Myeloid cells and fibroblasts involvement in different clusters were confirmed and linked to corresponding pathway activities. Collectively, our findings revealed more complex disease subtypes in SSc; Key gene mediators such as IL6, FGFR1, TLR7, PLCG2, IRK2 identified by network analysis underscored the scientific rationale for exploring additional targets in treatment of SSc.Pathophysiology of systemic sclerosis , an autoimmune rheumatic disease, comprises of mechanisms that drive vasculopathy, inflammation and fibrosis. Understanding of the disease and associated clinical heterogeneity has advanced considerably in the past decade, highlighting the necessity of more specific targeted therapy. While many of the recent trials in SSc failed to meet the primary end points that predominantly relied on changes in modified Rodnan skin scores (MRSS), sub-group analysis, especially those focused on the basal skin transcriptomic data have provided insights into patient subsets that respond to therapies. These findings suggest that deeper understanding of the molecular changes in pathways is very important to define disease drivers in various patient subgroups. In view of these challenges, we performed meta-analysis on 9 public available SSc microarray studies using a novel pathway pivoted approach combining consensus clustering and machine learning assisted feature selection. Selected pathway modules were further explored through cluster specific topological network analysis in search of novel therapeutic concepts. In addition, we went beyond previously described SSc class divisions of 3 clusters  and expanded into a much finer stratification in order to profile SSc patients more accurately. Our analysis unveiled an important 80 pathway signatures that differentiated SSc patients into 8 unique subtypes. The 5 pathway modules derived from such signature successfully defined the 8 SSc subsets and were validated by  Systemic sclerosis (SSc) is a heterogeneous autoimmune disease characterized by the dysregulated immune system, vasculopathy, and systemic fibrosis of the connective tissues. SSc is a rare disease with a prevalence around 240 patients per million in USA . Still, It is well accepted that both genetic and environmental factors contribute to the pathophysiology of SSc. Genome-wide association studies (GWAS) have identified some key genetic associations that confer susceptibility to SSc including major histocompatibility complex (MHC) II region as being the most significant in this disease –9. Toll-The disease of SSc is heterogeneous both clinically and genetically due to the involvement of multiple pathogenic pathways. Thus, a better understanding of the interplay of these factors is considered necessary in developing novel therapies for improved treatment options. Extensive efforts have been made to define and comprehend the disease mechanism at molecular level. Several microarray analysis studies or recent meta-analysis have delineated the existence of a few major key subtypes of SSc skin samples such as immune response dominant, fibro-proliferative and normal phenotypes –21. ThisS1 Table. Many of the studies we selected were clinical trials that involve medical intervention on patients; other studies have paired biopsy position (e.g. forearm vs back) design wherein non-involved skin area was sampled. We generally only considered baseline SSc patients and focused exclusively on involved (affected) skin samples on forearms to avoid any confounding factor. The availability of clinical information such as disease stage, severity and internal organ involvement varied a lot across studies, we hence concentrated on the factor of major disease phenotypes  in our research. Overall, we had a total of 141 SSc samples and 80 controls entered our meta-analysis, including 126 dSSc and 15 lSSc; the detailed sample distributions are listed in .All of the 9 SSc data sets were obtained by searching on 2 popular gene expression repositories: GEO or ArrayExpress databases, using key words “systemic sclerosis”, “systemic scleroderma” or “SSc”. We looked into studies published in the last 12 years and focused exclusively on microarray data. Next generation sequencing or other types of high throughput gene expression data were not considered since the absolute majority of public SSc datasets were based on microarray, and it is computationally more consistent to combine data sets of the same platform for meta-analysis. During the initial search, a total 26 SSc studies were identified from the two data repositories. They were subsequently screened for redundancy to exclude 3 studies sharing the same set of samples with others. An additional filter was applied to remove another 12 studies of non-skin samples. Lastly, we disqualified two studies (GSE9285 and GSE76806) as these data sets share much less common genes with others. The final list of studies included in our current investigation is specified in TM [TM entries using an incremental size of 50. The criterion was to include at least one pathway that contained a given key word when we searched from top to bottom. 