The PDZ family has drawn attention as possible drug targets because of the domains’ wide ranges of function and highly conserved binding pockets. The PICK1 PDZ domain has been proposed as a possible drug target because the interactions between the PICK1 PDZ domain and the GluA2 subunit of the AMPA receptor have been shown to progress neurodegenerative diseases. BIO124 has been identified as a sub µM inhibitor of the PICK1–GluA2 interaction. Here, we use all-atom molecular dynamics simulations to reveal the atomic-level interaction pattern between the PICK1 PDZ domain and BIO124. Our simulations reveal three unique binding conformations of BIO124 in the PICK1 PDZ binding pocket, referred to here as state 0, state 1, and state 2. Each conformation is defined by a unique hydrogen bonding network and a unique pattern of hydrophobic interactions between BIO124 and the PICK1 PDZ domain. Interestingly, each conformation of BIO124 results in different dynamic changes to the PICK1 PDZ domain. Unlike states 1 and 2, state 0 induces dynamic coupling between BIO124 and the αA helix. Notably, this dynamic coupling with the αA helix is similar to what has been observed in other PDZ–ligand complexes. Our analysis indicates that the interactions formed between BIO124 and I35 may be the key to inducing dynamic coupling with the αA helix. Lastly, we suspect that the conformational shifts observed in our simulations may affect the stability and thus the overall effectiveness of BIO124. We propose that a physically larger inhibitor may be necessary to ensure sufficient interactions that permit stable binding between a drug and the PICK1 PDZ domain. The PDZ (PSD-95/Dlg1/ZO-1) family is a large protein family that is involved in protein–protein interactions and regulating signaling pathways ,3,4,5,6.4-(C5)2 was shown to alleviate neuropathic pain [PICK1 (Protein Interacting with C Kinase-1) is an especially unique PDZ-protein as it is the only protein in the human proteome that contains both a PDZ domain and a BAR (Bin/amphiphysin/Rvs) domain ,37,38. Thic pain ,47. Whil50) of 0.51 µM, signifying good activity. Marcotte et al. produced a co-crystal structure of the PICK1 PDZ-BIO124 complex that revealed an interaction pattern that mimics the interactions with natural ligand GluA2.BIO124 is a sub µM inhibitor of the PICK1–GluA2 interaction that was identified by Marcotte et al. using a fluorescence polarization assay . The PICThe purpose of this study is to use all-atom molecular dynamics (MD) simulations to explore how the atomic-level interaction pattern between the effective sub-µM inhibitor BIO124 and the PICK1 PDZ domain affects the dynamics of the PICK1 PDZ domain. Because the PDZ family is a classic example of dynamic allostery in small modular domains, it has been of particular interest to explore how natural ligands trigger dynamic changes in various PDZ domains. We will apply MD simulations to explore how BIO124, which was designed to mimic natural ligand GluA2, alters the dynamics of the PICK1 PDZ domain. MD simulations have proved to be a reliable tool to explore the dynamics of the PDZ family ,55,56,57+ or Cl−) were added to neutralize the system at 293 K. Steepest-descent minimization and a 1 ns MD equilibrium simulation was carried out to generate equilibrated starting structures for the MD simulations. All bonds with hydrogen atoms were converted to constraints with the algorithm LINear Constraint Solver (LINCS) [We modeled the PICK1 PDZ–BIO124 complex with all-atom MD simulations. BIO124 is a small-molecule inhibitor designed by Marcotte et al. to mimic PICK1 PDZ-GluA2 interactions . The exp (LINCS) , and a N (LINCS) ,66 was uWe calculated the distance between binding pocket residue I37 and BIO124 to monitor the possibility of any dissociation events during the simulations. Distance plots are shown in Protein network analysis was used to describe the allosteric network between BIO124 and the PICK1 PDZ domain. Protein network analysis calculates the correlated movements between residues within a protein or protein complex by constructing residue-based and community-based weighted network graphs based on a trajectory. Each residue is represented by a node in a network and the links between nodes are the cross-correlation values between these nodes. The displacement of the Cα atoms are used to assess the magnitude of all pairwise cross-correlation coefficients . CorrelaTime-resolved force distribution analysis (TRFDA)  was usedHydrogen bond analysis was performed to explore the interaction pattern between BIO124 and the PICK1 PDZ domain in our simulations and to serve as a direct comparison to experimental results. The analysis revealed an interaction pattern that is in relatively good agreement with the crystal structure of the PICK1 PDZ-BIO124 complex  corresponds to the X-ray structure of the PICK1 PDZ–BIO124 complex (PDB ID: 6BJO) . The X-rOur simulations produce three binding conformations of BIO124 that correspond to three unique hydrogen bonding networks. While hydrogen bonds are one category of key atomic-level interactions in protein-ligand binding, we also wanted to explore the role of hydrophobic forces in each of the three states of BIO124. The crystal structure of the PICK1 PDZ-BIO124 complex reveals various hydrophobic interactions. In the crystal structure, the cyclopentyl group of BIO124 forms hydrophobic interactions with I37, A87, and I90. The piperidine core of BIO124 forms hydrophobic interactions with L32 and F53. Lastly, the bromophenyl moiety aligns with the carbon chain of K83. To explore the hydrophobic interactions in our simulations, we performed contact map analysis across each state of BIO124. We only consider the contact formed with heavy atoms of BIO124. The most probable contacts with the complex at each state are listed in While states 1 and 2 are not observed in the experimental structure of the PICK1 PDZ-BIO124 complex, many of the interaction patterns observed in these states are characteristic of the atomic-level interactions between the PICK1 PDZ domain and natural ligand GluR2 (PDB ID: 2PKU) , for whiTime-resolved force distribution analysis (TRFDA) was performed to reveal the punctual stress on each PICK1 PDZ residue as a result of interactions with BIO124 . TRFDA cThe analyses described above reveal that the three binding conformations of BIO124 correspond to three unique sets of atomic-level interactions between BIO124 and the PICK1 PDZ domain. We suspect that each set of interactions will uniquely affect the dynamics of the PICK1 PDZ domain. To explore this possibility, we performed protein network analysis to reveal the coupling of major movements by creating protein structure networks based off the primary motions of each residue. As shown in Protein network analysis reveals that the unique conformational states of BIO124 can induce unique dynamic changes to the PICK1 PDZ domain. We suspect that these dynamical differences are in direct relation to the atomic-level interaction pattern between BIO124 and the PICK1 PDZ domain. This begets the question—what specific atomic-level interactions between BIO124 and the PICK1 PDZ domain are responsible for the unique dynamic changes in each binding conformation? Here, we will specifically address the dynamic coupling between BIO124 and αA helix that occurs in state 0.Our recent review of allosterism in the PDZ family  revealedIn effort to explore the role of I35 in propagating signal to the αA helix, we performed distance distribution analysis and time-resolved force distribution analysis (TRFDA) to gain an in-depth understanding of the interactions between BIO124 and I35 in the three conformational states of BIO124. As shown in The purpose of this work is to use all-atom MD simulations to investigate how the atomic-level interactions between BIO124 and the PICK1 PDZ domain affect the dynamics of the PICK1 PDZ domain. We found that (1) BIO124 has multiple binding conformations with the PICK1 PDZ domain, (2) the three unique binding conformations of BIO124 result in unique dynamic changes to the PICK1 PDZ domain, and (3) interactions between BIO124 and I35 may be key to inducing dynamic allosterism at the αA helix.Our simulations reveal three unique binding conformations between the PICK1 PDZ domain and BIO124, referred to here as state 0, state 1, and state 2. Each conformation is characterized by a unique hydrogen bonding network. In state 0, BIO124 forms a hydrogen bonding network with the backbone of I33, G34, and I35. These hydrogen bonds agree with those observed in the experimental structure of the PICK1 PDZ–BIO124 complex . In statWhile the three binding conformations of BIO124 may indeed be an artifact of our simulations, they present a unique opportunity to explore the role of specific interactions in inducing specific dynamic changes to the PICK1 PDZ domain. Our results indicate that the conformation of BIO124 directly affects the dynamics of the PICK1 PDZ domain. In state 0, the major motions of BIO124 are coupled with the carboxylate-binding loop, βB strand, βC strand, and αA helix of the PICK1 PDZ domain. In state 1 and state 2, the major motions of BIO124 are coupled with the αB helix of the PICK1 PDZ domain. It is worth stressing that state 0 of BIO124 can induce dynamic changes to regions of the PICK1 PDZ domain that are distal from the binding pocket, including the βC strand and the αA helix. Previous work has identified the αA helix of the PDZ family as a region affected by dynamic allostery and has pointed to a pathway of signal transduction from structural equivalents of I35 to structural equivalents of A58 ,83,84. OInhibiting PICK1–GluA2 interactions may be an effective therapy for Alzheimer disease  and, potEach conformation of BIO124 dynamically couples with different regions of the PICK1 PDZ domain to form a stable complex. For example, in state 0, BIO124 dynamically couples with the βB strand, βC strand, and αA helix, and in states 1 and 2, BIO124 dynamically couples with the αB helix. Interestingly, natural ligands binding to well-studied PDZ domains such as PSD-95 PDZ3 and PTP-BL PDZ2 have been shown to induce allosteric networks that include residues from both regions described above ,82,86,87


Subcortical pathology is known to be highly prevalent in dementia and motor neurodegenerative conditions, however, how these subcortical changes impact on disease presentation, progression and symptomology is still highly debated. This is not trivial since virtually all cortical regions project or have feedback loops to subcortical structures, indicating that subcortical structures contribute to the well-known “cortical symptomology.”in-vivo characterization of regional and subnuclei changes within subcortical brain structures and their respective neural connections. Increasingly, the importance of subcortical pathology is being recognized in the early and even asymptomatic stage of neurodegenerative disorders, with overlapping patterns of involvement identified with increasing disease progression.Over the past decade, there have been significant advancements of The focus of this Research Topic was to highlight current advances in the understanding of subcortical pathology across the spectrum of dementia and motor neurodegenerative syndromes. In particular, it addresses how do these subcortical changes: (i) relate to early clinical diagnosis and phenotyping; (ii) are potential disease markers of motor, cognitive and behavioral impairment; (iii) relate to disease progression, and (iv) can be characterized using cutting-edge technologies. We present five articles highlighting the critical role of subcortical pathology across the spectrum of neurodegenerative conditions, including mild cognitive impairment (MCI), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).Jung et al. presented a novel 5 time-point longitudinal PET/MRI study of MCI patients in South Korea demonstrating a mediating effect of progressive subcortical abnormality on memory and executive function. Notably, time-varying change in thalamic volume, but not subcortical vascular burden, mediated progressive cognitive dysfunction. This builds upon the growing focus of thalamic abnormality as an important imaging biomarker of early and dissociable neurodegenerative pathology  that there is an important need to standardize imaging approaches toward subcortical structures. Such standardization, in the form of harmonization of acquisition and/or analysis protocols would allow more systematic investigation of subcortical changes across time and facilitate multi-center observational and intervention studies for neurodegenerative syndromes. Such standardization processes have been established for some time for cortical imaging but remain overlooked for subcortical regions.The compiled studies show the increasing progress and sophistication applied to subcortical analyses across neurodegenerative conditions and the importance of regional subcortical abnormality as imaging targets for capturing sub-clinical pathology, clinical phenotyping, and disease progression. Despite this promising progress, several limitations still emerge which need to be addressed in future studies. Specifically, the heterogeneity of imaging methods and their application makes it difficult to compare findings across studies, even within the same disease pathophysiology. It highlights a problem which has been recognized by several international working groups (hippocampus subfield group, for details see Temp et al. provide a timely perspective article introducing the unique advantages of Bayes theorem and applied parameter estimation as a methodological alternative to the more commonly applied p value focused Fisher statistics for detecting statistical significance. Specifically, the ability to allow for comparisons of multiple alternative hypotheses for small effects is of interest when analyzing subcortical structures, as it allows for discrimination between groups of patients or at the individual patient level. With recent neuroimaging publications of population-based brain development trajectories across the human life-span (Touching upon the impact of standardized analysis protocols, ife-span , it has Taken together, it emerges that cortical-subcortical loops and the integrity of subcortical structures are a critical aspect for further investigation, in particular for determining how subcortical changes contribute to “cortical symptomology.” There is still a common notion that subcortical changes in many neurodegenerative conditions emerge from Wallerian degeneration caused by cortical changes, based on the significant body of literature reporting cortical findings. However, clearly this is not yet established since subcortical changes might precede cortical changes for some neurodegenerative condition or certain symptoms. Better standardization and modeling of inter-individual differences will allow us to establish the relationship of cortical and subcortical changes in disease progression and symptomology across neurodegenerative conditions. Only then will it be possible to delineate cortical and subcortical contributions to neurodegenerative syndromes, without the current, inherent focus on cortical changes.We hope the readers enjoy our Research Topic submission as it highlights an important but under-researched area, which has great potential for future neurodegenerative disease diagnosis and management.ST, JM, and MH contributed equally to the interpretation and views expressed in the editorial. