Danggui-Shaoyao-San (DSS) is one of traditional Chinese medicine, which recently was found to play a protective role in diabetic kidney disease (DKD). However, the pharmacological mechanisms of DSS remain obscure. This study would explore the molecular mechanisms and bioactive ingredients of DSS in the treatment of DKD through network pharmacology. The potential target genes of DKD were obtained through OMIM database, the DigSee database and the DisGeNET database. DSS-related targets were acquired from the BATMAN-TCM database and the STITCH database. The common targets of DSS and DKD were selected for analysis in the STRING database, and the results were imported into Cytoscape to construct a protein-protein interaction network. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis and Gene Ontology (GO) enrichment analysis were carried out to further explore the mechanisms of DSS in treating DKD. Molecular docking was conducted to identify the potential interactions between the compounds and the hub genes. Finally, 162 therapeutic targets of DKD and 550 target genes of DSS were obtained from our screening process. Among this, 28 common targets were considered potential therapeutic targets of DSS for treating DKD. Hub signaling pathways including HIF-1 signaling pathway, TNF signaling pathway, AMPK signaling pathway, mTOR signaling pathway, and PI3K-Akt signaling pathway may be involved in the treatment of DKD using DSS. Furthermore, TNF and PPARG, and poricoic acid C and stigmasterol were identified as hub genes and main active components in this network, respectively. In this study, DSS appears to treat DKD by multi-targets and multi-pathways such as inflammatory, oxidative stress, autophagy and fibrosis, which provided a novel perspective for further research of DSS for the treatment of DKD. Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes and is the leading cause of end-stage renal disease . PatholoPaeonia lactiflora Pall., Alisma orientalis (Sam.) Juzep., Angelica sinensis (Oliv.) Diels., Poria cocos (Schw.) Wolf., Atractylodes macrocephala Koidz. and Ligusticum chuanxiong Hort. In 2009, Traditional Chinese medicine has been widely applied in the treatment of disease for thousands of years, and the application scope of many traditional Chinese medicine has been expanding . DangguiNetwork pharmacology is an emerging discipline that integrate virtual computing technology, public databases, and high-throughput data for revealing the complicated network relationship between drugs and diseases. Nowadays, many researchers have used network pharmacology to study the therapeutic targets of traditional Chinese medicine, laying a foundation for the clinical application of traditional Chinese medicine . DSS, ashttp://omim.org) (http://210.107.182.61/geneSearch/) (https://www.disgenet.org/) (https://www.uniprot.org/) and converted into a unified gene name (Human genes associated with DKD were gathered from the OMIM database (mim.org) , the DigSearch/)  and the et.org/) . Terms lene name . After dPaeonia lactiflora Pall., Alisma orientalis (Sam.) Juzep., Angelica sinensis (Oliv.) Diels., Poria cocos (Schw.) Wolf., Atractylodes macrocephala Koidz. and Ligusticum chuanxiong Hort. Active ingredients of every herb were mainly accessed through the TCMSP database (https://tcmspw.com/tcmsp.php), a platform provides information about Chinese herbal medicine, related targets and ingredients-targets-diseases networks (DSS prescription consists of the following herbs: networks . Names ohttp://bionet.ncpsb.org/batman-tcm/) (http://stitch.embl.de/)  (Targets of DSS active ingredients were acquired from the BATMAN-TCM database (an-tcm/)  and the mbl.de/) . Resultsot.org/) . FinallyThe screened DKD targets and DSS active ingredients related targets were imported into TBtools (version 1.0686)  and the https://david.ncifcrf.gov/, version 6.8) (http://www.bioinformatics.com.cn/). Protein-protein interaction (PPI) networks for DKD targets, DSS targets and common targets were established by STRING database  (Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis and Gene Ontology (GO) enrichment analysis were conducted by online function annotation tool DAVID (ion 6.8)  and visuon 11.0) . PPI nethttp://www.pdb.org) (https://pubchem.ncbi.nlm.nih.gov/) and were converted into Mol2 format files by Open Babel GUI software (version 2.3.1 http://openbabel.org)   and werebel.org) . Then thFlow chart of the network pharmacology is given in An aggregate of 162 targets were screened as DKD therapeutic targets from OMIM, DigSee and DigSee database . KEGG enTwenty-four active components of 6 Chinese herbal medicines were recognized according to the screening conditions. And 550 targets of these active components were further predicted by the BATMAN-TCM database and the STITCH database. KEGG enrichment analysis suggested that these targets of DSS are involved in a variety of metabolic pathways, such as glycine, serine and threonine metabolism, histidine metabolism, and fatty acid degradation . BesidesAs presented in Figure 6A, 28common targets were identified as DKD related proteins targeted by DSS. Subsequently, we constructed a network consisting of DSS herbs, active ingredients, common targets, interactions between targets and disease . Based oSubsequently, the pivotal intersection targets were screened by Cytohubba tool in Cytoscape software. As showed in Although there are many ways to slow the progression of DKD, there is still a lack of an effective way to prevent or cure it due to its complicate pathogenic mechanism. Therefore, it is urgent to find new and multi-effective drugs for the treatment of DKD from a broader perspective. DSS is one of promising drugs for DKD. Recent studies demonstrated that DSS plays a crucially reno-protective role in STZ-induced diabetic rats . UnfortuIn the current study, 28 DSS-DKD-common-targets were obtained and 16 core targets were acquired based on PPI network of these common targets by MCODE. The core targets include HIF1A, TNF, IL1B, IL10, ICAM1, NOTCH1, INS, AKT1, AGTR1, SERPINE1, PTGS2, NOS3, IGF1, ADIPOQ, PPARG, and APOE. Besides, KEGG pathways enrichment analysis of these 28 common targets found that DSS might ameliorate diabetic kidney injury through HIF-1 signaling pathway, TNF signaling pathway, mTOR signaling pathway and so on. Subsequent analysis by Cytohubba further ranked the DSS-DKD-common-targets. TNF and PPARG were identified as the most core targets.TNF is a pro-inflammatory cytokine that promotes the expression of a variety of transcription factors, cytokines, cell adhesion molecules, mediators of inflammatory processes, and acute-phase proteins by binding to its receptors . TNF wasPPARG is another important DKD-related gene. Previous study identified PPARG as a transcription factor modulating DKD target genes . And Shavia TNF and PPARG. However, there is no research on the role of poricoic acid C in DKD at present. Subsequent experiments can explore the reno-protective function of these active ingredients.Further molecular docking of TNF and PPARG with their predicted active ingredients of DSS was conducted. Among them, the docking of poricoic acid C and TNF had the lowest binding energy , followed by poricoic acid C and PPARG , and stigmasterol and TNF , mirroring a good binding activity in the molecular docking. Poricoic acid C is a kind of triterpenoid compounds. Previous study found that poricoic acid A could protect against acute kidney injury-to-chronic kidney disease transition in rats and attenuate fibroblast activation and abnormal extracellular matrix remodeling in renal fibrosis . PoricoiWe also explore the potential mechanisms of DSS against DKD through functional enrichment analysis. KEGG pathways enrichment analysis suggests that DSS participates in the regulation of several important pathways, such as HIF-1 signaling pathway, TNF signaling pathway, mTOR signaling pathway, AMPK signaling pathway, NF-kappa B signaling pathway and so on, some of which were also key pathways of DKD. These pathways are closely related to hypoxia injury, inflammation and autophagy. Previous studies also reported that DSS alleviated kidney injury through its anti-oxidant effect . In cereThe present study provides new insight into the pharmacology of DSS and the therapeutic mechanism for DKD, which also indicates that network pharmacology could be an effective way to explore the potential mechanism of intricate prescription for diseases. Our results using network pharmacology revealed that DSS play critically protective role in the treatment of DKD through multi-targets and multi-pathways .