2) We only kept the pathway that has enrichment scores over 3 in at least 5 samples out of 141 cases. In the end, a total of 693 pathways passed the filter and entered subsequent analysis.From each GEO study, the normalized gene matrix data  was carefully examined using PCA and boxplot profiling. We then applied GSVA algorithm   based on 90 percent of the samples, Euclidean distance and ward.D metrics. Optimal number of clusters were determined by visual inspection of consensus matrix pattern, CDF plot and delta plot from default function output as well as the plot of an external metrics: silhouette   . These mTable 1). In each cluster & pathway-module combination, the average of pathway enrichment scores was calculated (shown in S4 Table) and the value range was divided into 3 levels: High (> = 0.15), Moderate (-0.15~0.15), and Low (= <0.15) to reflect the pathway enrichment levels in each cluster and pathway module.Fast implementation of random forest method (ranger)  was chosTable 1) and performed two nodes prioritization algorithms  for each pathway set. In case of target list having more than 500 genes, only top 500 genes were used to cross-reference with MRSS in the following step.We extracted genes corresponding to the 5 pathway modules respectively  on  scoring  and querModified Rodnan skin score (MRSS) was extracted from one study from GEO. We then calculated Pearson correlation coefficients for each gene with MRSS based on quantile normalized gene expressions. MRSS associated genes were selected based on p value < = 0.05 and correlation coefficient > 0.We selected common genes across all 9 SSc studies and combined individual gene expression matrices into a gene level compendium matrix (12306 genes by 141 samples). Combat method   for the final meta-analysis. With a novel application of gene-pathway space projection, we sought to unify different SSc studies into a single pathway-based compendium before further dissecting their molecular signatures. Consensus clustering procedure was then applied to classify SSc samples into more discrete subgroups than previously documented 3 or 4 clusters [We surveyed PubMed and related GEO database of the past 15 years 2004 ~ 2019), selecting 9 relevant skin SSc studies through a multi-step screening and filtering process . Similarly, we failed to observe notable difference among diffuse, limited SSc samples and Controls, suggesting that the clinically groups may only differ on a subset of variable pathways. In addition, some of the disease samples were known to be highly similar to controls as documented by previous studies [Gene expression from individual study was transformed into pathway enrichment scores, and subsequently merged into one joint pathway compendium. We performed PCA on the compendium and found no potential batch effect or any outlier sample  were used in this process so that we did not exclude any interesting biological feature. As a result, a sum of 693 pathways passed the filters and entered downstream clustering analysis.Moderate filter steps based on Mean Absolute Deviation (MAD) and enrichment scores were applied to remove uninformative pathways. Relatively lenient cutoffs . Additionally, an external metric of silhouette scores was also generated  to demonstrate the optimal cutoff of SSc subtype numbers. Previous SSc studies mostly focused on 3 to 4 major SSc divisions [Fig 3A–3D). Eventually, we selected the optimal cluster number at N = 8 to strike a balance between minimal cluster size and a good stratification setting of SSc patients. Details on the cutoff determination methods of the 4 plots are specified in Methods section. The 8 subtype classification of 141 SSc patients defined a spectrum of disease clusters ranging from the biggest cohort of 28 (19.8%) samples to the smallest cohort of 8 (5.67%) patients (S2 Table). It was also noted that none of the clusters is significantly enriched with an individual SSc study; the overall association between cluster membership and GEO study sources is non-significant (Chi-square test: p = 0.15). In addition, neither dSSc nor lSSc patients was found to enrich a certain subtype (Chi-square test: p = 0.12), although some level of cluster associated lSSc depletion was observed in cluster 2 and cluster 4 (S3 Table). Overall, the 8 SSc clusters were established independent of study effects or clinical phenotypes (dSSc vs lSSc).We performed consensus clustering  on 221 Sivisions , 21, 37,Methods). A couple of algorithms reached more than 80% accuracy using all 693 pathways, however, only gbm, ranger and svmRadial could roughly uphold accuracy level when we rerun the cross-validation using top 50 or 100 features . Eventually, random forest (ranger) classifier was adopted since it on average achieved the highest level of accuracy in all 3 pathway modules (80.1%); In addition, it relied on a model based feature selection metric (Gini index) instead of random heuristic searching. We then adopted a step-up loop process using repeated cross-validation to determine an optimal number of top pathways that can reconstruct 8 SSc groups with comparative accuracy. As a result, a signature of top 80 pathways was elected whereby each SSc cluster can be interpreted biologically through its pathway enrichment pattern . We also compared the profiles of all SSc subtypes with that of control; highlighting 5 pathway modules that can literally differentiate the 8 clusters by their expression level combinations . It was readily evident that cluster 1 and 6 resembled control cohort, showing elevated level of cell metabolism pathways (Pathway-module Black). Meanwhile a minor difference was noted between the two subtypes that cluster 1 has slightly elevated level of immune response than cluster 6, suggesting a possible transitional role among cluster 1 samples. Such variation was also noted between cluster 2 and 4 wherein the average enrichment of Blue moderately differed between the 2 groups while both of them demonstrated a strong inflammation signature. Cluster 3, showing elevated level