Coronavirus disease 2019 (COVID-19) is a highly contagious disease and ravages the world.R. crenulata might have potential value in the treatment of COVID-19 patients by regulating the immune response and inhibiting cytokine storm.We proposed that Rhodiola crenulata (R. crenulata), against the immune regulation of COVID-19, and to provide a referenced candidate Tibetan herb (R. crenulata) to overcome COVID-19.We aimed to explore the potential molecular mechanism for R. crenulata were retrieved from the TCMSP database. GO analysis and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were built by R bioconductor package to explore the potential biological effects for targets of R. crenulata. The R. crenulata-compound-target network, target pathway network and protein–protein interaction (PPI) network were constructed using Cytoscape 3.3.0. Autodock 4.2 and Discovery Studio software were applied for molecular docking.Components and targets of l-rhamnoside and tamarixetin) and 159 potential targets of R. crenulata were identified from the TCMSP database. The result of GO annotation and KEGG-pathway-enrichment analyses showed that target genes of R. crenulata were associated with inflammatory response and immune-related signalling pathways, especially IL-17 signalling pathway, and TNF signalling pathway. Targets-pathway network and PPI network showed that IL-6, IL-1B and TNF-α were considered to be hub genes. Molecular docking showed that core compound (quercetin) had a certain affinity with IL-1β, IL-6 and TNF-α.Four bioactive components (quercetin, kaempferol, kaempferol-3-O-α-R. crenulata might play an anti-inflammatory and immunoregulatory role in the cytokine storm of COVID-19. Coronavirus disease 2019 (COVID-19) is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that ravages the world resulting in a pandemic of increasing death . Some stRhodiola), the genus Rhodiola in the family Crassulaceae, is herbaceous perennial plants. There are 96 species of Rhodiola in the world and most are found in different regions of China (73 species), such as Tibet. Rhodiola, also known as ‘oriental god grass’ and ‘plateau ginseng’, has great medicinal value. The main chemical components of Rhodiola include salidroside, flavonoids, terpenoids, sterols, tannins and other compounds. The functions of Rhodiola might be involved in promoting blood circulation and removing blood stasis, clearing lung and relieve cough, reducing fatigue and weakness, antiviral infecting and improving immunity  were utilized for retrieving information about functional annotation of genes. Gene ontology (GO) analysis and KEGG-pathway enrichment were built by R bioconductor package to explore the potential biological effects for targets of R. crenulata. GO terminology was annotated including biological process (BP), cellular component (CC) and molecular function (MF) categories. KEGG-pathway database (https://www.kegg.jp/kegg/) was applied for the targets mapped to the pathway. The target-pathway/function network was constructed using Cytoscape 3.3.0 to identify the relationships of R. crenulata targets with the involved pathways obtained through enrichment analysis.DAVID  is a search tool for retrieval of interacting genes/proteins [Screening for the immunity and inflammation cytokines in COVID-19 among proteins . Obtainehttp://www.rcsb.org/). ChemDraw software or PubChem (https://pubchem.ncbi.nlm.nih.gov/) was used to prepare the chemical structure of quercetin. Autodock 4.2 (http://mgltools.scripps.edu/downloads) and Discovery Studio software were applied for molecular docking.To obtain a deeper understand about the association of quercetin with TNF-α and IL-1β, molecular docking was applied to evaluate the strength and mode of interactions between quercetin and TNF-α/IL-1β. The crystal structure of TNF-α and IL-1β were obtained from RCSB Protein Data Bank , kaempferol (MOL000422), kaempferol-3-O-α-l-rhamnoside (MOL012777) and tamarixetin  and 165 edges. There were these components associated with targets, namely, quercetin (degree=144), kaempferol (degree=11), kaempferol-3-O-α-l-rhamnoside (degree=2) and tamarixetin (degree=4).The target proteins of the effective components were obtained from the TCMSP database. Finally, 159 potential targets (without repetition) of four bioactive components were collected . The phastructed , which iR. crenulata, the GO annotation and pathway-enrichment analyses were conducted. There were respectively 463 biological process (BP), 47 cellular component (CC) and 98 molecular function (MF) terms in total . Top ten significantly enriched BP, CC and MF categories were displayed in To recognize the potential biological functions of targets of R. crenulata targets, KEGG-pathway analysis was performed. Top 20 enriched pathways of R. crenulata targets were shown in R. crenulata were strongly associated with immune-related signalling pathways  and IL-1β, IL-6 and TNF-α proteins were analysed, and the results showed that quercetin had strong affinity with IL-1β, IL-6 and TNF-α proteins  and 159 potential targets of R. crenulata were identified from the TCMSP database. R. crenulata-compound-target network diagram displayed the potential synergy between multiple compounds and their targets. Moreover, GO annotation and KEGG-pathway-enrichment analyses were conducted to recognize the potential biological functions of targets of R. crenulata. The result showed that target genes of R. crenulata were associated with inflammatory response and immune-related signalling pathways, including IL-17 signalling pathway, TNF signalling pathway, NF-kappa B signalling pathway, Toll-like receptor signalling pathway, T cell receptor signalling pathway and MAPK signalling pathway. Targets-pathway network and PPI network showed that IL-6, IL-1B and TNF-α were considered to be hub genes. Molecular docking showed that quercetin (MOL000098) had certain affinity with IL-1β, IL-6 and TNF-α. These results suggested that R. crenulata might play an anti-inflammatory and immunoregulatory role in the cytokine storm of COVID-19.In this study, four bioactive components , a traditional Chinese medicine herb, can be mainly found at high altitudes in PR China such as Tibet, Qinghai. A recent study has revealed that the major bioactive components of R. crenulata have anti-inflammatory and antioxidant effects [R. crenulata could achieve a certain effect on improvement of pulmonary inflammation of the mice infected with influenza virus and release of inflammatory factors in serum and lung tissue [R. crenulata has the effect to improve the immunological functions of mice [R. crenulata inhibit activation of NF-κB pathway to reduce acute lung injury caused by sepsis [R. crenulata has an important role in the inflammatory response. Recent studies have revealed that R. crenulata possess multiple active ingredients. Here, four active ingredients of R. crenulata with OB ≥30 % and DL index ≥0.18 were found including quercetin (MOL000098), kaempferol (MOL000422), kaempferol-3-O-α-l-rhamnoside (MOL012777) and tamarixetin (MOL004083). Moreover, 159 potential targets (without repetition) of four bioactive components were collected. R. crenulata-compound-target network diagram displayed that the potential synergy between multiple compounds and their targets.Hongjingtian  could bind with IL-6, IL-1B and TNF-α. These results hinted that R. crenulata could regulate the formation of cytokine storms to reduce excessive inflammation in the body, thereby improving severe systemic damage in COVID-19 patients. However, the exact mechanism requires further validation in biological experiments.COVID-19 patients who succumb to pneumonia and hypoxia had one hallmark feature of the profound inflammatory state that marked elevation of serum inflammatory cytokines  and chemokines  . PPI netnstrated . IL-1B, nstrated . TNF-α rnstrated . SubsequR. crenulata were identified, and target genes of R. crenulata were associated with immune-related signalling pathways, especially IL-17 signalling pathway and TNF signalling pathway. Moreover, R. crenulata might play an anti-inflammatory and immunoregulatory role in the cytokine storm of COVID-19 by acting on IL-1β, IL-6 and TNF-α. However, further studies are necessary to elucidate the precise mechanism.In summary, bioactive components and potential targets of Click here for additional data file.


