The sixth Total Diet Study (TDS) of China included a countrywide study to assess the health effects of MSG (monosodium glutamate). MSG detection, consumption analysis, and risk assessment were conducted on 168 samples from seven food categories of the most typical Chinese daily diet. The highest value of MSG in the daily diet of the Chinese population was 8.63 g/kg. An MSG intake of 17.63 mg/kg bw/d for the general population of China was obtained from content measurements combined with food consumption, while the data from the apparent consumption survey alone gave 40.20 mg/kg bw/d. The apparent consumption did not consider the loss of MSG during food cooking, resulting in an overestimate. To offer a global perspective, MSG content, food category contributions, and ingestion levels across nations were summarized and thoroughly investigated. A realistic, logical, and precise risk assessment protocol for MSG daily intake was developed in this article. Monosodium glutamate (MSG), a flavor enhancer, is typically utilized in food processing in homes and the catering sector worldwide. The dangers of excessive MSG consumption on human health have drawn researchers’ attention repeatedly ,10,11,12Hence, the European Food Safety Authority (EFSA) re-evaluated the safety of glutamic acid and its derived glutamates in June 2017 and presented a population-acceptable daily intake (ADI) of 30 mg/kg bw/d, expressed as all inorganic salt forms of glutamic acid . AccordiIn this study, we investigated the types of daily diet and consumption in the general population of Chinese residents to obtain more exact information on the actual intake of MSG in the daily diet based on the sixth Total Diet Study (TDS) of China. The MSG content of all consumed foods in the ready-to-eat state was detected by our laboratory UPLC-MS/MS assay. The MSG daily intake was calculated by multiplying the food consumption and the related content. The daily intake of MSG in the general population of China was assessed at provincial and national levels based on an ADI value of 30 mg/kg bw/d for a standard Chinese man’s bw of 63 kg, on the risk of human health effects. Meanwhile, to compare the variability of risk assessment results between apparent MSG consumption and actual intake, a 3-day, 24-h dietary survey was conducted using a CHNS-like survey of over 10,000 households in a selected area covering 70% of China’s land area and 85% of its population and assessed in terms of apparent consumption. The real daily dietary intake of MSG in the general Chinese population was substantially lower than the apparent consumption, as shown by the comparison between the findings of the intake analysis and the apparent consumption analysis. This finding suggests that to appropriately assess the risk of MSG intake and prevent overestimation, losses of MSG during cooking and trans-oral processes should be taken into consideration.TDS, a continuous national study, monitors the levels of trace elements and harmful compounds in foods and is also used to estimate hazards of dietary exposure in Chinese populations. The China National Center for Food Safety and Risk Assessment (CFSA) conducted the sixth China TDS from 2016 to 2021 in twenty-four provinces, namely, Heilongjiang, Liaoning, Jilin, Hebei, Beijing, Henan, Shanxi, Shaanxi, Ningxia, Neimenggu, Qinghai, Gansu, Jiangxi, Fujian, Shanghai, Jiangsu, Zhejiang, Hubei, Sichuan, Guangxi, Hunan, Shandong, Guizhou, and Guangdong. In the sampling setting, provinces with a population above fifty million randomly set two different urban sampling points and four rural sampling points. Conversely, one urban sampling site and two rural sampling sites were randomly selected in provinces with a population of fifty million or fewer. The number of participants in each sampling site was not less than 30 households. According to the mixed food sample method used in the earlier TDS in China, the surveyed per capita food consumption was divided into thirteen categories, namely, cereals, legumes, potatoes, meats, eggs, aquatic foods, milk, vegetables, fruits, sugar, beverages, alcohol, and condiments (including cooking oil). The dietary composition of the surveyed areas or populations was collected by grouping the different food items consumed into the categories to which they belonged. Various food samples were collected from food purchasing points such as vegetable farms, side stores, grain stores, farmers’ markets, or farmers’ homes, near the neighborhood committee or village where each survey site was located. Food and condiments for cooking were prepared in designated restaurants, kitchens, or laboratories according to the cooking utensils and methods customarily used locally. All seasonings were added to other food groups as required by the recipe during the cooking process. Then, the ready-to-eat dietary samples were stored at −20 °C and shipped to CFSA.3·H2O was added for cartridge equilibration. After sample loading, water (4 × 0.5 mL) and methanol (2 × 0.5 mL) were used to remove impurities. Then, 0.5 mL of 2% formic acid (v/v) in methanol was used to elute the target. All eluent fractions were combined and dried by nitrogen flow, and the residue was reconstituted in 0.25 mL of 50% aqueous acetonitrile. Finally, depending on the level of MSG content in the dietary sample, 1 mL of the eluate was diluted 10−3 times and then detected for content. MSG identification and quantification were performed using Shimadzu LC 8050 UPLC-MS/MS . The positive ion mode was used in the ESI ionization. The ion spray voltage was +4.5 kV. Nitrogen was used as the atomization gas and the drying gas, with a flow rate of 3 L min−1 and 10 L min−1, respectively. The heating gas velocity was 10 L min−1 of air. Additionally, meanwhile, the desolvation line and heating module temperatures were 300 °C and 400 °C, respectively.One hundred and sixty-eight samples from twenty-four provinces of seven food items, namely, cereals, legumes, potatoes, meats, eggs, aquatic foods, and vegetables, were examined for MSG content determination. Following the local food recipe, these foods used MSG or other analogous flavors during the cooking procedure. MSG was extracted using 4 mL of 0.1 M HCl from 1.0 g of the dietary sample thawed at room temperature during the sample pretreatments. The extraction solution was centrifuged above 4000 rpm for 10 min. Before the sample was cleaned, 1 mL of supernatant was aliquoted from the extraction solution and neutralized with 1 mL of 0.1 M NaOH. An ion exchange cartridge  was used for sample clean-up. Firstly, 2 mL of 5% NH™ UPLC HSS PFP column  was used for MSG chromatographic separation. The temperature of the column oven was 40 °C. The mobile phase was water (phase A) and acetonitrile (phase B) containing 0.2% formic acid (v/v). The steps of the gradient elution program were: 0–3 min: 50% phase B and 50% phase A, 3–6 min: phase B content increasing linearly to 100% and kept for 2 min. The flow rate was 0.20 mL/min, and the injection volume was 1 μL. The retention time of MSG was 2.748 min under these conditions.An ACQUITY+ > 84.05+ and 147.90+ > 56.05+, with collision energies of 16 eV and 23 eV, respectively, in MS separation.Quantitative and qualitative ion pairs on the multiple reaction monitor (MRM) scanning modes were 147.90The dietary intake of each food category was calculated by multiplying the MSG contents and their consumption. The whole MSG intake was the accumulated sum of every food intake. Additionally, then, the sum was divided by 63 kg of the standard Chinese man for MSG intake risk assessment. The items with “not detected” data represented half of the Limits of Detection (LODs) when processing the dietary intake. The risk assessment was based on whether the general population’s intake in each province was above 30 mg/kg bw/d, and areas where MSG intake exceeds this value need to be a cause for concern.The parameters of the assay, such as LODs, Limits of Quantitation (LOQs), and spiked recovery experiment, were jointly validated by three other national laboratories. All methodological sensitivities, reproducibility, and specificities are reflected in the additional information. The LODs of MSG in the food composites ranged from 1.5 to 3.5 μg/kg in different food matrices. Additionally, the LOQs of MSG were 5.0 μg/kg in cereals, legumes, and potato products and 10 μg/kg in meat, egg, aquatic, and vegetable products, respectively. In the recovery test, blank samples were selected from the seven food matrixes in which the MSG concentrations were below the LODs. Three spiking levels of 10, 100, and 1000 μg/kg were evaluated. The validation results of this determination method are seen in It is clear from There was a wide range of MSG intakes, from 4.27 to 32.35 mg/kg bw/d, among different regions in China. The mean (±SD) was 17.63 ± 2.50 mg/kg bw/d at the national level. The top three highest intake provinces were Shanghai, Heilongjiang, and Hunan, with 32.35, 27.78, and 27.56 mg/kg bw/d. The province with the lowest intake was Shandong Province, with a value of only 4.27 mg/kg bw/d, as The risk evaluation included an investigation into the dietary contribution of MSG consumption. This data analysis enabled us to comprehend the variations in dietary practices between regions and to lower risk by limiting items with a high proportion of contaminants. Vegetables were the primary source of essential intake at the national level in our contribution margin analysis, contributing at a rate of 44%, as shown in Since MSG was first used in flavoring, there have been different opinions on its safety in various countries and institutions. The US Food and Drug Administration (FDA) considers the addition of MSG to foods to be “generally recognized as safe” (GRAS). In some countries and regions, MSG is even seen as the main alternative to reduce sodium salt intake. Although our group has reservations about the suitability of this ADI for the Chinese population, it has been used as a tentative evaluation criterion for the risk assessment of MSG. A uniform unit of measurement was needed for MSG intake to be consistent with the criteria used to determine results when comparing different regions of the world. The MSG intake units mentioned previously in In this study assessment, animal-based foods such as meats and aquatic had higher MSG levels than plant-based foods. Compared to plant foods, animal-based foods have more protein amino acid groups, and these free proteins may undergo hydrolysis to produce glutamate under complex external conditions. Many high-protein animal foods have high levels of MSG, and a large proportion of this should be contributed by these free glutamates . This fiWe also carried out a comparative