw, they were instructed to ingest 75 g of dextrose (10 oz of glucose solution) within ten minutes. Two hours later, a second blood sample was collected.To extract and quantify PFAS from human serum, a 50 L aliquot was first diluted with formic acid and injected into a commercial column switching system for concentration on a solid-phase extraction column. This method of extraction was used to separate the analytes from other serum constituents. High-performance liquid chromatography was then employed to separate the analytes from each other. Detection and quantification of the PFAS was achieved using tandem mass spectrometry, which converts liquid phase ions into gas-phase ions. To detect and quantify the PFAS, an electrospray ionization source was modified to produce negative-ion Turbo Ion Spray (TIS) ions. This method has a low limit of detection (LOD) in the low parts per billion range, which enables the rapid detection of these PFAS in human serum. Overall, this method provides a reliable and sensitive approach for the detection and quantification of PFAS in human serum.All PFAS analytes in the dataset had a constant detection limit of 0.10 ng/mL. For each analyte, two variable names were provided in NHANES: a value of “0” indicated that the result was at or above the detection limit, while a value of “1” indicated that the result was below the detection limit. In cases where the analytic result was below the lower limit of detection, an imputed fill value was inserted into the analyte results field. This value was calculated as the lower limit of detection divided by the square root of 2 (LLOD/sqrt(2)), which was 0.10/√2 = 0.07. Consequently, the LOD for each PFAS was either 0.10 or 0.07 ,20.2), while glycohemoglobin was measured using the A1c G7 HPLC Glycohemoglobin Analyzer . The Roche/Hitachi Modular P Chemistry Analyzer was used to measure urine creatinine . Finally, a non-competitive, double-antibody method was employed for measuring human urinary albumin, involving covalently attaching an antibody to human albumin to polyacrylamide beads, reacting the solid-phase antibody with a urine specimen, and measuring the fluorescence of the resulting complex using a fluorometer.The markers of interest that went into the allostatic load were: Systolic blood pressure—SBP, diastolic blood pressure—DBP, triglycerides, high-density lipoprotein (HDL) cholesterol, and total cholesterol, inflammatory (C-reactive protein—CRP), and the metabolic systems . CRP was measured via latex enhanced nephelometry and quantified using anti-CRP antibodies with a hydrophilic shell and polystyrene core of CRP particles. BMI was calculated as weight (kg) divided by the square of standing height —PFDE, PFNA, PFOS, PFUA, PFOA, and PFHS—on allostatic load, which was the dependent variable. Allostatic load, a measure of physiological dysregulation, was assessed using established biomarkers. The six PFAS were considered the independent variables, and we aimed to model their combined effect on allostatic load through statistical analysis. This study employed BKMR analysis to explore the relationship between the independent and dependent variables and assess the magnitude and significance of their associations.We used BKMR with the hierarchical variable selection method due to highly correlated variables and collinearity of key variables within the dataset. In this study we built BKMR models in the R program via the R package (bkmr). BKMR models evaluated the role of mixtures or multipollutant exposures  on allostatic load using the kmbayes function.Using BKMR we modeled the effect of multiple PFAS on allostatic load  adjusting for covariates “Gender”, “Age”, “Cigarette smoking”, “Physical activity”, “Ethnicity”, “Occupation”, “Income”, “Alcohol Consumption”, “Education”, “Birthplace”, and “Time in the US” in our primary analysis and then in subsequent analysis adding lead (Pb) to the covariates in order to account for the demonstrated association of blood lead levels with allostatic load.Binary outcomes were included in the BKMR package using the probit model via a general linear model for efficiency and convenience of computation and to overcome some of the issues that may arise in the dataset, such as collinearity under Bayesian inference ,34. FinaThe overall analytical plan for the first part of this study was to assess the combined effect of PFDE, PFNA, PFOS, PFUA, PFHS, and PFNA on allostatic load  while adjusting for the following covariates: Gender, Age, Cigarette smoking, Physical activity, Ethnicity, Occupation, Income, Alcohol Consumption, Education, Birthplace, and Time in the US.The posterior inclusion probabilities (PIPs) of selected variables in the model range from 0 to 1, where a higher value indicates a stronger association with the outcome variable, which is allostatic load in this study. PIPs close to 1 suggest that the variables are important predictors of allostatic load and play a significant role in the model. Conversely, variables with PIPs closer to 0 are less relevant to the outcome of interest and do not impact allostatic load significantly. These results are presented in The study firstly compares different models  and finds that the BKMR model is suitable for analyzing multipollutant exposures. The BKMR model provides a better fit than other models for both individual variables and the overall model, as shown in The modeling below was repeated with lead (Pb) added as a covariate to see if the association between allostatic load and blood lead levels significantly altered the outcomes of the study.To investigate the relationship between multiple PFAS and allostatic load, we modeled allostatic load as a continuous variable rather than a binary variable, as the latter approach could have missed important nuances in the relationship. We employed the Bayesian Kernel Machine Regression method to evaluate the impact of multiple PFAS exposures on allostatic load, while controlling for covariates such as gender. This approach allowed us to comprehensively analyze the associations between PFAS and allostatic load and examine potential nonlinear and interactive effects. The