Diabetes damages the eye in many ways, but the most common eye complication is retinopathy. Of the 246 million people with diabetes worldwide, about 30% will be at risk of developing diabetic retinopathy. In addition to impairing vision, the presence of diabetic retinopathy also means an increased risk of life-threatening systemic vascular complications. To prevent the occurrence and development of diabetic retinopathy, the research and development of new drugs for diabetic retinopathy is the primary task. Because of its characteristics of holistic view and dialectical view, traditional Chinese medicine has a good effect in the treatment of complex diseases. However, due to the characteristics of complex components, numerous targets, and synergistic effects that are important, and its compound prescriptions, the modernization of traditional Chinese medicine has been hindered. The rise of network pharmacology in recent years provides a good opportunity for the development of traditional Chinese medicine. Network pharmacology aims to explore the relationship between drugs and diseases from a holistic perspective, discover drug targets, and guide the development of new drugs. In terms of methodology, network pharmacology has the characteristics of integrity and systematicness, which just coincides with the principle of consultation in traditional Chinese medicine. In this paper, from the perspective of the application of traditional Chinese medicine, TCMSP software was used to retrieve the active ingredients in traditional Chinese medicine, and the corresponding targets of the active ingredients were obtained. We then performed GO annotation analysis and KEGG pathway analysis on the targets using DAVID software. We further used Cytoscape software to build an active ingredient-target-pathway network model. Through network pharmacology research, it has been shown that Fufang Xueshuantong may treat diabetic retinopathy through multiple pathways such as intravascular growth factor signaling pathway, mitogen-activated protein kinase signaling pathway, and Toll sample receptor signaling pathway, reflecting the multicomponent traditional Chinese medicine compound and multitarget and multichannel characteristics. The research in this paper provides a theoretical basis for the pharmacological mechanism of traditional Chinese medicine in the treatment of diabetic retinopathy. Diabetes mellitus is a common endocrine disease dominated by glucose metabolism disorders, and the prevalence of diabetes and the number of diabetic patients are increasing rapidly –4. The IAs the duration of diabetes increases, the risk of various complications caused by diabetes also increases, and the most serious complication is Diabetic Retinopathy (DR). Diabetic retinopathy is one of the three major eye diseases that cause blindness in humans, and it is caused by diabetes. When diabetic patients have poor blood sugar control, the blood will flow through various blood vessels and organs in the body with high concentrations of blood sugar. When excess sugar flows into the fundus and retina, it is very likely that polysaccharide particles will form to block the capillaries in the fundus, preventing normal blood flow. Long-term blockage will cause local hypoxia and rapid reduction of hemoglobin in the retinal tissue, and further risk of blood vessel rupture, which may eventually lead to visual impairment or even blindness in patients –8. AccorDiabetes has a thousand years of history in Chinese medicine. Diabetes is the closest to diabetes in Chinese medicine. In modern medicine, the two are often equated. DR belongs to diabetes mellitus cancer. Most physicians call DR the diabetes mellitus disease, which belongs to the categories of bird's eye, cataract, and violent blindness. Liver and kidney deficiency and heart and spleen deficiency lead to eye orifice dystrophy. After many clinical practices, it has been proved that traditional Chinese medicine has a significant effect in the prevention and treatment of diabetic retinopathy. However, it also faces the relatively slow theoretical research on traditional Chinese medicine compounds due to the complex components and unclear mechanism of action. Moreover, it is difficult to achieve the preset purpose by using traditional pharmacological experimental methods. With the gradual improvement of systems biology and the vigorous development of network pharmacology combined with computer technology, multidisciplinary research methods have emerged. These methods have been gradually applied in the field of traditional Chinese medicine. The emergence of these emerging scientific technologies has provided new methods and strategies for exploring the theory, clinical research, and drug development of diabetic retinopathy. The research mode of network pharmacology has also changed from single drug and single target to disease-gene-target-drug. The overall analysis of the biological system network, the prediction of the pharmacological mechanism of the drug, and the evaluation of the clinical effect of the drug are helpful for the related research of traditional Chinese medicine compounds and the development of new multitarget traditional Chinese medicine drugs.Network pharmacology has the characteristics of integrity and system, which is in harmony with the holistic view of traditional Chinese medicine and the principle of dialectical treatment. This article briefly reviewed the opportunities and challenges of the modernization of traditional Chinese medicine and the generalization of the formation of network pharmacology. On this basis, the network pharmacology method was used to analyze the drug-target-disease relationship of traditional Chinese medicine in the treatment of diabetic retinopathy, and to corroborate the biological mechanism of highly expressed genes. The research in this paper can provide a basis for further clinical research on diabetic retinopathy.The retina is a transparent and soft membrane, which is located behind the inner wall of the eyeball. It is rich in blood vessels and is the only blood vessel that the human body can see. In clinical practice, doctors use the retinal images of the fundus to judge the changes in the blood vessels of the patient's body. The retina contains tissues such as the macula, optic disc, and blood vessels. The optic disc, also known as the optic disc, is the initial end of the optic nerve and is in the shape of a reddish disc. The macula is located on the temporal side of the optic disc and is darker than the surrounding retinal area, with a depression in the center of the macula called the fovea. As shown in Endothelial cells, pericytes, and basement membrane together make up retinal capillaries. Endothelial cells participate in the immune activity of the body, which can affect vasoconstriction and vasodilation and regulate local blood flow and vascular permeability in retinal capillaries. Endothelial cells connect with pericytes to form the blood-retinal barrier and maintain the stability of retinal capillaries. Pericytes can also regulate the permeability of local blood flow in retinal capillaries and can also provide support for endothelial cells and inhibit endothelial cell proliferation. It has been reported that the activity of antioxidant enzymes in retinal tissue decreases during hyperglycemia, resulting in the accumulation of a large amount of ROS in vascular endothelial cells and pericytes, resulting in oxidative stress. Under normal biological conditions, ROS remain in balance, but their overproduction can lead to a biological process called oxidative stress, which is considered to be the main pathogenesis of DR. ROS can damage intracellular biomacromolecules, cause increased oxidative stress, and ultimately lead to mitochondrial dysfunction and retinal cell apoptosis.Astragalus is a qi-tonifying medicine with a lukewarm nature. DR often has the phenomenon of yin deficiency and qi deficiency. Astragalus belongs to the spleen and lung and has the effect of tonifying qi. Studies have confirmed that astragaloside IV in Astragalus has anti-inflammatory and antioxidant effects and has a preventive effect on DR and balances oxidative stress responses. Astragalus polysaccharides can be anti-inflammatory and antioxidant, reduce VEGF expression, and reduce the adhesion of leukocytes to the retina, thereby preventing and treating DR. Panax notoginseng can remove blood stasis and stop bleeding, warm in nature, and return to the liver and stomach. It has the effect of stopping bleeding without leaving stasis. Panax notoginseng can reduce retinal vascular permeability, decrease blood viscosity, protect capillary structure, and delay the development of DR. The study found that the traditional Chinese medicine Ginkgo biloba, Pueraria, Angelica, and its extracts significantly improved many indicators of DR patients.The pathogenesis of DR is complex. Although there is no cure for traditional Chinese medicine, traditional Chinese medicine has great advantages in conservative treatment of DR. Traditional Chinese medicine can improve vision and delay the development of DR, which reflects the broad prospects and unique advantages of traditional Chinese medicine in the treatment of DR. In terms of treatment, traditional Chinese medicine pays more attention to the functions of regulating qi and activating blood, nourishing the liver and kidney. Studies have confirmed that, in the process of DR development, blood stasis has always been an important pathogenesis of DR, so the method of promoting blood circulation and removing blood stasis is one of the effective means of treating DR. There are many reasons for blood stasis: yin deficiency, qi and blood stasis, phlegm-dampness caused by blood stasis, blood heat caused by blood stasis, etc. Traditional Chinese medicine summarizes DR as endogenous dryness and heat, yin deficiency constitution; or inappropriate diet, causing damage to the spleen and stomach; or excessive fatigue and insufficient yin. Deficiency of both qi and yin and deficiency of liver and kidney can cause vision loss; dry eyes and long-term high sugar conditions can lead to microvascular disease. At the same time, yin deficiency and dryness heat will make the blood flow not smooth, and blood stasis will endogenously lead to blockage of the eyes and collaterals, which will aggravate DR . Since aAt present, many Chinese patent medicines are developed by some Chinese medicine compounds, which improves the patient's compliance and provides convenience for patients to take medication under certain conditions. There are many compound Chinese patent medicines used for the treatment of DR in clinic, including tablets, capsules, and granules. There are also many corresponding clinical trials.The medical treatment is guided by the holistic view and dialectical treatment, and the traditional Chinese medicine compound formula with the compatibility principle of “monarch, minister, assistant, and envoy” is the main form of clinical disease prevention and treatment. Based on the characteristics of integrity and dynamics, Chinese network pharmacology advocates the principle of multitarget and multichannel drug delivery. Therefore, in the process of TCM modernization, some TCM researchers draw on the research ideas of network pharmacology to explore the essential attributes of TCM “prescriptions for symptoms.” These essential attributes reveal the multichannel, multitarget, and multicomponent comprehensive effects of traditional Chinese medicine and its compounds, and good staged results have been achieved. The basic characteristics of traditional Chinese medicine lie in the holistic view and dialectical treatment, and it is characterized by the combination of diseases and the corresponding prescriptions and syndromes, that is, the diagnosis and treatment mode of “disease prescription.” Symptoms are the core content of the characteristic diagnosis and treatment system of traditional Chinese medicine, which is the holistic view, dialectical treatment, and compound intervention. The difference between dialectical treatment and “symptom” treatment is that the former treats the “symptom” that reflects the overall change, while the latter treats the symptoms point-to-point. On