analysis of the global MSG food content. As can be seen in To further explore the influence of the geographical distribution and dietary consumption habits of the Chinese general population on MSG intake, we mapped the geographical distribution of total dietary MSG in China . In geneIn addition, we conducted inter-country comparisons of MSG-related or similar data on the MSG dietary intake of the world population, and the results are shown in Comparatively, the European diet was dominated by high-quality baked goods, soups, broths, sauces, meat, meat products, seasonings, and condiments, all of which were high in MSG and consumed in significant amounts ,35. The In this study, we considered the intake derived by multiplying the food content and the corresponding consumption as the risk assessment value to be accurate. This strategy takes full account of factors such as the low levels of MSG added to fresh or home-cooked foods, and the fact that MSG is dissolved in the water of the dish during cooking and consumption of the food, and this water reduces the imported intake of MSG as a non-edible fraction in the actual intake. Therefore, a realistic approach to measuring MSG intake has the advantage of authenticity and precision. Although a risk assessment of health effects based on MSG consumption would lead to overestimation, high MSG consumption should be a concern in China.In summary, our group conducted a comparison of apparent MSG consumption surveys and real intake using the TDS study to analyze the risk of MSG quickly and accurately for the public using vast data covering over 70% of China’s land and 85% of its population. This is the most representative and up-to-date MSG intake and risk assessment for China to date. We have, for the first time, examined the geographic variation of MSG intake for different food categories in China. This analysis will serve as a local point of reference and offer information on how to restructure diets healthily. However, in this paper, the results of our risk assessment were carried out mainly based on MSG intake in the general population. Therefore, we have shortcomings in the analysis of individual consumption and risk of MSG. In a subsequent study, we will reinforce the probabilistic assessment of the risk of MSG intake in the Chinese population on specific indicators such as gender, age group, and BMI for the individuals participating in the survey, to more precisely target the population groups at high risk of MSG intake.In summary, we conducted a comparison of apparent MSG consumption surveys and real intake using TDS study to analyze the risk of MSG quickly and accurately for the public using vast data covering over 70% of China’s land and 85% of its population. A risk analysis of MSG intake in the general population of China was obtained from both assessments. This is the most representative and up-to-date MSG intake and risk assessment study for China to date. Additionally, considering the MSG loss caused by MSG in whole food cooking and non-edible parts of the dishes, we believe that the strategy of obtaining the maximum intake with MSG apparent consumption through a 3 d, 24 h room survey causes overestimation. This provides a more reliable technical solution for the future risk assessment of relevant condiments. We have, for the first time, examined the geographic variation of MSG intakes for different food categories in China. This analysis will serve as a local guideline and offer information on how to restructure diets healthily. Last but not least, a global comparative analysis of food content and population intake in dominant studies around the world was conducted. This provides a global view of MSG population intake and risk assessment for our colleagues around the world, providing a strategy for a rapid, accurate, and realistic MSG risk assessment of daily household dietary intakes for related work.


Alzheimer’s disease (AD) is an age-associated neurodegenerative disease, and the currently available diagnostic modalities and therapeutic agents are unsatisfactory due to its high clinical heterogeneity. Necroptosis is a common type of programmed cell death that has been shown to be activated in AD.In this study, we first investigated the expression profiles of necroptosis-related genes (NRGs) and the immune landscape of AD based on GSE33000 dataset. Next, the AD samples in the GSE33000 dataset were extracted and subjected to consensus clustering based upon the differentially expressed NRGs. Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. Finally, we developed a diagnostic model for AD by comparing four different machine learning approaches. The discrimination performance and clinical relevance of the diagnostic model were assessed using various evaluation metrics, including the nomogram, calibration plot, decision curve analysis (DCA), and independent validation datasets.Aberrant expression patterns of NRGs and specific immune landscape were identified in the AD samples. Consensus clustering revealed that patients in the GSE33000 dataset could be classified into two necroptosis clusters, each with distinct immune landscapes and enriched pathways. The Extreme Gradient Boosting (XGB) was found to be the most optimal diagnostic model for the AD based on the predictive ability and reliability of the models constructed by four machine learning approaches. The five most important variables, including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1, were used to construct a five-gene diagnostic model. The constructed nomogram, calibration plot, DCA, and external independent validation datasets exhibited outstanding diagnostic performance for AD and were closely related with the pathologic hallmarks of AD.This work presents a novel diagnostic model that may serve as a framework to study disease heterogeneity and provide a plausible mechanism underlying neuronal loss in AD. Accordientified . Neverthin vivo data demonstrating its participation in development of cognitive deficits in APP/PS1 mice  controls, and then explored the immune profiles of the samples. Next, AD samples in the training set were extracted and subjected to consensus clustering based on differentially expressed NRGs. Results revealed that the patients could be classified into two necroptosis clusters, each with distinct immune landscape and enriched pathways. Subsequently, key genes related to necroptosis clusters were identified using WGCNA algorithm, and intersected with the key gene related to AD, thereby obtaining the shared genes between module-related genes in AD and in necroptosis clusters. Subsequently, a diagnostic model for AD was developed by comparing various types of machine learning approaches, with the nomogram, and its discrimination performance and stabilities in the diagnosis of AD were determined using the calibration plot, decision curve analysis (DCA), and independent validation datasets. Furthermore, we performed correlation analyses for the hub genes included in the diagnostic model with the age and major risk factors influencing the generation of Aβ, and further elucidated its correlation with the pathologic hallmarks of AD. The flow chart of the study was shown in Microarray data of transcription profiles associated with AD were acquired from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database .Necroptosis-related genes were retrieved from the Deathbase database  with a rBased on the differentially expressed NRGs, we employed the “ConsensusClusterPlus” package  to clustp-value less than 0.05 calculated by student’s t-test were displayed on a barplot, with red and blue representing up- and downregulated pathways, respectively.Gene set variation analysis enrichment analysis was performed to determine the biological processes and pathways related to the disparate clusters using “GSVA” packages, based on the gene sets of “c2.cp.kegg.v7.2.symbols” provided by the Molecular Signatures Database (MsigDB).To explore the connections between gene modules and disease traits in the context of AD, we employed WGCNA in conjunction with hierarchical clustering. This analysis, performed using the “WGCNA” package, enabled us to identify crucial genes that are closely associated with the pathogenesis of AD. Briefly, the genes with the top 25% variance were extracted from the GSE33000, and used to cluster the samples and remove outliers. Next, Pearson’s correlation value between varying gene pairs was calculated, and utilized to construct a similarity matrix, the latter of which was converted into the adjacent matrix with a suitable soft threshold power. Furthermore, a topological overlap matrix (TOM) was generated, and genes that shared similar expression pattern were categorized into disparate modules via a dynamic tree-cutting algorithm using minimum module size of 100 genes a threshold. Finally, we defined the hub genes within each module as those exhibiting a gene significance (GS) value greater than 0.2 and a module membership (MM) value exceeding 0.6.CIBERSORT algorithm was used to identify the relative composition of 22 immune cells based on expression profiles of the samples in GSE33000 dataset, which is a widely used analytical tool that is based on linear support vector regression principle for deconvolution analysis , 2022. TAfter intersection analysis of the genes in the most significant modules in WGCNA, “caret” R package was utilized to divide the samples into two datasets using the createDataPartition function, and a 70 to 30% split was applied, ensuring a representative distribution of samples across the sets. Then, the key genes with high diagnostic potential for AD were identified using four machine learning algorithms, including Random Forest (RF), Support Vector Machines (SVM), Extreme Gradient Boosting (XGB), and Generalized Linear Model (GLM), were applied to rank features by importance in the training set via “kernlab,” “randomForest,” “xgboost” R packages. In the RF algorithm, we implemented the train function with the “rf” method, and trainControl function was employed to establish the settings for repeated cross-validation. Additionally, we defined the p_fun function to predict class probabilities. For the SVM model, we employed the train function with the “svmRadial” method for training, and the parameter prob.model was set to TRUE to enable probability modeling. In the case of the XGB model, we constructed it using the train function with the “xgbDART” method, and the cross-validation settings were specified via the trainControl function. Similarly, we used the train function with the “glm” method to create the GLM model. To handle binary classification, the family parameter was set to “binomial.” Besides, diseases