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure. In the field of machine learning research, supervised learning methods have already witnessed the outstanding performance in many applications. The key point of supervised learning is to collect sufficient labeled datasets for model training, which also limits the usage of supervised learning in scenarios with a lack of training data. Furthermore, data annotating is usually a time-consuming, labor-expensive, or even unrealistic task. To settle this situation, domain adaption (DA) is a promising methodology that aims to learn an adaptive classifier for the target domain tasks by making use of labeled data from source domains ,2,3,4. IHowever, due to the mismatch between the source domain data and the target domain task, there is an uncertainty in DA when source domain data transfers to tasks of the target domain. As shown in Most DA research works adopted metric learning to minimize the data differences between the source and target domain for getting an adaptive classifier. Some works map the source and target data instances into a common feature space by minimizing the gap between the data distributions of the source and target domain, such as transfer component analysis (TCA) , correlaThe uncertainty is important for evaluating the adaptation degree of the source domain data about target tasks. The study of uncertainty has been successfully applied in traditional machine learning, such as bayesian-based uncertainty , evidencTo solve these problems, in this paper, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise a reliable adaptive classifier with the uncertainty measure. Specifically, we first construct an evidence net based on evidence theory for measuring the uncertainty of source domain data about the target domain classification task. It can calculate the proportion of uncertainty for each source domain instance in the target domain classification task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine (SVM). The contributions of this paper are summarized as follows.Designing an evidence net based on evidence theory to measure the uncertainty of source domain data about a target domain classification task.Designing a general loss function with uncertainty measure for learning of the adaptive classifier.Extending the SVM by the general loss function with uncertainty measure for enhancing its transferred performance.The remainder of the paper is organized as follows. We start by reviewing related works in In this section, we discuss previous works on domain adaptation that minimizes the data difference between the source and target domain. In addition, we introduce the evidence theory that is most related to our work.We will briefly introduce the domain adaptation with metric learning. These methods leverage the metric methods to reduce the data difference between two domains.Maximum mean discrepancy (MMD)  takes adKullback–Leibler (KL) divergence  can measJensen–Shannon (JS) divergence is similar to KL divergence and measures the difference between the source domain and target domain. However, the JS divergence solves the asymmetry problem of KL divergence. Joshua Giles et al.  use JS dThe Wasserstein distance derives from the optimal transport problem. It can be used to measure distances between two probability distributions. Shen et al.  reduce tIn summary, the core idea of most methods is to minimize the distribution difference between the source and target domain. However, they ignore the uncertainty between the source domain data and the target domain task.Evidence theory can be considered a generalized probability ,40. It ckth category, and Let A itself and A is called a focal element. In classification problems, if The mass function x belong to green based on evidence E. For example, we assume a classification problem that distinguishes colors. The frame of discernment is k is the degree of conflict between Dempster’s rule reflects the combined effect of evidence. Let In domain adaptation, the key problem of the uncertainty measure is how to evaluate the uncertainty in the target domain classification task for each source domain data. We consider that the lower uncertainty of instance represents less information loss in domain adaptation. To achieve this, we construct an evidence net based on evidence theory. It consists of two key steps (1) obtaining a trusty evidence set, and (2) designing the evidence net based on evidence theory. We describe them separately below.Let us consider a simple scenario with a large number of instances labeled source-domain n target domain instances similar to the source domain instances Given a source-domain instance lated ashxs,Φt=ϕxBased on the above analysis, the objective function of Equation  to obtaiThe optimal evidence set Equation  can be sx about the classification task. In domain adaptation, In the evidence theory, suppose that In this section, motivated by evidential k-Nearest Neighbor  and neurk classes, we decompose the evidence set lth element in the evidence subset.According to According to the decomposition of the evidence set As shown in n nodes, and we denote the node of layer (1) Layer k nodes, and we denote the node as (2) Layer (3) In layer Algorithm 1 The uncertainty measure based on evidence net for source domain dataInput: source domain Output: source domain for alldo 1:  2: Generate an evidence set Equation . 3: Estimate uncertainty end for 4: return 5: w is the parameter of the model. In order to verify its effectiveness, we extend the loss function with an uncertainty measure to support vector machine (SVM).The learning algorithm with uncertainty measure can be transformed into a problem of regularized risk minimization with uncertainty Based on the general loss function, we propose an improved support vector machine with an uncertainty measure (SVMU), which integrates the uncertainty of the source domain instance about the target domain task to SVM. The SVM uses only one penalty factor to control the balance between margin maximization and misclassification. However, in domain adaptation, due to domain differences, the classification hyperplane controlled by only one penalty factor cannot effectively distinguish classes of the target domain. The SVMU can change the penalty factor by the uncertainty measure. It makes the instances of the source domain that are beneficial to the target domain classification task become the new support vectors and diminishes the importance of some instances that have negative effects. Thus, SVMU is more flexible and superior in domain adaptation than SVM. The details of SVMU are described as follows.b is the bias parameter.SVM maps the input points into a high-dimensional feature space and finds a separating hyperplane that maximizes the margin between two classes in this space. According to the general loss function, Equation , the optTo solve this optimization problem, we construct the Lagrangian functionTo find the saddle point of By applying these conditions to the Lagrangian function , problemThe optimal solution of  can be dC controls the balance between margin maximization and misclassification. A larger C allows the SVM to have fewer misclassification and a narrower margin. Conversely, a smaller C makes the SVM ignore more training points and obtains a larger margin. Due to the existing uncertainty of the source domain data about the target domain task, with only one penalty factor, it is difficult to control the balance between margin maximization and misclassification in the target domain task. This may result in negative transfer when using SVM as the classifier.In the traditional SVM, the only penalty factor C becomes C. Thus, the selection of support vectors does not rely on a single penalty factor but is determined by the uncertainty C by uncertainty Based on the above analysis, applying uncertainty to SVM, it can be found that the single penalty factor In the experiments, we evaluate the adaptive classifier with an uncertainty measure on various kinds of data, including texts and images. The descriptions of the datasets are listed below.B), dvds (denoted as D), electronics (denoted as E), and kitchen appliances (denoted as K). Each review is assigned to a sentiment label, Amazon product reviews dataset  is the bA, images downloaded from online merchants), Webcam , DSLR , and Caltech-256 (denoted as C). The dataset includes 10 classes: backpack, touring bike, calculator, head Caltech, phones, computer keyboard, laptop-101, computer monitor, computer mouse, coffee mug, and video projector. There are 8 to 151 samples per category per domain and 2533 images in total. In this dataset, we construct 12 cross-domain multi-classification tasks: Office+Caltech dataset  is commoy is the ground truth label of x, and In the experiment, for each domain adaptation classification task, we use the classification accuracy of the target domain as the evaluation criterion. Suppose To evaluate the transfer performance of SVM with the uncertainty measure (SVMU), we compared it with 9 domain adaptation methods on Amazon product reviews dataset and Office+Caltech datasets, respectively. The methods of comparison include transfer component analysis (TCA) , correla(1)Testing on Amazon product reviews datasetIn this testing, we evaluate SVM with an uncertainty measure (SVMU) on the Amazon product reviews dataset. The classification accuracies of the comparative study are listed in As shown in (2)Testing on Office+Caltech datasetsIn this testing, we evaluate SVM with an uncertainty measure (SVMU) on Office+Caltech datasets. In each cross-domain classification task, we extract the features of images by speeded up robust features (SURF). The classification accuracies of the comparative study are listed in It is obvious that SVMU achieves better performance than the methods of comparison on Office+Caltech datasets. Specifically, the average classification accuracy of SVMU on 12 cross-domain classification tasks is 50.60%, which gains significant performance improvements of 4.16%, 4.1%, 5.22%, 4.04%, 4.55%, 4.63%, 3.23%, and 2.93% compared to the baseline methods. The experimental results reveal that the improved SVM with the uncertainty measure is reliable and effective in cross-domain image classification tasks.In this experiment, we verify the effectiveness of the uncertainty measure from three views: (1) Testing on synthetic data, visualizing the classification hyperplane of an adaptive classifier with and without an uncertainty measure. (2) Testing on real-world datasets, comparing the performance of SVM with and without an uncertainty measure. (3) Case study, explaining the role of uncertainty measure.In order to demonstrate the effectiveness of the adaptive classifier with an uncertainty measure in domain adaptation, we visualize the classification hyperplane of an adaptive classifier on a synthetic dataset. The synthetic dataset is generated from a Gaussian distribution In To further explain the effectiveness of the adaptive classifier with uncertainty, we compare the SVM with and without uncertainty on the Amazon product reviews dataset.Based on the above sub-experiments, it can be verified that the uncertainty measure is able to enhance adaptive classifier transfer performance. To explain the role of the uncertainty measure on the transfer process for an adaptive classifier, we use the Caltech-256 image data (complex background) as the source domain and the Amazon image data (no background) as the target domain. When the Caltech-256 dataset transfers to the Amazon dataset, the uncertainty values of some instances in the backpack and bicycle categories in the Caltech-256 dataset are shown.As shown in We use the evidence net to calculate the uncertainty between (a1)–(a6) and the target domain task; as shown in In this article, based on evidence theory, we revisited the domain adaptation from source domain data uncertainty and thereby devised a reliable adaptive classifier with the uncertainty measure. Specifically, for solving the uncertainty measure between the source domain data and target domain tasks, we designed an evidence net based on evidence theory. To solve the problem of model learning with a data uncertainty measure, we proposed a general loss function with an uncertainty measure for an adaptive classifier and extended the loss function to support vector machine. Experiments on the text dataset and image dataset validate that the proposed uncertainty measure is effective at improving the transfer performance of an adaptive classifier. In the future, we plan to extend the classifier with the uncertainty measure to handle the domain adaptation with multiple source domains and the domain adaptation on open sets.