P < 0.05 after filter process, only 9 SNPs meet the expected cut-off P-value, however, none of them had association with SAPHO syndrome based on published literatures. And then, 15 pathways were found involved in SAPHO pathogenesis, of them, 6 pathways including osteoclast differentiation, bacterial invasion of epithelial cells, et al., had strong association with skin, osteoarticular manifestations of SAPHO or inflammatory reaction based published research. This study identified aberrant osteoclast differentiation and other pathways were involved in SAPHO syndrome. This finding may give insight into the understanding of pathogenic genes of SAPHO and provide the basis for SAPHO research and treatment.SAPHO syndrome is a rare chronic inflammatory disease which is characterized by the comprehensive manifestations of bone, joint, and skin. However, little is known about the pathogenesis of SAPHO syndrome. A genome-wide association study (GWAS) of 49 patients and 121 control subjects have primarily focused on identification of common genetic variants associated with SAPHO, the data were analyzed by classical multiple logistic regression. Later, GWAS findings were further validated using whole exome sequencing (WES) in 16 patients and 15 controls to identify potentially functional pathways involved in SAPHO pathogenesis. In general, 40588 SNPs in genomic regions were associated with  PSTPIP1, PSTPIP2, NOD2 or LPIN2 genes in SAPHO samples  syndrome, with the clinical manifestations including auto-inflammatory osteoarticular disorders and dermatological conditions, is a rare disease with an estimated prevalence of less than 1 in 10,000 . It was  samples . There anfection , impairenfection . It was nfection . Apart fnfection . To datenfection .p-value assuming-independence between markers. In this study, we set up a GWAS study in SAPHO patients, followed by pathway-based analyses of GWAS data that focused on the integrated effects of numerous loci, each making a small direct contribution to estimate of disease susceptibility, which might provide understanding the genetic basis of chronic diseases . The GWAS findings were further validated using whole exome sequencing (WES) to discover genetic variants and abnormal pathways involved in SAPHO patients.Genome-wide association studies (GWASs) has showed remarkable success in detecting the genetic factors of complex diseases by identifying multiple variants associated with complex clinical phenotypes . A commoThe GWAS study contained 52 SAPHO patients and 124 healthy controls  according to the manufacturer’s instructions. For the GWAS, all samples were genotyped individually using Illumina Infinium OmniZhongHua-8v1-3_A1 by the BioMiao Biological Technology Company . For the WES, 3 μg of purified gDNA was fragmented to 180–280 bp and subjected to DNA library creation using established Illumina paired-end protocols. The Agilent SureSelect Human All ExonV6 Kit  was used for exome capture according to the manufacturer’s instructions. The Illumina Novaseq 6000 platform  was utilized for genomic DNA sequencing by Novogene Bioinformatics Technology Co., Ltd  to generate 150-bp paired-end reads with a minimum coverage of 10× for ∼99% of the genome (mean coverage of 100×).1 and PLINK version 1.90 beta2. Genotype data were cleaned before analysis by excluding SNPs or individuals that did not fulfill the QC criteria, which included: SNP call proportion ≥ 95%, subject completeness proportion ≥ 95%, SNP minor allele frequency ≥ 0.01, and SNP conformity with Hardy-Weinberg equilibrium expectations (P ≥ 0.01 in controls). A comparison of cases and controls was made using Pearson’s chi-square tests or Fisher’s exact test. Because this study examined the functional relationships of genes and proteins, we considered gene-level significance rather than that of single SNP in the traditional GWAS studies. To that end, SNPs in the GWAS were mapped to functional genes according to SNP locations and gene locations by MAGMA software (v1.07beta)  (.07beta) . In ordee (v6.8) , and proe (v6.8) .For WES, after quality control (QC) and preprocessing of sequencing data, the clean data in fastq format was aligned to the human reference genome hg19 (GRCh37) using the Burrows-Wheeler Aligner (bwa)  along wiP < 0.00001). Six samples were deleted due to quality control, finally 49 cases and 121 controls were left for subsequent analysis. The mean genotyping rate in the remaining individuals was 99.73%.This GWAS study contained 52 SAPHO patients  and 124 controls . The number of SNPs were reduced from 878,000 to 802,276 after filtering for low call rate (<90%), minor allele frequency (<0.01) and deviation from Hardy-Weinberg equilibrium , rs2243861 was located in the intron region of the IQ motif containing with AAA domain 1 like gene (IQCA1L), and the other 7 SNPs  were mapped to genomic regions with no known functional genes. Based on previously published literature, none of the 9 SNPs had an association with SAPHO syndrome or other inflammatory disease. Next, we lowered the significance threshold and 84 SNPs with a P-value below 5.6 × 10–7, a level roughly 10-times the expected threshold, were selected for further analysis. However, no further SNPs or genes were identified . Because of the multiple roles of some genes and complex interactions in cellular activities between protein pathway networks, candidate genes were always involved in different pathways. For example, mitogen-activated protein kinase 12 (MAPK12) gene, an important transduction factor of extracellular signals, was involved in osteoclast differentiation, amyotrophic lateral sclerosis (ALS), Fc epsilon RI signaling pathway, VEGF signaling pathway, Rap1 signaling pathway, MAPK signaling pathway, and T cell receptor signaling pathway , phagosome , Fc epsilon RI signaling pathway , Rap1 signaling pathway , Fc gamma R-mediated phagocytosis pathway , and bacterial invasion of epithelial cells pathway  were highly correlated with SAPHO syndrome based on bone and skin manifestations of SAPHO. Other pathways may affect the pathological process of SAPHO syndrome as well, but their direct association requires further research.To dissect the pathways involved in SAPHO disease, we proposed a pathway-oriented analysis of the GWAS result. We analyzed a list of differentially expressed genes and a P < 0.05 . For theP < 0.05  includin pathway . PreviouCSF1, FCGR2B, PIK3CD, MAP2K6, LILRB5, PPP3CA and MAPK12, CNV aberrations were found in LILRA3, FCGR2B, and CTSK genes); 7 samples had gene aberrations in the phagosome pathway ; 6 samples had gene aberrations in the Fc epsilon RI signaling pathway ; 7 samples had gene aberrations in Rap1 signaling pathway ; 5 samples had gene aberrations in the Fc gamma R-mediated phagocytosis pathway ; 11 samples had gene aberrations in the Bacterial invasion of epithelial cells pathway   , 7.P < 6.24 × 10–8). Among them, 7 SNPs located in the regions without known functional genes, rs4505038 (PEX16) and rs2243861 (IQCA1L) located in the introns of known genes. Pex16 plays an critical role in adipose tissue peroxisomal biogenesis, and mice deficient for the Pex16 gene showed increased diet-induced obesity and impaired thermogenesis ability without skin or osteoarticular manifestations  networks can provide valuable biological clues . In thisstations . The IQCimmunity . Based oGiven the complex symptom and etiology of SAPHO syndrome, we speculate that no single genetic variant accounts for all the complicated manifestations of this disease. Thus, in the following analysis, we reanalyzed the GWAS data by adopting pathway and network-based analysis. We found several pathways were altered in SAPHO samples, and six of these had evidence with skin, osteoarticular manifestations of SAPHO syndrome or inflammatory reactions, including osteoclast differentiation pathway, phagosome pathway, Fc epsilon RI signaling pathway, Rap1 signaling pathway, Fc gamma R-mediated phagocytosis pathway, and bacterial invasion of epithelial cells pathway.The osteoclast differentiation pathway, a key regulator of resorption and formation of bone tissue, was the most significant aberrant pathway in SAPHO patients. Previous studies reported disruption of the osteoclast differentiation or function leads to inhibited bone resorption, which further can result in bone marrow deficiency and no teething . On the Mycobacterium tuberculosis to escape bacterial killing and survive within host phagocytes  were highly associated with SAPHO syndrome, suggesting phagocytosis has an important role in SAPHO syndrome. Phagocytosis is an evolutionarily ancient process whereby cells engulf large particles. It is an important core mechanism in some immune processes, including defense against infectious agents, inflammation, tissue remodeling, and antigen degradation and presentation . Phagocyagocytes . In thisP = 0.013035, 9 genes). In accordance, Li et al. reported a SAPHO patient with elevated serum immunoglobulin E levels, and demonstrated methylprednisolone treatment achieve long-term remarkable remission on clinical manifestations  and bacterial invasion of epithelial cells , suggesting damage to the cell barrier contributes to the complicated manifestation of SAPHO syndrome. The function of the small G-protein Rap1 is to regulate endothelial barrier function controlled by cell–cell adhesion and the actin cytoskeleton. When this process is activated, numerous signaling cascades are induced by Rap1 to enhance the endothelial barrier function. Of note, Rap1 activation results inhibit of Rho to decrease radial stress fibers and activate Cdc42 to increase junctional actin (In this study, we found two pathways associated with aberrant cell barrier function in SAPHO patients, Rap1 signaling pathway (al actin . These aal actin  and retial actin . Moreoveal actin .Unlike other immunologically relevant diseases, except chronic multifocal osteomyelitis, SAPHO patients suffer from recurrent demographic manifestations, including palmoplantar pustulosis, psoriasis vulgaris, and severe acne. On the base of the findings in this study, we inferred a single pathway was not responsible for this complicated syndrome, two or more pathways probably act simultaneously. For example, an impaired cell barrier or inflammatory cytokine release induced by allergic reactions might promote the demographic manifestations and elevated inflammatory factors, moreover aberrant phagocytosis and osteoclast differentiation pathways might cause alterations to bone resorption and formation, ultimately leading to osteoarticular deformation. These pathways are closely linked and might affect each other, for example, an impaired cell barrier and pathogen infection or allergic reaction might lead to the over-expression of inflammatory factors, which increase the permeability of the skin or endothelial cells, thus increasing infection. Moreover, the disruption of phagocytosis might allow bacteria to escape and enhance infection.In conclusion, this GWAS study combined with pathway-based analysis and WES identified aberrant pathways including the osteoclast differentiation pathway involved in SAPHO syndrome. This finding may provide insights into the pathogenic genes of SAPHO and provide the basis for SAPHO research and treatment. Further studies should be conducted to validate this conclusion in a larger sample size and in other ethnic backgrounds.HRA000288. All data is available from the corresponding author upon request.The raw sequence data reported in this article have been deposited in the Genome Sequence Archive  in BIG DThe studies involving human participants were reviewed and approved by the Ethics Committee of the National Research Institute for Family Planning. The patients/participants provided their written informed consent to participate in this study.RC and YD: planning of the project, analysis and interpretation of data, and drafting the manuscript. MF, YF, and JG: analysis and interpretation of data. YZ, ZC, and FH: collecting of the data. CG and XM: putting forward research ideas and planning of the project. 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.