phenotype was used as the response variable, whereas the genes obtained by WGCNA were used as the explanatory variable. Next, the explain function of “DALEX” R package was employed to perform exploratory analysis for the model and plot function was used to generate the cumulative residual distribution map and residual boxplot, thereby identifying the optimal diagnostic model combined with time-dependent receiver operating characteristic (ROC) curve with evaluation of the area under the ROC curve (AUC) through “pROC” package. Finally, five most important variables were used to construct a five-gene diagnostic model, and such a diagnostic model was validated through multiple validation sets.t-test was applied to analyze normally distributed data. Correlation analyses were conducted using the Spearman correlation test. These analyses were performed using the R software 4.1.2, and p < 0.05 was considered statistically significant.The nonparametric Wilcoxon test was used to compare two groups of non-normally distributed data, while student’s We searched for previously reported NRGs from public databases and identified 46 NRGs. To investigate the expression profiles of NRGs in AD, 310 postmortem brain samples of patients with AD and 157 ND samples in the GSE33000 were analyzed after log2 transformation. Results showed that 36 out of the 46 NRGs were differentially expressed in AD samples. The expression levels of RIPK1, RIPK3, MLKL, CASP8, TNF, CASP6, TRPM7, FADD, PELI1, PGLYRP1, TNIP1, TP53, TNFRSF1A, BIRC2, MEFV, AIM2, TNFAIP3, SERTAD1, TRAF2, NFKB1, CFLAR, FAS, GSK3B, TRADD, and TLR3 were significantly enhanced in AD samples, whereas that of CYLD, ITPK1, MAP3K7, SPATA2, SIRT3, HMGB1, UCHL1, CTSB, MAPK14, SFTPA1, and FASLG were downregulated in AD samples , and a h+ T cells, follicular helper T cells, activated NK cells, and eosinophils. Subsequent correlation analysis revealed that the expression levels of the afore-mentioned differentially expressed NRGs were closely linked with the abundance of immune cells in the local environment  and Cluster 2 , with the clinical characterization of necroptosis clusters of AD samples being displayed in Next, 310 AD samples in the training set were extracted and subjected to consensus clustering based on the differentially expressed NRGs. It was observed that the most optimal number of clusters was two in consensus matrix plots k = 2; , with th2 was equal to 0.85  produced predominantly by perivascular macrophages has been shown to drive microglial phagocytosis of synapses, thereby contributing to the development of amyloid-induced neurodegeneration (To close the gap existing in our understanding of the molecular mechanisms of AD, we systematically and comprehensively investigated the transcriptomic profiles of necroptosis regulators between AD and ND samples, to identify the aberrant expression patterns of NRGs in AD. Our findings demonstrate the underlying connections of necroptosis in the pathogenesis of AD. Intriguingly, we uncovered the specific immune cell landscape in the microenvironment of AD, and the subtypes of T cells were significantly varied between AD and ND individuals, implying that T cells play key roles in the progression of AD. Specifically, AD individuals exhibited high relative abundance of naive and resting memory CD4 T cells, whereas ND samples had markedly higher number of CD8l models  and cerel models , in whicl models . Notablyneration . Likewisneration , suggestneration . Such obLately, machine learning-based algorithms, combing the clinical, digital pathology, multi-omics data, have been developed and applied in the diagnosis of diseases , drug deThe CACNA2D3 gene encodes the α-2δ subunit of the voltage-dependent calcium ion channel, and is known to contribute to late-onset AD following genomic imbalance . Besidesin vivo and in vitro data are needed to elucidate the detailed mechanism of the identified hub genes in the pathophysiology of AD.Despite the important findings of this study, there are several limitations that need to be considered. First, it should be acknowledged that the disease cohorts investigated in this study were primarily derived from public database. Therefore, the reliability of the diagnostic models should be validated using external datasets. Further, Collectively, our study presents the expression profiles of NRGs in AD and normal individuals, and further reveals the distinct necroptosis clusters identified using consensus clustering in diseased samples, each with unique immune profiles. Moreover, a five-gene XGB-based diagnostic model for AD was constructed, which performed well in the training and independent validation datasets. Our work provides a novel diagnostic model that may serve as a framework to study disease heterogeneity and the mechanism underlying neuronal loss in AD.The original contributions presented in the study are included in the article/LL designed the study, drafted the manuscript, provided the funding, and supervised the study. HW, CW, and YY performed literature search, conducted statistical analyses, and visualized the results. All authors contributed to the article and approved the submitted version.


Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography–mass spectrometry (GC–MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa , whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa . This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients ( The gut microbiota plays an important role in maintaining the healthy metabolism in the host. This regulation of the host health relies on the production of different classes of metabolites by the microbiota and their underlying molecular mechanisms in both healthy and diseased conditions ,2. ThereThe gut microbiome has been linked with a plethora of diseases, gastrointestinal disorders, and different types of cancer, including gastric cancer and colorectal cancer ,6,7. ThiThe identification of volatile metabolites as potential biomarkers of a disease through non-invasive analysis still remains a challenge. Breath analysis is an easy, non-invasive, real-time, and innovative method used to analyze the chemical composition of exhaled breath ,9. In paActinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Verrucomicrobia, Fusobacteria, and Synergistetes [Fusobacterium nucleatum, Escherichia coli, and Bacteroides fragilis were correlated with colorectal cancer [Dialister invisus, Parabacteroides spp., Lachnospiraceae, and Ruminococcaceae, as well as strains such as Faecalibacterium prausnitzii and Clostridium clostridiiforme, have been associated with metabolite disturbances, including SCFAs, bile acids (BAs), choline, and specific pathways in several gastrointestinal and inflammatory disorders [Several well-established volatile breath metabolites, such as aldehydes, ketones, and hydrocarbons; bacterial fermentation products, including alcohols; and short-chain fatty acids (SCFAs), such as acetate, butyrate, and propionate have been correlated with gut microbiota and are exhaled via the breath ,16. Howegistetes . Gut micl cancer . Severalisorders ,21,22. TIn the last decade, the advancement and the application of metagenomics, metabolomics, breathomics, and omics data analysis  have led to a better understanding of the fecal and breath volatilome and their contribution to pathogen virulence and overall gut health . A numbeStudies have identified over 2700 VOCs in human breath [The analysis included a total of 49 participants. All participants provided both breath and fecal samples. Out of these participants, 16 were gastric cancer patients and 33 were healthy controls. The patient group showed different clinical stages of gastric cancer, starting at IA and going up to IV. An overview of clinical and demographic data is presented in Bacteroidetes at 24.9% and 30.2%; Firmicutes at 67.5% and 60.7%; Actinobacteria at 3.3% and 3.5%; and Proteobacteria at 2.6% and 3%, respectively , as in both cases only 4.7% and 3.0% of the variance could be explained, respectively.A total of 6.2 billion raw reads were obtained from all the samples analyzed. After quality filtering, trimming , and host sequence removal, 6 billion paired reads remained with an average of 123 million paired reads per sample, in a range between 86 and 178 million. The negative control added during the extraction did not provide enough DNA for library construction, thus indicating a low possibility of reagent and human contamination. Overall, there were 1324 bacterial species identified after the initial analysis of the microbiome composition using the MetaPhlAn4 marker database. At the phylum level, the fecal microbiota of cancer and the control patient samples were, on average, dominated by ectively . After oIn order to remove possible false-positive results (misidentifications) and rare species that have little importance for the downstream breath VOC analysis, only the species present in at least 30% of the samples were retained for the analysis, which left 395 bacterial species in the analysis dataset. The taxonomic analysis was performed on the full dataset.A non-metric multidimensional scaling (NMDS) plot, colored according to the sample groups, is presented in p-value < 0.05), namely Bifidobacterium dentium and Lactobacillus mucosae  and octane, 1,1′-oxybis- . We also identified that these two metabolites did not overlap between the study groups, even after a sensitivity analysis, and were present exclusively in cancer cases. These results demonstrated that 1-octanol and octane, 1,1′-oxybis- could be potential breath biomarkers for gastric cancer. Importantly, studies had previously associated these VOCs in exhaled breath with a number of bacteria-related metabolic pathways associated with cancer [A table with the descriptive statistics and h cancer ,28,29,30To reduce the dimensionality of the dataset and obtain a more similar number of features and samples, the feature space of both the breath-VOC and microbiome data subsets were transformed using a sparse principal component analysis (S-PCA).p-value = 0.045, in the cancer data subset and a correlation of 0.871, p-value = 0.0007, in the control data subset). x-axis represents the canonical variate for the breath VOCs, whereas the y-axis represents the canonical variate for the fecal microbiome data. Each point corresponds to integrated data from the exhaled breath and the fecal samples collected from the same participants.To detect the possible relationships between the breath VOCs and the fecal microbiota specific to gastric cancer, separate analyses of the data related to the cancer patients and the data of the control group were carried out. The CCA analysis of the S-PCA transformed data showed a