the cognitive level, the latter is shallower and belongs to perceptual cognition; the former is deeper, with a leap from perceptual cognition to rational cognition, which is the stage of cognition that goes deep into the internal laws of things. Elucidating the biological basis of symptoms is one of the keys to the modernization of traditional Chinese medicine. Decades of modern research on symptoms have shown that symptoms are complex systems composed of many factors, which are difficult to express with a single physiological and biochemical index. Therefore, with the increase of research depth and complexity, it is necessary to develop a suitable way to discover TCM symptom characteristics from complex systems. Network pharmacology integrates the information of “disease-phenotype-gene-drug” to understand the relationship between disease phenotype and life macromolecules from a systematic perspective. Its idea of “disease phenotype-biomolecule” network construction serves to guide the research on the biological basis of TCM symptoms. Many researchers have carried out research on the biological basis of syndromes from the perspective of biomolecular networks, formed a research framework suitable for interpreting the connotation of the system of disease-syndrome prescriptions, and further proposed the concept of biomolecular network markers for syndromes. Through network function analysis, the researchers found that the biomolecular network of cold syndrome is dominated by the functional modules of hormones, the biomolecular network of heat syndrome is dominated by the functional modules of cytokines, and the functional modules of neurotransmitters are distributed in the two networks. The researchers also analyzed the possibility of applying syndrome molecular network markers to research fields such as syndrome objectification and individualized diagnosis and treatment, clinical effect evaluation of traditional Chinese medicine, prescriptions, and medicinal properties of traditional Chinese medicine.Pharmacodynamic substances in traditional Chinese medicine refer to the chemical composition system that exerts pharmacological effects in traditional Chinese medicines and compound prescriptions. The pharmacodynamic material basis of traditional Chinese medicine cannot be attributed to a specific effective chemical component, and its mechanism of action does not act on a specific target but is the result of the overall regulation of multiple components through multiple targets and multiple links. In network pharmacology, a drug-drug network can be constructed based on the similarities between drugs and drugs in terms of structure and efficacy, and the efficacy of drugs and the chemical composition of efficacy can be predicted. This method of network pharmacology can also be applied to the prediction of the efficacy of effective and effective components of traditional Chinese medicine and the analysis of the chemical components of the effective substances. Many studies , 17 predPanax notoginseng, Astragalus, Salvia miltiorrhiza, and Scrophulariaceae were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database to collect chemical constituents-target information. The DR-related targets were then retrieved from the database of five disease-related targets, OMIM, TTD, pharmGkb, DiGSeE, and GAD. DiGSe is an online text mining tool that can mine disease-related genes. Using the Uniprot database, input the target name obtained from TCMSP and 5 databases to obtain the corresponding gene name and correct the protein name. We preferentially select gene names that are included in Swiss-prot and have a high degree of matching. The intersection of Fufang Xueshuantong and DR-corrected genes were obtained. Next, Gene ontology (GO) annotation analysis and Kyoto encyclopedia of genes and genomes (KEGG) were performed on the obtained intersection genes using the DAVID database. Intersection genes can be directly mapped onto pathways. According to the target prediction results of the above chemical components of Fufang Xueshuantong, Cytoscape software was used to construct a chemical component-intersection target-pathway network model. In the network, nodes represent chemical components, intersecting genes and pathways. If a target is a potential target of a compound, it is connected by an edge. When the target is involved in a certain pathway, the target and the action pathway are also connected by an edge. The multicomponent, multitarget, and multipathway characteristics of Fufang Xueshuantong in the treatment of DR were revealed by constructing a network. We used UniProt search to select the PDB ID of the appropriate target. The 3D structure files of the docking protein and control ligands were downloaded using RSCB PBD, where the control ligands were automatically matched by the RSCB PBD system. Then, use PubChem to download the 3D structure file of the compound, and finally use the SystemsDock online molecular docking tool for online molecular docking.Firstly, the chemical constituents of Panax notoginseng, 202 kinds of Salvia miltiorrhiza, 87 kinds of Astragalus root, and 47 kinds of Scrophulariaceae. A total of 427 chemical components were deleted, including compounds such as flavonoids, terpenoids, glycosides, carbohydrates, fatty acids, and amino acids, such as luteolin, apigenin, dehydrotanshinone II, salvianolic acid b, ursolic acid, and L-lysin . Some researchers measured the main chemical components in Fufang Xueshuantong capsule by UPLC-MS/MS method and found that the contents of tanshinone II-A, salvianolic acid B, and ursolic acid were 1.135, 2.343, and 0.560 (mg/g), respectively. The chemical components of Fufang Xueshuantong correspond to 376 genes, such as VEGF, mitogen-activated protein kinase 8, MAPK8, and IL-1β.We retrieved and found that there are 119 chemical constituents of A total of 247 DR-related genes were obtained from 5 databases, including vascular endothelial growth factor A, MAPK1, and MAPK8. Through DAVID analysis, the Toll-like receptor signaling pathway, MAPK receptor signaling pathway, VEGF signaling pathway, and other pathway information were obtained, as shown in β, PTGS2, and MAPK8. We annotated the biomolecular functions of the 37 intersecting genes through the DAVID system and performed GO annotation analysis and KEGG pathway analysis on the acquired targets. The GO annotation analysis is divided into three parts, namely, molecular function, cellular component, and biological process. Among the molecular functions of the intersecting genes, the protein binding and heparin binding functions are the most involved. Related genes include JUN, SRC, and VEGFA. Intersection genes were most distributed in the extracellular space, cytosol, and cell surface. Genes such as superoxide dismutase, IL-6, and IL-1β are involved. Intersection genes and inflammatory response, positive regulation of RNA polymerase II promoter transcription, positive regulation of endothelial cell proliferation, and other biological processes are the most numerous. Genes such as NOS1, IL-1β, and VEGFA are involved.The intersection of Fufang Xueshuantong target genes and DR-related genes showed that there were 37 identical genes, including VEGF, MMP, IL-1P < 0.05, and finally, 14 pathways and 22 genes were obtained. We used Cytoscape software to construct chemical component-gene-pathway network model. In this model, there are a total of 169 nodes and 458 edges. There are many effective chemical components acting on MAPK8, IL-6, IL-1β, and COX-2. These targets involve more pathways including TLR signaling pathway, MAPK signaling pathway, and VEGF signaling pathway. The chemical components acting on the above targets and pathways include luteolin, apigenin, ursolic acid, and K-lysine. The action targets of the main active components of Fufang Xueshuantong are distributed in different signaling pathways, which coordinate with each other and play a role together.By KEGG analysis, a total of 18 pathways were obtained, including 24 intersection genes. Pathways were screened by the criterion of It has been reported in the literature that the mechanism of compound Xueshuantong in the treatment of DR mainly includes dilation of blood vessels, improvement of antihypoxia ability, inhibition of oxidative stress, and reduction of apoptosis. These findings are consistent with the network prediction results in this paper. This study can reveal the drug mechanism of Fufang Xueshuantong in the treatment of DR from multiple perspectives.Two targets of COX-2 and VEGFR2 were selected, and 5 compounds of luteolin, quercetin, kaempferol, and ursolic acid  and control ligands of 4 targets were selected for Molecular docking, as shown in Fufang Xueshuantong in the treatment of DR has been clinically proven. However, the pharmacological mechanism of Fufang Xueshuantong in the treatment of DR is still unclear. In 2008, Hopkins clearly proposed the concept of network pharmacology. This paper uses the research ideas of network pharmacology to explore the potential targets and mechanisms of Fufang Xueshuantong in the treatment of DR and predict the possible targets and pathways of Fufang Xueshuantong in the treatment of DR.Inflammation is the initial reaction of retinal blood vessels stimulated by high glucose, manifested as leukocyte aggregation and a large increase in the secretion of inflammatory factors. Neovascularization is the hallmark pathological feature of DR. In this paper, the analysis of DR-related genes found that VEGF signaling pathway, TLR signaling pathway, and MAPK signaling pathway play a central role in the process of inflammation and vascular proliferation. COX-2 is an important inducible enzyme in the inflammatory process. The promotion of angiogenesis by COX-2 may be related to its induction of tissue production of VEGF. VEFG specifically acts directly on endothelial cell surface receptors to promote angiogenesis. VEGFA is an important member of the VEGF family, which can promote neovascularization and increase vascular permeability.VEGF signaling pathway, TLR pathway, and MAPK pathway coordinate with each other and influence each other. For example, both COX-2 and TLRs can induce tissue production of VEGFA, which binds to its receptor KDR and activates MAPK1/8 or c-Jun N-terminal kinase, thereby affecting the MAPK signaling pathway. MAPK is a regulator of FGF2-induced angiogenesis. FGF2 has chemotactic and mitotic effects on endothelial cells, promotes angiogenesis, and leads to angiogenesis. Studies have shown that both exogenous and endogenous FGFs can induce VEGF production and affect angiogenesis.β, MAPK8, JUN, and MMP2, which regulate VEGF signaling pathway, TLR signaling pathway, and local adhesion. Moreover, in the pathogenesis of DR, multiple chemical components of Fufang Xueshuantong can also correspond to a common target, such as luteolin, apigenin, and ursolic acid, which act together on VEGFA.In this study, combined with the data analysis of TCMSP and DAVID, it was found that a total of 92 compounds, including luteolin, ursolic acid, quercetin, kaempferol, and apigenin, may have 21 targets for the above-mentioned 3 pathways. The data analysis in this paper found that, among the chemical components of Fufang Xueshuantong, one chemical component can correspond to multiple targets and participate in multiple signaling pathways. For example, the corresponding targets of ursolic acid in Scrophulariaceae are IL-1In conclusion, this subject analyzed the components and targets of Fufang Xueshuantong and collected DR-related genes. We used network pharmacology to obtain the relevant pathways of the two intersection genes and predicted that Fufang Xueshuantong acts on multiple signaling pathways such as VEGF, TLR, and MAPK through multiple chemical components such as ursolic acid, quercetin, and kaempferol. This study shows that Fufang Xueshuantong is an important compound, and its chemical components may directly act on the key pathway targets of DR and treat DR by inhibiting inflammation and vascular proliferation. This study provides a typical basis for further revealing the typical pharmacological mechanism of traditional Chinese medicine, represented by compound Fufang Xueshuantong, in the treatment of DR.