in Blue module only, formed a distinct cohort. Clusters 5 and 8 are again diverging solely on Pathway module Yellow although they are not so dissimilar to each other; both subsets demonstrated strong inflammatory, pro-fibrotic and cell-metabolism signatures. Lastly, cluster 7 appeared to be a separate “normal-like” cohort, enriching merely in a couple of metabolism pathways of Pathway module Yellow. Collectively, the 8 SSc cluster subtyping system was a more pertinent and finer stratification compared to the paradigmatic 3-type classifications  by previous literatures [S5 Table.A total of 693 pathways, many of which redundant, were used to establish the 8 cluster SSc classification. The sheer number of pathways involved in consensus clustering actually hampered our effort in deriving meaningful biological interpretations, not to mention questing relevant gene targets. In addition, we arrived at the 8 SSC subsets through a consensus clustering approach based on Euclidean distance metrics and the robustness of their cluster memberships may need further validation by a different method. To address these issues, we adopted a machine learning procedure aiming to reconstruct the cluster membership and reduce the number of pathways through an accompanying feature selection process. The goal of such process was to find a highly predictive pathway subset that can achieve the same level of classification accuracy as that of all 693 pathways. We evaluated a diverse set of popular multiclass machine learning algorithms based on 10-fold cross-validation of the training set  and used them as start nodes to query against the MetabaseTM biological network [After identifying 8 SSc subtypes using the pathway signature, we further explored the underlying gene interactions of each cluster through network analysis. The aim of such inquiry was to uncover latent gene hubs or upstream regulators that lay behind the pathway pattern; these genes, if established, could serve as potential drug target candidates for related SSc patient groups. To achieve this goal, we extracted all genes from each of the 5 pathway modules identified previously  n network . Both meS6 Table) based on 5 pathway modules, of which 92 unique genes emerge if we combine module sources. We picked a list of 9 genes  based on their MRSS associations and biological assessment, showing their profiles in Fig 5.Still, each algorithm led to hundreds of potential target genes ranked by their hub scores, we further reduced the target lists by overlapping with external clinical features such as modified Rodnan Skin Score (MRSS) which is an important clinical marker of SSc skin phenotype. We extracted MRSS from one study and correlated with genomic expressions. Genes significantly associated with MRSS were selected, and overlapped with the potential targets identified in pathway module based network analysis. This procedure generated a concentrated target list of 164 genes . Identified as key hub in its subnetwork, pursuing those three targets with small molecule compound or monoclonal antibody will possibly destabilize the topology of subnetwork, and normalize the aberrant biological pathways. A recent approval of Nintedanib (OFEV®) targeting FGFR for SSC-associated interstitial lung disease (ILD) substantiates our effort for drug targets based on PPI network analysis. Another possible target is IRAK2, a key serine/threonine kinase involved with IL1R and its downstream upregulation of NF-kB and its pathway. The function of IRAK2 in myofibroblast had been illustrated in a murine IRAK2 knockout model in which fibroblasts showed reduced reaction to multiple TLR ligands. In summary, our analyses has not only demonstrated some important biological pathways relevant to the disease, but also identified some important key hub genes/proteins within the biological pathway networks.We further focused on 3 genes of interest  from the 9 gene list and investigated their subnetwork connections . Endothelial cells and macrophages were also included in the analysis as a comparison. A follow-up pairwise t-test was carried out to compare among the clusters and corresponding results  were recorded in S7 Table. The overall cellular enrichment pattern appeared highly consistent with the cluster pathway features . Specifically, cluster 1 showed low level of fibroblasts and endothelial signatures, corresponding to the under-enriched Pathway module Blue, on the other hand, it had moderate enrichment in myeloid and macrophage that corresponds to Pathway module Red. Cluster 2 exhibited elevated level of myeloid + macrophages (Pathway module Red & Green) and moderate enrichment in fibroblasts + endothelial (Pathway module Blue). Cluster 3 only enriched in fibroblasts + endothelial which was reflected by Pathway module Blue in Fig 4B. Cluster 4, with strong expression in all 4 cell types, had elevated enrichment in Pathway modules Blue, Red and Green. In the similar manner, cluster 5, 6, 7 and 8 respectively displayed enrichment pattern of the 4 cell types that linked to 3 Pathway modules. In summary, Red and Green pathway groups are strongly associated with myeloid signature while Blue pathways correspond steadily with fibroblasts activity. The establishment of such cells-to-pathway affiliation also helped us defining the main biological functions of each pathway module (Table 1). In contrast to the previous studies which demonstrated differential expression levels of multiple immune cell lineage gene signatures, our cellular deconvolution demonstrated the prevalent myeloid cell signature among