statistically significant correlation between the components , there were 14 VOCs and 33 species of bacteria remaining in the cancer subset and 7 VOCs and 17 species of bacteria in the control subset. Similarly, we applied a threshold of ≥0.5 for the correlations in both datasets to obtain the final and most important exhaled VOCs and fecal bacterial taxa.The analyses of the VOCs and fecal bacteria were continued by analyzing the significant correlations ≥ 0.5 and by investigating their biological relevance in the available literature, as well as in the metagenome sequencing results. The VOCs were evaluated according to the pathways of the bacteria with which they were correlated in order to determine whether they could explain the observations related to the specific VOCs and their potential roles in cancer.Clostridium_SGB6179) overlapped between the two groups.The chemical identities of the 21 exhaled volatile metabolites that had the highest contribution to the overall correlation, found in both the cancer and control groups, are shown in The correlations between the most significant volatile components and the detected species of bacteria in the two study groups are represented in Bacteroides xylanisolvens and Gemmiger formicilis bacteria. In addition, 2-pentanone, a ketone, was clearly linked to the Dorea formicigenerans and Clostridiaceae bacterium_OM08_6BH strains. We observed dimethyl disulfide correlated to the Dorea longicatena, G. formicilis, D. formicigenerans, Clostridium_sp_AM22_11AC, and Oscillospiraceae bacterium_NSJ_64 species. Furthermore, ethanol, 2-phenoxy- in the exhaled breath of cancer patients was related to Fusicatenibacter saccharivorans, Oscillibacter_sp_ER4, Clostridium_sp_AF20_17LB, Bilophila wadsworthia, Bittarella massiliensis, Blautia faecicola, and Clostridium_sp_AM22_11AC.In B. xylanisolvens, Ruminococcaceae unclassified_SGB15257, Holdemanella biformis, and Olsenella_sp_GAM18, trended towards the white color or had strong positive correlations with a majority of the breath volatile metabolites, as compared to the other bacteria.Furthermore, after comparing the rows, different bacterial species were significantly different from each other. Bacteria species, such as The correlation heatmap for the breath VOCs and the fecal bacterial taxa that contributed most to the correlations in the control group is shown in Enterocloster aldensis and Actinomyces sp_ICM47, all other bacterial species exhibited strong correlations with the levels of VOCs previously mentioned.Interestingly, we found statistically significant correlations between the individual breath VOCs and fecal bacteria. Of the 7 preselected VOCs from the 33 healthy controls, benzyl alcohol; dl-erythro-1-phenyl-1,2-propanediol; 1-decanol; methacrolein; and 3-methylcyclopentyl acetate were strongly correlated with the abundance of most of the bacterial species under observation. Likewise, except for The correlation tables showing the magnitudes of the correlation coefficients between the breath metabolites and fecal bacterial taxa in the cancer and control subsets are provided in The present study aimed to investigate the possible correlations between specific breath VOCs, as identified by GC–MS, and the gut microbiota, as evaluated by shotgun metagenomics sequencing, in relation to gastric cancer. The exhaled breath samples and the fecal samples from a total of 49 participants  were analyzed. We hypothesized that the breath VOCs would be related to the fecal microbiota composition, directly or indirectly, and their presence and levels would vary between the disease and healthy states.Altogether, our results revealed a significant relation between the fecal microbiota and the volatile metabolites in exhaled breath, as they differed between the cancer patients and the healthy controls. The exhaled breath analysis was integrated with the fecal microbiome analysis, and together, they could serve as non-invasive diagnostic-and-screening tools for gastric cancer, as this disease has been associated with changes in the volatile metabolic profiles and dysregulation of the gut microbiota, its composition, and the related metabolic pathways ,32. HoweWhile microorganisms are present throughout the human digestive tract, studying their presence in fecal samples is crucial to understand the composition of fecal microbiota and their interactions with the host and their contributions to host phenotypes . AlthougRecent studies have shown that the concentrations of VOCs in the breath and the abundance of fecal microbes differed according to the state of gastrointestinal diseases. Smolinska et al. highlighted a strong link between the specific VOCs in exhaled breath and the specific fecal bacterial strains in a study that included a cohort of patients with Crohn’s disease, a type of inflammatory bowel disease . The samIn this study, we found distinct breath metabolites and microbiota compositions, as well as distinct correlations, in the analyzed samples of the cancer and control groups. This suggested different underlying metabolic processes or mechanisms could exist between the groups. The CCA analysis showed a slightly stronger correlation in cancer cases (correlation 0.891) than in the healthy controls (correlation 0.871). Similarly, a larger number of breath VOCs and bacterial species were observed in the cancer patients than in the controls, once again attesting to the distinct microbiome compositions and metabolisms associated with each group. Interestingly, the observed VOCs did not overlap between the groups, whereas a single bacterial taxon was present in both; therefore, the correlations found in this study were dependent on the state of disease or health. Long-term dietary patterns could alter microbiota composition and metabolic activity . Herein,B. xylanisolvens and H. biformis, in cancer patients. B. xylanisolvens has been linked with high-fiber dietary patterns [Helicobacter pylori and Mycobacterium avium. Furthermore, 2-pentanone, commonly reported in the breath and fecal samples in numerous studies, was produced via the β-oxidation of fatty acids or the oxidation of 2-pentanol catalyzed by either alcohol dehydrogenases (ADHs) or cytochrome p450 (CYP2E1) [D. formicigenerans and Clostridiaceae bacterium_OM08_6BH of Firmicutes revealed significant correlations with 2-pentanone. The D. formicigenerans strain has been associated with obesity-related inflammation.Similar to other studies of gastrointestinal disorders, we found statistically significant correlations among specific hydrocarbons , one ether , and bacteria patterns . The met(CYP2E1) ,50. The Prebiotic supplementation with pectin, a dietary fiber, did not have an impact on fecal microbiota composition, SCFAs, or exhaled VOCs in young adults versus elderly subjects . Low-fibD. longicatena, D. formicigenerans, G. formicilis, and Clostridium_sp, showed a strong positive correlation to DMDS. However, the relation of DMDS to G. formicilis had not been demonstrated previously. Complex benzenes and toluene, as volatile aromatic hydrocarbons, were reported in higher concentrations in human breath, urine, and blood, and have been commonly attributed to environmental exposure, smoking, and pathogens, such as H. pylori, Pseudomonas aeruginosa, and M. avium [B. xylanisolvens, whereas benzene and toluene were sparsely correlated to the G. formicilis species. B. xylanisolvens was shown to degrade polysaccharides in the gut and produce antibodies (IgM) against the carcinoma-associated Thomsen–Friedenreich (TFα) antigen, in a dose- and time-dependent manner [DMDS, a sulfur-containing compound, was most likely related to the proteolytic fermentation by human fecal microbiota and is an important breath biomarker . Indeed,M. avium ,57. In ot manner .Ruminococcus, Prevotella, and Bifidobacterium, among others, that could convert alcohols into aldehydes and SCFAs [E. aldensis, a strain of Lachnospiraceae, which was metabolically less active and could be responsible for producing certain alcohols and SCFAs. However, the relation of Phenol, 2-methyl-4-- to E. aldensis had not been investigated previously. We also found statistically significant correlations between the species Candidatus Schneewindia gallinarum and Pseudoflavonifractor SGB15156, and 4-ethylbenzamide (a benzamide derivative), in the healthy controls. This metabolite has been detected in the breath of smokers and emphysema patients, and has often been regarded as a possible inhibitor of Mycobacterium tuberculosis [Several primary and branched alcohols were found to be present in both the cancer and control groups, in varying concentrations. They could be produced via the microbial fermentation of alcohols, including from methanol to heptanol . Bacterind SCFAs . It is irculosis . In sharrculosis . In the rculosis . In otheConsequently, several classes of VOCs could be identified in exhaled breath associated with the fecal microbiome and related to different metabolic pathways. Including our results of the correlated exhaled breath metabolites in future research with a larger study cohort could further our understanding of the relationship and the origin of the metabolites. In addition to the sample size, a potential limitation in the present study could have been related to the confounding chemicals present in the ambient air, as a possible source of background bias. Similarly, the expression of certain breath VOCs in relation to the correlated bacteria is not yet fully understood. The presence of outliers in the fecal bacterial communities and their respective VOCs may suggest the presence of some rare and unclassified bacterial taxa.We did not identify any noteworthy differences in the gut microbiome composition based solely on taxonomic composition between the gastric cancer and control groups that would enable us to distinguish between the two. This lack of differentiation was not unusual, given the limitations of the sample size, the variability of the tumor types, and the clinical stages represented in this study, as well as the restraints of microbiome DNA sequencing. While this method was not entirely quantitative, the presence of DNA alone did not necessarily indicate the production of a particular metabolite. To obtain more precise information about the processes occurring in the gut, one would need to use other approaches, such as RNA sequencing and metabolomics. Alternatively, breath analysis of VOCs has the potential to provide a less expensive, more direct, and non-invasive measure of the microbiome’s status, which would be a more practical approach in clinical settings where rapid and cost-effective testing is necessary.In line with our findings, the established link between exhaled metabolites