The quantitative model allows the prediction of the pesticide residual level in the process from wheat to flour.Pesticide residues in grain products are a major issue due to their comprehensive and long-term impact on human health, and quantitative modeling on the degradation of pesticide residues facilitate the prediction of pesticide residue level with time during storage. Herein, we tried to study the effect of temperature and relative humidity on the degradation profiles of five pesticides  in wheat and flour and establish quantitative models for prediction purpose. Positive samples were prepared by spraying the corresponding pesticide standards of certain concentrations. Then, these positive samples were stored at different combinations of temperatures  and relative humidity . Samples were collected at specific time points, ground, and the pesticide residues were extracted and purified by using QuEChERS method, and then quantified by using UPLC-MS/MS. Quantitative model of pesticide residues was constructed using Minitab 17 software. Results showed that high temperature and high relative humidity accelerate the degradation of the five pesticide residues, and their degradation profiles and half-lives over temperature and relative humidity varied among pesticides. The quantitative model for pesticide degradation in the whole process from wheat to flour was constructed, with R Wheat flour is one of the most common food ingredients in the world, and it occupies an important position in the food industry ,2. PestiThe common pesticides in wheat include fungicides , herbicides , insecticides , plant growth regulators , of which five pesticides  are used more frequently in wheat cultivation. Pesticide residue problems often occur in wheat and wheat products ,6,7.The commonly used methods for pesticide extraction and purification include dispersive liquid–liquid microextraction, solid-phase extraction, solid-phase microextraction, QuEChERS  method ,9,10,11.The commonly used methods for the detection of pesticides in food are gas chromatography, gas chromatography–tandem mass spectrometry, liquid chromatography, liquid chromatography–tandem mass spectrometry, and ultra-high performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) ,15,16,17In this study, the degradation patterns of five typical pesticides during the storage of wheat and flour under various storage conditions were investigated, using QuEChERS method coupled with UPLC-MS/MS. The five pesticides included carbendazim, a broad-spectrum fungicide, bensulfuron methyl, an herbicide, triazophos, an organophosphorus acaricide, chlorpyrifos, a thiophosphate insecticide, and carbosulfan, a carbamate insecticide. A typical wheat supply chain includes the storage of wheat grain, the milling process, and the storage of wheat flour. The degradation profiles of these five pesticide residues, combined with their processing factors during wheat milling, will facilitate the model construction to predict the pesticide residue levels in the wheat supply chain.18 column  was purchased from Waters . QuEChERS purifier and salt package were purchased from Beijing Dima Outai Science Technology Co., Ltd. .Wheat (Jimai 22) was provided by Crop Research Institute, Shandong Academy of Agricultural Sciences . Flour (Fuqiang) was supplied by Beijing Guchuan Food Co., Ltd. . The pesticide standards, including triphenyl phosphate (TPP), carbendazim, bensulfuron methyl, triazophos, chlorpyrifos, and carbosulfan , were purchased from Accustandard Inc. . The five pesticides  chemical structures are shown in Stock solutions of each pesticide standard (100 mg/L) in the same volumetric flask were prepared with chromatographic grade methanol. The stock solutions were prepared with methanol as a mixed stock solution (2 mg/L) and stored at 4 °C in a refrigerator for use. A series of concentrations of working solutions  were obtained by sequentially diluting the mixed stock solution with wheat and flour blank matrix solution.According to the standard “Maximum Residue Limits of Pesticides in Food of National Food Safety Standard” (GB 2763-2021), and the limit of detection (LOD) and limit of quantification (LOQ) of the experiment, a mixed solution containing 10-fold maximum residue limit (MRL) of carbendazim, triazophos, and carbosulfan, 1-fold MRL of chlorpyrifos, and 20-fold MRL of bensulfuron methyl were prepared in methanol. Then, 5.0 g of each wheat and flour samples were sprayed with the above prepared mixed solution and left sealed for 24 h for storage experiments.Different concentrations of glycerol were prepared and placed into closed drying containers to obtain environments with relative humidity of 50%, 60%, 70%, 80%, respectively. The wheat and flour samples sprayed with pesticides were placed in containers with different humidity control, and then each container was placed in incubators with constant temperature at 20 °C, 30 °C, 40 °C, and 50 °C, respectively. Different combinations of temperature and humidity were obtained.3COONa, 6 g anhydrous MgSO4) and vortexed to mix [g for 10 min, the supernatant (6 mL) was collected in a centrifuge tube (10 mL). The samples were purified by adding a purifier  to the supernatant, and then vortexed and mixed. After centrifugation at 2000× g for 10 min, the supernatant (2 mL) was aspirated into a new centrifuge tube (10 mL). After blowing nitrogen to near-dryness at 40 °C, 2 mL methanol was added to redissolve, and then vortexed and mixed. Finally, the purified extracts were filtered through a 0.22 µm nylon membrane filter to analyze with UPLC-MS/MS.The extraction and purification methods for wheat and flour were based on the classical QuEChERS method with minor change ,22,23. Wd to mix ,25,26. A18 column . The mobile phase consisted of 0.1% formic acid in a mixed solvent of water (A) and acetonitrile (B). The gradient elution procedure was as follows: 10% B (0–1.5 min), 50% B (1.5–4 min), 90% B (4–10.5 min), and 10% B (10.5–13 min). The flow rate of the mobile phase was set at 0.4 mL/min, and the injection volume was 5 µL. The samples were measured by multiple reaction monitoring (MRM) in positive ion. The MRM parameters are shown in The five pesticides were separated on a UPLC-MS/MS (Waters ACQUITY UPLC I-Class/Xevo TQ-S)  equipped with positive mode (ESI+) and Waters BEH CThe data of this study were collected under Masslynx 4.1 software . Microsoft Excel 2020 software  was used for preliminary sorting of experimental data, and Minitab 17 software  was used for response surface analysis. Origin 2019b software  was used to draw plots and calculate the Area Under Curve (AUC).2) of the five pesticides in wheat and flour blank matrices were obtained using the internal standard method. As shown in 2 of the standard curves of the five pesticides were all greater than 0.9990, indicating good linearity in a certain linear range.In this study, the standard curves and coefficients of determination  ratio, and the LOQ was obtained according to a ten-fold S/N ratio . Spike rThe supply chain from wheat to flour includes raw material storage, cleaning, milling, sieving, cleaning, final product storage, packaging, and circulation, etc. Through previous literature research and investigation, we found that raw material storage, milling, and final product storage are the critical points in wheat flour supply chain ,29. The 0, C1, C2, and C3 were pesticide residue concentrations at corresponding stage, and Tw, tw, and RHw are the temperature, time, and relative humidity during wheat storage, and Tf, tf, and RHf are the temperature, time, and relative humidity during storage. The processing factor (PF) was used to describe the effect of milling process on the degradation of pesticides degradation and defined as the ratio of C2 to C1. PF less than 1 indicates that the processing method can effectively reduce the amount of pesticide residues, and the lower the PF value, the lower the amount of pesticide residues [residues ,31.The changes in pesticide residues in the wheat and flour storage process are complex, and it is susceptible to the influence of storage conditions such as temperature and relative humidity. Therefore, the degradation pattern of pesticide residues during wheat and flour storage is mainly explored in the following study to construct the degradation model of pesticide residues in the wheat flour supply chain.The second-order mathematical models of the degradation of five pesticides in wheat and flour at different times, temperatures, and relative humidity were constructed using Minitab 17 software, and the degradation models of five pesticides at different storage conditions are shown in 2 of five pesticides is above 0.796 by building models, which indicates that the fitting result is good and reaches the expected level. By constructing the mathematical model, the degradation pattern of pesticide residues in wheat and flour under different storage conditions can be reasonably predicted.As can be seen in The degradation patterns of pesticide residues of carbendazim, bensulfuron methyl, triazophos, chlorpyrifos, and carbosulfan in wheat and flour at different storage temperatures are shown in As shown in As shown in 10 coefficient of these reactions to temperature are different, and this may also explain the different susceptibility to temperature of pesticides [In general, with the increase of storage temperature, the chemical reaction rate of pesticide degradation process is accelerated, and the volatility of the pesticide is enhanced, so the half-lives of all pesticide residues tested decreased. However, the susceptibility to temperature of the five pesticides are different, with carbosulfan as the most unsusceptible one. Temperature influences the volatility of pesticides, and those with lower boiling point may be more susceptible. Temperature also promotes the chemical reaction rate involved in the process of pesticide degradation, and Qsticides .The degradation patterns of pesticide residues of carbendazim, bensulfuron methyl, triazophos, chlorpyrifos, and carbosulfan in wheat and flour at different storage relative humidity are shown in As shown in As shown in In general, with the increase of storage relative humidity, the chemical reaction rate of the pesticide degradation process is accelerated, and the half-lives of all pesticide residues tested decreased. However, different structures of pesticides have different sensitivity to relative humidity. Relative humidity influences the hydrolysis reaction of pesticides, and those with more ester bonds may be more susceptible . Relativp < 0.05) affected the AUC of the five pesticides. With the increases of temperature and relative humidity, the AUC of five pesticides increased to a