Knee osteoarthritis is a common joint degenerative disease. Xiao Huoluo Pills (XHLP) has been used to treat degenerative diseases such as osteoarthritis and hyperosteogeny. However, XHLP’s specific effective ingredients and mechanism of action against osteoarthritis have not been explored. Therefore, bioinformatics technology and molecular docking technology are employed in this study to explore the molecular basis and mechanism of XHLP in the treatment of knee osteoarthritis.P value < 0.01, |log2 FC|≥1.0). The Venn package in R language and the BisoGenet plug-in in Cytoscape are adopted to predict the potential molecules of XHLP in the treatment of knee osteoarthritis. The XHLP-active component-target interaction network and the XHLP-knee osteoarthritis-target protein core network are constructed using Cytoscape software. Besides, GO/KEGG enrichment analysis on core genes is performed using the Bioconductor package and clusterProfiler package in the R language to explain the biological functions and signal pathways of the core proteins. Finally, molecular docking is performed through software such as Vina, LeDock, Discovery Studio 2016, PyMOL, AutoDockTools 1.5.6, so as to verify the binding ability between the active components of the drug and the core target protein.Public databases  are used to find the effective active components and corresponding target proteins of XHLP . Differentially expressed genes related to cartilage lesions of knee osteoarthritis are obtained based on the GEO database  was screened out of 1672 differentially expressed genes, including 913 upregulated genes and 759 downregulated genes, displayed as heat maps and volcano maps. Besides, 33 core target proteins are calculated by Venn data package in R and BisoGenet plug-in in Cytoscape. The enrichment analysis on these target genes revealed that the core target genes are mainly involved in biological processes such as response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation. These core target genes are involved in signaling pathways related to cartilage degeneration of knee osteoarthritis such as TNF signaling pathway and PI3K-Akt signaling pathway. Finally, the molecular docking verification demonstrates that some active components of the drug have good molecular docking and binding ability with the core target protein, further confirming that XHLP has the effect of inhibiting cartilage degeneration in knee osteoarthritis.In this study, based on the research foundation of bioinformatics and molecular docking technology, the active components and core target molecules of XHLP for the treatment of cartilage degeneration of knee osteoarthritis are screened out, and the potential mechanism of XHLP inhibiting cartilage degeneration of knee osteoarthritis is deeply explored. The results provide theoretical basis and new treatment plan for XHLP in the treatment of knee osteoarthritis. The characteristic case changes of knee osteoarthritis (KOA) include articular cartilage degeneration, subchondral bone reactivity, osteophyte formation at the joint edge, synovial lesions, ligament laxity, and contracture , 2. WithTraditional Chinese medicine has been used to treat KOA for many years. Starting from the local application of knee joint medicine and the whole body, it can effectively relieve pain symptoms, improve joint function, and protect joint structure , 10. XHLThe traditional Chinese herbal medicine treatment KOA has passed the test of long-term medical practice. However, the “multi-component” and “multi-functional” of Chinese medicine has not been recognized by the world because its specific mechanism of action remains unclear . Networkhttps://tcmspw.com/tcmsp.php) in turn for search. Oral bioavailability (OB) refers to the speed and degree of absorption of oral drugs into the circulation of the body (including the bioavailability and utilization rate of the drug). Drug likeness (DL) is a concept based on the physical and chemical properties and molecular structure of existing drugs, used to evaluate the similarity between a compound and a known drug. According to the common screening criteria of the TCMSP database and previous literature results [XHLP is composed of six traditional Chinese medicines: Chuanwu, Caowu, Dilong, Araceae, Frankincense, and Myrrh, with the composition ratio of 1.2:1.2:1.2:1.2:1.0:1.0. XHLP’s constituent medicines “Chuanwu,” “Caowu,” “Dilong,” “Araceae,” “Frankincense,” and “Myrrh” were entered into the TCM pharmacology system database   and the UniProt database (https://www.uniprot.org). The species is set as Homo sapiens. Comparison and gene name correction are performed on the obtained target protein, converting into the target gene name and deleting the target protein without corresponding gene name. The obtained active component of the medicine and the active component establish a corresponding relationship with the target. The drug-active component-target interaction network model is obtained by Cytoscape 3.7.2 software [The TCMSP database is used to search for the target of the active components of the drugs obtained by screening, and all the target protein names obtained from the screening of the TCMSP database are imported into the DrugBank database (software . The “Nesoftware  is emplohttps://www.ncbi.nlm.nih.gov/geo/). The screening conditions are (1) osteoarthritis, (2) human, and (3) cartilage tissue. The whole-genome sequencing data GSE114007 that meets the screening criteria are obtained, including 18 normal knee cartilage tissue samples from K-L I patients and 20 knee cartilage tissue samples from K-L IV KOA patients. All the above sequencing data sets are downloaded based on the GEO database of GPL11154 and GPL18573 platforms. The general clinical information of patients is presented in Table The high-throughput sequencing data of cartilage tissue genes related to osteoarthritis are downloaded in the GEO (gene expression omnibus) database . The online analysis website ClustVi s[https://biit.cs.ut.ee/clustvis/) is adopted to draw the heat map and cluster analysis of the differential genes obtained by screening. Then, the adjust P value in the processed data is converted to -log10. According to log2 FC, -log10  is divided into upregulated genome, downregulated genome, no statistical difference, and non-expressed difference genome. The processed data are imported into GraphPad Prism 7 to draw a volcano map.The original sequencing data of GSE114007 are downloaded from the GEO database. The FastQC software is adopted to perform quality assessment and quality control processing on the original data. Besides, the Hisat2 software is used to select a reference genome to compare and analyze the clean data obtained after quality control processing. Then, the number of gene reads is quantitatively analyzed using the HTSeq Python package to obtain the count file. Next, the counts data obtained by sorting are filtered. Filter conditions are (1) variance filtering and (2) low abundance filtering. Besides, the data are standardized using the LogClustVi s (https:/http://www.interactivenn.net/) [The Venn diagram of the common target genes in the target of the active component of the drug and the target of the disease is displayed through the online Venn diagram production website InteractiVenn (nn.net/) . The obtnn.net/)  to constnn.net/)  is used To further explore the potential mechanism of XHLP active components on KOA and the function of common target genes, we use the Bioconductor package and clusterProfiler package  in the Rhttps://tcmspw.com/tcmsp.php). The Chem3D software is used to make 3D structures of effective active components. Then, the 3D structure of the core protein gene is downloaded from the PDB database (http://www.rcsb.org/). The PyMOL software is employed to perform operations such as dewatering and phosphate removal of proteins. Besides, the AutoDockTools 1.5.6 software [−1 is selected as the screening basis to evaluate the reliability of biological information analysis and prediction [The molecular docking verification of XHLP and potential targets is conducted to explore the interaction between them. The structural formula of the active components of XHLP is downloaded from the TCMSP database  identification by the herbal group. Under the screening conditions of OB ≥ 30% and DL ≥ 0.18, 71 effective active compounds were selected from the active components of XHLP, including 3 species of Radix Aconiti and 8 species of Radix Aconiti, 3 species of Earth Dragon, 7 species of Araceae, 8 species of Frankincense, and 45 species of Myrrh; besides, there is 1 common active component of Radix Aconiti and Radix Aquilinum, and 2 common active components of Araceae and Myrrh  is divided into upregulated genome, downregulated genome, and no statistically different genome). The results were imported into GraphPad Prism 7.0 to draw a volcano map  and 18 cases of normal cartilage tissue samples  from the sequencing data set GSE114007. The general clinical characteristics of the patients are presented in Table https://string-db.org/); hide unconnected targets were set, and “medium confidence > 0.400” in the lowest interaction score was determined; the result data of protein-protein interaction were exported  mainly focuses on response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation; cellular component (CC) is mainly concentrated in collagen-containing extracellular matrix, endoplasmic reticulum lumen, and fibrillar collagen trimer; molecular function (MF) is mainly manifested in activating transcription factor binding, growth factor binding, and core promoter sequence-specific DNA binding Table A–C. The −1 and DS can find the docking site, the results of the active component receptor output by the LeDock software were imported into Pymol, and the 3D molecular docking display was performed with the protein ligand  and the top 6 potential core target genes in the 2.4 results  were selected for molecular docking verification. The Chem3D software was employed to draw the corresponding 3D structure according to the structural formulas of the 5 effective active components, and output in mol*2 format. The 3D structures of 6 core proteins were downloaded from the PDB database and output in pdb format. The AutoDockTools 1.5.6 software was used to convert active components and core proteins into pdbqt format to find active pockets, that is, the ligand is combined with one or more amino acid residues to form H bonds, H-π bonds, H-π bonds, or π-π bonds and other active sites. Vina script, LeDock, and Discovery Studio 2016 software were operated to calculate the binding energy of ligand and receptor. The results suggested that quercetin can form a stable docking model with CCND1, JUN, MMP9, and MMP2, respectively; beta-sitosterol can dock with JUN and MMP2 protein ligands; izoteolin and ellagic acid can form stable docking with JUN, MMP9, and MMP2 protein ligands , information management, and analysis has become an urgent problem to be solved. Therefore, data mining can help us find valuable information from massive amounts of data and convert it into meaningful knowledge. At present, life science research is transforming from experimental analysis combined with data accumulation to data analysis to guide experimental verification . NetworkIn this study, the XHLP-active component-target network was constructed. The network topology analysis revealed that the potential active components of XHLP treatment KOA mainly include quercetin, Stigmasterol, beta-sitosterol, Izoteolin, and ellagic acid. Hu et al.  discoverThrough the mutual mapping of the XHLP target and the differentially expressed genes of KOA, 33 potential molecular targets were obtained. Furthermore, Cytohubba, BisoGenet, and CytoNCA plug-in in Cytoscape software were used to obtain 6 potential therapeutic targets for XHLP treatment of KOA, namely VEGFA, CCND1, MYC, JUN, MMP9, and MMP2. A large number of studies have revealed that the expression of VEGFA is elevated in patients with advanced OA –50. VEGFThe enrichment analysis of target genes GO/KEGG indicated that “oxygen levels,” “mechanical stimulus,” “collagen-containing extracellular matrix,” “TNF signaling pathway,” “PI3K-Akt signaling pathway,” and “Fluid shear stress” may be a potential way of XHLP treatment of KOA. Many studies –59 have Finally, the binding ability of the active components of XHLP with molecular target domains was verified in this study through molecular docking technology. The results demonstrated that quercetin can form a stable docking model with CCND1, JUN, MMP9, and MMP2, respectively; beta-sitosterol can dock with JUN and MMP2 protein ligands; Izoteolin and ellagic acid can form stable docking with JUN, MMP9, and MMP2 protein ligands. According to the calculations with Vina, LeDock, and DS software, quercetin can form the most stable molecular docking with the 6ESM ligand of MMP9 protein; MMP9 is a pro-inflammatory protein, and can exert a direct anti-inflammatory effect to treat KOA cartilage degeneration when combined with quercetin, which is the effective active component in XHLP. However, this study still has some limitations. First, the results of this study lack cell and animal experimental verification. Another limitation is the false negative due to the targets rooted from a different database, which may have biased impact because of different experimental conditions. We are also aware of the public databases that have limited information. Moreover, Earth Dragon failed to retrieve the active components in the TCMSP database and Batman-TCM database.In this study, the potentially effective active components, molecular targets, and action pathways of XHLP in the treatment of KOA cartilage degeneration were analyzed using bioinformatics. Besides, 71 effective active components were discovered for potential treatment of KOA; 1672 differentially expressed genes in cartilage degeneration with KOA were discovered through bio-information analysis. Mutual mapping was used to obtain 33 potential targets for the XHLP treatment of KOA. This study discovered 33 molecular targets such as VEGFA, CCND1, MYC, JUN, MMP9, and MMP2. These molecular targets are associated with the functions of “inflammatory response,” “mechanical stimulus,” “TNF signaling pathway,” and “PI3K-Akt signaling pathway,” involved in signaling pathways related to inflammation and chondrocyte apoptosis. Moreover, molecular docking verification was conducted to verify that the active components of XHLP can form a stable docking model with potential molecular targets. To sum up, the XHLP treatment of KOA plays a synergistic role in anti-inflammatory and inhibiting chondrocyte apoptosis through the characteristics of “multi-component, multi-target, multi-pathway.”