Active compounds of EJSW were identified by high-performance liquid chromatography-diode array detector (HPLC-DAD) and high-performance liquid chromatography-mass spectrometry (HPLC-MS) and their targets were obtained from HERB and SwissTargetPrediction, and ITP targets were obtained from Comparative Toxicogenomics Database (CTD) and GeneCards. STRING and Cytoscape were used for protein-protein interaction (PPI) network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses by WebGestalt yielded a gene-pathway network, Autodock molecular docking was applied to screen targets and active compounds, and cytokines were detected using a cytometric bead array (CBA) human inflammation kit. We identified 14 compounds and 129 targets, and 1,726 ITP targets. RAC-alpha serine/threonine-protein kinase (AKT1), tumour necrosis factor (TNF), interleukin-6 (IL6), caspase-3 (CASP3) and tumour suppressor protein (TP53) were core targets (nodes and edges). Functional annotation identified cofactor binding and coenzyme binding, and 20 significantly enriched pathways. Active compounds of EJSW were successfully docked with ITP targets. Tumour necrosis factor alpha (TNF-α) and interleukin-1 beta (IL-1β) were upregulated in ITP patients, vascular endothelial growth factor A (VEGF-A) and vascular endothelial growth factor D (VEGF-D) were downregulated, and EJSW treatment reversed these trends. EJSW may regulate key ITP targets based on the  DecreTraditional Chinese medicine (TCM) is considered an effective auxiliary strategy for treating chronic diseases, including ITP. It can increase PLT counts and reduce bleeding with significant efficacy , 14 and The chemical components of EJSW were analysed by HPLC-DAD and HPLC-MS techniques. This method is reliable and reproducible, and can more accurately determine the true content of chemical components. It is widely used in laboratories for quality control of traditional Chinese medicines. Network pharmacology is a new discipline integrating systems biology and pharmacology. Using extensive biological information, existing molecular biology data are systematically and comprehensively analysed, and interaction mechanisms linking organisms and diseases are deciphered based on protein abundance, gene expression, and levels of small molecules (metabolites) . It can 1 and SwissTargetPrediction2. ITP-related targets were then derived from Genecards and Comparative Toxicogenomics Database (CTD). The mechanisms through which EJSW treats ITP were analysed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The findings lay a foundation for the clinical application of EJSW to treat ITP.In the present study, a network pharmacology approach was used to explore the mechanisms and molecular targets of EJSW for the treatment of ITP. Active compounds of EJSW were identified by HPLC-DAD and LC-MS and their targets were obtained using HERBChinese herbal medicine pieces of ASR, CR, PRA, RRP, ACC and their precision compound granules were provided by the Chinese Pharmacy of Xiyuan Hospital of China Academy of Chinese Medical Sciences. A 2.5 g of precision compound granules were taken and added 20 ml of 50% methanol for ultrasound for 40 min. After cooling, it was weighted with 50% methanol. The supernatant was taken and injected through 0.22 μm filter membrane for quantitative analysis by HPLC-DAD. The contents of gallic acid, albiflorin, paeoniflorin, senkyunolide I, ligustilide, oxidised paeoniflorin, catechin, caffeic acid, verbascoside, chlorogenic acid, alanine, L-proline, glycine and L-hydroxyproline as the markers were detected.The chemical analysis was conducted on a high performance liquid chromatograph  equipped with DAD detector. The column  was used for chromatographic separation. The mobile phase was acetonitrile (B), 0.1% phosphoric acid aqueous solution (A), with a gradient elution procedure . The data was collected by scanning at full wavelength, and chromatograms with detection wavelengths of 276 and 324 nm were extracted, respectively. The flow rate was 1 ml/min, and the column temperature was 35°C, and the injection volume was 10 μL.The powder of Ejiao was weighed for 2.69 g, accurately added with 25 ml of pure water, weighed, sonicated for 30 min, cooled, weighed, supplemented with pure water, shaken, filtered, and used for HPLC-MS.The liquid system is Shimadzu ultra-high performance liquid chromatograph (Shimadzu LC30), and the mass spectrometry system is SCIEX 5,600 + mass spectrometer . The chromatographic column: ACQUITY UPLC HILIC Column 1.7 μm, 2.1 mm × 100 mm; mobile phase: A is 0.1% formic acid water, B is acetonitrile, and the elution program is shown in the following Mass spectrometry conditions: the mass spectrometry adopts positive ion mode (ESI+), independent data acquisition mode (IDA), the collision voltage was 35 V, the capillary voltage was 5,500 v (positive ion), the ion source temperature was 550°C (positive ion), and the de clustering voltage was 60 V. The curtain gas flow was 25 l/min, the gas flow of Gas 1 and Gas 2 were both 50 l/min, and all gases were nitrogen.3 and CTD .A disease-compound-target network was constructed and visualised using Cytoscape 3.7.1ompounds . Proteinnce >0.4 . The oth8 (p < 0.05), relationships between genes and GO terms were visualised using a chord picture, and relationships among enriched GO terms were visualised using a DAG picture. WebGestalt assigned the KEGG database for pathway analysis, and pathways with significant changes (p < 0.05) were subjected to further analysis. Visualisation of the results of GO and KEGG analyses was constructed by Bioconductor (version 3.89) within R (version 3.5.3). Genes that significantly regulated pathways were selected for gene-pathway network analysis, and a gene-pathway network was constructed to screen key target genes through which EJSW treats ITP.Gene Ontology analysis of biological process (BP), cellular component (CC) and molecular function (MF) categories was carried out using the WebGestalt web-based gene set analysis toolkit8 . These f10. Crystal structures of core target proteins were downloaded from the PDB database11, and operations such as dewatering, removing original ligands, adding hydrogens, and calculating charges were performed using PyMOL (version 2.4.012) and autodocktools (version 1.5.613). Molecular docking was performed using autodock Vina14, and PyMOL was employed for visualisation.The 3D structures of active compounds were obtained through the PubChem databaseIn order to confirm the screening results based on network pharmacology, various cytokines including IL-1β, TNF-α, VEGF-A, and VEGF-D were assessed. Twenty-two ITP patients and 15 healthy volunteers (HV) were recruited from Xiyuan Hospital of China Academy of Chinese Medical Sciences . All patients were treated with EJSW for 2 months. After this period, 2 mL of peripheral blood from ITP patients and HV group members was collected, centrifuged , and the resulting serum was to assess IL-1β, TNF-α, VEGF-A and VEGF-D by a cytometric bead array (CBA) human inflammation kit according to the manufacturer’s instructions  . Analysip < 0.05.This study was approved by the Clinical Research Ethics Committee of Xiyuan Hospital, China Academy of Chinese Medical Sciences  and was conducted in compliance with the Declaration of Helsinki. All analyses were performed using SPSS 23.0 . The threshold of significance was defined as two-sided We obtained a total of 14 active compounds of EJSW and they were gallic acid, albiflorin, paeoniflorin, senkyunolide I, ligustilide, oxidised paeoniflorin, catechin, caffeic acid, verbascoside, chlorogenic acid, alanine, L-proline, glycine and L-hydroxyproline. Among them, 10 active compounds including gallic acid, albiflorin, paeoniflorin, senkyunolide I, ligustilide, oxypaeoniflorin, catechin, caffeic acid, verbascoside, and chlorogenic acid were detected by HPLC-DAD, as shown in Active compounds of EJSW were identified by HPLC-DAD and LC-MS and their targets were obtained using HERB (see text footnote 1) and SwissTargetPrediction (see text footnote 2). Eleven of the 14 active compounds had targets, and the remaining 3e had no corresponding targets. Overlapping results from Gene Cards and CTD databases yielded 1726 ITP-related targets , and 58 in silico analyses was generated, which included 58 nodes and 518 edges  are shown in The results of GO and KEGG pathway enrichment analyses , including complement and coagulation cascades, AGE-RAGE signalling in diabetic complications, ABC transporters, antifolate resistance, one-carbon pool related to folate, arginine biosynthesis, bile secretion, parathyroid hormone synthesis, secretion and action, glyoxylate and dicarboxylate metabolism, and VEGF signalling. In p-value. The distribution of targets in the 20 pathways through which EJSW affects ITP treatment are shown in Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed to identify pathways via which EJSW influenced ITP treatment. Twenty significantly enriched pathways were identified  identified in the PPI network were validated by molecular docking with key compounds in EJSW. The results showed that the binding affinities of six key compounds to the core targets were below −5 kcal/mol , which sWe measured TNF-α, IL-1β, VEDF-A, and VEGF-D expression levels in ITP patients (before and after treatment by EJSW) and HV group members. As shown in p < 0.05). We also recorded bleeding in ITP patients before and after treatment with EJSW. WHO Bleeding Scale were used to document and evaluate bleeding in patients. As shown in 2χ = 6.023, p < 0.05].Meanwhile, we measured the platelet counts of these 22 ITP patients before and after EJSW treatment. As shown in Immune thrombocytopenia is an autoimmune hemorrhagic disease. Specifically, the destruction of platelets by the monocyte-macrophage system in ITP patients, resulting from the production of anti-platelet auto-antibodies, is relevant to immune disorders. However, a decrease in platelet count increases the risk of bleeding, and ITP patients present with petechiae in the skin and other bleeding symptoms, such as epistaxis and gingival bleeding. Purpura and ecchymosis can occur in any part of the skin or mucosa, and commonly in the lower extremities and distal upper extremities. Although glucocorticoids have many undesirable side effects, such as osteoporosis, femoral head necrosis, and psychiatric symptoms, they remain the first-line treatment for ITP , 32. HowThe medical theory system of TCM has been used for thousands of years to treat diseases in China. Combining two or more compatible TCMs can typically improve the