certain extent and then decreased.The interaction between temperature and relative humidity on degradation of pesticide residues was studied by response surface analysis using Minitab 17 software, as shown in p < 0.05) affect the AUC of carbendazim, bensulfuron methyl, and triazophos, and relative humidity could significantly (p < 0.05) affect the AUC of chlorpyrifos and carbosulfan. There was a significant (p < 0.05) interaction between temperature and relative humidity on the AUC of triazophos, chlorpyrifos, and carbosulfan, with p values of 0.031, 0.032, and 0.038, respectively. The AUC of five pesticides decreased with increasing temperature and relative humidity.The interactional relationship between temperature and relative humidity on AUC of flour stored for 60 days is shown in The reason may be that different structure of pesticides have different sensitivity to temperature and relative humidity. In general, high temperatures make the molecular structure of pesticides vulnerable to damage, and the degradation rate of the pesticide increases with the temperature. Changes of relative humidity can affect the hydrolysis mechanism of pesticides due to interaction between lipid molecules in pesticides and water molecules, destroying the structure of pesticide molecules and leading to the degradation of pesticide residues. Therefore, pesticides with different structures have different rates of residue degradation .In this study, the five pesticides in wheat and flour were extracted and purified by the QuEChERS method and quantified by UPLC-MS/MS. The linear range, linear equation, LOD, LOQ, recovery rate, and precision of the method were investigated, and the results showed that the method was simple, rapid, with high accuracy and good applicability.2 reached above 0.817 in wheat, and the R2 reached above 0.796 in flour, with good fitting effect. The model could be used to predict the degradation of pesticide residues at given time points of the wheat flour supply chain from wheat grain to the final product, i.e., wheat flour.A quantitative model was constructed to predict the pesticide residue degradation during the storage of wheat and flour. The results showed that the RIn flour and wheat, the five pesticide residues gradually decreased with the increase of storage time, and the degradation rate was faster in the early stage and slower in the later stage. The degradation rates of the five pesticides increased with increasing temperature and reached a peak at 50 °C. The degradation rates of the five pesticides increased with increasing relative humidity and reached a peak at 80%. Temperature may influence the volatility of pesticides and the rate of chemical reactions involved in the degradation process. Relative humidity may influence the hydrolysis reaction of pesticides and the growth and reproduction rate of microorganisms. Results showed that high temperature and high relative humidity accelerate the degradation of the five pesticides residues, and their degradation profiles and half-lives over temperature and relative humidity varied among pesticides.


UBE3A gene is presumed to be the primary driver of Dup15q pathophysiology, given that UBE3A exhibits maternal monoallelic expression in neurons and that maternal duplications typically yield far more severe neurodevelopmental outcomes than paternal duplications. However, studies into the pathogenic effects of UBE3A overexpression in mice have yielded conflicting results. Here, we investigated the neurodevelopmental impact of Ube3a gene overdosage using bacterial artificial chromosome–based transgenic mouse models (OEUbe3a) that recapitulate the increases in Ube3a copy number most often observed in Dup15q. In contrast to previously published Ube3a overexpression models, OEUbe3a mice were indistinguishable from wild-type controls on a number of molecular and behavioral measures, despite suffering increased mortality when challenged with seizures, a phenotype reminiscent of sudden unexpected death in epilepsy. Collectively, our data support a model wherein pathogenic synergy between UBE3A and other overexpressed 15q11.2–q13.1 genes is required for full penetrance of Dup15q syndrome phenotypes.Chromosome 15q11.2–q13.1 duplication syndrome (Dup15q syndrome) is a severe neurodevelopmental disorder characterized by intellectual disability, impaired motor coordination, and autism spectrum disorder. Chromosomal multiplication of the  Human chromosome 15q11.2–q13.1 is exceptionally vulnerable to structural abnormalities that result in neurological disorders –11. ThesGenomic imprinting underlies the monoallelic, parent-of-origin–specific expression of different 15q11.2–q13.1 genes. Consequently, paternal and maternal 15q11.2–q13.1 deletions produce distinct pathophysiologies, which in turn result in Prader-Willi and Angelman syndromes, respectively . MaternaUBE3A  mouse Ube3a coding region as well as large stretches of flanking, untranslated sequence both upstream (~50 kb) and downstream (~20 kb). Subsequent recombineering and flippase-mediated (FLP-mediated) recombination in E. coli culminated in a floxed construct with the capacity for Cre-mediated cessation of expression (OEUbe3a mouse lines (lines A–F), 2 of which (lines C and E) proved to harbor transgene insertions within chromosome 3, as determined by targeted locus amplification mapping using sequencing primer sets designed to provide coverage of Ube3a and nearby flanking sequences (https://doi.org/10.1172/jci.insight.158953DS1). Using complementary droplet digital PCR (ddPCR) assays targeting both endogenous and loxP-containing genomic DNA sequences, we further determined that each line carried a 2-copy transgenic Ube3a insertion  individuals. OEUbe3a transgene copies contain a residual FRT site, a by-product of our recombineering strategy (OEUbe3a sublines of mice (+1Ube3a) with high construct validity for UBE3A overdosage in interstitial Dup15q, which we exploited by crossing +2Ube3a mice to a highly efficient FLPo deleter line  transgenic approach to generate potentially novel mouse models of UBE3A overexpression, henceforth referred to as pression . Our effnsertion . These 2strategy . This prter line   mice should overexpress UBE3A 1-fold, 2-fold, and 4-fold, respectively . In fact, we observed more modest percentage gains in UBE3A protein expression relative to WT: +1Ube3a = 172.5% ± 9.23%; +2Ube3a = 229.3% ± 11.01%; +4Ube3a = 306% ± 16.7%  model mice (AS/+2Ube3a), as the latter are devoid of UBE3A expression in mature neurons  mice  the rotarod, open field, marble burying, nest building, and forced swim tasks. Taken together, these tests consistently reveal motor dysfunction, deficits in species-typical innate behavior, and anxiety-like phenotypes in AS model mice , 42. If AS) mice . Indeed,terparts , whereasterparts , 42. We terparts , though +2 group , A–C. Sehe brain , A and Bhe brain , C–H. Co+2Ube3a mice  for UBE3A expression levels to be maintained within an optimal range during their development. As such, we speculated that an overlapping group of tasks, including those that we used to assess the functionality of transgenic UBE3A protein in AS/a+2 mice , might bme tasks  — despithe brain , D–G — s4 groups , A–C. Be4 groups  42), we, weUbe3acontrols . Sequentcontrols . In the controls , as floaa+4 mice ; hence, analyses  and in tanalyses , D–G.OEUbe3a mice in behavioral domains relevant to these clinical symptoms of Dup15q syndrome.Dup15q individuals generally present with moderate to profound forms of intellectual disability, ASD-like characteristics, and recurrent seizures that, in a minority of cases, result in sudden unexpected death in epilepsy (SUDEP) , 44–46. OEUbe3a mice to an associative learning and memory task. We implemented a classical fear conditioning paradigm based on determining a mouse’s capacity to associate an unanticipated, fear-provoking foot shock with a specific environmental context (learning), and to later recognize this context (memory) after both short and long intervals postconditioning  or long-term (28 days) contextual memory tests , 48Ube3ary tests . These r+2Ube3a, and +4Ube3a mice alike , 63Ube3aon day 1 . This reon day 1 , furtherhallenge . This cohallenge  and indiOEUbe3a mice had flurothyl-induced seizures no more readily than WT, but they were far more likely to die from them: approximately 35% of +1Ube3a mice and more than 50% of Ube3a+2 mice died during the 8-day induction period, whereas WT mice seldom succumbed (+1Ube3a and +2Ube3a mice compared with WT controls despite modest (+1Ube3a) or no (+2Ube3a) differences in baseline flurothyl seizure susceptibility or rates of flurothyl kindling , rating ≥6 on a modified Racine scale. However, in contrast to WT mice, when OEUbe3a mice had THLE seizures, they rarely survived them , in both hippocampus  origin, making it likely that epigenetic factors and monoallelically expressed 15q11.2–q13.1 genes are principal pathophysiological drivers of this disorder . As UBE3lication –68, respradation . At highOEUbe3a model has led us to the overarching conclusion that UBE3A overexpression is generally well tolerated by the developing nervous system; by and large, our experiments failed to detect phenotypic deficits in OEUbe3a mice relative to WT controls. OEUbe3a mice actually outperformed their WT counterparts on the rotarod and showed a reduced tendency to float during the forced swim task. This is opposite of AS mice, which exhibited poor rotarod performance and increased floating in the forced swim task, suggesting that in at least some cases, loss of UBE3A expression and UBE3A overdosage mediate reciprocal phenotypic outcomes. It is also noteworthy that increased UBE3A dosage rendered OEUbe3a mice challenged with a course of flurothyl seizure kindling more susceptible to seizure-associated death, a phenotype reminiscent of SUDEP in Dup15q individuals  will be required to determine whether this is truly a Ube3a dose–sensitive phenotype.Extensive molecular, electrophysiological, and behavioral testing in the ividuals , 44–46. Our findings contradict those from alternative mouse models of UBE3A overexpression, in which elevated UBE3A levels were associated with numerous behavioral abnormalities ranging from learning deficits, increased anxiety-like behavior, and reduced seizure thresholds  to core Ube3a copy number (4–9 copies) and  unique off-target effects of transgenic insertion — each likely to be exacerbated by breeding to homozygosity as was the case in these studies. If valid, an essential implication of these findings is that UBE3A E3 ligase activity (a presumed but unverified feature of the untagged Anderson model) is dispensable for the observed pathophysiological effects. This possibility, and what (if