Objective: This work aims to explore the potential targets and underlying therapeutic mechanisms of celastrol in autoimmune hepatitis (AIH) through network pharmacology and experiments on Laboratory Animals.Methods: A drug-target interaction network was constructed to predict the possible targets of celastrol and their potential relationship with the drug; docking studies were also performed for validation. This study used both acute and chronic rodent models of autoimmune hepatitis. Gross appearance of liver and spleen were obtained from murine models, hematoxylin-eosin staining and Sirius red staining were performed to examine hepatic inflammation and fibrosis respectively. By combining molecular docking and enrichment analysis results, the most prominent signaling pathway was selected and further confirmed by Western blot in AIH models administered with celastrol.Results: In total, 82 common targets of celastrol and AIH were obtained from databases, identified by network pharmacology, and adequately enriched. Among them, PIK3R1, SRC, MAPK1, AKT1, and HRAS were selected as the top 5 closely related targets to celastrol. They all performed effectively in molecular docking, with AKT1 and PIK3R1 exhibiting more-prominent binding energy. Subsequently, celastrol administration significantly ameliorated hepatitis and liver fibrosis by reducing AKT1 and PI3K phosphorylation in both acute liver injury and chronic models of autoimmune hepatitis.Conclusion: In summary, celastrol significantly attenuates autoimmune hepatitis by suppressing the PI3K/AKT signaling pathway, confirmed by validated animal models. These findings may help identify the mechanism involved in the anti-inflammatory action of celastrol in autoimmune hepatitis and provide ideas for future comprehensive studies. Autoimmune hepatitis (AIH) is a chronic inflammatory disorder of the liver, characterized by elevation of serum immunoglobulin G (IgG), presence of autoantibodies and interface hepatitis on liver histology . It occuThe concanavalin A (ConA) model, mediated by T cells, is a well-accepted rodent model for studying acute immune-mediated liver injury with partial features resembling autoimmune hepatitis . Since iTraditional Chinese medicines (TCM) comprise a treasure house for novel drug research. Previous literature has indicated that celastrol, isolated from the root bark of Tripterygium wilfordii Hook. f., has important therapeutic significance . BecauseHerbal medicines harbor various bioactive ingredients acting against multiple targets rather than a single target . In 2008http://lsp.nwu.edu.cn/index.php) and PharmMapper (http://lilab-ecust.cn/pharmmapper/) were used to predict the potential targets of celastrol. After integrating the data and removing duplicates, all proteins were confirmed using the UniProt database (https://www.uniprot.org/). Genes related to the term “autoimmune hepatitis” were extracted from the GeneCards database (https://www.genecards.org/).A two-dimensional (2D) structure of celastrol was obtained from the PubChem database. The online resources in systems pharmacology was used to construct a PPI systematic network and visualize the result. The shared proteins with a combined score of >0.9 were selected for PPI analysis, and disconnected nodes were hidden in the network. Network visualization and analysis were conducted using the Cytoscape software 3.6.0  .Gene enrichment analysis of the screened drug targets and intersectional targets was performed to further elucidate the mechanisms underlying the potency of celastrol. Both KEGG signaling pathway enrichment and GO biological functional enrichment analyses were performed using Metascape (n/step1) . All genhttp://www.rcsb.org/) was used to obtain the crystal structures of candidate targets of celastrol. Docking studies were conducted between celastrol and key targets using Autodock 4.2. The structure modification process included ligand and water removal, hydrogen addition, amino acid optimization, and patching (The PDB database (patching .n = 6), celastrol (Cel) group (n = 6), ConA group (n = 7) and ConA + celastrol (ConA + Cel) group (n = 7). According to the existing literature and the results of our pre-experiments, the mice in the celastrol group and the ConA + Cel group were perfused with celastrol once daily in doses of 3 mg/kg for a week  were supplied by the Laboratory Animal Centre of Tongji Hospital. They were housed in the SPF environment with an alternating 12 h light/dark cycle and had free access to food and water. Concanavalin-A (ConA) was purchased from Sigma-Aldrich; Merck KgaA, celastrol was obtained from MCE (HY-13067). Mice were randomly divided into normal control group (r a week . After 5r a week .n = 6), CYP2D6 (CYP) group (n = 8), CYP2D6+celastrol (CYP + Cel) group (n = 8), celastrol-chronic (Cel-c) group (n = 6). For mice in the CYP and CYP + Cel groups, multiple high-pressure tail vein injections of plasmid were used to develop AIH mouse models as reported in previous literature  staining was analyzed to evaluate the liver injury. Meanwhile, Sirius-red staining was performed to evaluate the degree of fibrosis in chronic models.Blood samples were collected by extracting the eyeball after anesthesia. The whole blood was collected in tubes, and the serum was isolated through centrifugation at 3,500 rpm for 10 min. Subsequently, serum levels of AST and ALT were measured by the clinical laboratory of Tongji Hospital .Liver tissue samples were stored at −80°C, then digested in RIPA buffer containing phosphatase inhibitor, cocktail, and PMSF . All samples were centrifuged at 12,000 x g for 10 min after grinding and sonication. Protein concentration was measured via the a BCA assay. Then, 60 μg protein was loaded on 10% Bis-Tris gel and separated by SDS-PAGE. The membranes were blocked with TBST containing 5% BSA  at room temprature for 1.5 h. The primary antibodies applied overnight at 4°C included: anti-AKT1 ; anti-p-AKT1 ; anti-PI3K ; anti-p-PI3K ; anti-GAPDH . On the next day, secondary antibodies were incubated at room temperature for 1.5 h. All membranes were washed 3 times using TBST for 10 min, then exposed to hypersensitive electrochemiluminescence (ECL) reagents . WB images were analyzed using the Image J software.t-test was used to analyze the comparison between two groups. p < 0.05 was considered statistically significant.The resulting data were analyzed using GraphPad Prism 8.0 software. The results were presented as mean ± standard deviation (SD). The Student’s Celastrol is an active ingredient isolated from Tripterygium wilfordii Hook. f.; its 2D structure is shown in As indicated in the STRING database, 82 collected genes were used to construct the protein-protein interaction network comprising 329 edges. The average node degree was 8.02, whereas the average local clustering coefficient was 0.5 . In thisEventually, based on the degree and above-average betweenness centrality, we defined 5 key players in the activity of celastrol against AIH: PIK3R1, SRC, MAPK1, AKT1, HRAS .Genes encoding proteins targeted by celastrol are involved in signaling pathways  includintuberculosis (ko05152). The top 20 clusters are shown in The pathway enrichment analysis showed that the shared genes encoding proteins, also treated as the potential drug targets are associated with pathway in cnacer (hsa05200), proteoglycans in cancer (ko05205), JAK-STAT signaling pathway (hsa04630), bladder in cancer (ko05219), and p < 0.01), the top 10 significantly enriched terms were selected in BP, MF, and CC categories, respectively , membrane raft (GO:0045121), and receptor complex (GO:0043235) were considered to be the top 3 clusters.Meanwhile, based on their significance level  is a progressive autoimmune-mediated liver inflammatory disorder with a poorly understood etiology and no specific therapeutics . Its undInformation from public databases was integrated to explore interactions between celastrol and its potential protein targets in AIH, as well as relevant signaling pathways in which celastrol targets participate. As a consequence, 82 collective targets of celastrol and AIH were identified, among which PIK3R1, SRC, MAPK1, AKT1, and HRAS exhibited a closer association with autoimmune hepatitis. Also, docking studies were performed to predict specific interactions between celastrol and its predicted targets; notably, AKT1 and PIK3R1 bound well with celastrol. AKT1 is the central member of the PI3K/AKT signaling pathway, and PIK3R1 codes for phosphatidylinositol 3-kinase (PI3K) regulatory subunit alpha (p85-ALPHA) . As sugga Carmen et al. revealed that the PI3K/AKT pathway is closely related to the proliferation of T cells in graft-versus-host disease; meanwhile, the ability of pathway inhibitors (BKM120 and BEZ235) to suppress activated T cell proliferation were confirmed  induced by CCL4 . The PI34 . Previou4 . TherefoIn conclusion, we developed comprehensive network pharmacology to identify targets of celastrol in AIH and preliminary confirmed the suppressed PI3K/AKT pathway-mediated treatment efficacy in two typical rodent models. This study provides a basis for further research determining the molecular targets of celastrol in AIH as well as drugs against other inflammatory and autoimmune diseases.