therapeutic effect in clinical treatment through synergism. According to TCM theory, ITP is thought to be a disease characterised by bleeding and blood deficiency, hence haemostasis and blood enrichment are necessary to treat ITP. However, it is common knowledge that platelets influence haemostasis. Thus, promoting platelet production is as important as preventing platelet destruction in view of modern medicine.We identified 14 compounds in EJSW, of which gallic acid was able to intervenes in major inflammatory pathobiologies . PlateleA network pharmacology approach can be used to probe the multi-component and multi-target characteristics of TCM compounds. In the present study, this technique was used to explore the mechanisms of EJSW in the treatment of ITP. The results showed a complex and interlaced network among target proteins of EJSW. Fifty-eight common targets of EJSW related to ITP were identified, 20 of which were major targets, including AKT1, IL6, CASP3, TNF, TP53, IL1B, PTGS2, MAPK8, HSPA4, IL2, MAPK1, CASP9, ICAM1, IL4, MCL1, MAPK14, CSF2, HMOX1, IFNG, and NFKB1, according to cutoff scores.AKT1 can influence platelet activation , while TMolecular docking validation indicated strong binding between key compounds and their targets. These targets are involved in multiple functions in diverse biological processes, and the functions of the main targets can be summarised as inflammatory responses, angiogenesis, vascular integrity, and platelet apoptosis.We performed GO analysis of the targets for EJSW against ITP. The results of enrichment and target attribution analyses showed that EJSW regulates complex biological processes, including metabolic processes, biological regulation, and multicellular organismal processes. Enriched cellular components included membrane, membrane-enclosed lumen and vesicle subcategories. The main molecular functions included protein binding, ion binding, nucleotide binding and hydrolase activity.Thus, EJSW regulates some key steps in metabolic and immune function processes, such as cofactor binding, carboxylic acid binding, transcription factor activity, direct ligand regulation, nuclear receptor activity, coenzyme binding, vitamin binding, organic acid binding, and others. These processes may be linked to immune disorders, vascular endothelium defects, and platelet apoptotic processes.Immune thrombocytopenia is a type of autoimmune disease. Some genetic diseases can also lead to abnormal immune regulation and developed autoimmune haematological disorders, such as haemolytic anaemia and immune thrombocytopenia. But the genetic symptoms often have typical features and a clear genetic basis , which iThe results of KEGG pathway analysis also showed that EJSW influences ITP through multiple pathways, including complement and coagulation cascades, AGE-RAGE signalling in diabetic complications, ABC transporters, antifolate resistance, ErbB signalling, and VEGF signalling. These pathways are closely related to cancer, metabolism, angiogenesis, inflammation, autoimmune diseases, coagulation, and other processes.Complement and coagulation cascades are intimately associated with inflammatory responses and ensuring haemostatic maintenance . Indeed,The AGE- RAGE signalling pathway has strong links with vascular injury and inflammatory reactions , 63. Infde novo formation of the embryonic circulatory system) and angiogenesis (the growth of blood vessels from pre-existing vasculature)  constitute a sub-family of growth factors that stimulate the growth of new blood vessels . VEGFs aulature) . VEGF-A ulature) , 74. VEGThus, inflammatory factors, VEGFs, and immune imbalance are all correlated with the development of ITP. According to TCM theory, another function of EJSW is tonifying blood and arresting bleeding. The functions of the above target genes and signalling pathways conform to TCM theory. Therefore, the opinion that downregulating inflammatory factors is helpful to reduce the risk of bleeding is supported, due to restoration of immune balance and decreased platelet apoptosis. EJSW may treat ITP by inhibiting the expression of TNF-α, IL-1β, and other inflammatory factors, and by protecting vascular integrity through AGE-RAGE signalling, VEGF signalling, and complement and coagulation cascade signalling pathways. In the present study, TNF-α and IL-1β were upregulated in ITP, while VEGF-A and VEGF-D were downregulated. Conversely, after effective treatment with EJSW for 2 months, TNF-α and IL-1β were downregulated while VEGF-A and VEGF-D were upregulated. However, expression of TNF-α and IL-1β was still increased and expression of VEGF-A and VEGF-D was still decreased compared with normal controls. These results are consistent with those of earlier studies  reportinin vitro and in vivo functional experiments are needed to validate the data. Overall, we explored the mechanisms and molecular targets of EJSW for treating ITP using network pharmacology and molecular docking. And our study found that the therapeutic activity of EJSW against ITP might be related to downregulating inflammatory factors and reducing vascular endothelial injury and regulating relevant pathways . Therefore, this study provide a scientific basis for clinical treatment of ITP, a direction for screening indicators of the follow-up clinical efficacy of EJSW, and new ideas for exploring the potential mechanisms of EJSW.However, this study has certain limitations. We only analysed data from a bioinformatic approach, and further specific in vivo and in vitro experiments are needed to unequivocally identify the main regulatory targets of EJSW.The therapeutic activity of EJSW against ITP might be related to downregulating inflammatory factors and reducing vascular endothelial injury, which may enhance the platelet count. The effects of EJSW may involve regulating VEGF signalling, AGE- RAGE signalling, and complement and coagulation cascade signalling pathways. The results provide a scientific basis for clinical treatment of ITP, a direction for screening indicators of the follow-up clinical efficacy of EJSW, and new ideas for exploring the potential mechanisms of EJSW. We identified some core factors, but further screening and https://doi.org/10.6084/m9.figshare.20069915.v1.The data presented in this study are deposited in the Figshare repository: The studies involving human participants were approved by the Clinical Research Ethics Committee of Xiyuan Hospital, China Academy of Chinese Medical Sciences  and was conducted in compliance with the Declaration of Helsinki.MW and XH: conceptualisation and design of the work, writing, review, and editing. MS, JM, ZW, XD, HC, XZ, and YSo: data acquisition and analysis. MW, YSu, and YSo: writing original draft preparation. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Milk oligosaccharides (MOS) and galactooligosaccharides (GOS) are associated with many benefits, including anti-microbial effects and immune-modulating properties. However, the cellular mechanisms of these are largely unknown. In this study, the effects of enriched GOS and MOS mixtures from caprine and bovine milk consisting mainly 6'-galactosyllactose, 3'-sialyllactose, and 6'-sialyllactose on Caco-2 cells were investigated, and the treatment-specific metabolomes were described. In the control, the cells were treated with a sugar mix consisting of one-third each of glucose, galactose and lactose.A local metabolomics workflow with pathway enrichment was established, which specifically addresses DI-FT-ICR-MS analyses and includes adaptations in terms of measurement technology and sample matrices. By including quality parameters, especially the isotope pattern, we increased the precision of annotation. The independence from online tools, the fast adaptability to changes in databases, and the specific adjustment to the measurement technology and biomaterial used, proved to be a great advantage.For the first time it was possible to find 71 active pathways in a Caco-2 cell experiment. These pathways were assigned to 12 main categories, with amino acid metabolism and carbohydrate metabolism being the most dominant categories in terms of the number of metabolites and metabolic pathways. Treatment of Caco-2 cells with high GOS and glucose contents resulted in significant effects on several metabolic pathways, whereas the MOS containing treatments resulted only for individual metabolites in significant changes. An effect based on bovine or caprine origin alone could not be observed. Thus, it was shown that MOS and GOS containing treatments can exert microbiome-independent effects on the metabolome of Caco-2 cells. These include prebiotic, anti-adhesive, anti-microbial, and immune-modulating effects . In addio models , 2017. To models . MOS areo models . Caprineo models . GOS, aso models . Since to models . In the o models . Howevero models . The maio models . Howeverin vitro system to study the interaction between intestinal cells and oligosaccharides are Caco-2 cells. Caco-2 cell lines are a continuous line of heterogeneous human epithelial colorectal adenocarcinoma cells , e.g., with Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), is able to cover a large range of metabolites in a short measurement time, even for complex sample matrices without prior chromatographic separation. In addition, it has extremely high resolution (xHR) in comparison to various LC-MS analytical techniques and very high sensitivity in comparison to NMR . In partThe first aim of the study was to establish a metabolomics workflow which improves the identification level for DI-FT-ICR-MS analyses, which is usually referred to the lowest level G  or 1 as All solvents listed were obtained from Carl Roth, Karlsruhe, Germany. All consumables were purchased from Sarstedt, Nümbrecht, Germany.K. lactis. The high GOS (HG) fraction contained different mono-, di-, tri- and tetrasaccharides, with 6'-galactosyllactose as main product. Bovine and caprine skimmed ultrafiltrated (UF) milks were subjected to nanofiltration. For better separation of MOS from residual sugars, lactose in the UF-permeate was hydrolyzed by ß-galactosidase. High GOS/bMOS (GM) from cow’s milk fraction contained the bovine MOS N-acetyl-β-D-galactosamine-lactose, 6'-sialyllactose, and 3'-sialyllactose as main products. Low GOS/cMOS (LGM) from goat’s milk contained 6'-sialyllactose and 3'-sialyllactose as main component of the caprine MOS fraction beside other minor MOS. Both fractions contained in addition comparable amounts of GOS, monosaccharides and lactose from the lactose hydrolysis during sample preparation and further milk components