multiple SSc clusters, especially the co-variation between myeloid and macrophage gene signatures. The dysfunction of macrophage can lead to aberrant tissue repair and uncontrolled tissue modelling by communicating with epithelial cells, endothelial cells and fibroblasts by producing pro-fibrotic mediators and enhance the survival and activation of myofibroblasts. Hence, our analysis substantially underscored the contribution of myeloid cells to SSc pathogenesis.When examining genes identified in network analysis, we recognized that many of them were highly related to myeloid cell and fibroblast signatures. A cell type enrichment analysis was then performed to profile fibroblasts and myeloid components in each cluster . For instance, cluster 1 and 6 resembled the control cohort, showing active cellular metabolism pathways (Pathway module Black) similar to the normal control group. However, we noticed a subtle difference between the two subtypes that cluster 1 was actually mixed with elevated levels of immune response genes. Cluster 2 and 4 classified as Pathway module Blue, demonstrated moderate difference in terms of the extent of the enriched inflammation signature. Cluster 3, showing elevated levels in the Blue module only, formed a distinct cohort. Consistent with literature, Cluster 5 and 8, characterized as Pathway module Yellow exhibited a mixture of strong inflammatory, pro-fibrotic and cell-metabolism signatures. Lastly, cluster 7 appeared to be a separate “normal-like” cohort. Collectively, the 8 SSc cluster subtyping system was a more pertinent and finer stratification compared to the paradigmatic type classifications  by previous literatures [Previous SSc meta-analysis integrate multiple datasets with varying source effects by merging data together after normalization and baseline correction procedure . We adoperatures , 37, 38.The 8 SSc subtypes we assessed in the study can be grouped to inflammatory, fibro-inflammatory, metabolic-fibrosis, metabolic, and normal-like phenotypes. For the inflammatory dominant clusters, such as cluster 2 and cluster 4, it is evident that IL17, IL22 pathways were highlighted, suggesting the hyperactive Th17 cells in such groups of patients. Current knowledge indicates complex roles of Th17 in fibrogenesis: One study demonstrated that IL-17 derived from Th17 cells promoted collagen production and fibroblast growth . AnotherAnother distinct computational approach we adopted was to analyze the topology of PPI networks derived from each cluster using two algorithms: 1) random walk  and 2) nin silico cellular deconvolution was applied to determine to what extent each immune or stromal cell subset is associated with the identified 8 disease subtypes. Our analyses showed that the SSc metabolic phenotype normally correlated with low enrichment of cellular components–except for cluster 8, which are enriched by all cellular components. The general moderate-to-low patterns of cell type enrichment shown in Fig 7 suggest that the SSc subtypes with dominant metabolic pathways are associated with a “normal” like phenotype. In contrast, clusters 2, 3, 4, and 5, which are annotated as immune response dominant, exhibited substantially elevated levels of myeloid lineage enrichment. In particular, Cluster 4, with strong expression in all 4 cell types, has elevated enrichment in the Pathway module of pro-fibrotic and/or immune responses. In general, this analysis underscores the important involvement of myeloid cells, particularly macrophages, linked to fibroblast pathway genes. The establishment of such cells-to-pathway affiliation helped to define the main biological functions of each pathway module.To expand the biological discoveries from our meta-analysis, Our meta-analysis on SSc public studies, on the other hand, is not without limitations: Firstly, it has always been challenging to achieve enough sample size in meta-analysis of this type of rare human disease. In addition, there is a severe unbalanced distribution of lSSc and dSSc patient numbers in our analysis, which limited the power to delineate the molecular differences between the two categories. Thirdly, all the SSc public datasets available so far are based on microarray platforms that have limited resolution due to probe designs. Last and most importantly, the lack of important clinical information–e.g. disease duration or treatment history, in majority of the public datasets may hamper our effort in accurately defining the disease phenotypes. For example, some subtype of patients such as the normal-like group, may simply represent the early stage of the disease if we have disease duration data available. In view of these concerns, a more comprehensive future longitudinal study of SSc patients is needed to unveil critical biological mechanisms behind the etiology, disease progression, and prognostics of this dangerous autoimmune disorder.In summary, our findings based on the current pathway driven meta-analysis indicate that there exists more complex disease structure in SSc than previously described. These results also confirmed the important role of myeloid cells in SSc pathogenesis. Nevertheless, the extent of contribution to disease phenotype through myeloid cell involvement still warrants further examination. Meanwhile, important hub genes identified through our module specific network analysis overlapped strongly with current clinical evidences; some of the genes are potential drug targets and may serve as the foundation for next generation of SSc treatment.S1 Checklist(DOC)Click here for additional data file.S1 FigSamples are colored by study sources and clinical phenotypes are denoted in different shapes.