With recent advances in the computer-aided synthesis planning (CASP) powered by data science and machine learning, modern CASP programs can rapidly identify thousands of potential pathways for a given target molecule. However, the lack of a holistic pathway evaluation mechanism makes it challenging to systematically prioritize strategic pathways except for using some simple heuristics. Herein, we introduce a data-driven approach to evaluate the relative strategic levels of retrosynthesis pathways using a dynamic tree-structured long short-term memory (tree-LSTM) model. We first curated a retrosynthesis pathway database, containing 238k patent-extracted pathways along with ∼55 M artificial pathways generated from an open-source CASP program, ASKCOS. The tree-LSTM model was trained to differentiate patent-extracted and artificial pathways with the same target molecule in order to learn the strategic relationship among single-step reactions within the patent-extracted pathways. The model achieved a top-1 ranking accuracy of 79.1% to recognize patent-extracted pathways. In addition, the trained tree-LSTM model learned to encode pathway-level information into a representative latent vector, which can facilitate clustering similar pathways to help illustrate strategically diverse pathways generated from CASP programs. Tree-structured long short-term memory neural model learns to understand the retrosynthesis design strategies from patent-extracted retrosynthetic pathway data. During this process, single-step retrosynthetic reactions can be proposed using reaction templates  or template-free retrosynthesis models.4,11–14 For each intermediate molecule, there could be numerous valid strategies to transform it into corresponding precursors. To avoid the combinatorial explosion during recursive expansion to find viable multistep retrosynthesis pathways, either heuristic rules15,16 or data-driven ranking models2,5,8 can be implemented to prioritize promising single-step retrosynthetic reactions. Depending on the constraints that users set for the retrosynthesis search, such as search time and number of single-step expansions allowed per intermediate, a successful retrosynthetic search could result in thousands of potential retrosynthesis pathways. For example, the open-source program, ASKCOS,5,17,18 gave a total of 1498 different retrosynthesis pathways for hydroxychloroquine with only 30 seconds search time on a 20-core workstation.Computer-aided synthesis planning (CASP), initially proposed by Corey,Two challenges naturally arise with the large number of pathways proposed by the modern CASP programs:(1) Prioritizing strategic retrosynthesis pathways. In spite of the effort to improve the quality of the single-step retrosynthetic transformation, the final retrosynthesis pathways found may not be useful even though each single-step reaction is valid and selective. As an intuitive example, protection and deprotection reactions are important steps in the retrosynthesis design; however, without pathway-level guidance during the retrosynthetic search, the program could produce pathways composed of a series of nonproductive protection and deprotection reactions.(2) Clustering similar retrosynthesis pathways. A majority of the retrosynthesis pathways proposed differ only at a sub-portion level, leaving users overwhelmed by similar pathways, and making it hard to focus on the pathways that are strategically different.e.g. a series of protection and deprotection reactions). Schwaller et al.4 and Lin et al.14 designed customized scoring functions, which aggregates the single-step reaction likelihood and the degree of molecule simplification, to evaluate candidate retrosynthesis reactions in the tree search. These heuristic scoring functions will guide the tree search towards simple precursors. Alternatively, Badowski et al.7 excluded protection and deprotection reaction rules during the retrosynthetic search to focus only on the productive disconnections. They treated protection reactions as a mask for the incompatible functional groups. However, this is only possible with their expert-encoded reaction rules that have extensive information about reaction type and functional group tolerance. In addition, application-oriented metrics can also be used to sort pathways. For example, price of the final target is one of key considerations for process chemistry. Badowski et al.19 developed a price estimator that used recursive formulae to assign cost to individual components along the pathways, and price penalties were applied to strategically similar pathways to ensure diversity in the top-ranking routes. Despite their inclusion of many expert-designed considerations when estimating the price, such as reaction yield and reaction cost composed of labor plus equipment/solvent/purification, target compound price estimation may still remain challenging without accurate prediction of the reaction stoichiometry, reaction concentration, and separation efficiency.Simple heuristics can be implemented to partially mitigate these two challenges. Sorting retrosynthesis pathways by the number of reaction steps can easily prioritize pathways that contain no or fewer nonproductive steps  can help to identify pathways in the network. However, a reaction network of the whole database will contain single-step reactions from various literature sources, where the roles of products and reactants could be reversed creating the possibility of cyclic reaction paths. As a consequence, it could be difficult to define a meaningful retrosynthesis pathway algorithmically. Considering that drug or fine chemical patents are typically preparation-oriented, single-step reactions extracted from a single patent would be highly related with fewer cyclic patterns. As the example shown in i.e. compounds only appearing as products and not as reactants, traversing through the network with a complete depth-first search (DFS) algorithm will give all the retrosynthesis pathways embedded in the network. Reagents were omitted from the network to make the neural model focus on assessing the retrosynthesis design strategy, i.e. how a target molecule is decomposed step by step towards commercially available precursors, rather than on minor differences in reagent choices for a particular transformation. To improve data quality, we implemented the state-of-the-art atom mapping algorithm, RXNmapper,26 for reaction validation and accurate differentiation between reactants and reagents.Converting single-step reactions into a reaction network (24 The extraction process would work similarly on other single-step reaction databases that contain reaction source identifiers (e.g. USPTO23 database with patent numbers and Reaxys22 database with literature identifiers). 85% of patents provided fewer than 10 pathways each  model to encode the overall pathway information. The tree-LSTM algorithm was initially proposed for tasks such as semantic relatedness of two sentences and sentiment classification.via intermediate compounds as the edges. The Morgan fingerprints of products29 and reactions30 with 2048 bits and a radius of 2 as implemented in RDKit31 were used to encode the reaction node information.2,29,30 Using both reaction fingerprint and product fingerprint as inputs gives the model a complete picture of the reaction core and the unchanged fragments. This encoded reaction representation was then fed into a reaction embedding neural network. The structure of the tree-LSTM network is identical to the structure of the pathway tree, and each LSTM node takes in the corresponding learned reaction node embedding as input  for the patent pathway compared to its accompanied artificial pathways. With this training procedure, the SLScore is interpreted as a relative quantity that is only used for comparing pathways with the same target molecule. To be noted, the SLScore absolute value of an individual pathway has little meaningful information itself, or when comparing across pathways with different targets. The trained tree-LSTM pathway ranking model gave a top-1 accuracy of 79.1% on the testing dataset described above  top-1 accuracy.via splitting at branching nodes, and then tracks the intermediates' complexity flow through each linear pathway with a complexity vector. We used SCScore developed by Coley et al.20 to quantify the complexity of each compound. For multi-reactant reactions, the most complex compound was selected to represent the complexity. The constructed complexity vector was then passed through a FFNN to generate a score for each linear pathway, followed by min-pooling to aggregate the scores of all linear pathways belonging to the same retrosynthesis pathway as its score. The min-pooling was used since the strategic level assessment of retrosynthesis pathways should be dominated by the least strategic linear pathway . 6000 randomly selected single-step reactions from the testing dataset belonging to 10 different frequently used reaction types were embedded using the trained reaction node embedding NN from the tree-LSTM model, giving a vector representation of each single-step reaction. These 6000 vector representations were projected to a two-dimensional space using t-Distributed Stochastic Neighbor Embedding (t-SNE) method32 To illustrate how this approach can help organize a large number of pathways generated, we selected vadadustat 77 as the target molecule. After searching pathways for 45 seconds using ASKCOS, we selected the top 2000 pathways found for the following analysis . e.g. appearance frequency of different reaction types) may exist, biasing the model towards patterns that appear more frequently in patent-extracted pathways than in artificial pathways. Looking forward, these limitations can be mitigated by (1) having multiple patent-extracted pathways (currently only one) for the same target molecule, (2) having more accurate and richer labelling of different pathway designs, and (3) having more examples with tactical retrosynthesis designs (e.g. the use of directing groups).The tree-LSTM model was demonstrated to understand strategic retrosynthesis design and cluster strategically similar pathways. Nevertheless, due to limitations in data labelling, the tree-LSTM model was trained to differentiate patent-extracted pathway and artificial pathways, with the assumption that patent-extracted pathways should be considered more strategic than artificial pathways. Thus, the model, to a certain extent, will ignore creative artificial pathways with comparable or improved strategic levels compared to the patent-extracted pathways. In addition, since the sources of patent-extracted and artificial pathways are different, certain data discrepancy  by Nextmove. Despite that data was deeply cleaned and curated with the state-of-the-art atom mapping algorithm, the potential data quality issue may still mislead the tree-LSTM model to using some minor features that have never appeared in the artificial pathways for ranking. Thus, using high-quality or even human-curated pathway dataset can further refine the model's ability of understanding the retrosynthesis design strategies.via ranking patent-extracted retrosynthesis pathways higher than the artificial ones. The model was able to achieve a top-1 ranking accuracy of 79.1%, which significantly outperformed the other three heuristic baseline models. Case studies on the correctly and incorrectly ranked results showed that tree-LSTM model was indeed able to recognize strategic synthesis designs and penalize nonproductive or non-selective reaction sequences. The trained tree-LSTM model can also serve as a tool to cluster pathways with strategically similar designs by encoding the pathway into a learned pathway embedding, so that users can focus on strategically different pathways proposed by the retrosynthesis program.This work implemented a tree-LSTM neutral network structure to encode pathway-level retrosynthesis design information. In order to facilitate learning how chemists design synthetic routes in practice, we curated a retrosynthesis pathway database from the single-step patent reaction database. For each target molecule in the pathway, 5–300 artificial pathways were generated by the ASKCOS program. The tree-LSTM model was trained to understand the strategic level of the retrosynthesis pathways 31 was used for molecule/reaction parsing, molecular fingerprint conversion, and various cheminformatics calculations. PyTorch 1.4 . All scripts were written in Python 3.7. RDKitorch 1.4  was usedThere are no conflicts to declare.SC-012-D0SC05078D-s001SC-012-D0SC05078D-s002SC-012-D0SC05078D-s003