like milk salts. Control (C) consisted of glucose, galactose, and lactose , as they were also part of the GOS and MOS fractions. All GOS/MOS contents were determined using high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD). A detailed overview of the oligosaccharides compositions is summarized in Oligosaccharides were produced according to −1]; Biochrome AG, Berlin, Germany) at 37°C and 5% CO2. For all experiments cells were seeded at a density of 1 × 105–2×105 cells cm−2 to ensure confluency after O/N incubation. Medium was changed every second day. After 20 days, cells were treated in accordance with the experiments of −1  between passage 24 and 50 were cultured in T-175 polystyrene flasks  with Caco-2 medium . The upper phase was transferred to a new tube and kept at 4°C. A second extraction cycle was conducted as described above. The resulting upper lipophilic phase and middle hydrophilic phase were transferred into 15 ml tubes, and the lower proteinogenic phase was discarded. The phases were dried with a speedvac and resuspended with the following liquids: The hydrophilic compounds (middle phase) were resuspended in methanol/water  with 0.1% acetic acid (500 µL). The lipophilic compounds (upper phase) were resuspended in isopropanol/chloroform  with 0.1% acetic acid (500 µL). Samples were stored at −80°C until measurement. This work focused on the investigation of the hydrophilic compounds.Cells were removed from the culture flasks under standardized conditions using a cell scraper. Thereafter, 3 times 2 ml of ultra-pure water with 0.1% (v/v) formic acid was added and treated for 2 min each. Cell disruption was conducted by ultrasonication  on ice. The cell lysate was dried by means of a speedvac  and resuspended in 1.5 ml ultra-pure water. Extraction was performed using a modified SIMPLEX approach based on measurementvia FT-ICR-MS (A), peak alignment (B) was conducted using MetaboScape 4.0 SR1 . For data analysis and metabolite annotation (C) in the first step (C1.1), an organism-specific database was selected and downloaded n”] were not considered for further analysis. In addition, only sufficiently large (≥60Da) and thus potentially measurable compounds were considered. Drugs and associated pathways were also excluded. The resulting, cleaned local database was then used for metabolite annotation in MetaboScape. Thereafter, the theoretical masses were calculated based on the sum formulas and compared and matched with the measured masses (C2.1). The resulting preliminary annotations were again subjected to filtering according to quality parameters (C2.2). These quality parameters included the mass error and the isotope pattern (see 2.2.5). Confirmed formulas and thus confirmed IDs of tentative metabolites were merged into a new export file, which in turn was used to map KEGG compounds to pathways in which the compounds participate (C3.1). The resulting pathway information and associated metabolites were used for pathway enrichment analysis. To evaluate and filter the enrichments, the following quality parameters were considered (C3.2): percentage of coverage, enrichment factor, occurrence of pathway-specific sum formulas, and number of linked metabolites (see 2.2.7).The workflow for the metabolomics analyses is presented in ormulas; ). The refeature statistics were performed and included principal component analysis (PCA) as well as differential feature statistics to identify differences in feature intensities between treatments. In the final step (E), the results of the workflow were compared with two different web-databases.(D) In parallel, non-targeted comparisons MassTRIX  were used. Further settings for MassTRIX: database KEGG, HMDB, Lipidmaps without isotopes, organism Homo sapiens. For the comparative web-annotation, the data resulting from Step B were used. The measured masses were exported as a bucket table . From the web-based comparisons, only metabolites for which a KEGG ID was available were used for further analyses. Based on this data, a new local database was created for MetaboScape, which also passed through workflow steps C1.1 to C3.1.For the Germany; ) and HMDGermany; ) were usFor the mass spectrometric analysis, a FT-ICR-MS system  equipped with an ESI source was used. For the direct injection (DI), an Infinity 1260 HPLC autosampler and pump  was linked. The eluent consisted of methanol and water  with 0.1% acetic acid. For analyses samples were ionized in positive or negative modes by electro spray ionization. The mass range was between 60 and 1,500 m/z. The main parameters were: Nitrogen as dry gas (4 L/min and 200°C) and nitrogen as nebulizer (1 bar); the time-of-flight section was set to 0.35 ms; the quadrupole mass was set to 200 m/z; the quadrupole RF was 2 MHz and the sweep excitation power of the ICR-cell was 18%. All methods were calibrated using sodium trifluoroacetate. The stability of the measurement was monitored using quality control samples (QC). The QC represents a pool of all samples measured every 20th analysis .FT-ICR-MS data were processed by MetaboScape 4.0 . All datasets were recalibrated by matrix compounds within the samples  with a tolerated error <0.5 Da. To reduce noise, only peaks were selected which were present in at least 20 of 96 samples. Further, the intensity threshold was set to 500,000 counts.hsa, and to the measurement technique. Peaks were annotated if the mass error was below 2 ppm and if the mSigma (isotope error) was below 300 . The annotation was considered individually for each ion: [M + H]+, [M + Na]+, and [M + K]+ (positive mode), and [M-H]− and [M + Cl]− (negative mode), respectively. The annotated data were exported as tab-separated tables of the feature intensities for subsequent statistical analysis in R version 4.1.1. If a metabolite was present in the form of several ions, the ion with the highest intensity count was used for the statistical analysis.For metabolite annotation, the database resulting from the workflow was used with adaptations to the organism, i.e., p-values that describe the significance of different feature intensities between treatments were obtained by likelihood-ratio tests of the full model (with treatment as a fixed effect) against the reduced model without treatment type as a variability-explaining factor. Median fold changes  How many metabolites of a pathway were found in the metabolomics data relative to the number of metabolites in the same pathway that could theoretically be measured using the FT-ICR-analysis if above the LOD? 2) Which pathways are enriched in metabolites that have been identified to be significantly abundant, dependent on the treatment type based on pairwise-treatment comparisons using a linear mixed effect model statistic (see 2.2.6)? The metabolites ATP, ADP, AMP, NAD, NADH, NADP, NADPH, coenzyme A, phosphate, and diphosphate were not considered in the pathway enrichment analysis as these metabolites participate in a wide range of diverse pathways and are therefore not informative for the activity of specific pathways. Furthermore, only metabolites that are theoretically measurable by the FT-ICR-MS approach were included in the pathway enrichment analysis, which excludes metabolites with a mass of less than 60 Da and metabolites with ambiguous sum formula . Enrichment factors of significant treatment-dependent metabolites for each pathway (HG), a combination with a high content of GOS/bMOS (GM), a combination with a low GOS/cMOS content (LGM), and a control (C) which consisted of one-third each of glucose, galactose, and lactose. Since the treatments were not pure substances, and also contained e.g., mono- and disaccharides, the different treatments were compared in six combinations.• Each individual treatment vs. control• The two GOS/MOS treatments against each other• The two GOS/MOS treatments individually against the high GOS treatmentIn a semi-targeted metabolomics approach, the bovine and caprine milk- (b- and cMOS) and galactooligosaccharide (GOS) treatment-specific Caco-2 metabolomes should be investigated. For this purpose, four different sugar combinations were used: a high content of GOS The multi-comparisons allow us to distinguish whether differences between metabolomics profiles are due to the monosaccharides, the disaccharides, or the presence of GOS or MOS. Moreover, general and concentration-dependent effects could be inferred .(A), peak alignment (B), data analysis (C), and statistics (D). Element E evaluates comparability with workflows using web-based approaches with databases or online tools. A key objective of the presented workflow is the reduction of false-positive results at the level of metabolites and pathways through specific filtering steps using various quality parameters (see 2.2.5/2.2.7).A local workflow has been developed and applied, which specifically addresses the highly complex metabolomics data from DI-FT-ICR-MS analyses. This workflow includes five basic elements: Measurement Based on this workflow we identified all metabolites and associated pathways characterizing the Caco-2 metabolome and treatment-specific effects using different sugar mixtures. The numeric results of the individual steps of the metabolomics workflow are shown in via MetaboScape considering the quality parameters.Peak alignment resulted in 5,945 features (B), a feature is defined as the measured mass (m/z) without any annotation. Based on the annotation list obtained after the first filter step (C.1.2), containing all potentially measurable metabolites and sum formulas, 211 sum formulas with 468 corresponding KEGG IDS were assigned to the 5,945 features. This corresponds to a percentage share of 3.6%. Matching of metabolites to the corresponding metabolic pathways by KEGG mapping resulted in 83 preliminary metabolic pathways with at least 3 hits (C3.1); metabolites listed in 2.2.7 were excluded. Based on the specified quality parameters of the pathway enrichment analyses, a further 12 of 83 pathways were excluded (C3.2). Multiple comparisons in the statistical analyses revealed that 25 of the remaining 71 metabolic pathways were significantly regulated (D). Step E shows the difference between annotation with web-based approaches and the results validated Pathway enrichment analyses were used to reduce the number of false-positive annotations by applying various quality criteria (see 2.2.5/2.2.7). These included a pathway coverage of at least 33.33%, the occurrence of at least three directly linked non-isobaric metabolites within one pathway, and the occurrence of unique sum formulas.versus those sum formulas which have been determined, a coverage rate of 22.86% is expected across all 83 pathways. 