any) relevance it may have to pathogenic mechanisms in Dup15q syndrome, remain to be directly tested. Notably, enzymatically competent UBE3A is essential to prevent Angelman syndrome pathogenesis  have enabled studies of UBE3A overexpression in concert with the overexpression of several other genes in the 15q11.2–q13.1 region . By all ed genes . This cled genes . What colication , and ourHERC2 is an especially promising candidate. HERC2 protein, itself a HECT E3 ubiquitin ligase, has been shown in vitro to physically interact with — and modulate the catalytic activity of — UBE3A (HERC2 missense mutation was reported to be associated with Angelman-like features (A) receptor genes in the 15q11.2–q13.1 region has been strongly linked to excessive β oscillations in Dup15q individuals. This EEG phenotype, which mimics the effects of benzodiazepine treatment, is equally penetrant in maternal and paternal Dup15q and thus occurs independently of UBE3A overdosage  region, of which deletion leads to PWS, a neurodevelopmental disorder characterized by hypotonia, feeding difficulties, motor delay, and cognitive impairments . Full, uncut images of protein blots are included in the published online supplemental material.A detailed description of the methods can be found in P less than 0.05 was considered significant. P values.All statistical analyses were done using GraphPad Prism software , except for models of changing seizure severity in the course of flurothyl kindling, which were estimated using GLIMMIX in SAS . All data are presented as mean ± SEM unless specified otherwise; All experiments were performed in strict compliance with animal protocols approved by the Institutional Animal Care and Use Committee of the University of North Carolina at Chapel Hill and in accordance with European Commission Council Directive 2010/63/EU .MCJ designed and validated the mouse model and performed Western blotting, quantitative PCR, immunofluorescence, and flurothyl kindling experiments. BNW and PFL performed and analyzed flurothyl kindling experiments. AMP, SB, and DDH performed behavior experiments and corresponding Western blotting experiments. MSS and NSJ performed operant extinction experiments. CRD performed and analyzed quantitative PCR experiments. MSF performed and analyzed Western blotting experiments. PJC performed statistical analysis of flurothyl-induced seizure severity. AMP and WFJVI performed and analyzed the RNA-Seq experiment. MAJ performed the LTP experiments. BDP and YE supervised the project. AMP, MCJ, BDP, and YE wrote the manuscript. All authors reviewed the manuscript. The order of co–first authors and of co–senior authors was determined based on their overall contribution to the study.


At the first step of phage infection, the receptor-binding proteins (RBPs) such as tail fibers are responsible for recognizing specific host surface receptors. The proper folding and assembly of tail fibers usually requires a chaperone encoded by the phage genome. Despite extensive studies on phage structures, the molecular mechanism of phage tail fiber assembly remains largely unknown. Here, using a minimal myocyanophage, termed Pam3, isolated from Lake Chaohu, we demonstrate that the chaperone gp25 forms a stable complex with the tail fiber gp24 at a stoichiometry of 3:3. The 3.1-Å cryo-electron microscopy structure of this complex revealed an elongated structure with the gp25 trimer embracing the distal moieties of gp24 trimer at the center. Each gp24 subunit consists of three domains: the N-terminal α-helical domain required for docking to the baseplate, the tumor necrosis factor (TNF)-like and glycine-rich domains responsible for recognizing the host receptor. Each gp25 subunit consists of two domains: a non-conserved N-terminal β-sandwich domain that binds to the TNF-like and glycine-rich domains of the fiber, and a C-terminal α-helical domain that mediates trimerization/assembly of the fiber. Structural analysis enabled us to propose the assembly mechanism of phage tail fibers, in which the chaperone first protects the intertwined and repetitive distal moiety of each fiber subunit, further ensures the proper folding of these highly plastic structural elements, and eventually enables the formation of the trimeric fiber. These findings provide the structural basis for the design and engineering of phage fibers for biotechnological applications. Bacteriophages, which specifically infect the bacterial hosts, are the most abundant entities on the planet , and havEscherichia coli phage T4 possesses two kinds of fibers: the long tail fibers (LTFs) composed of four different protein components and the short tail fibers (STFs) of gp12 protein [The phage tail fibers identified to date are elongated trimeric proteins that contain distinct domains, featuring intertwined extended regions and triple β-helices ,10,11. A protein . Given t protein . Therefo protein ,16. As r protein . In cont protein  and Mu [ protein  have bee protein . As the  protein . It remaAnabaena PCC 7120 [As widely distributed phages that infect the ancient cyanobacterial hosts, cyanophages can modulate the seasonal succession of cyanobacteria in aquatic ecosystems and therefore represent a potential environmentally friendly biological agent for controlling cyanobacterial blooms ,22. EngiPCC 7120 . HoweverMyoviridae cyanophage Pam3, which was isolated from the Lake Chaohu in China [In this study, we investigated the structures of the tail fiber and its chaperone of the in China . Pam3 hagp24 and gp25 were amplified by polymerase chain reaction (PCR) from the genomic DNA of Pam3. The complete gp24-gp25 DNA segment was cloned to a modified pET-28a vector with an N-terminal His6-tag on gp24. The individual gp24 or gp25 gene was also cloned in the pET-28a vector with an N-terminal His6-tag (E. coli strain BL21 (DE3)  in LB medium containing 30 μg/mL kanamycin at 37 °C. When the optical density of cells at 600 nm (OD600) reached 0.8–1.0, protein expression was induced with 0.2 mM isopropyl β-D-thiogalactoside (IPTG) for 1 h at 37 °C. The cells encoding gp24-gp25 were harvested by centrifugation at 8000× g for 3 min and were resuspended in the binding buffer . Then, the cells were lysed using 30 min of sonication followed by the centrifugation at 12,000× g for 15 min. The supernatant containing the target proteins was transferred to a Ni-NTA column  that was pre-equilibrated with binding buffer. The protein impurity was removed with 50 mM imidazole in binding buffer. The target protein was eluted with 500 mM imidazole, and further purified by size-exclusion chromatography using a HiLoad 16/600 Superdex 200 pg column. The purity of proteins was assessed using SDS-PAGE (the concentration of stacking gel is 5% and the separating gel is 4–15%) and stored at −80 °C for further use. The protein marker is GoldBand Plus 3-color Regular Range Protein Marker .The genes His6-tag . ProteinThe molecular weight of gp24-gp25 complex and gp24 in solution were determined by size exclusion chromatography with multi-angle light scattering (SEC-MALS). The Superdex 200 Increase 10/300 GL column  pre-equilibrated with binding buffer was connected to the DAWN HELEOS II light scattering detector  and the Optilab T-rEx refractive index detector (Wyatt Technology). Protein samples  were eluted through the column, and the results were processed and analyzed using ASTRA 7.0.1 software (Wyatt Technology). The final results were analyzed and plotted using Origin 2022.g for 10 min at 4 °C to remove the precipitated proteins. The protein concentration in the supernatant was determined using a NanoDrop . The proteins retained in the supernatant were compared with the unheated sample to calculate the soluble proportion at each temperature. The data points were fitted with a Boltzmann equation to calculate the Tm value using Origin 2022.The purified gp24 proteins were concentrated to 2 mg/mL. Each protein sample of 15 µL was heated for 5 min at various temperatures from 57 to 87 °C with an interval of 2 °C by PCR Amplifier  and centrifuged at 12,000× v/v) NP40 (Nonidet P40). The 3.5 µL protein samples were loaded on a 300-mesh carbon-coated golden grid  and flash frozen in liquid ethane using the Vitrobot (FEI). Grids were blotted for 8 s and incubated for 30 s with a blot force of −2 in 100% relative humidity at 8 °C. After removing the excess sample using the filter paper, the golden grid was quickly transferred to liquid nitrogen for storage.The purified gp24-gp25 proteins were concentrated to 4.7 mg/mL and incubated with 0.03% (2) were collected with a magnification of 120,000× in the super resolution mode with a defocus range of −1.5 to −2.5 μm. More than 959,630 particles were automatically picked by using cryoSPARC [The cryo-EM datasets were collected using an FEI Titan Krios 300 kV transmission electron microscope equipped with the K3 electron-counting K3 detector at the Cryo-EM Center at University of Science and Technology of China. A total of 1,435 movies . The statistics for cryo-EM data collection and processing, model building and refinement were listed in The structures of gp24 and gp25 were predicted using AlphaFold2 (version 2.1)  by imporhttps://blast.ncbi.nlm.nih.gov, accessed on 12 May 2021) and HHpred [The molecular functions of gp24 and gp25 were predicted by BLASTp  to the long tail fiber tip protein gp38 of phage S16. By contrast, the BLASTp search of gp25 gave hits of previously identified tail chaperones, such as tail assembly proteins (TACs) and TFAs. Moreover, HHpred prediction showed that gp25 shares a high similarity to the TFA of phage Mu. Mass spectrometry analysis of the intact Pam3 virions detected the gp24 protein, but not gp25 , further6-tagged full-length gp24 (His-gp24), gp25 (His-gp25) or co-expressed His-gp24 with gp25 in E. coli. Overexpression of individual gp24 yields a large fraction of recombinant proteins in inclusion bodies, leaving a small fraction of soluble proteins, which showed two peaks in size-exclusion chromatography corresponding to the trimeric and monomeric gp24, respectively  monomer , with a Further analysis of these GRMs revealed a similar conformation and organization for each GRM a. RamachStructural similarity search using DALI  revealedEach gp25 subunit consists of two distinct domains: the N-terminal β-sandwich domain (residues Met1~Ala55) and the C-terminal α-helical domain (residues Pro64~Phe162) connected by an eight-residue linker. The β-sandwich domain has two β-sheets composed of two and three antiparallel β-strands, respectively, in addition to a short helix α1, whereas the α-helical domain consists of four α-helices (α2~α5) and an η-helix c. In gp2Mu) (PDB 5YVQ), with an RMSD of 3.0 Å over 48 Cα atoms. Superposition of gp25 