Integrative multi-omics analysis has become a useful tool to understand molecular mechanisms and drug discovery for treatment. Especially, the couplings of genetics to metabolomics have been performed to identify the associations between SNP and metabolite. However, while the importance of integrative pathway analysis is increasing, there are few approaches to utilize pathway information to analyze phenotypes using SNP and metabolite. We propose an integrative pathway analysis of SNP and metabolite data using a hierarchical structural component model considering the structural relationships of SNPs, metabolites, pathways, and phenotypes. The proposed method utilizes genome-wide association studies on metabolites and constructs the genetic risk scores for metabolites referred to as genetic metabolomic scores. It is based on the hierarchical model using the genetic metabolomic scores and pathways. Furthermore, this method adopts a ridge penalty to consider the correlations between genetic metabolomic scores and between pathways. We apply our method to the SNP and metabolite data from the Korean population to identify pathways associated with type 2 diabetes (T2D). Through this application, we identified well-known pathways associated with T2D, demonstrating that this method adds biological insights into disease-related pathways using genetic predispositions of metabolites. The advances in biological techniques have led to the generation of multiple omics (multi-omics) data, which contribute to a better understanding of biological mechanisms and diseases. For instance, the next-generation sequencing (NGS) technology for genome-wide data and mass spectrometry for quantitative metabolic data allow us to generate multi-omics data from the same samples at a low cost . These tGenome-wide association studies (GWAS) have been conducted worldwide to identify single nucleotide polymorphisms (SNPs) associated with various diseases or phenotypes.An intermediate variable, linking genetic variants and phenotype, is suggested to consider the effects of genes and environmental factors in overcoming the limitation of GWAS . One of Since pathway analysis can give a more intuitive interpretation of the biological system, several methods have been proposed for pathway analysis that focuses on identifying significant pathways related to certain traits of interest . SpecifiHere, we propose an integrative pathway analysis of SNP and metabolite data using a hierarchical structural component model. This method calculates genetic risk scores of metabolites and investigates pathways associated with phenotypes through the genetic risk scores. This approach is based on our earlier work Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) . PHARAOHThe genetic metabolomic score (GMS) is calculated by summing the effects of the corresponding SNPs on each metabolite and then is used for pathway analysis in PHARAOH. HisCoM-SM adopts the ridge penalties to both GMSs and pathways to identify pathways while controlling for potential correlations between GMSs and between pathways.Here, we apply HisCoM-SM to SNP and metabolite data from Korean Association REsource (KARE) cohort to identify pathways associated with T2D. Note that T2D is a metabolic disorder that is affected by genetic factors and environmental exposure simultaneously . Throughhttps://statgen.snu.ac.kr/software/HisCoM_SM.The HisCoM-SM is available at The SNP data was generated by the Affymetrix Genome-Wide Human SNP array 5.0. from the Korea Association REsource (KARE) project. KARE is based on Ansan and Ansung Korean population cohort among 10,038 participants which was initiated in 2001 . This chhis work .The serum metabolites in the 691 participants were quantitatively analyzed by a targeted metabolomics approach using liquid chromatography-mass spectrometry (LC-MS). 64 metabolites were measured in this work. The metabolites of each subject were measured at the fifth follow-up in the KARE dataset. Among 64 metabolites, 53 were mapped to 101 pathways. The 53 metabolites were classified into eight categories. The criteria for diagnosis of T2D are 1) fasting blood glucose (FBS) The framework of HisCoM-SM consists of two steps. The step 1 is to calculate genetic risk scores of metabolite data referred to genetic metabolomic scores (GMSs). Genetic effects of metabolites are estimated and then used to calculate the GMSs. The step 2 is to perform pathway analysis using the calculated GMSs by step 1. To perform pathway analysis, a hierarchical structural component model (HisCoM) is used. HisCoM consists of three layers which are input layer, latent layer, and outcome layer. In our work, GMSs are used as input variables, pathways are used as latent variables, and binary phenotype is used as outcome variable. The two steps of the process are described in more detail below.To perform pathway analysis using SNP and metabolite data, we first construct the GMSs. Here, we used two methods for calculating GMSs. The first score was derived from the single-SNP association test for metabolites. To do that, we applied linear regression for each metabolite adjusted for age and sex and calculated the GMSs by clumping and thresholding to remove redundant correlated effects due to linkage disequilibrium (LD) using PLINK . Clumpinr2>0.2) . Threshor2>0.2) . Then, tr2>0.2) .The second score is based on the genetic best linear unbiased prediction (GBLUP) method from Genome-wide Complex Trait Analysis (GCTA) software . All SNPAfter constructing the GMSs, pathway analysis is performed. To estimate the parameters of the model, we maximize a penalized log-likelihood function  and use After estimation, the phenotype is resampled 100,000 times through permutation to generate the null distribution of coefficients of pathways to calculate empirical p-values. To correct the multiple comparisons problem, the false discovery rate (FDR) is applied . Here, wTo detect genetic variants associated with metabolites, we performed the mGWAS using linear regression, adjusting for sex and age. Out of 53 metabolites, only two metabolites have significantly (p < 1e-8) associated SNPs. Specifically, we identified 17 SNPs associated with Glycine which is related to insulin sensitivity and secretion . These SHisCoM-SM was applied to SNP and metabolite data of T2D/preT2D patients and normal samples from the KARE dataset which is a large Korean population-based cohort. We first mapped the KEGG pathway database with metabolite data. Among 64 metabolites, 53 metabolites were mapped to 101 pathways. Then, the GMSs were used as components of pathways, which are latent variables in the model. Note that we used the two methods to construct GMSs: 1) Single-SNP association-based GMSs and 2) GBLUP-based GMSs. Those two methods are discussed in detail in the Methods section. The lists of identified pathways with HisCoM-SM based on single-SNP association denoted by HisCoM-SM (single) and HisCoM-SM based on GBLUP denoted by HisCoM-SM(GBLUP) are shown in Trans-4-Hydroxy-L-proline, and Hippurate were significant in both HisCoM-SM methods. Arginine facilitates the action of glucose to stimulate insulin release  had the highest absolute effect value and the lowest q-value in both HisCoM-SM (single) and HisCoM-SM(GBLUP). Among the 49 metabolites contained in this pathway, Arginine, Tryptophan, Lactate, Trimethylamine N-oxide (TMAO),  release . In addi release . In addi release . These rFor comparison purposes, we applied the conventional HisCoM to KARE metabolite data to identify the T2D related pathways . Table 3In Several studies have suggested that pathway analysis using multi-omics data allows more insights into biological systems. Pathway analysis using more than one omics data is becoming increasingly common. However, few studies can identify disease-related pathways considering SNPs and metabolites together.We proposed a novel pathway analysis integrating SNP and metabolite data. Our method introduced novel genetic metabolomic scores (GMSs) for pathway analysis. We used a single-SNP association and a GBLUP approach to construct GMSs. The calculated GMSs were used as components of pathways in a hierarchical model. The coefficients can be estimated by analyzing GMSs and pathways simultaneously, considering the correlations between these scores and between pathways, respectively.We applied HisCoM-SM to the KARE cohort dataset. Our HisCoM-SM successfully identified pathways that were reported to be related to T2D. In our result, the pathways identified by HisCoM-SM and conventional HisCoM were almost overlapped, indicating that HisCoM-SM and HisCoM yielded quite consistent results, and the GMSs can be utilized for pathway analysis. Moreover, HisCoM-SM could identify the T2D-related pathways that the conventional HisCoM using only metabolite data could not detect. Since 53 targeted metabolomics in our analysis may cover only a small portion of the metabolome, modeling the effects of SNPs on these metabolites resulted in similar results from the conventional HisCoM method using only metabolites. We are planning to modify HisCoM-SM so that it allows for each pathway to have inputs from both genes and metabolites simultaneously. In other words, SNPs can directly contribute to pathways (not through metabolites), which also makes a more biological sense. The new model with a rewired structure is expected to improve the performance. We will leave it as a near-future study.Here, we applied the clumping and thresholding process to generate genetic metabolomic scores using p-values from linear regression models. Instead of linear regression models, other approaches such as Kernel regression can be applied to detect non-linear relationships between SNPs and metabolites. Our HisCoM-SM can also use other GMSs such as the ones derived from the LD pred method . Also, oWe believe that our method may add practical biological insights into the disease-related pathways by genetic predispositions of metabolites and contribute to the understanding of molecular mechanisms and treatment for the disease.