and the fecal microbiota could provide information on the origin of the volatile compounds in the breath, indicate the state of health, and assess recent or long-term exposure to various contaminants. Additionally, this could generate new knowledge on the mechanisms and potential of breath VOCs for gastric cancer screening in exhaled breath analysis.This study included a total of 49 individuals, aged 24–77 years, recruited at the Riga East University Hospital, Latvia Oncology Center , who underwent the analyses. Among them, 16 were diagnosed with gastric cancer, and 33 were healthy controls. The patient group had different clinical stages of cancer, ranging from stage IA to stage IV . The stuThe clinical data, breath, and fecal samples were collected during the hospital visit. Breath samples were collected in a dedicated room within the oncology department for the GC–MS analysis. The fecal samples from the same participants were taken for microbiome analysis.Fast for at least 12 h;Refrain from drinking coffee, tea, and soft drinks, for at least 12 h;Refrain from smoking for at least two hours;Avoid alcohol intake for at least 24 h;Refrain from tooth-brushing, mouthwash, or flossing;Avoid chewing gum and mouth fresheners for at least 12 h;Refrain from using cosmetics/fragrances prior to the procedure on the day of the test;Avoid excessive physical activity  at least two hours prior to the test.Exhaled breath samples were taken randomly at room temperature, in a room free of potential sources of VOCs, such as food, kitchen waste, drugs, solvents, and disinfectants, to prevent the risk of background bias. All participants were exposed to the same test environment, temperature, and humidity conditions, without any medical intervention. Prior to the sampling, the study subjects were advised to:2 sensor cell  was connected to the other end of the elbow. The elbow was equipped with a ¼″ port, enabling the connection of industry standard 1/4″ Tenax TA sorbent tubes. Prior to the sample collection, the sampling end of the sorbent tube was inserted into the elbow, so it protruded 5–6 mm into the interior of the elbow and was fixed with a ¼″ PTFE nut. The other end of the sampling tube was connected to a 250 mL gas-tight glass syringe  using a 1/8″ Teflon tube. The subject could freely inhale/exhale through a mouthpiece without pneumatic resistance. The sampling was achieved manually by drawing a volume of 500 mL during subsequent end-tidal exhalation segments, as identified via the CO2 measurement. On average, 10–15 mL of breath was drawn per exhalation. Effectively, two breath samples (each 500 mL) were taken per subject. In parallel to the breath samples, one air sample of the room was collected via a manual draw of 500 mL of ambient air into a Tenax sorbent tube. Immediately after sampling, both ends of the sorbent tube were closed with brass ¼″ nuts, and the tubes were frozen at −80 °C. Samples were stored at −80 °C and transported on dry ice. An effort was made to reduce the storage time to 4 weeks. The volatile metabolites were measured by GC–MS.Prior to the breath sample collection, the medical staff interviewed the study subjects using a clinical questionnaire containing questions on personal data, VOC exposure, confounding factors, and 24 h dietary recall. Breath samples were taken using an in-house custom-made breath sampler. The breath sampler consisted of a disposable mouthpiece  installed on a disposable elbow , and the COAll standard mixtures were produced using high-purity liquid substances. Reference chemicals with stated purities of 95–99.9% were purchased from Merck  and Fluka . Standards were prepared by injecting and evaporating several microliters of the liquid compound into evacuated and heated 1 L glass bulbs . Next, primary standards were diluted by transferring appropriate volumes from the bulb mixtures into 3–10 L Tedlar bags , that were pre-filled with purified and humidified air (Relative Humidity 100% at 34 °C). The stainless-steel industry-standard thermal desorption tubes , pre-packed with Tenax TA, were purchased from Markes International Limited . Prior to each sampling, sorbent tubes were reconditioned according to the manufacturer’s recommendations. The calibration protocols were outlined elsewhere .A two-stage thermal desorption of breath VOCs was performed using a commercial thermal desorber  and an autosampler . During the primary desorption, sorbent tubes were heated to 280 °C for 6 min under a flow rate of 6.0 (99.9999%) helium at 20 mL/min. VOCs desorbed during this phase were refocused into a cold trap packed with graphitized carbon-black and maintained at 5 °C. The injection of VOCs into the capillary column was performed via the rapid heating of the cold trap to 320 °C for 1.5 min in a splitless mode.−1. The column temperature program was as follows: 40 °C for 10 min; followed by 5 °C min−1 and up to 150 °C; hold for 5 min; then 10 °C min−1 and up to 280 °C; and finally remaining at 280 °C for 5 min. The untargeted VOC analysis was performed using the mass spectrometer working in SCAN mode with the associated m/z ranging from 20 up to 250. The peak integration was based on the extracted m/z ratio chromatograms, and this approach allowed for a proper separation of the majority of the peaks of interest from their neighbors. The quadrupole, ion source, and transfer line were kept at 150 °C, 230 °C, and 280 °C, respectively.The VOC analysis relied on an Agilent 7890A/5975C GC–MS system . Extracted compounds were separated using an Rxi-624Sil MS column  operated in a constant helium flow of 1.5 mL minIdentification of breath VOCs was performed in two steps. Firstly, the peak spectrum was evaluated against the National Institute of Standards and Technology (NIST) mass spectral library database. Next, the NIST identification was confirmed by comparing the respective retention times with the retention times obtained for the reference standards prepared from pure compounds, as outlined previously.The study utilized sample collection bottles  specifically designed for fecal immunochemical occult blood testing. These bottles were used to collect, transport, and store the fecal samples. After conducting a quantitative immunochemical fecal occult blood test, all the returned bottles were frozen at −80 °C and kept frozen during the transport and storage, until they were ready for further analysis for the microbiome research. The samples were processed within 60 days from the time of collection. Prior to DNA extraction, the entirety of the buffer content containing the fecal matter (~1.5 mL) was collected from the sample bottles using a sterile, disposable syringe and then concentrated through lyophilization. The resulting lyophilized sample was used to extract microbial DNA using the MagPure stool DNA LQ  and FastPrep 24 5G instrument. Additionally, DNA was extracted from a negative control.MGI Tech Latvia laboratories were responsible for the sequencing process. The extracted DNA was fragmented into roughly 400 bp fragments, and a sequencing library was created using the MGIEasy PCR-Free DNA  library preparation protocol, in accordance with the manufacturer’s standard procedure. The DNBSEQ-T7 platform  was used to perform shotgun metagenomic sequencing with 150 bp paired-end reads.The primary processing of the raw metagenomics sequencing data was conducted on high-performance computing clusters at Riga Technical University  using an in-house Snakemake-based workflow. Adapter removal and base quality trimming was conducted with fastp tool. The host reads were extracted with Kraken2 (version 2.1.2) using the GRCh38 human reference genome. The taxonomic assignment was carried out using the MetaPhlAn4 (version 4.0.3) and vJan21_CHOCOPhlAnSGB_202103 marker database, along with Kraken2, against a database built from all available bacteria full genomes in RefSeq (accessed July 2022). A confidence value of 0.2 of the Kraken2 results was re-estimated at species level with Bracken (version 2.8) in order to improve the taxonomic abundances. The functional annotation was performed using HUMAnN 3 (version 3.6) with a full UniRef90 database (accessed December 2022), and the results were normalized at all levels by community and expressed in copies-per-million.The results were summarized and further transformed by in-house python scripts and analyzed in an R software environment (version 4.0.5.).The statistical analysis to evaluate the connection between the VOCs from exhaled breath and the bacteria species from the fecal microbiota was carried out using CCA. The CCA found the relationship between the two multivariate sets of variables that characterized the same study participants by creating linear combinations of variables from each set , so they would have a maximum correlation between them ,67. The The most significant breath VOCs and fecal microbiome species were identified using sensitivity analyses by backward elimination of the VOCs and bacteria, and then evaluating the changes in significance of correlation among the canonical components. The least significant VOCs and bacterial species had little impact on the correlation when removed. The final set of breath VOCs and bacteria species included only the significant markers, whose removal would lead to a significant drop in correlational significance.Since the counts of unique VOCs and unique bacterial species were significantly larger than the number of study participants, the dimensionality was reduced using S-PCA .p-value < 0.05 was deemed to indicate statistical significance.The analysis and visualization were conducted using RStudio 2022.02.3 (Build 492) and the R libraries CCA, CCP, and sparsepca. A Overall, this study investigated the significant and informative correlations between breath volatile metabolites and fecal microbiota compositions. The production of microbe-associated breath VOCs could affect the disease pathology. Using GC–MS for the VOC measurements served to complement the fecal microbiome analysis using metagenomic sequencing, in addition to an integrated approach involving a multivariate statistical analysis and bioinformatics. The proposed integrated analyses could be beneficial for a better understanding of the underlying biological processes of microbiota composition and breath VOCs. Moreover, this prospective study offers potential directions for advanced screening and developing microbiome-directed novel therapeutic targets and biomarkers for gastric cancer. Our findings further highlighted how breath VOCs were an excellent medium for the investigation of microbial metabolic functions and promising biomarker candidates that could eventually deliver on the promise of non-invasive cancer screening.