β-sandwich domain against TfaMu showed that the central β-sheet is well aligned, whereas the surrounding α-helices vary greatly , which contribute to tail fiber binding in phage Mu [Mu was not solved, we predicted its structure by AlphaFold2 [Mu might also form a helical bundle that mediates the trimerization to ensure the correct assembly of trimeric tail fiber. Similar to TfaMu, the chaperone gp25 also adopt a modular structure in which the non-conserved N-terminal β-sandwich domain binds to the tail fiber, whereas the conserved C-terminal α-helical domain mediates the trimerization and assembly of tail fiber.Structural similarity search demonstrated that the β-sandwich domain of gp25 is most structurally similar to the TFA N-terminus of phage Mu  [E. coli phage K1F (PDB 3GW6) [Bacilllus virus GA1 (PDB 3GUD) [In addition, the overall structure of gp25 is also similar to the intramolecular chaperones located at their C-termini of phage tail fibers , includiDB 4UW8) , the taiDB 3GW6)  and the DB 3GUD) . They al2. The interactions are mainly mediated by β2 and α1 of gp25, and β5~β7 of gp24, forming extensive hydrophobic interactions, in addition to seven pairs of hydrogen bonds (In the complex structure of gp24-gp25, the trimeric gp25 embraces the gp24 trimer mainly via the N-terminal β-sandwich domains a. The β-en bonds a. The elen bonds b.The present complex structure provides insights into the assembly process of gp24 trimer, assisted by the chaperone gp25. We propose that the chaperone gp25 assemble into a trimer via the C-terminal α-helical domains, from which three N-terminal β-sandwich domains protrude outwards and function as three “tentacles” to search the nascent gp24 subunits. Once binding to the highly flexible C-terminal region of gp24, the chaperone protects these intertwined elements of gp24 and eventually enable their proper folding. Notably, the N-terminal α-helical domain of the tail fiber could trimerize as well in the absence of the chaperone. Once the trimeric tail fiber is correctly folded and registered, the chaperone should be released, most likely thanks to a relatively small interface area between gp24 and gp25 in addition to an eight-residue flexible linker that bridges the two domains of the chaperone.In this study, we report the cryo-EM structure of the simplified tail fiber complexed with its chaperone from the myocyanophage Pam3, which provides insights into the assembly mechanism of the tail fiber assisted by an intermolecular fiber chaperone. The tail fiber gp24 adopts an intertwined and elongated trimeric structure with a modular organization different from the previously reported ones. The gp24 has a TNF-like domain of two parallelly aligned β-sheets that is rarely found in phages. The TNF-like domain stretches out and forms a four-layer structure of the glycine-rich domain harboring 12 GRMs, which is the most prominent feature of gp24. Compared to a much more complex assembly of tail fibers in other myophages, such as T4  and S16 Mu and other Tfa proteins. These results suggest the intermolecular chaperone of tail fiber also adopts a modular organization, a totally distinct N-terminal domains in combination with a highly conserved C-terminal domain.A key finding of our study is that the full-length structure of the chaperone gp25 adopts a modular organization. The chaperone gp25 is composed of two separate domains: the N-terminal β-sandwich domain responsible for binding to the tail fiber, and the C-terminal α-helical domain mediates the trimerization. This modular organization is commonly found in other protein chaperones, such as phage Mu TFA  and RuBiIn conclusion, our complex structure of gp24-gp25 provides insights into the assembly mechanism of phage tail fiber assisted by an intermolecular chaperone. Structural analysis clearly shows that the role of chaperone is to initiate the correct assembly of tail fiber at the distal moiety, thus constraining the intertwined structural elements to be orderly aligned, which provides a model for the action of tail fiber chaperones. Our studies might also pave the way for further understanding and synthetic engineering of myophages with a simplified tail fiber.


In addition, an increase of bioactive compounds content and antioxidant capacity of the breads were revealed. As expected, the starch hydrolysis index increased, as well as GI. Nevertheless the values were really close to low eGI (56), which is a relevant result for a sweet bread. Apple flour showed good technological and sensory properties as a sustainable and healthy food ingredient for GF bread.Baking bread without gluten presents many challenges generally related with poor sensorial and nutritional characteristics, and strategies to overcome this issue are needed. Despite many gluten-free (GF) bread studies, to the best of our knowledge, few are dedicated to sweet GF bread. Sweet breads have traditionally been an important type of food and are still frequently consumed worldwide. Apple flour is naturally GF, and is obtained from apples which do not accomplish market quality requirements and are being wasted. Apple flour was, therefore, characterized in terms of nutritional profile, bioactive compounds, and antioxidant capacity. The aim of this work was to develop a GF bread with incorporation of apple flour, in order to study its effect on nutritional, technological, and sensory characteristics of sweet GF bread. Additionally, in vitro starch hydrolysis and glycemic index (GI) were also analyzed. Results demonstrated the influence of apple flour in dough’s viscoelastic behavior, increasing G’ and G’’. Regarding bread characteristics, apple flour led to better acceptance by the consumer, with firmness increasing , and consequently specific volume decreasing (1.38; 1.18; 1.13 cm Worldwide, at least 1% of the population lives with Celiac Disease (CD); the prevalence of this disease varies according to the geographical area, and can reach 2.6% ,4. AccorGluten plays an essential role in normal breadmaking, as it is responsible for the viscoelastic behaviour of the dough, higher volume, and softer texture of wheat breads . Thus, gSweet breads are a traditionally important type of food, and many different countries around the world have numerous recipes, having as main ingredients: wheat flour, a sweetener—sugar or honey, fat, and spices—cinnamon or fennel —recipes The use of by-products has been considered by many authors as a good solution to improve GF bread’s nutritional, functional and technological quality ,4,13,14.In the present work, the flours selected for the formulation with apple flour were buckwheat flour (BWF), sweet potato flour (SPF), and lupin flour (LF), owing to its good performance in GF bread formulation and also low GI. Southgate et al. , and alsThe aim of the present work was developing a GF common bread and analyzing the effect of apple flour incorporation in the nutritive value, antioxidant capacity, in vitro starch hydrolysis and predicted glycemic index, technological quality, and sensory analysis of the GFB. Moreover, converting the GFB into a sweet bread using a sustainable source of sugar.®, Lesaffre, Marcq-en-Baroeul, France), gelling agent hydroxipropylmethylcellulose—HPMC , sugar, sunflower oil, salt and water. All ingredients were purchased from the named companies, except sugar, yeast, salt and oil, which were purchased in the local market.Ingredients used for bread formulation were: buckwheat flour , white lupin flour , sweet potato flour , apple flour , dried yeast . Standards for starch (93%), D-glucose and resistant starch (52.5%), together with GOPOD reagent were purchased from Megazyme . Folin–Ciocalteau’s reagent, acetic acid—glacial extra pure (99.7%), ethanol, potassium persulfate, sodium hydroxide, methanol, hydrochloric acid, iron (III) chloride, sodium acetate, sodium carbonate, sulphuric acid (98%), and potassium hydroxide (98%) were acquired from Panreac . α-amylase from porcine pancreas , 2,2-diphenyl-1-picrylhidrazyl radical (DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) diammonium salt (ABTS), 2,4,6-Tripyridyl-s-Triazine (TPTZ iron reagent), and calcium chloride dihydrate were obtained from Sigma-Aldrich . 3,4,5-trihydroxybenzoic acid , 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), and 2,4,6-Tripyridyl-s-Triazine (TPTZ iron reagent), were purchased from Fluka . Finally, amyloglucosidase 1100 BG was acquired from Novozymes .Apple flour was prepared by Terrius company without removing skin or seeds, from a Portuguese apple cultivar named “Bravo de Esmolfe”. Apples were sliced, dried (40 °C for 7 h) and ground. Apple flour was sieved to obtain a particle size of approximately 0.16 mm to achieve a uniform particle size. Apple flour (AF) was characterized in terms of nutritional profile, bioactive compounds and antioxidants . The samples were taken from 3 different packages of 2.5 kg, from 2 different batches. All analyses were performed at least in triplicate.Based on a GFB formulation from our research group , prelimiTo study the impact of apple flour, its level of incorporation was 17% and 23% of total flours, replacing buckwheat flour. The apple flour level was selected after developing trials with different levels of incorporation, and analyzing the behavior of the dough and the bread, focusing on what could be maximum amount of apple flour, allowing feasible baking to obtain bread, which was 23%. This incorporation level is considered as a “technological limit” for the developed formulation. The other level was selected after lower levels had been tested, since levels below 17% did not present relevant sensory impact (results not shown). All the formulations are summarized in The moisture of each flour was determined through an automatic moisture analyzer PMB 202  with the following results: BW—10.62%; SPF—7.22% LF—6.12%; and AF—3.68%. Thermo processor equipment  was used to mix ingredients. Firstly, the yeast was activated, with the addition of distilled water, yeast and sugar for 2 min at 27 °C at velocity 1 (gentle agitation). The other ingredients were added and mixed for 10 min with a dough mixing program (wheat ear symbol); the dough was poured in a lubricated aluminum pan, and fermented for 50 min at 29 °C in a fermentation chamber Arianna XLT133 . Finally, the dough was baked for 50 min at 180 °C in an electric oven, model Johnson A60 , and allowed to chill for 2 h, at room temperature.For each formulation, three breads were prepared, and all analyses were performed at least in triplicate: three measurements were taken from each slice individually, in three independent slices from each bread. Mean values and standard deviation measured from each determination were recorded.