models were adjusted for relevant covariates to ensure the accuracy and reliability of the results.The following three figures  illustraFor example, in 2007–2008, the lower quartile, median, and upper quartile concentrations of PFDE were 0.070, 0.090, and 0.200, respectively. The interquartile range was 0.130, indicating that the middle 50% of the data was between 0.025 and 0.155. The same pattern can be observed for the other PFAS variables in each cycle.Overall, In this study, we used BKMR to assess the relationships between allostatic load and six PFAS combinations in a sample of US adults from the NHANES. The concentration of various PFAS were relatively homogenous across the four cycles of data making the data ideal for our study. Specifically, the findings from the study reveal important insights into the concentrations and time trends of the PFAS of interest in the study cohort. The results indicate that the concentrations of PFAS were relatively stable over time, with only small variations observed.BKMR modeling is a statistical technique that is employed to evaluate the significance of variables based on their posterior inclusion probability, and the multivariable relationships between the predictor variables and outcome variables, including univariate, bivariate, and multivariate exposure-response relationships, as well as their effects on one another. This method was used to identify the most important PFAS and their interactions in producing high allostatic load, while accounting for potential confounders and covariates. By considering the relationships between multiple variables simultaneously, BKMR modeling enabled a more comprehensive understanding of the complex interplay between the exposures and outcomes. Among the PFAS examined in this study, BKMR analysis revealed that higher serum concentrations of PFDE, PFNA, and PFUA tended to be associated with a higher allostatic load with these PFAS being identified as the most important in the relationship between PFAS and allostatic load. When allostatic load was modeled as a continuous variable the most critical PFAS were PFDE, PFOS, and PFNA. The fact that PFOS became more important in the analysis suggests that the nature of the data and the underlying relationship between allostatic load and PFAS is complex. To best capture nonlinear interactions between the variables, modeling allostatic load as continuous may paint a more accurate picture of the effects of multiple PFAS. This is so because allostatic load modeled as a continuous variable has more granularity to detect subtle changes, and as a categorical variable allostatic load only provides information on the category to which each observation belongs, and not on specific values within the category. Finally, as a continuous variable, allostatic load has increased sensitivity to changes or variations in data.This study is the first of its kind to model PFAS with allostatic load using BKMR. That said, other studies may offer additional insight. Ding et al. conducted a study to examine the association between PFAS exposure and adipocytokine concentrations and found significant correlations between PFAS mixtures and the health-related outcomes of leptin and adiponectin in their models. Specifically, the PFAS chemicals PFDA, PFOS, PFNA, and PFHpS were identified as important contributors to these associations . Tian etIn our study, using BKMR’s feature of being able to estimate hierarchical importance, PFNA was found to have a larger influence on allostatic load, followed by PFDE and PFUA when modeling allostatic load as a binary variable. Such insight can be critical for public health officials working in environments where the PFAS exposure is vast and contains various PFAS. By considering all exposures in a multivariable space and connecting the exposure with the health outcome via the Gaussian kernel function, the BKMR model allows for an excellent exploration of the relationship between PFAS and allostatic load.The BKMR model’s conclusions about the direct relationship between PFNA, PFDE, and PFOS with allostatic load when modeled as a continuous variable are interesting in that the most critical variables in order from high to low were PFOS, PFDE, and PFNA. Thus, when allostatic load is viewed as a continuous variable the traditional PFAS, PFOS, becomes the most important variable. This is especially insightful since BKMR modeling correct for the confounding effect, which is brought on by the strong correlations between PFAS when compared to traditional multiple linear regression models . SpecifiPFAS compounds in combination with one another may interact and produce a cumulative effect that is either more or less than the total of the fractions of the individual PFAS because people are typically exposed to numerous PFAS at once from various sources . The mecThe strengths of this study are as follows. The study used mixture analysis to evaluate the combined exposure of multiple PFAS on allostatic load. In addition, we used a nationally representative sample of adults. Finally, the study adjusted for the relevant confounders which lowered the bias that may have occurred. The cross-sectional study design, one of the study’s drawbacks, makes it challenging to conclude whether a causal relationship between PFAS exposure and allostatic load exists. Finally, we did not employ the NHANES survey weights in BKMR models, comparable to other earlier studies using NHANES data, because weighting algorithms were not included in the R packages for BKMR.In the current investigation, we found direct correlations between exposure to selected PFAS and allostatic load. The PFAS mixtures showed statistically significant overall direct correlations with allostatic load. Our study demonstrated that exposure to particular PFAS in combination might adversely promote the body’s stress response.Based on the results of the current study, there are significant associations between allostatic load and exposure to PFDE, PFNA, PFOS, and PFUA. Our findings suggest that an accumulation of PFAS exposure could have negative effects on the body’s ability to cope with stress.