Computational approaches for identifying the protein–ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein–ligand binding affinity and achieve significant performance improvement. However, protein–ligand binding affinity prediction still has fundamental challenges. One challenge is that the mutual information between proteins and ligands is hard to capture. Another challenge is how to find and highlight the important atoms of the ligands and residues of the proteins.To solve these limitations, we develop a novel graph neural network strategy with the Vina distance optimization terms (GraphscoreDTA) for predicting protein–ligand binding affinity, which takes the combination of graph neural network, bitransport information mechanism and physics-based distance terms into account for the first time. Unlike other methods, GraphscoreDTA can not only effectively capture the protein–ligand pairs’ mutual information but also highlight the important atoms of the ligands and residues of the proteins. The results show that GraphscoreDTA significantly outperforms existing methods on multiple test sets. Furthermore, the tests of drug–target selectivity on the cyclin-dependent kinase and the homologous protein families demonstrate that GraphscoreDTA is a reliable tool for protein–ligand binding affinity prediction.https://github.com/CSUBioGroup/GraphscoreDTA.The resource codes are available at  Identification of high binding affinity values for candidate small molecules plays an important role in drug discovery and drug design . Considering the interactions between small molecules and proteins, our model also includes a mutual information mechanism to learn and exchange the representations of proteins and small molecules. Unlike existing graph neural networks, GraphscoreDTA mainly has the following novelties: (i) bitransport information mechanism is constructed to bridge the gap between protein and ligand feature extraction; (ii) skip connections are employed in the GNNs to avoid gradient vanish problem; (iii) multihead attention mechanism is designed to weigh the contributions from individual atoms of a ligand and individual residues of a protein, respectively; (iv) gated recurrent units (GRUs) are introduced to dominate the information proportion between the graph general nodes and super nodes; and (v) Vina distance terms are used to optimize the model prediction. To further evaluate the generalization ability of GraphscoreDTA, two variants of the model were trained based on compound and protein similarity splitting strategy, respectively. The results showed that the models have better performances on the test sets compared with the state-of-the-art methods. In particular, we showed that (i) employing 8 Å as the cutoff of protein contact map may help capture both long-range interactions and short-range interactions; (ii) our model can identify important residues and atoms for proteins and ligands, which may reveal the recognition mechanisms among protein–ligand pairs; and (iii) the modeling structures are predictive even when the number of experimentally solved protein structures is insufficient. Furthermore, we showed the potential of GraphscoreDTA for predicting novel inhibitors for disease-related targets.Ki, Kd, and IC50 . In addition, the other two high quality test sets, namely, CASF2016 benchmark  database , and concordance index (CI) are used to measure the predictive performance. As shown in R values of GraphscoreDTA in the CASF2016 test set are 11.5%, 8.6%, 10.2%, 35.1%, and 11.4% larger than DeepDTA, DeepDTAF, Pafnucy, BAPA, and the fusion model. Similar improvement was also observed in the CASF2013 test set. Among remaining metrics, our model achieves best performance of 0.819 (0.039 improvement), 1.249 (reduced by 0.177), 0.981 (reduced by 0.149), and 1.216 (reduced by 0.192) for CI, RMSE, MAE, and SD, respectively. More notably, the SD metric of our model has highest drop with a value of 0.192 than other metrics, while the CI metric has the least improvement (0.039). Furthermore, we also evaluated the model performance based on protein similarity clustering, and found that our model consistently outperformed the other models in all the metrics  may be more effective than model III (protein–ligand interaction GNNs) for affinity prediction. The ablution studies show that combining different configurations, such as the mutual information, individual protein and ligand GNNs, interaction GNNs, and Vina optimization terms, can indeed improve the prediction performance.The ablation studies were conducted to evaluate the effectiveness of main strategies adopted in the modeling . Here, sConsidering the information transfer between different nodes of protein graph, we used a contact map to represent the edges in a protein graph. Here, we calculated the contact maps using different distance cutoffs, then the models with different contact maps were evaluated on the CASF2016 test set. As shown in Here, two representative protein–ligand complexes were selected to visualize the attention contribution of our constructed model. The model can produce multihead attentions by inputting protein contact maps and ligand graphs, where the weights represent the contribution of each amino acid of the protein and each atom of the ligand. Thus, the amino acid/atom with a larger weight value indicates that it is more important for affinity identification. The top-15 weighted residues and atoms were selected to check the overlap with the actual interaction sites . In HIV-Proteins possess specific structures for different folding recognition. In this study, the tertiary structures were needed for identifying the protein–ligand binding affinity which might not be always available. Here, we evaluated the model performance using the predicted structures rather than the experimental structures on the CASF2016 test set. To avoid the sequence error, protein sequences in UniProt database must completely cover the sequence parsed from the PDB dataset. Then AlphaFold2 . In thesR, CI, RMSE, MAE, and SD values are 0.876, 0.704, 0.470, 0.376, and 0.484, respectively. The results suggest that the predicted values are extremely close to the measured values (The cyclin-dependent kinase (CDK) family plays a vital role in the regulation of the eukaryotic cell cycle and has important functions in apoptosis, transcription, and differentiation . Cyclin-d values . For exaFurthermore, we evaluated the performance on the other CDKs including CDK1, CDK2, and CDK8. We collected 5 crystal structures of CDK1 in complexes with compounds, 77 crystal structures of CDK2 in complexes with compounds, and 21 crystal structures of CDK8 in complexes with compounds from the constructed datasets. We calculated the total number of hits among the top 1, top 10, and top 8 predicted candidate compounds for CDK1, CDK2, and CDK8, respectively. More specifically, the top 1 of the known compounds with best measured binding affinity value for CDK1 target was correctly predicted as top ranked by our model . MeanwhiWe applied GraphscoreDTA to the task of drug–target selectivity. Here, the drug–targets with large differences in binding affinity values were considered to evaluate the model performance. For drug catechol, we aim to find the effective targets with the highest pairwise binding affinities . It is kη) with 10 ligands, 3-dehydroquinate-dependent protein kinases-1 (DHQD) with 19 ligands, 3-phosphoinositide-dependent protein kinase-1 (PDPK1) with 25 ligands, acetylcholine receptor (AChR) with 29 ligands, β-glucosidase (GBA3) with 20 ligands, biotin carboxylase (BC) with 10 ligands, protein kinase A (PKA) with 45 ligands, and dual-specificity phosphatase (DSP) with 33 ligands , a potential target, can bind different small molecules and play a relevant role in the treatment of Hepacivirus’s disease. It is interesting to note that the sizes of pocket and small molecules relate to the protein–ligand binding affinity . Therefobtad340_Supplementary_DataClick here for additional data file.