To the editors,1 In the present study, we analyzed the risk of cholelithiasis according to the degree of urine dipstick proteinuria.2 Our analysis showed that heavy proteinuria group (urine dipstick proteinuria ≥2+) had the higher risk for incident gallstone compared with the negative proteinuria group. This finding suggests that urine dipstick proteinuria ≥2+ is potentially associated with the increased risk factor for cholelithiasis.We appreciate Shih-Wei Lai for his interest and valuable comments on our study.3,4 Urine dipstick proteinuria can be affected by physiological conditions, like dehydration and orthostatic position, other than pathologic problems.5 However, it is recognized that our study participants with increased urine dipstick proteinuria tended to have worse renal function. In our study groups, urine dipstick proteinuria showed an inverse relationship with estimated glomerular filtration rate (eGFR) and a proportional relationship with serum creatinine. Additionally, compared with negative urine dipstick proteinuria, the increased risk of cholelithiasis was observed only in the heavy proteinuria group (urine dipstick proteinuria ≥2+) presenting worst renal function. These findings imply that poor renal function may mediate the association between urine dipstick proteinuria and the risk of cholelithiasis. Therefore, The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to calculate eGFR.6 Our analysis indicated that the risk of cholelithiasis was inversely associated with quartile levels of eGFR , and that in the heavy proteinuria group was not so high (2.39%). Our results only show that the group with heavy urine dipstick proteinuria had a higher risk of cholelithiasis than the group with negative urine dipstick proteinuria. However, these findings do not guarantee the reliability and cost-effectiveness of urine dipstick test for proteinuria in predicting cholelithiasis. Therefore, further studies should be conducted to identify the pathological mechanisms and mutual interactions among urine proteinuria, renal disease, and cholelithiasis. These studies may reveal the useful markers for cholelithiasis regarding renal pathology.


Carnobacterium maltaromaticum. A phenotypic assay indicated that four of six C. maltaromaticum strains showed weak (Cm 6-1 and ATCC 35586) or even delayed (Cm 3-1 and Cm 5-1) citrate utilization activity. The remaining two strains, Cm 4-1 and Cm 1-2 gave negative results. Additional analysis showed no or very limited utilization of citrate in media containing 1% glucose and 22 or 30 mM citrate and inoculated with Cm 6-1 or ATCC 35586. Two potential pathways of citrate metabolism were identified by bioinformatics analyses in C. maltaromaticum including either oxaloacetate (pathway 1) or tricarboxylic compounds such as isocitrate and α-ketoglutarate (pathway 2) as intermediates. Genes encoding pathway 1 were present in two out of six strains while pathway 2 included genes present in all six strains. The two potential citrate metabolism pathways in C. maltaromaticum may potentially affect the sensory profiles of milk and soft cheeses subjected to growth with this species.In the present study, we describe the identification of potential citrate metabolism pathways for the lactic acid bacterium (LAB)  Carnobacterium maltaromaticum isolated from Mozzarella cheese, are also able to metabolize citrate, which leads to the production of aroma compounds . T. Tcit opC. maltaromaticum citrate metabolism was tested on a citrate-containing agar medium [Enterococcus faecalis ATCC 29212, Lactiplantibacillus pentosus DSMZ 20314, and Lactiplantibacillus plantarum LMG 11405, all showed strong activity, whereas Pediococcus acidilactici PAH and Lactococcus lactis ATTC 11454 showed no activity, which was also the case for Carnobacterium gallinarum LMG 9841 and Carnobacterium divergens LMG 9199. Four C. maltaromaticum strains  showed weak activity, among which the Cm 3-1 and Cm 5-1 strains had delayed positive phenotypes during aerobic incubation at 25 °C  or log 6.3 CFU/mL (ATCC 35586) to log 8.7 CFU/mL (Cm 6-1) or log 9.1 CFU/mL (ATCC 35586) after 9 days of incubation at 25 °C. The medium with the lowest initial concentration of citrate (22 mM) contained either 18.7  or 19.5  mM citrate at end of incubation. One ATCC 35586 sample that only contained 12 mM was noted as an outlier and was not included in the average. Similarly, the medium with the highest initial concentration of citrate (30 mM) contained 28.25 mM at the end of incubation irrespective of strain . It will be of interest to conduct further studies on whether citrate pathway 1 genes are localized on plasmids in order to evaluate the stability of their presence in individual strains.None of these pathways were detected in five strains of ium spp. . BLAST acitM-CDEFGT cluster nomenclature [cit promoters [2-–Mg2+ complex [The overall organization of the pathway 1 gene cluster is similar in the four strains Cm 4-1, Cm 6-1, JIP 2891, and 10040100629 . Followiromoters ,31. Locu complex ,33. LocuL. lactis biovar. diacetylactis CRL264 strain, citrate transport, and metabolism are composed of the chromosomal citM(ψcitO)-I-CDEFXG cluster [citM(H)-CDEFGT(X) without citI gene.In the  cluster . HoweverE. faecalis [The gene products associated with locus tags 3138 and 3139 are related to transmembrane function; region and transmembrane helices were predicted for the 3139 gene product by use of the TMHMM server. These proteins may be associated with the functionality of the OAD-beta chain, and the latter was also predicted to contain transmembrane helices. Transmembrane OAD has previously been identified in LAB and has been associated with the type II citrate pathway in faecalis ,35. Soluble OAD are classified into three groups  based on the malic enzymes (MEs). Notably, the soluble OAD (EC1.1.1.40) was identified in the Cm 6-1 strain (locus tag 1547), indicating an additional potential ability to convert oxaloacetate to pyruvate ,37.+ symporter, CimH, locus tag 1548 in Cm 6-1 sequence) encoded by a gene present in all six strains (Pathway 2 includes the conversion of citrate into isocitrate and α-ketoglutarate by a truncated tricarboxylic acid (TCA) cycle containing the genes encoding aconitate hydratase and isocitrate dehydrogenase but lacking the genes encoding the reactions from α-ketoglutarate to succinate . This pa strains  are presCarnobacterium maltaromaticum citrate degradation did not induce pathway 1, as indicated by the negative result for strain Cm 4-1 that contains the genes for pathway 1 but showed a negative phenotype  or Cm 6-1 (with genes encoding pathways 1 and 2) or to affect the production of short-chain fatty acids to any significant extent. The presence of glucose may suppress the conversion of citrate in E. faecalis but not in L. lactis, Lacticaseibacillus casei, or L. plantarum [C. maltaromaticum citrate catabolism. Among other carbohydrates, galactose may exert an enhancing effect for citrate metabolism in E. faecalis, L. casei, and L. plantarum. Suppression of citrate metabolism by lactose was not observed in L. casei, L. plantarum, or Leuconostoc mesenteroides [E. faecalis [Furthermore, APT broth with 1% glucose and 22 or 30 mM citrate did not appear to support citrate utilization by lantarum ,42. As tteroides  and depefaecalis . Lc. lactis and Leuconostoc spp. [Lc. lactis, L. casei, and L. plantarum [E. faecalis and L. mesenteroides [C. maltaromaticum during growth in APT broth with citrate.pH also affects citrate catabolism, with the highest activity observed between pH 5.5 and 6.0 in toc spp. , whereaslantarum ,46. Citrteroides ,47,48,49L. pentosus and L. plantarum [C. maltaromaticum. Succinate is a product of the reductive TCA cycle for some LAB, such as strains belonging to lantarum ,51. Impllantarum ,51. UsinC. maltaromaticum for the production of lactic and acetic acid, formic acid, CO2, acetoin, diacetyl, and 2-3-butanediol [C. maltaromaticum [C. maltaromaticum are not known. It will be of interest to examine this further, especially with regard to the synthesis of the flavor compounds diacetyl and 3-methyl-butanal.Pyruvate serves as a substrate for tanediol . Oxaloactanediol . These tomaticum ,52,53,54C. maltaromaticum appear to encode two different pathways for metabolizing citrate. To our knowledge, this is the first genetic analysis on potential citrate metabolism pathways for the Carnobacterium genus. Currently, the conditions that activate the citrate pathways are not known and further studies are required to illuminate them. As this species has potential as an adjunct culture in soft cheese products, it would also be of interest to investigate whether citrate catabolism in C. maltaromaticum by pathway 1 and/or pathway 2 may result in the production of flavor compounds under conditions prevailing in such products. We are currently researching that direction by examining the external regulation of citrate catabolism in Carnobacterium maltaromaticum.In conclusion, this study shows that some strains of


Ulcerative colitis (UC) is a type of chronic idiopathic inflammatory bowel disease with a multifactorial pathogenesis and limited treatment options. The aim of the present study is to investigate the hydrogen deuterium exchange mass spectrometry (HDX-MS) behaviors of fermented deoxyanthocyanidins and their molecular mechanisms to alleviate UC by using quantum chemistry and network pharmacology. R package was used for Gene Ontology (GO) and KEGG pathway analyses, and the protein–protein interactions (PPIs) of the targets were assessed using Search Tool for the Retrieval of Interacting Genes (STRING). Finally, molecular docking was implemented to analyze the binding energies and action modes of the target compounds through the online tool CB-Dock.  Tandem MS indicated at least two fragmentation pathways through which deuterated vinylphenol-deoxyanthocyanidins could generate different product ions. Quantum calculations were conducted to determine the transition states of the relevant molecules and analyze their optimized configuration, vibrational characteristics, intrinsic reaction coordinates, and corresponding energies. The potential targets of deoxyanthocyanidins in UC were screened from a public database. The  Quantum calculations indicated two potential fragmentation pathways involving the six-membered ring and dihydrogen cooperative transfer reactions of the vinylphenol-deoxyanthocyanidins. A total of 146 and 57 intersecting targets of natural and fermented deoxyanthocyanidins were separately screened out from the UC database and significant overlaps in GO terms and KEGG pathways were noted. Three shared hub targets  were selected from the two PPI networks by STRING. Molecular docking results showed that all deoxyanthocyanidins have a good binding potential with the hub target proteins and that fermented deoxyanthocyanidins have lower binding energies and more stable conformations compared with natural ones.  