(TIF)Click here for additional data file.S1 TableMethod section. 1 Whitefield Lab from department of Genetics, Dartmouth Medical School, Hanover, NH, USA; 2 Assassi Group from University of Texas Health Science Center, Houston, TX, USA; 3 Bioinformatics Core facility, University of Manchester, Manchester, UK.Some samples are repeated at different time series points, before-after treatments or biopsy positions. Detailed inclusion or exclusion standards are specified in (DOCX)Click here for additional data file.S2 Table*Percentages in the table represent total percentages.(DOCX)Click here for additional data file.S3 Table*Percentages in the table represent total percentages.(DOCX)Click here for additional data file.S4 TableTable 1 was calculated and shown.The average of pathway enrichment scores for each cluster & pathway-module combination from (DOCX)Click here for additional data file.S5 TablePathways modules identified from (DOCX)Click here for additional data file.S6 TablePearson correlation coefficient and related p value are used to denote association between MRSS and gene expression, the algorithm sources  and pathway module (1~5) are shown in column 1 and 2.(XLSX)Click here for additional data file.S7 TableOne-sided Welch T test was performed to compare cell type enrichment level among the 8 SSC clusters and the control group. P value matrix (corrected by Bonferroni procedure) was generated. The comparison was based on the alternative hypothesis that cluster in row label > cluster in column label(XLSX)Click here for additional data file.


Hypertensive vascular remodeling (HVR) is the pathophysiological basis of hypertension, which is also an important cause of vascular disease and target organ damage. Treatment with Fructus Tribuli (FT), a traditional Chinese medicine, has a positive effect on HVR. However, the pharmacological mechanisms of FT are still unclear. Therefore, this study aimed to reveal the potential mechanisms involved in the effects of FT on HVR based on network pharmacology and molecular docking.  We selected the active compounds and targets of FT according to the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Swiss Target Prediction database, and the targets of HVR were collected from the Online Mendelian Inheritance in Man (OMIM), GeneCards, and DrugBank databases. The protein-protein interaction network (PPI) was established using the STRING database. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and network analysis were performed to further explore the potential mechanisms. Finally, molecular docking methods were used to evaluate the affinity between the active compounds and the main target.  Seventeen active compounds of FT  and 164 potential targets for the treatment of HVR were identified. Component-target and PPI networks were constructed, and 12 main active components and 33 main targets were identified by analyzing the topological parameters. Additionally, GO analysis indicated that the potential targets were enriched in 483 biological processes, 52 cellular components, and 110 molecular functions. KEGG analysis revealed that the potential targets were correlated with 122 pathways, such as the HIF-1 signaling pathway, ErbB signaling pathway, and VEGF signaling pathway. Finally, molecular docking showed that the 12 main active components had a good affinity for the top five main targets.  This study demonstrated the multiple compounds, targets, and pathway characteristics of FT in the treatment of HVR. The network pharmacology method provided a novel research approach to analyze potential mechanisms. Hypertension has become one of the most threatening public health problems worldwide . It is tTribulus terrestris L. [Traditional Chinese medicine (TCM) has high activity and low toxicity in preventing and treating HVR, playing an irreplaceable role. Fructus Tribuli (FT), which is the dried fruit of stris L. , is renostris L. , could bstris L. , had neustris L.  and memostris L. , and prostris L. , antidepstris L. , anti-instris L. , and antstris L. .κB signaling pathway [κB p65 [In recent years, the efficacy of FT in HVR has attracted more and more attention. Zhang et al. found that the furostanol saponins from FT could inhibit the expression of myocardial transforming growth factor and connective tissue growth factor in ventricular muscle, reduce systolic blood pressure, and inhibit myocardial fibrosis in spontaneously hypertensive rats (SHRs). Thus, vascular remodeling induced by hypertension was significantly ameliorated . Guo et  pathway . Jiang e [κB p65 . In addi [κB p65 . HoweverTCM has the characteristics of integrity and diversity and can play a comprehensive role in multilevel, multilink, multitarget, and bidirectional regulation. Network pharmacology is a new field that integrates systems biology, omics, and computational biology to reveal the mechanism of drug action from an overall perspective and possesses integrity, synergistic effects, and dynamic characteristics . This rehttps://tcmspw.com/tcmsp.php), which is considered a special platform for Chinese herbal medicine based on systems pharmacology that provides the relationships between drugs, targets, and diseases, as well as comprehensive data on absorption, distribution, metabolism, and excretion (ADME) properties for each compound [https://pubchem.ncbi.nlm.nih.gov/).The chemical information about FT was obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  database were applied to identify the potential