−1 in relation to flours) 107.5; 95.5; 93.7, respectively, for the formulations C, A17, A23, and also presented in In order to obtain the mixing properties, flour blends’ water absorption (WA) adjustment was performed in a micro-doughLAB 2800 , at 14% moisture basis. Using manufacturer’s farinographic protocol at slow 63 rpm (AACCI Method 54-21.01) , an optiThe pasting properties of each GF flour blend formulation, were tested using a micro-doughLAB 2800 , following the manufacturer’s cooking protocol with some modifications . The mixDough samples from the three developed formulations  were characterized through dynamic rheometric measurements—Small Amplitude Oscillatory Shear Measurements (SAOS), using a controlled stress rheometer  coupled with a UTC–Peltier system, to perform rheology tests. It used a serrated parallel-plate PP20 (diameter of 20 mm), where the gap between plates was 1.0 mm (previously optimized for bread dough).Each dough sample was prepared as presented in −1 were accomplished to identify dynamic linear viscoelastic region of each sample. Then, frequency sweeps (from 0.00628 rad s−1 to 6.28 rad s−1) were performed at constant shear stress of 10 Pa. Temperature was maintained at 5 °C during the measurement to prevent dough fermentation. Before starting the test, samples were left for 15 min, to stabilize the temperature, and allow relaxing of residual stress. All rheology measurements were run at least in triplicate.Stress sweep tests at a frequency of 6.28 rad sbb is the weight of the dough before baking and Wab is the bread weight after baking and cooling.Bake Loss: was determined using the following expression :Bake losSpecific Bread Volume: was determined using the rapeseed displacement method .w): was measured with an HygroPalm-HP23 , after mincing interior crumb from bread slices into the sample holder.Water activity : was performed after baking (2 and 24 h), in a texturometer TA.XT.plus . Bread slices with a thickness of 20 mm were prepared to perform a “two bite test” (double puncture test). Test conditions were: 25 mm diameter acrylic cylindrical probe, 1 mm sfrom TPA .Bread Crumb Color: was measured using a colorimeter Croma-Meter CR 400 , by CIE  L*a*b* system with following parameters: L*—lightness , a*—intensity of green (−60 < a* < 0) or red (0 < a* < +60), b*—intensity of blue (−60 < b* < 0) or yellow (0 < b* < +60). In order to measure the change in the visual perception of the bread’s color, Delta E (ΔE*) was calculated using the expression:−1 DW). Samples were analyzed for ash, protein, crude fat content, and fiber according to the Association of Official Analytical Chemists methods [−1): control 3.08 ± 0.10; A17: 3.07 ± 0.22; A23: 3.11 ± 0.10. Samples were stored in food safe plastic bags (with 50 g), at room temperature and protected from light.Breads’ nutritional composition was evaluated in triplicate, after drying and grinding bread samples , through gravimetric measurements, when reaching constant weight at 130 °C.Total Ash Content: was measured gravimetrically, incinerating bread samples at 550 °C in a muffle furnace L 9/R .Crude Fat Content: was determined by using petroleum ether in a Soxhlet extractor Det-gras N , dried in the oven (overnight), and finally fat content was determined gravimetrically.Total Protein Content: was determined using the Kjeldhal method. The nitrogen conversion factor used was 5.65, FAO’s reference for sweet potato, since it was the common ingredient used in the same share for the three bread samples.Dietary Fiber: soluble, insoluble and total fiber of GF breads  were determined by using a “Total Dietary Fiber Assay Kit” from Megazyme , following manufacturer’s instructions. All procedures were performed at least in duplicate.Carbohydrates: were determined by difference (100—% moisture—% ash—% protein—% fat—% fiber).To calculate total starch content, an enzymatic procedure was used, as earlier presented by Goñi et al. , with soResistant starch content was assessed using the method proposed by Goñi et al. , slightl−1 and sodium phosphate buffer pH 6.9) was added, and the mixture was kept in incubation, at 37 °C with constant shaking (120 rpm). Along the time of incubation, at 15, 30, 60, 90, 120, 180 min, aliquots of 2 mL were taken from the Erlenmeyer, boiled for 5 min to inactivate the enzyme and kept in an ice bath (4 °C). The DNS reagent was added to the supernatants of the aliquots and glucose content was determined using maltose standard curve at 540nm.In vitro starch hydrolysis and glycemic index estimation were determined according to Goñi et al.  and GermFor eGI estimation, starch in vitro digestibility was graphically drawn, and area under the curve (AUC) was calculated using the trapezoids method. HI (starch hydrolysis index) of standard wheat bread = 100 and for the HI of each sample, HI = (AUC of sample bread/AUC of standard wheat bread) *100. eGI= 0.549×HI + 39.71 .ortho-diphenols, flavonoids contents) were analyzed using spectrophotometric methodologies previously reported in [•+ and DPPH•, as well as ferric reducing antioxidant power (FRAP) assay, performed as described by [Bioactive compounds  for each sample.All analyses were completed in 96-well micro plates , using a microplate reader Infinite M200 , and were evaluated in triplicate (v/v) was added. Samples were stirred at the highest speed, for 30 min, at room temperature, and then centrifuged at 5000 rpm for 15 min at 4 °C. Supernatant was collected. The final volume was made up to 5 mL with methanol/distilled water . The procedure was repeated three times.To perform bioactive compounds extraction from bread samples, 40 mg of each bread type previously dried and ground in powder were weighed and 1.5 mL of extracting solvent  [To assess total phenols content in bread samples, Folin–Ciocalteau reagent was used, with gallic acid as standard. This method consists in the reduction of phosphowolframate–phosphomolybdate complex through bread’s phenolic compounds, yielding blue reaction products. Briefly, 20 μL of sample and 100 μL of previously diluted Folin–Ciocalteau reagent were mixed and vortexed. Then, 80 μL of sodium carbonate was added and mixed in a vortex. Finally, after 30 min of incubation in the oven at 40–45 °C in the dark, the absorbance was read at 750 nm. Results were expressed in milligrams of gallic acid per gram of dry weight (mg GA g−1 DW) .−1). After waiting 5 min, 28 μL of aluminum chloride (100 g L−1) was added and the mixture reacted for 6 min. Next, 120 μL of sodium hydroxide (1 M), was added to the mixture, the microplate was shaken for 30 s, and absorbance was measured at 510 nm. Flavonoid content was quantified using catechin as standard, and results are presented as milligrams of catechin per gram of dry weight (mg CAT g−1 DW) [Flavonoids in bread samples were assessed through the following experimental procedure: 24 μL of sample were mixed with 28 μL of sodium nitrite .s el al. . 40 μL o• was calculated using the equation:DPPH antioxidant capacity was measured placing 10 μL of sample or Trolox standard and 190 μL of the DPPH to each well of the microplate. The mixture was incubated for 30 min in the dark at room temperature, and absorbance read at 520 nm. Inhibition of free radical DPPH• samples scavenging capacity was determined by interpolation of the calibration curve for Trolox, and results were expressed as mmol Trolox per g of dry weight (mmol Trolox g−1 DW) [DPPH g−1 DW) . ABTS ra−1 DW) [−1 DW) [The antioxidant capacity of the extracts was determined by interpolation of the calibration curve for Trolox, and results were expressed as mmol Trolox per g of dry weight (mmol Trolox g−1 DW) . To dete [−1 DW) .Sensory evaluation of bread samples was carried out in a tasting room, using a structured scale, with a group of randomly selected 40 non-trained panelists, from Instituto Superior de Agronomia Food Science Department (both students and staff), according to ISO 11136:2014 . PanelisEthical Statement: All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of LEAF Research Centre.p < 0.05). All data were presented as mean value and respective standard deviation.Results were analyzed with OriginPro 2019 . The statistical tool chosen to compare experimental data, was analysis of variance (one-way ANOVA). In order to evaluate means differences comparison, Tukey Test was used in a confidence level of 95%, with differences between mean values, being considered statistically significant at (−1): 13.3 protein; 3.4 lipids; 61.6 carbohydrates; 10.0 total fiber.Apple flour (AF) was analyzed in order to obtain its nutritional profile as well as bioactive compounds and antioxidant capacity. Results are presented in ortho-diphenols and flavonoids content were really high. Comparing with other authors, TPC values obtained for apple flour were within the range of values obtained for the peel (7.08-12.33 in mg GAE g DM−1) from different apple varieties, and much higher than the values presented for the apple cortex from the same varieties [−1), respectively, of 7.61, and 6.80. Moreover, in a research study that studied seedless apple flour, TPC was even lower, 5.48 GAE g DM−1 [−1 DW, which difference could indicate a higher content in hydrophilic antioxidants, since DPPH does not identify these type of antioxidants due to the fact that is not soluble in water [−1 DW, which is a much lower value that our results. Regardless of differences in methodologies, the absence of seeds could be one of the reasons for the difference in these values [−1 DW.It is generally agreed by different authors that apple is rich in bioactive compounds. The major groups found were: hydroxycinnamic acid, flavan-3-ols, procyanidin, anthocyanin, flavanols, and dihydrochalcone classes. The main compounds included: chlorogenic acid, quercetin glycosides, procyanidin B1 and B2, catechin, epicatechin, and phloridzin ,56. In parieties . This sharieties ,32. Feliarieties  and Serrarieties , obtaineE g DM−1 , highligE g DM−1 . Regardiin water . The ante values . Regardie values  for applAfter preliminary experiments, a peak torque value of 48 mN·m ± 4%, corresponding to 95.0% WA, was considered as optimum for better baking performance of the control formulation. A17 and A23 formulations were kept in this range of torque (±4%) by modifying the water addition. From micro-doughLAB mixing curves, water absorption value (14% moisture basis) of A17, and A23 formulations was, respectively, 81.0%, 79.0%.In Regarding pasting curves, C1 represents the maximum torque of mixing flours, C2 is related to protein weakness, C3 is starch gelatinization, C4 concerns gel stability and amylase activity, and C5 corresponds to starch retrogradation . When prGelatinization viscosity (C3) reached 52, 60, and 55 m·Nm of torque, respectively, for