Oxidative stress, the alteration of mitochondrial function, and the neurovascular unit (NVU), play a role in Amyotrophic Lateral Sclerosis (ALS) pathogenesis. We aimed to demonstrate the changes in the plasma redox system and nitric oxide (NO) in 32 new ALS-diagnosed patients in treatment with Acetyl-L-Carnitine (ALCAR) compared to healthy controls. We also evaluated the effects of plasma on human umbilical cord-derived endothelial vascular cells (HUVEC) and astrocytes. The analyses were performed at the baseline (T0), after three months (T1), and after six months (T2). In ALS patients at T0/T1, the plasma markers of lipid peroxidation, thiobarbituric acid reactive substances (TBARS) and 4-hydroxy nonenal (4-HNE) were higher, whereas the antioxidants, glutathione (GSH) and the glutathione peroxidase (GPx) activity were lower than in healthy controls. At T2, plasma TBARS and 4-HNE decreased, whereas plasma GSH and the GPx activity increased in ALS patients. As regards NO, the plasma levels were firmly lower at T0–T2 than those of healthy controls. Cell viability, and mitochondrial membrane potential in HUVEC/astrocytes treated with the plasma of ALS patients at T0–T2 were reduced, while the oxidant release increased. Those results, which confirmed the fundamental role of oxidative stress, mitochondrial function, and of the NVU in ALS pathogenesis, can have a double meaning, acting as disease markers at baseline and potential markers of drug effects in clinical practice and during clinical trials. Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by a progressive deterioration of upper and lower motorneurons (MNs), whose underlying mechanism has not yet been clarified ,2. IndeeThe uncertainties relating to the pathogenetic mechanisms may account for the lack of a truly effective treatment. To date, only riluzole ,9,10 andIn addition, many experimental findings showed that ALCAR could protect mitochondria against oxidative stress . With reA lack of detailed information about the ALCAR mechanisms of action can limit the clinical application. Indeed, a better understanding of the actions of ALCAR could be useful in clarifying its role in the management of patients affected by ALS. A possible target could be represented by the neurovascular unit (NVU), which is a network of vascular cells, glial cells, and neurons, whose alteration could hamper the blood–brain barrier (BBB) or blood–spinal cord barrier (BSCB), and lead to MNs damage due to harmful factors entering the CNS ,25,26. TIn this study, we have therefore focused on the analysis of the plasma redox state in ALS patients treated with ALCAR, and on the effects of plasma on cell viability, mitochondrial membrane potential, nitric oxide (NO), and mitochondrial ROS (mitoROS) release by members of the NVU unit, such as vascular endothelial cells and astrocytes.The study was performed on 32 consecutive patients diagnosed with ALS, according to the El Escorial criteria ,29 at thWe adhered to the following inclusion criteria for the patients’ recruitment: (1) aged 18–75 years; (2) defined, clinically probable, and probable laboratory-supported ALS ; (3) within 24 months of symptoms onset; (4) patients on riluzole treatment from at least one month before starting the ALCAR treatment; that is, T0; (5) patients without relevant comorbidities ; (6) patients were able to provide informed consent or had a legally authorized representative willing to do so.C9orf72, SOD1, TARDBP, and FUS). Data were collected from clinical records using an anonymous data form. An alpha-numeric code was randomly assigned to each patient to keep data collection and analysis anonymous. Patients received acetyl-L-carnitine as per clinical practice 3 g/day per os. The clinical and laboratory investigations were conducted at the time of recruitment before starting the ALCAR treatment (T0) and after six months of treatment (T2). For the plasmatic dosage of the oxidants/antioxidants and NO, we also collected blood after three months (T1). In particular, at each timing point, clinical data and plasma samples were collected. For healthy controls, plasma sample collection was executed at T0, only.For each participant, we collected demographic and clinical features, including age at onset, sex, phenotype  and diagnostic delay. We also collected the ALS Functional Rating Scale–Revised (ALSFRS-R) score, the Forced Vital Capacity percentage (FVC%), Body Mass Index (BMI), and the mutational status . The centrifugation of the plasma samples was performed by means of a centrifuge , at 3100 rpm, 4 °C, for 10 min. Thereafter, we aliquoted plasma into 5 tubes, which were kept at −80 °C at the Physiology Laboratory, Università del Piemonte Orientale. We used the samples to quantify the redox state markers and to stimulate the human vascular endothelial cells (HUVEC) and the astrocytes. A pseudonymized condition was followed when handling plasma samples.g; 4 °C), 150 μL of each sample was transferred to a 96-well plate to quantify MDA, by means of a spectrophotometer , at 530–540 nm excitation/emission wavelengths. A reference standard curve with the TBARS Standard was prepared to perform an accurate analysis of TBARS in the samples. The levels of TBARS were expressed as MDA (µM). We executed each measurement in triplicate.Plasma lipid peroxidation was quantified by means of the TBARS assay Kit , that examines the production of malondialdehyde (MDA) ,30. In oPlasma lipid peroxidation was also examined through the 4-HNE ELISA Kit , in which the sample or standard competes with a fixed amount of 4-HNE on the solid phase supporter for sites on the Biotinylated Detection Antibody specific to 4-HNE . Brieflyg for 2 min, then we collected the supernatants and added 50 µL /mL of TEAM reagent to increase the pH. Then, we transferred 50 µL of each sample to a 96-well plate for the quantification of GSH by means of a spectrophotometer (VICTOR™ X Multilabel Plate Reader), at 405–414 nm excitation/emission wavelengths. In order to execute an accurate analysis of GSH plasma levels (as µM), we prepared a standard curve, as suggested by the Glutathione Assay Kit, and executed each measurement in triplicate.The plasma GSH levels were measured by means of the Glutathione Assay Kit  ,31, whicThe plasma glutathione peroxidase activity was measured by means of the Glutathione Peroxidase Assay Kit , which measures the GPx activity via a coupled reaction with glutathione reductase . Briefly® Ultra filter  in order to remove proteins. For the NOx measurement, 10 µL of each sample was mixed with 70 µL assay buffer in a 96-well plate, followed by addition of the enzyme cofactor and of the nitrate reductase mixture. After incubation for 2 h, 2,3–diaminonaphthalene (DAN) and NaOH were added in each well and the absorbance was read through a spectrophotometer, as specified below. In order to measure the nitrites, after the addition of 10 µL of sample and 70 µL assay buffer, the DAN Reagent was immediately added in the 96-well plate and left in incubation for 10 min. After the addition of NaOH, the plate was immediately read at the excitation wavelength of 365 nm and emission wavelength of 430 nm through a spectrophotometer (VICTOR™ X Multilabel Plate Reader). Standard curves were built to compare the nitrites and NOx, which were expressed as μM. We executed each measurement in triplicate.We analyzed the plasma NO as both nitrites and nitrites/nitrates (NOx) through the Nitrate/Nitrite Fluorometric Assay Kit . Briefly, the plasma samples were ultrafiltered through the AmiconFor the in vitro experiments, the plasma of 10 patients at T0 and T2 and of 5 healthy controls at T0 was used. Non-treated cells were also included in the analyses. In particular, we used specific Transwell inserts in order to analyze cell viability (MTT assay), mitochondrial membrane potential, mitochondrial ROS (mitoROS), and NO release in HUVEC and astrocytes treated with plasma samples . As in pHUVEC , and mice immortalized astrocytes (kindly provided by prof. Dmitry Lim) , were cuIn HUVEC and astrocytes, the viability was examined using the 1% 3--2,5-diphenyl tetrazolium bromide  dye ,34,35. TWe used the JC-1 assay in order to examine the mitochondrial membrane potential of HUVEC/astrocytes, as was the case in previous experiments ,34,35. BThe MitoROS release was examined using the Cayman’s Mitochondrial ROS Detection Assay Kit  ,35,36. IWe used the Griess assay (Promega), to quantify the NO release in HUVEC and astrocytes ,34. To ep-value < 0.05 was considered statistically significant. STATA software  and Graph PAD  were used for statistical analysis.The Research Electronic Data Capture software  was used to collect data. For each patient, the mean of the multiple measurements was considered for the analysis. The median and interquartile range (IQR) were used to summarize quantitative variables. The Mann–Whitney test was used to test the differences between two groups and the difference between two time points was evaluated using the Wilcoxon signed-rank test. The correlation between quantitative variables was evaluated using the Spearman’s correlation coefficient. A p-value > 0.05). At T1 (after three months), five patients left the study (due to death or loss at follow-up) and at T2, 11 patients left the study. In total, we analyzed 32 patients at T0, 27 patients at T1, and 21 patients at T2.Thirty-two patients , with a median age of 67.00 (IQR: 58–70), were recruited for this study. The demographic and phenotypic features of enrolled patients are shown in At T1, the median ALSFRS-R was 37 (IQR: 30.50–41.00), with an FVC% of 72 (IQR: 55–95) and BMI of 22.25 (IQR: 19.98–25.88). At T2, the median ALSFRS-R was 33 (IQR: 31.00–38.00), with an FVC% of 69 (IQR: 51–90) and BMI of 23.45 (IQR: 19.93–26.88).In ALS patients at T0 and T1, the plasma TBARS and 4-HNE were higher A,B, wherRegarding NO evaluated as nitrites and NOx, the plasma levels were significantly lower at T0 and T1 than those measured in healthy