targets related to the active compounds of FT. Swiss Target Prediction is a web server for potential drug target prediction. Then, each of the predicted targets was inputted into the UniProt database (https://www.uniprot.org/) with “Homo sapiens” selected as the organism. After deleting repeated and nonhuman targets, we obtained the targets for each active compound.The TCMSP and Swiss Target Prediction (http://www.omim.org/) database, the GeneCards database (https://www.genecards.org/), and the DrugBank database (https://www.drugbank.ca/). OMIM is a comprehensive research resource of human genes and genetic diseases [Targets associated with HVR were retrieved from the Online Mendelian Inheritance in Man  to perform protein-protein interaction (PPI) analysis. STRING is a database that predicts direct and indirect interaction and builds networks between proteins and proteins [To identify the potential targets of FT in the treatment of HVR, we intersected the active compound targets and disease targets. The common targets were considered to be potential targets. The obtained common targets were imported into STRING . DAVID can provide systematic, comprehensive biological functional annotations for large-scale gene or protein lists, including biological processes (BP), cellular components (CC) molecular functions (MF), and pathways. In the GO and KEGG analyses, the lower the P value, the stronger the correlation between the pathway and target; the higher the count, the more targets in the pathway. A threshold P value of <0.05 was considered statistically significant.All potential targets were utilized for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the DAVID database  was searched to download the major target protein structure and import it into Discovery Studio Client for preprocessing such as deleting the water molecules and preparing the protein. The original ligand position was used as the active center to expand a certain range, and the amino acid residues in this range were defined as the active site. The default settings were used for the other settings. The receptor-ligand interaction module was used for molecular docking. The software finds the optimal binding conformation by locating the active sites of small molecule compounds and proteins and calculates and scores the affinity between the receptors and the ligands. LibDock scores between the compound and protein higher than the docking score between the ligand and the protein indicate that the component has a good affinity for the target. Finally, PyMOL software was used to visualize the docking results of the major active compound with their corresponding higher-scoring protein.The molecular structures of the major active compounds in the Mol2 file format obtained from the TCMSP in the simulation description format (SDF) downloaded from the PubChem database or in the canonical simplified molecular-input line-entry specifications (SMILES) were imported into the Discovery Studio Client software for modification. The RCSB PDB database , and the count of each term is also displayed in The 164 common targets were inputted into the DAVID database for GO and KEGG analysis. Of them, 645 GO terms were ascertained, including 483 BP terms, 52 CC terms, and 110 MF terms. The top 20 enriched GO terms were partially displayed by a bar chart according to the −log10 (P values of less than 0.05. The top 20 signaling pathways were listed in a bubble chart and are shown in P values. The enrichment pathways of FT in treating HVR were mainly concentrated in the HIF-1 signaling pathway, ErbB signaling pathway, VEGF signaling pathway, TNF signaling pathway, FoxO signaling pathway, Fc epsilon RI signaling pathway, Rap1 signaling pathway, and the cAMP signaling pathway.KEGG pathway annotation showed that the 164 common targets were involved in 122 pathways with The active compounds, potential targets, and signal pathways were imported into Cytoscape software for visual processing to construct a compound-target-pathway network. To further analyze and verify the target-compound interactions, the top five main targets of AKT1, MAPK3, VEGFA, SRC, and IL6, which had higher degrees, were selected for molecular docking with the 12 major active compounds of FT. The LibDock score of the 12 small molecule compounds was obtained, as shown in Hypertension is one of the most common cardiovascular diseases, with an increasing prevalence rate worldwide. Vascular remodeling is a significant pathological feature of hypertension, in which the early stage is an adaptive process that eventually becomes maladaptive, damaging target organs such as the heart, brain, and kidneys, and causing the complications of hypertension. TCM has a long history of treating hypertension, with the characteristics of maintaining stable hypotension and protecting target organs with fewer adverse reactions. FT has been used as a TCM for thousands of years and has a certain effect on the treatment of HVR. Although some preliminary research has been conducted on FT, the molecular targets and mechanisms of FT in treating HVR have not been fully explored. Therefore, elucidating the potential mechanisms is of great significance.Network pharmacology is a new strategy and method, which helps to investigate the interaction between drugs and diseases and explores the underlying mechanism of a particular therapeutic efficacy. In this study, we constructed a compound-disease target interaction network and found 12 major active compounds. Meanwhile, three topological parameters, degree, betweenness centrality, and closeness