the C, A17 and A23 flour blends. The corresponding temperature was 83 °C for C and A17, while for A23 it was 90 °C. From these results it was possible to understand that AF contributed to increase gelatinization torque value, and also the temperature. Bread is a very complex matrix, with interaction among all the ingredients, which led to different dough behaviors. Particularly, sugars revealed an effect of concentration; however, the obtained values could not exclusively be attributed to sugars’ role in the bread, but also to the interactions between proteins, starch, HPMC and fibers . Rocha PNext, starch granules reach a rupture point due to the maximum water absorption with temperature at 90 °C, and viscosity started decreasing until a minimum (C4). The torque values were 48 m·Nm for C flours blend and 36 m·Nm for both A17 and A23. This viscosity decay is the breakdown (difference between C3 and C4), which shows the resistance to heat and shear of the dough, and it is lower with higher heat resistance. This was seen in C, where breakdown viscosity was really low, and increased with the addition of AF, with values, respectively, of 24.0 and 19.0 for A17 and A23. This could be explained due to replacing of BWF which has more resistance to heat and shear stress when compared with apple flour, ascribed to its higher starch and protein content .Finally, when temperature started decreasing, viscosity kept increasing and reached a maximum with starch retrogradation in the end of the test (C5). Retrogradation torque revealed a progressive decrease from C to A17 and to A23, respectively, 78, 71, 65 m·Nm, attributed to the reduction of protein and starch content in the flour blends, according to the different levels of AF incorporation in the dough. Since retrogradation is the reorganization of molecules and restructuring of amylose, the flours blend with apple, leading to lower values due to the less structured matrix. As previously mentioned, the interaction between all bread ingredients influences the GF dough rheology, affecting also starch pasting properties [−1 (1 Hz) are presented in In p > 0.05) differences among them. Similar results were obtained in previous work, where acorn flour replaced similar percentages of buckwheat flour (23% and 35%), and differences were not found between the tested breads [In d breads .p < 0.05) decreased from control bread to both AF incorporation levels. These results shown that the incorporation of AF developed a more consistent bread, having a lower volume per weight unit. This could be ascribed to the fiber increase, besides starch and protein content reduction, owing to the replacement of buckwheat by apple flour, which reduces air incorporation ability, leading to a bread’s lower specific volume. These results are in accordance with other authors who have studied apple pomace incorporation in GF bakery, which particularly indicate a reverse relation between volume and firmness, as we also found in the present work [Regarding bread’s specific volume, it was possible to perceive that the values significantly . Regarding firmness at 24 h, the values for the three different breads were also different (p < 0.05), showing a similar behavior as previously described in firmness at 2 h. These results were aligned with the reduction of the volume previously explained, attributed to the effect that AF generates in the bread. The first level of the tested flour (17%) increased bread’s firmness and accordingly reduced the specific volume when compared to control bread. The higher amount of incorporation (23%) revealed also a firmness increase, comparing with control, but with lower values than A17. It seems that the level of added flour reveals higher impact in the firmness increase than in specific volume decrease, which could be explained due to the interaction between fiber increase, starch and protein reduction, and consequent reduction in air incorporation ability. This type of firmness performance has also been identified with other apple-sourced ingredients tested in GF formulations [p < 0.05). Water activity significantly decreased (p < 0.05) with the incorporation of apple flour, among the three breads C, A17, and A23. AF has about three times less moisture when compared with BWF, so the replacement of 17% and 23% by AF explains the water activity reduction, since the water available is lower in apple flour-containing breads. Additionally, the water absorption was also higher in C bread (95%) than in A17 (81%) and A23 (79%), which together with the starch, protein, sugars and fiber content of each formulation, also explains the differences in water activity, due to the water movements and availability along the bread’s microstructure [Analyzing the results obtained for texture parameters, it was possible to observe that firmness value at 2 h increased from C to A17, and decreased when raising the level of apple flour incorporation to A23, being all different  between the control and both A17 and A23. These results showed a reduction of lightness in bread, meaning a slightly darker color, as we can observe from p < 0.05) by incorporation of AF, but without differences between AF breads, revealing more reddish and yellow color. Similar color parameters were also obtained in other studies where apple-derived ingredients were added to bakery products, with bread crumb darkness increasing as the main effect [Regarding bread crumb color, the L* parameter reduced with the incorporation of AF, with difference  decrease from control to A17 and A23. As mentioned in As regards moisture, the results have shown a significant  between control bread and sweet AF breads, decreasing from C to A23, but not showing significant differences between A17 and A23. According to Hager et al. [−1. Accordingly, when replacing 17% and 23% of BWF by AF, bread’s protein level was expected to undergo a reduction. Comparing our results with other studies about the addition of apple pomace in rice flour GF cakes, and apple pomace in brown rice crackers, a reduction of protein content was also observed [The protein content was different  whereas no differences were observed between C and A17, and between breads with different levels of apple flour incorporation. This could be due to the increased amount of sugar from apple flour, which triggered glucose catabolite repression, and under such conditions, yeast cells direct used acetyl-CoA for lipid synthesis rather than the TCA (tricarboxylic acid) cycle [As concerns fat content, the results showed differences between the breads. In particular, C and A23 breads differ in their fat content (d) cycle ,65. In gd) cycle .Total fiber in breads A17 and A23 increased when compared with the control (C), as was expected, since AF has a higher content in total fiber, around 12.85%, while BWF presents around 2.20%. Thus, the replacement of BWF by AF led to a sweet GFB with higher fiber content. Insoluble fiber was higher in A17 and A23 also due to differences between BWF and AF insoluble fiber content. Other authors studying apple-derived ingredients incorporation in baking products, also obtained an increase in fiber content ,24. The Regarding carbohydrates, the amount is quite similar between the three breads C, A17 and A23, but with different profiles. According to the results in the next section, as shown in p < 0.05) when BWF was replaced by AF (A17 and A23), since the latter ingredient has a low starch content. For the same reason, the resistant starch value was also reduced significantly (p < 0.05) in breads A17 and A23, when compared with C.In The postprandial response is related to carbohydrate digestion, and can be estimated by the glycemic index (eGI), through a model behavior developed to allow comparison of starchy foods ,48. Glycp < 0.05) when breads were supplemented with AP, and consequently, eGI also increased. This result was expected since sweet GFB (A17 and A23) presents higher fructose and total carbohydrates content  for both levels of apple flour incorporation, and for all compounds group analyzed: total phenols, ortho-diphenols and flavonoids content. As regards antioxidant capacity, it can be seen that for both apple flour levels, and for the three determination methodologies, the values were significantly (p < 0.05) higher than for control bread. For DPPH, ABTS, and FRAP determinations, no significant differences between A17 and A23 were found (p > 0.05). According to different authors, apple is a source of bioactive compounds and antioxidants [ortho-diphenols and flavonoids content as well as antioxidant capacity. Other authors obtained also the increase of bioactive compounds and antioxidant capacity [−1 and 0.063 mg Catechin g−1 for total phenols and flavonoids content, for its higher level of incorporation in crackers, nevertheless it was 9%, much lower than in the present work. Espinosa-Solis et al. [−1 for ABTS antioxidant activity [The developed GFB bioactive compounds’ content and antioxidant capacity are presented in oxidants ,56. Thuscapacity ,23,24. Icapacity  obtainedactivity . Neverthactivity .Results of sensory evaluation are represented in Supplementation with apple flour presents promising results in developing bakery products with potential health benefits and innovative sensory attributes, especially in sweet breads, cakes and cookies. At the same time, with apple flour it is possible to follow the trend of rich-fiber, natural health promoting ingredients, sustainability and also deliver apple characteristics to the bread.Apple flour improved technological and sensory characteristics of sweet gluten-free (GF) bread, being a source of bioactive compounds and antioxidants. The starch hydrolysis index increased, as well as eGI, nevertheless with values really close to the low eGI category (56). Nevertheless, the results have demonstrated apple flour’s impact on dough’s pasting properties and rheology (increasing G’ and G’’), leading to a more compacted dough structure and breads with higher firmness. Sensory results show that this impact on texture was compensated by the positive effect on flavor.Furthermore, the apple flour samples presented a high concentration of bioactive compounds; nevertheless, from the sustainability perspective, it is important to study its bioavailability. Apple flour is a valuable by-product that can be used as natural sweetener in GF bakery products, giving a sweet gluten-free bread with better acceptance by the consumer, rich in fiber, antioxidants and other bioactive compounds. In conclusion, apple flour seems to be an ingredient with a positive impact in GF bread quality in terms of nutrition, bioactivity and sensory properties, while at the same time it is a resource saved from being wasted.