Deoxyanthocyanidins may provide anti-inflammatory, antioxidative, and immune system regulatory effects to suppress UC progression. It is proposed for the first time that fermentation of deoxyanthocyanidins can help adjust the structure of the intestinal microbiota and increase the biological activity of the natural compounds against UC. Furthermore, HDX-MS is a helpful strategy to analyze deoxyanthocyanidin metabolites with unknown structures. Ulcerative colitis (UC) is a chronic nonspecific inflammatory bowel disease (IBD) characterized by diffuse mucosal inflammation of the colon and rectum that may lead to permanent fibrosis, tissue damage, and a high risk of colitis-related colon cancer , 2. The The gut microbiota plays important roles in the cultivation of host immunity, digestion of food, regulation of intestinal endocrine function and neurological signaling, modification of drug action and metabolism, elimination of toxins, and production of numerous compounds influencing the host . EvidencAnthocyanins are a type of flavonoids commonly found in richly colored flowers and fruits . AnthocyLactobacillus plantarum, novel vinylphenol and pyrano adducts of deoxyanthocyanins  and directly injected into the LTQ-Orbitrap HRMS with ESI source at a flow rate of 15 μL min−1. The HDX reactions were initiated by mixing each fermented deoxyanthocyanidin with 1 mL of precooled methanol-d4/D2O  to decrease H/D scrambling. The MS experiments included full scan and tandem MS in the positive ion mode for the fermented deoxyanthocyanidins and corresponding deuterated deoxyanthocyanidins. The ESI source was performed using the following parameters: spray voltage 4.5 kV; dry gas (N2) flow rate 15L min−1; capillary voltage 35V; capillary temperature 150 °C; and nebulizer (N2) pressure 35 psi. The higher energy collision-induced dissociation (HCD) mass spectra were obtained with a mass resolution of 17500 and normalized collision energy (NCE) from 15 to 50 eV.The detailed description of structural identification and the MS experiments for fermented deoxyanthocyanidins were published before . BrieflyDFT calculations were performed for geometry optimizations of deoxyanthocyanins by employing B3LYP/6-31G(d) hybrid functional implemented using the Gaussian 16 program . Furtherhttp://www.swisstargetprediction.ch/) was used, which is a web server that predicts the most probable protein targets of small molecules based on the combination of 2D and 3D similarity measures of known ligands [https://www.genecards.org/) and DrugBank (https://www.drugbank.ca). Targets with hit scores greater than 9.0 were selected as the UC-related genes in the GeneCard database, and the UniProt database (http://www.uniprot.org/) was used for comparison of target information and gene symbol standardization. Subsequently, the regulatory networks of natural deoxyanthocyanidins-UC and fermented deoxyanthocyanidins-UC were generated by using Cytoscape software, respectively.The 2D structures of deoxyanthocyanidins were drawn through ChemDraw software, and the geometric configurations were optimized by Gaussian software. To obtain the targets of deoxyanthocyanidins, the SwissTargetPrediction database  analysis is a commonly used approach to define genes and their RNA or protein products to determine the unique biological characteristics of high-throughput transcriptome or genome data . It is mhttps://string-db.org/) with a confidence score ≥0.4 [PPI is the basis of most biological processes in living cells and is essential for understanding cell physiology in normal and disease states. PPI network mappings originated from deoxyanthocyanidins and UC targets were performed using an online tool STRING  (ore ≥0.4 .http://cao.labshare.cn/cb-dock/), which can automatically recognize the binding sites of a given protein, calculate the center position, customize the docking box size, and use the popular docking program (AutoDock Vina) to dock with the query ligands [http://www.rcsb.org), and the selected deoxyanthocyanidins were optimized by Gaussian software. Then, the protein and the deoxyanthocyanidins were uploaded to CB-Dock for docking. The docking scores were used to compare the theoretical binding affinities of natural and fermented deoxyanthocyanidins to the key targets.In order to investigate the relationship between the target proteins and ligands at the molecular level, molecular docking experiments were conducted to evaluate the interaction strength and mode between deoxyanthocyanidins and the targets. The docking simulation was performed by CB-Dock  in m/z 281.0805 in protic media corresponded to a molecular formula of C17H13O4+ and the loss of phenol. In deuterated methanol/water, deuterated compound 7 gave a precursor ion of m/z 379.1477 corresponding to a molecular formula of C23H15D4O5+ and the replacement of four protons with deuterium (m/z 283.0931 (C17H11D2O4+) and m/z 284.0993 (C17H10D3O4+) in The typical structures of new fermented deoxyanthocyanidins were identified and confirmed by NMR analysis and chemical synthesis , and 3D euterium . Howeverm/z 283.0931 (C17H11D2O4+), the structure of transition state 1 (TS1) is shown in via a six-membered ring transition (m/z 284.0993 (C17H10D3O4+), the potential fragmentation pathway involved dihydrogen cooperative transfer reactions in TS2 of 17H10D3O4+. The comparison of Gibbs free energy displayed that the energy of TS1 was approximately 47.45 kcal/mol lower than that of TS2, so TS1 was relatively easily to take place. The new proposed fragmentation pathways of deuterated compound 7 are shown in In order to illustrate the two deuterated fragment ions produced by deuterated compound 7 in ansition . Accordiansition . As for Drugs can bind to multiple targets, and molecular targets can participate in multiple processes . Thus, iThe deoxyanthocyanin–UC regulatory networks presented in R package. The gene functions were divided into three categories, namely, biological process (BP), cellular component (CC), and molecular function (MF). The first seven enrichment results of GO analysis were visualized as bar graphs. For natural deoxyanthocyanins, BP is mainly involved in cellular response to oxidative stress, response to reactive oxygen species, and cellular response to reactive oxygen species; CC is mainly related to vesicle lumen, ficolin-1-rich granule lumen, and peroxisome; MF is mainly involved in heme binding, tetrapyrrole binding, and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen (G protein-coupled chemo attractant receptor activity (The functions of the target genes were determined by Gene Ontology (GO) analysis using the r oxygen . For feractivity .The potential pharmacological mechanisms of deoxyanthocyanins against UC were revealed by KEGG enrichment analysis. According to the results of KEGG enrichment, the mechanisms of natural deoxyanthocyanins in the alleviation of UC mainly focus on proteoglycans in cancer, microRNAs in cancer, endocrine resistance, estrogen signaling pathway, prostate cancer, etc ; the mecFollowing the prediction of target gene interactions using the STRING database, PPI networks were constructed to identify the most important proteins and biological molecules responsible for the effect of deoxyanthocyanins against UC. For natural deoxyanthocyanins, a total of 33 nodes and 160 edges were modulated by the PPI network , and theThree typical deoxyanthocyanins were selected as molecular docking ligands to compare the effects of natural and fermented deoxyanthocyanins against UC. The optimized structures of apigeninidin, compound 7, and compound 9 were separately uploaded to CB-Dock for docking analysis with PTGS2, ESR1, and EGFR as targets. Vina scores lower than 0 indicate that the target protein and ligand can bind spontaneously, and lower docking scores reflect more stable conformations. The docking scores of the first five conformations in each docking result are shown in via the reaction of natural deoxyanthocyanins  with the decarboxylation products of phenolic acids, such as hydroxycinnamic acid, coumaric acid, caffeic acid, and ferulic acid [In the present study, a comprehensive network pharmacological method based on novel fermented vinylphenol/pyrano-deoxyanthocyanins and a disease database search was used to identify the most useful molecular targets and mechanisms of deoxyanthocyanins to alleviate UC. The novel fermented deoxyanthocyanidins were created lic acid . The uselic acid . In the lic acid . In MS2,lic acid . Quantumlic acid ,36. UsinR package. The results are given as follows: (1) for BP, the targets of natural and fermented deoxyanthocyanins had significant enrichment in response to oxidative stress, cellular response to reactive oxygen species, reactive oxygen species metabolic process, response to reactive oxygen species, and response to oxygen levels; (2) for CC, both of the targets were significantly enriched in membrane raft, membrane microdomain, vesicle lumen, and basal plasma membrane; (3) for MF, both of the targets were enriched in heme binding, tetrapyrrole binding, serine-type endopeptidase activity, and serine-type peptidase activity. For KEGG pathway analysis, both of the targets were particularly enriched in proteoglycans in cancer, microRNAs in cancer, prostate cancer, EGFR tyrosine kinase inhibitor resistance, and endocrine resistance (P < 0.05).Comprehensive and user-friendly information on all annotated and predicted human genes is provided by GeneCards, which also integrates genomic, transcriptomic, proteomic, and clinical information related to genes . DrugBanvia the STRING online database. We comprehensively considered the top 10 targets of the two groups and selected PTGS2, ESR1, and EGFR as the hub targets of deoxyanthocyanins against UC. PTGS2  plays a crucial role in the regulation of cellular responses to inflammation, which is a key factor in the pathogenesis of gut inflammation [κB, induction of PPAR-γ, and modification of the mucosal barrier function [α) is a ligand-activated transcription factor that forms a homo- or heterodimer with ERβ in the nucleus; this protein consists of several domains essential for hormone binding, DNA binding, and transcriptional activation [The PPI target networks of natural and fermented deoxyanthocyanins were separately constructed ammation ,40. Whenfunction . PTGS2 afunction . ESR1  and compared their binding energies with those of natural and fermented deoxyanthocyanins through the molecular docking method. Although the underlying mechanisms of deoxyanthocyanins toward their targets require further characterization in vivo and in vitro, in silico predictions clearly show that the effects of deoxyanthocyanins on alleviating UC are significantly enhanced following fermentation. For the first time, this work shows that HDX-MS is suitable for the analysis and identification of unknown deoxyanthocyanidin metabolites and the increase of pharmaceutical effects to alleviate UC after the metabolism of probiotics via network pharmacology.The purpose of this study is to analyze the mass spectrometric behavior of vinylphenol-deoxyanthocyanins by using DFT calculations and identify the molecular mechanisms of these molecules in alleviating UC