certainty were calculated to identify the main targets in the PPI network. Then, GO and KEGG pathway enrichment analyses were carried out to further explore the potential mechanisms of FT in the treatment of HVR. At last, we used molecular docking methods to evaluate the binding activity between the targets and active compounds of FT.Through the PPI network, we found 33 main target proteins of FT in treating HVR. Among them, the regulation of the MAPK target was of great significance in the treatment of HVR. MAPK mediates a large variety of biological functions including gene expression, cell mitosis, metabolism, motility, survival, apoptosis, and differentiation . One stuκB and MAPK pathways, leading to the gradual progression of the disease. The FoxO signaling pathway has important roles in cell fate decisions including apoptosis, cell-cycle control, glucose metabolism, oxidative stress resistance, and longevity [The KEGG analysis provided a further understanding of the mechanism of HVR. HIF-1 is a transcription factor that regulates the homeostasis of oxygen concentration and is activated under hypoxic conditions, resulting in the upregulation of its target genes, which include VEGF. Many studies have confirmed that the HIF-1 signaling pathway is related to cardiovascular diseases. Mo et al. found that the onset of capillary leakage was associated with an upregulated HIF-1a/VEGFA signaling pathway, causing dysregulation in the immune response and affecting vascular permeability . One celongevity . These rAlthough the efficacy of FT in treating HVR has been confirmed by many studies, the molecular mechanisms of FT remain unclear. In the present study, we screened 12 major active compounds and 33 major targets. Functional enrichment analyses including GO and KEGG pathway analyses were performed, and 645 GO terms and 122 pathways were ascertained. Last, we used molecular docking methods to evaluate the binding activity between the targets and active compounds of FT. This study clarified the multiple compounds, targets, and pathway characteristics of FT in the treatment of HVR and provided a systematic view of the potential mechanisms from the combination of network pharmacology and a literature search.


Cannabis sativa that differ from each other by composition of cannabinoids, terpenes and other molecules. The medicinal properties of these cultivars are often very different, with some being more efficient than others. This report describes the development of a method and software for the analysis of the efficiency of various cannabis extracts to detect the anti-inflammatory properties of the various cannabis extracts. The method uses high-throughput gene expression profiling data but can potentially use other omics data as well. According to the signaling pathway topology, the gene expression profiles are convoluted into the signaling pathway activities using a signaling pathway impact analysis (SPIA) method. The method was tested by inducing inflammation in human 3D epithelial tissues, including intestine, oral and skin, and then exposing these tissues to various extracts and then performing transcriptome analysis. The analysis showed a different efficiency of the various extracts in restoring the transcriptome changes to the pre-inflammation state, thus allowing to calculate a different cannabis drug efficiency index (CDEI).There are many varieties of  In the twentieth century, enormous strides have been made in combatting various diseases. Treatment of chronic diseases still remains to be more challenging than acute ones. This is often due to substantial differences in individual responses to known drugs. Over the last few decades, with the advent of genomics and epigenomics, research has focused on the development of personalized medicine. A need has arisen to develop diagnostic tools for use in the characterization of personalized aspects of chronic diseases.Intracellular signaling pathways (SPs) regulate numerous processes involved in normal and pathological conditions, including development, growth, aging and cancer. Many intracellular signaling pathways or maps are available at online websites. The information relating to signaling pathway activation (SPA) can be obtained from the massive proteomic or transcriptomic data . AlthougThe transcriptomic methods like next-generation sequencing (NGS) or microarray analysis of RNA can routinely determine the expression levels for all or virtually all human genes . TranscrPro- and antimitotic SPs that determine various stages of cell cycle progression remained in the spotlight of computational biologists for more than a decade ,5. TodayMany bioinformatics tools have been developed that analyze SPs. One group of bioinformatic approaches integrated the analysis of transcriptome-wide data with the models employing the mass action law and Michaelis–Menten kinetics . HoweverIn addition, all the contemporary bioinformatical methods that were proposed for digesting large-scale gene expression data, followed by recognition and analysis of SPs, have an important disadvantage: they do not allow tracing the overall pathway activation signatures and quantitively estimate the extent of the SPA ,15. ThisThus, there remains an unmet, urgent and increasing need to provide effective personalized non-toxic disease therapies, as well as models for selecting a personalized optimal therapy for an individual.Here, we propose a method for quick, informative and large-scale screening of changes in SPA in cells and tissues. These changes may reflect various conditions, such as