controls. Despite the improvement observed at T2, however, they continued to be very low even 6 months after the start of ALCAR treatment T2; E,F.We used the plasma of 10 ALS patients taken at T0 and T2 to stimulate HUVEC. We examined the effects on cell viability, mitochondrial membrane potential, mitoROS, and NO release compared with those elicited by plasma of five healthy controls at T0. As shown in After 6 months of ALCAR treatment (T2), the plasma of ALS patients was still able to reduce viability and mitochondrial membrane and to increase the release of mitoROS in HUVEC compared with what was observed for the plasma of healthy controls. However, the effects were lower than those found with the plasma of the same patients at T0 A–C. It sSimilar results to those found in HUVEC were observed in astrocytes . In factC9Orf72 mutation, we did not observe any difference between the two groups at baseline and over the course of the disease in terms of oxidative stress markers. Interestingly, at T1, we observed higher levels of TBARS and 4-HNE in fast-progressing patients compared to slow progressors . No other variables were associated with functional progressions.No statistically significant correlation was found between clinical and demographic features  and plasmatic values of TBARS, 4-HNE, GSH, GPx activity and NO at baseline and over time. Similarly, comparing the sporadic patients vs. a group of patients’ carriers of the This study confirms that the balance between oxidants and antioxidants is affected in ALS and demonstrates that the treatment with ALCAR can improve the altered redox state condition in ALS patients. As has already been highlighted, the levels of MDA, expressed as TBARS, and the amount of 4-HNE in the plasma of patients affected by ALS at the time of recruitment were higher than those found in the group of healthy controls. A reduction in the plasma GSH rate and of the GPx activity accompanied this observation.It is noted that the assays we used to quantify the plasma lipid peroxidation and the amount of the antioxidants in ALS patients and healthy controls are those widely used to conduct this type of investigation ,40,41,42Regarding MDA, its levels have been shown to predict worse clinical outcomes in patients with cardiovascular diseases, Alzheimer’s, and multiple sclerosis, and they are taken as a marker of ferroptosis . Also, tIn addition, the 4-HNE levels, which we found to be increased in plasma, although with a decreasing trend in all time-points in ALS patients, have been associated with a greater clinical decline over an 18-month follow-up period .Also, in this study, we used the DTNB-based spectrophotometric method for the quantification of the GSH levels, which was found to provide similar results to those of the chromatography-based method  and has Overall, therefore, our data confirm the previous conclusions relating to the presence of an alteration in the balance between oxidants and antioxidants in patients affected by neurodegenerative diseases, such as ALS. Hence, an increased level of protein carbonyl in the spinal cord and motor cortex of patients with sporadic ALS was shown ,50. AlsoRegarding GSH, which is well known to non-enzymatically react with ROS, a reduction in the GSH/oxidized glutathione (GSSG) ratio and of GSH levels has been observed in the cerebrospinal fluid of ALS patients . In partIn order to better investigate the level of antioxidants, in this study, we examined the activity of the GPx, as well. The results obtained confirm those relating to GSH, since the GPx activity was much lower than that found in plasma of controls at all time-points. Our data are also in agreement with previous observations found in plasma of ALS patients and in postmortem brain homogenates ,52,56.That alteration in the redox state could be associated with cellular dysfunction with particular regard to mitochondria. This was confirmed by the in vitro studies we performed by stimulating HUVEC and astrocytes with plasma of ALS patients. As previously found , the resOur data would support the knowledge that compromised mitochondria and oxidative stress could act as contributing factors for ALS pathology. Changes in mitochondria morphology have been shown in neurons and glial cells from ALS patients and animal models ,58. ThesThe treatment of ALS patients with ALCAR was able to improve the redox state of ALS patients. Hence, the levels of TBARS and 4-HNE were, in fact, reduced, whereas those of GSH and the GPx activity were already increased at 3 months after the initial ALCAR administration, compared to T0. The improvement in the oxidative stress condition was maintained even after 6 months after the onset of ALCAR treatment.The beneficial effects found in plasma of patients were also observed in vitro, where we obtained a reduction in the harmful effects elicited by plasma of ALS patients. In fact, our results showed a decrease in mitochondrial damage and of mitoROS production in HUVEC and astrocytes. It could be underlined that the data we obtained can provide information on the protective effect of ALCAR. Currently, little knowledge is available regarding its actions on astrocytes and existing research mainly refers to the damage induced by ethanol or caused by spinal cord injury ,63.In addition, our findings corroborate the role of ALCAR as a protective agent in ALS through the modulation of the mitochondria function and oxidative stress ,19,20. MInterestingly, at T1, throughout the disease course, we observed a difference in lipid peroxidation markers (TBARS and 4-HNE) between fast-progressing patients and slow progressors, which could indicate a stronger dysfunction in the oxidative stress in patients with a more severe disease and, likely, a minor beneficial effect of the available treatment. Our data on 4-HNE are, therefore, in agreement with the previous data, which showed a correlation between the aforementioned marker and a greater disease severity .The results about plasma NO, which was evaluated based on nitrites and NOx levels, are worthy of discussion. They showed that plasma nitrites and NOx levels were lower not only in ALS patients than those of the healthy controls at T0, but even at T1, which was at 3 months from the start of ALCAR treatment. An increase in those plasma values was observed at T2, but it settled at levels much lower than those observed in healthy controls.Our data, therefore, highlighted the presence of an endothelial dysfunction in ALS patients, which was not affected by the treatment with ALCAR.This finding was confirmed by the in vitro experiments, since the plasma of ALS patients was able to reduce the NO release by HUVEC at both T0 and, in this case, more strongly, at T2.Those results could add information about the role of endothelium in the ALS genesis, which has not yet been well investigated. Endothelial cell degeneration and vascular leakage was reported in SOD1 mutants prior to MNs damage and neurovascular inflammatory response, indicating that this damage plays a central role in ALS initiation. Also, reduced levels of tight junction proteins ZO-1, occludin, and claudin-5 were demonstrated before ALS onset, as was a reduction in blood flow through the cervical and lumbar spinal cord in pre-symptomatic G93A SOD1 mice . FinallyIn this way, therapeutic approaches aiming to protect the endothelium either as direct cytoprotection or through eliminating microenvironment influences should be improved ,69.In astrocytes treated with ALS plasma, we observed an increased NO release at T2, which is in agreement with previous data about this issue ,71,72,73Even if this aspect has not been analyzed, we could hypothesize a different activation–expression of various NO synthase (NOS) isoforms in HUVEC and astrocytes in response to plasma from ALS patients, which would be related to the condition of oxidative stress and/or inflammation. It is well known, in fact, that while the endothelial NOS isoform is involved in the physiologic and low amount of NO release, the inducible NOS isoform is responsible for the increased NO production which could be observed in conditions characterized by oxidative stress/inflammation . It coulRegarding the effects on oxidative stress and mitochondria, it was shown that ALCAR can be metabolized in neuronal mitochondria to free carnitine and acetyl-CoA . The latOur study has limitations: firstly, all patients were taking ALCAR and riluzole, although administration of these medications started one month before the baseline. As riluzole is the only drug approved to treat ALS in Europe, from an ethical point of view, it is not possible to hypothesize a control arm without riluzole and, similarly, in clinical practice, most of our patients routinely take ALCAR. Moreover, in this study, we used the same methods for assessing plasma oxidative stress as those adopted in the previous study, which focused on the evaluation of the same markers in ALS patients at T0 (before ALCAR treatment), in order to come to more meaningful conclusions about the effects of ALCAR treatment. However, it could be helpful to broaden the analysis of the oxidative stress markers by adding the quantification of hydrogen peroxide or superoxide. Also, the NO levels could be examined through a flow injection analysis. In addition, it could be useful to investigate the intracellular pathways implicated in plasma effects in HUVEC and astrocytes, particularly concerning NO release, and perform cross talk experiments between members of the NVU.From the above analysis, we observed a fundamental role of oxidative stress, the alteration of mitochondrial function, and of the NVU in ALS pathogenesis at baseline and over time. We also reported a significant improvement in these parameters over time during the concomitant ALCAR treatment. These results can have a double meaning, acting both as disease markers at baseline and as potential markers of drug effects in clinical practice and during clinical trials.