46 pathways were above the expected value, eight of them significantly . Applying the quality parameters resulted in 35 pathways passing the 33.33% level, 42 containing at least one unique molecular formula and, 28 having at least 3 directly linked metabolites. In the following the percentage distribution of the 83 pathways based on the number of fulfilled criteria is listed:• 5 of 83 pathways fulfilled 3 criteria, corresponding to 6.02%• 24 of 83 pathways fulfilled 2 criteria, corresponding to 28.92%• 42 of 83 pathways fulfilled 1 criterion, corresponding to 50.60%• 12 of 83 pathways did not fit any of the criteria, corresponding to 14.46%Pathway mapping based on the annotated metabolites resulted in 83 preliminary pathways having a minimum of 3 hits. Based on all sum formulas that could potentially be determined 12 pathways did not meet any of the criteria and were thus excluded from further analyses. For detailed information see All 71 remaining identified pathways, which fulfilled at least one criterion, were assigned to 24 different main categories based on the KEGG nomenclature , i.e., a percentage coverage of 48.83%, two metabolites with unique sum formulas, and more than 3 linked metabolites. Thus, the pathway is considered to be confirmed. Both metabolites fructose-6-phosphate and glucose-6-phosphate are present in this pathway and directly linked to each other, i.e., fructose-6-phosphate is derived from glucose-6-phosphate, and subsequent metabolites such as fructose-1,6-bisphosphate, sedoheptulose-7-phosphate, and 2-deoxy-ribose phosphates have also been detected. Therefore, the co-occurrence of both isobars is plausible.Furthermore, based on ascorbate and aldarate metabolism, it was possible to exclude the arabinonates, since neither precursor molecules nor subsequent metabolites were detected. But the presence of isobaric compounds xylonates and lyxonates is proposed, since two precursor molecules were detected. Ascorbate and aldarate metabolism fulfilled all 3 criteria with a coverage rate of 42.31%Based on pyrimidine metabolism, the presence of the isobars glutamine and 3-ureidoisobutyrate would be excluded, as they appear without directly linked metabolites in the pathway. However, glutamine can be confirmed by alanine, aspartate, and glutamate metabolism, as it is directly linked to two further metabolites in this pathway. Pyrimidine metabolism is (with 24.56%) below the minimum percentage coverage but fulfills the other two criteria. Alanine, aspartate, and glutamate metabolism fulfills only the minimum criterion of linked non-isobaric metabolites.To visualize overall similarities and differences in the cell metabolome caused by the treatments, and to give a general overview, a PCA was used. The metabolomes of the control, GM, and LGM exhibited similar scattering patterns in the PCA, whereas HG showed clear clustering in PC1 vs. PC2 and PC2 vs. PC3 in the score plots . In accop -value < 0.05). These pathways are categorized in Across all previously described comparisons, there were a total of 25 significantly regulated pathways . Regulation of 2-isopropylmaleate, 1-aminopropan-2-ol-O-phosphate, and 2-amino-3-phosphonopropanoate was found for a high GOS amount (HG vs. C/GM/LGM) and of 3-dehydro-threonate for HG vs. C. Creatine was significantly regulated in the context of the MOS fraction (GM/LGM vs. HG and GM vs. C) and regulation of shikimate-3-phosphate, 3-hydroxy-2-methylpyridine-4,5-dicarboxylate, ribosyl-homocysteine, and 1-myo-inositol-1,4,5 triphosphate was identified for GM (GM vs. HG/C).The first aim of the study was to establish a local metabolomics workflow which improves the identification level for DI-FT-ICR-MS analyses, and to investigate the presence of isobaric compounds.The second aim was to characterize the Caco-2 metabolome in detail, objectively applying the new workflow and investigating how milk oligosaccharide (MOS) and galactooligosaccharide (GOS) containing sugar mixtures influence the metabolome of Caco-2 cells.Using the presented workflow, we were able to identify 71 pathways, 202 sum formulas, and 445 KEGG IDs based on 5,945 features measured. Comparing these numbers with the web-based annotations using HMDB (397 sum formulas and 735 KEEG IDs) or the web-based tool MassTRIX (766 sum formulas and 1857 KEGG IDs) the number of identified metabolites were lower in our workflow. The differences can be explained by our filtering steps, which included quality parameters that evaluated isotopic patterns and fine structures (see 2.2.5), thus leading to a higher precision in identification. Further, pathway enrichment analyses were used as filtering elements which resulted in a lower number of confirmed annotations. Further reduction was caused by the exclusion of drug metabolism pathways, and metabolites with non-defined residues or with non-specified number of repetitions for structural elements in our study. Nevertheless, our results are plausible and comparable with online based workflows using HMDB and MassTRIX. After entering workflow step C2.1/C2.2 with the resulting sum formulas from HMDB or MassTRIX, followed by pathway mapping, all pathways identified in our workflow were covered. Repeating the evaluation step C2.1/C2.2 was necessary because the web databases only use mass error for identification, while local processing in our workflow also considers the isotopic pattern, and thus reduces the number of false-positive annotations see . A compaAnother important quality parameter to reduce the number of false annotations, was pathway coverage, which was calculated based on sum formulas. Here we followed the conservative procedure according to Further, evaluating the co-existence of isobaric compounds was improved for the first time using pathway enrichment analyses. This step allowed us to analyze the plausibility of metabolite annotations by integrating them into a pathway and directly linking metabolites to the biological context. Thus, in the pentose phosphate pathway, the co-existence of the two isobars fructose-6-phosphate and glucose-6-phosphate was verified. In addition, the occurrence of 3-ureidoisobutyrate could be excluded, whereas the occurrence of the isobaric compound glutamine was confirmed by alanine, aspartate, and glutamate metabolism. However, there are also limitations; terminal isobaric compounds of a pathway with the same precursor molecules cannot be further evaluated for their presence without additional analysis. The same applies to the occurrence of isobaric compounds with precursor molecules and degradation products that are also isobaric compounds.In vivo, intestinal epithelial cells are the interface between host and microbiome and are exposed to many microenvironmental metabolites  and fructose and mannose metabolism (C vs. HG). Caco-2 cells are able to transfer glucose from the surrounding medium into the cell via the GLUT1 or SGLT1 transporters  by contributing to the regeneration of oxidized glutathione (We showed a significant regulation of pentose phosphate metabolism (C/LGM vssporters . The p-vment see . It is ktathione . An incrtathione . This covia transcytosis (A significantly higher content of tri- and disaccharides was observed in the HG-treated cells (HG vs. C/GM/LGM). 6'-galactosyllactose is a neutral oligosaccharide, which can enter Caco-2 cells by transcellular transport scytosis , 2002. Ivia glutaminase, which is in turn metabolized to alpha-ketoglutarate and supplied to the citrate cycle, resulting in malate. Malate was also detected in the HG-treated cells with significantly higher content compared to the control. Malate can cytosolically be degraded to pyruvate or, alternatively, via oxaloacetate, to aspartate and via this contribute to asparagine regeneration and nucleotide synthesis , including glutamate, glutamine, aspartate, and asparagine. An increase in glutamate in Caco-2 cells in the context of inulin treatment was also described by ynthesis . The amiynthesis . Since v+ symporters. It can be assumed that there is an excess of Na+ ions in the cell in connection with the increased uptake of amino acids. Taurine contributes to preventing this imbalance.The significantly higher amino acid content in the cells treated with HG may also be due to increased transport activity, as all amino acids were also part of the medium. Significantly higher levels of histidine and glycerol-3-phosphates were detected in all treatments containing GOS. This could be related to increased proton shuttle processes. The glycerol-3-phosphate shuttle system contributes to the transport of cytosolically produced NADH into the mitochondria, which is subsequently transferred to the respiratory chain for ATP production. The inner mitochondrial membrane is impermeable to NADH, so that a shuttle-dependent transport must take place . Histidivia paracellular transport in Caco-2 monolayers. As described by Significantly higher levels of the second messenger 1-myo-inositol-1,4,5-triphosphate (IP3) were observed in GM-treated cells (GM vs. C/HG/LGM). Thus, the present response must be based on cell surface sensing. The regulation could be related to the sialyllactose present in the bMOS fraction. Sialyllactose is an acidic oligosaccharide due to its side chain. The established local workflow offers an alternative to web-based approaches. It considers isotope pattern as well as mass error, additional quality parameters that lead to higher precision in identification and annotation. By incorporating the biological context through the pathway enrichment analyses and the criteria established for the enrichment analyses, it is possible to evaluate the co-presence of isobars. A further advantage of the local workflow is its independence and fast adaptability. The entire workflow can be applied to other cell culture systems and organisms as well as to various treatments and measurement techniques, whereby isotope fine structure can only be used as a quality parameter for xHR instruments.Applying the presented workflow allows a detailed characterization of the Caco-2 metabolome and forms the basis for further investigations of bioactive compounds in cell culture experiments.Moreover, it was shown for the first time that GOS and MOS can directly, i.e., independently of the microbiome, affect the metabolome of Caco-2 cells, ranging from single metabolites to the regulation of entire pathways. Most importantly amino acid pathways (D-glutamine and D-glutamate metabolism and histidine metabolism) were upregulated by GOS vs. the other treatments.


The face blurring of images plays a key role in protecting privacy. However, in computer vision, especially for the human pose estimation task, machine-learning models are currently trained, validated, and tested on original datasets without face blurring. Additionally, the accuracy of human pose estimation is of great importance for kinematic analysis. This analysis is relevant in areas such as occupational safety and clinical gait analysis where privacy is crucial. Therefore, in this study, we explore the impact of face blurring on human pose estimation and the subsequent kinematic analysis. Firstly, we blurred the subjects’ heads in the image dataset. Then we trained our neural networks using the face-blurred and the original unblurred dataset. Subsequently, the performances of the different models, in terms of landmark localization and joint angles, were estimated on blurred and unblurred testing data. Finally, we examined the statistical significance of the effect of face blurring on the kinematic analysis along with the strength of the effect. Our results reveal that the strength of the effect of face blurring was low and within acceptable limits (<1°). We have thus shown that for human pose estimation, face blurring guarantees subject privacy while not degrading the prediction performance of a deep learning model. Human pose estimation is a highly important task in the field of computer vision. The focus of human pose estimation is the calculation of human body keypoint coordinates based on images. Combined with kinematic analysis, it has the potential for many applications in different fields, e.g., ergonomics ,2,3,4 orPreservation of subject privacy in datasets for computer vision tasks is an emerging research topic. So far, few studies have investigated approaches, such as face blurring, to preserve subject privacy with a minimal impact on the performance of deep learning models ,19,20,21Therefore, previous works have focused on the task of classification, action recognition, or 2D human pose estimation in videos. Thus far, the effect of face blurring on 3D human pose estimation and, more importantly, subsequent kinematic analysis has not yet been investigated on a consistent cohort. Considering their importance in biomechanical and ergonomic domains, we study the statistical significance as well as the strength of the effect of face blurring on 3D human pose estimation and kinematic analysis in this paper. Our contribution consists of three main parts. First, to the best of our knowledge, this study is the first one focusing on the effect of face blurring on multi-view 3D human pose estimation. Second, based on the 3D keypoint coordinates obtained from the human pose estimation, we calculated joint kinematics and analyzed the eventual impact of face blurring. Third, both statistical significance and strength were calculated to more comprehensively evaluate the effect of face blurring on the performance of a deep learning model.In this study, we first performed subject face blurring on an image dataset acquired in our previous gait study. Then, using different training strategies, we obtained distinct deep learning models and tested the performance of each model on the face blurring or the original dataset, thus examining the effect of face blurring. The dataset used in this research was a multi-view human gait dataset, namely, the ENSAM dataset collected in a previous study . The datWith the camera parameters collected in the ENSAM dataset, the reference annotations of the head and the neck were projected onto the corresponding images. A circle covering the subject’s face was drawn based on these projections in the images. The pixels inside the circle were blurred using Gaussian blur. A Gaussian kernel size of 25 × 25 was carefully selected with which the faces of different sizes could all be blurred properly. The standard deviation of the kernel was set to 4.1 and determined automatically using OpenCV . Using tAs in , the 3D −5 and 10−4 for the algebraic and the volumetric network, respectively. The training and evaluation of the neural network during the experiment was effectuated on a Linux server under Ubuntu 20.04.1 LTS 64 bits. The machine consisted of an AMD Ryzen 9 3900X 12-core processor and 125 GB RAM. It was equipped with 2 Nvidia TITAN RTX GPUs with 24 GB of RAM, one of which was employed in this study. Three training experiments were conducted, namely, #1, #2, and #3. The model training setup is summarized in For the three models obtained in the experiments, their performances for human pose estimation and the subsequent kinematic analysis were analyzed on the original or the face-blurred test set. The human pose was defined by the 3D coordinates of the 17 keypoints in our human model. Using the 17 coordinates, joint angles were computed for the lower and upper extremities. For the joint angles of the lower limbs, we followed the calculation method proposed in . For theThe coordinate system of the trunk was defined as follows: The Y-axis is the vector The method employed in  was adopj = max − μmin|j|μ, where the maxμ and minμ were the maximum and minimum values among all the 3 inference results estimated with different models on joint j. The evaluation metric MPJPE (Mean Per Joint Position Error) was chosen in this paper to examine the performance of different models by comparing the joint positions estimated via the neural networks against the references acquired from the marker-based motion capture system. MPJPE was computed as follows :(1)MPJPETo analyze the effect of face blurring on kinematic calculations, we performed statistical tests on joint angles differences (with the reference system) calculated using different data sets based on different models.Since the accuracies of human pose estimation on the original and the face-blurred test set are subject-by-subject matched, a one-way repeated ANOVA test was used to examine the effect of face blurring. In this test, we had one within-subject factor, namely, experiment setup, which had three levels, i.e., experiment #1, #2, and #3. The root mean square error (RMSE) of the joint angle of each subject was used as the dependent variable. We assumed that our data were consistent with the assumption of sphericity since different joint angle calculations did not affect each other. j, i.e.,j, with Finally, to further evaluate the strength of these effects, we quantitatively investigated the variations in joint angle estimation using different models. As a measure of the strength of the effect, the maximum variation Δ of angle estimation due to face blurring was defined for each joint Joint localization performances from the above experiments are shown in μ = 11.1) and #3 (μ = 13.8), respectively. Unlike the head, #1 (μ = 11.0) and #3 (μ = 10.0) were the worst- and best-performing settings for the neck, respectively. The keypoints with the highest variations were the head and neck, with a Δ of 2.7 mm and 1.0 mm, respectively. For the head, the best- and worst-performing settings were #1  by the different experiment setups.The statistical test results shown in We have presented the statistical significance of the effect of face blurring on the joint angle calculation using ANOVA tests. To further investigate the strength of the effect of face blurring, variations of joint angle computation were quantified.The maximum variations Δ of joint angles are also presented in In this study, we aimed to assess the significance of the effect of face blurring on landmark localization performances and the effect on the subsequent kinematic analysis. To that end, a comparison between a control model  and models trained or finetuned (#1 and #2) and evaluated on blurred images was led.Concerning keypoint localization, regardless of the different experiments, the errors in the upper extremities  were larger than those in the lower extremities . One possible reason was that the annotations in the training set of lower limb keypoints were refined by the 3D reconstructions from the biplanar radiographs. Another important reason is that the arm activities during movement show more variation between different subjects, so the algorithm is less robust when predicting the keypoints’ positions.Regarding the average performance, the MPJPE of experiment #1 was comparable to that of experiment #3. For experiment #2, the MPJPE decreased marginally, indicating that we can train the model directly on the face-blurred images without pre-training on the original images. As expected, the most impacted keypoints by face blurring were the head and neck. Surprisingly, the head localization showed lower differences in model #1 than the control model #3. On the other hand, the other keypoints were impacted almost negligibly (maximum average variation was 0.7 mm).In addition to the experiments presented in this paper, we also evaluated the performance of the three models for all possible combinations with both blurred/original test sets. As expected, we found that when the training data were of different types than the test data, keypoint localization performance slightly decreased.One-way repeated measures ANOVA tests revealed that elbow flexion was statistically significantly affected, although for all other joint angle calculations we did not observe a statistically significant effect of different experimental settings on kinematic calculations. However, whether the strength of these effects was within acceptable limits needed to be analyzed quantitatively. As stated in , a jointWe have demonstrated in There are also several limitations in this present study. The dataset used in the research was a gait dataset, and other types of movements were not investigated. Most subjects in our dataset were masked because of the context of the COVID-19 pandemic. Moreover, a single camera setup was investigated. It would be then interesting to examine the effect of face blurring on other datasets including other motions ,26, unmaIn this study, we present the first comprehensive investigation of the effect of face blurring on 3D human pose estimation. We have performed subject face blurring on an image dataset acquired in a previous gait study and investigated the impact of face blurring on human pose estimation and the subsequent kinematic analysis. Following this, we examined the statistical significance of the effects of face blurring on joint angle calculations with a further analysis of the strength of these effects. The results show that training the model on face-blurred images does not have a large impact on the performance of the model. The effects of face blurring are not found statistically significant on kinematic calculations for all joint angles except one . Moreover, we can train the neural network directly on face-blurred images without pre-training on the original images. Our findings indicate that it is feasible to utilize face-blurred image datasets for human pose estimation and effectively protect the privacy of subjects in training datasets without loss of performance in the subsequent kinematic analysis, thus facilitating data sharing that can accelerate convergence of clinical or ergonomic applications.