differences in physiological state, aging, disease, treatment with drugs, media composition, additives, etc. One of the potential applications of SPA studies may be to utilize mathematical algorithms to identify and rank the medicines based on their predicted efficacy. In this paper, we give examples of the analysis of transcriptomics data for three sets of experiments involving the analysis of the anti-inflammatory properties of cannabis extracts. Such a method can potentially be used for the detection of the efficiency of various other botanical extracts or single compounds, although the versatility of the method will have to be confirmed using other data sets.Several methods were proposed for the assessment of drug efficiency based on gene/protein expression ,17,18,19CDEI is our original approach for assessing the efficiency of a cannabis drug’s application regarding individual persons. CDEI is calculated based on high-throughput gene expression profiling and a signaling pathway topology, by comparison of gene expression and signaling pathway profiles between case and normal reference states for prediction of drug action in individual cases.As input data, the CDEI operates with the results of various “omics” data stemming from the cells of individual patients and the healthy individuals. These data may include transcriptomic , non-coding-RNAomic, proteomic and epigenomic data, etc.The data of the full mRNA/protein abundance are integrated by the CDEI into the assessment values for activation of different cellular pathways .https://www.qiagen.com/ru/shop/genes-and-pathways/pathway-central/).The topology for the signaling, metabolic and cytoskeleton pathways, etc., were obtained from the database of QIAGEN SABiosciences  method . Among tG where Imagine a pathway graph, PF), for the all genes g of the pathway K,g expression level in a given sample compared with the average value for the pool of normal samples. The latter term expresses the summation over all the genes γ that belong to the set gU of the upstream genes for the gene g. The value of downn (γ) denotes the number of downstream genes for gene γ. The weight factor g: g, and g. The search for upstream/downstream genes is performed according to the depth-first search method [Consider then the values, called perturbation factors  from the previous paragraph, resulting in the accuracy value, I is the identity matrix, and∑gAcc(g) .Obtain the signaling pathway impact analysis (SPIA) for each drug for each biological pathway.pw) factor as follows: For pathways with a positive mean SPIA score of the case samples, pw = ((number of case samples with positive SPIA score)/). For pathways with a negative mean SPIA score of the case samples, pw = ((number of case samples with negative SPIA score)/).Calculate the values of the pathway weight (μ = mean(SPIA)·pw.Adjust the mean SPIA score of each pathway by the weight factor, SPIAμ for the pool of case samples are different from 0 . During the Student’s t-test, the following case classes are taken into account: the untreated case (U), i.e., the pathological state before drug application, should be far from the control (C).-T), i.e., the pathological state after drug application, should be close to the control;Treated case  − 0.5).Calculate the cannabis drug efficiency index (CDEI) for each drug for a specific disease, wherein CDEI = 2 . So, a t-statistic is calculated for each dataset and the CDEI metric is one value for each case sample dataset.Note also that the mean score of the case samples in each pathway is first calculated/adjusted and then the set of the mean scores in each data set are t-tested for their difference from 0 . Cuttings from the mother plants of the same age (~6 months) were made and allowed to root. After rooting (~10 days), plantlets were acclimated for another week and then were transplanted to a larger pot. Four plants per variety were grown at 22 °C for 18 h in light and 6 h in the dark, for 4 weeks, and then transferred to the chambers for a 12 h light/12 h dark regime to promote flowering. Plants were grown to maturity and flowers were harvested and dried. Approximately 5 g of the dry, lower samples from each of four plants per variety were combined and then used for extraction.Three grams of plant tissue was ground to a powder using a fine coffee grinder and the powdered plant tissue were weighed using an analytical balance. Plant material was placed inside a 250 mL Erlenmeyer flask. A total of 100 mL of ethyl acetate was poured into the flask containing the plant material. The flasks were then wrapped with tin foil and shaken continuously (120 rpm) in an incubator at 21 °C overnight and in the dark.After overnight solvent extraction, the extracts were filtered through cotton into a 100 mL round bottom flask. The extracts were concentrated to around 2–3 mL using a rotary vacuum evaporator. The extracts were then transferred to a tared 3-dram vial (cat# 60975L Kimble obtained from Fisher Scientific). The leftover solvent was evaporated to dryness in an oven overnight at 50 °C to eliminate the solvent completely. The mass of each crude extract was recorded. All extractions were repeated twice.The levels of cannabinoids were analyzed using an Agilent Technologies 1200 Series HPLC system. The extract stocks were prepared from the crude extracts whereby 3–6 mg of crude extract was dissolved in DMSO  to reach a 60 mg/mL final concentration and stored at −20 °C. The appropriate cell culture media (RPMI + 10% FBS or EMEM + 10% FBS) were used to dilute the 60 mg/mL stock to make a working medium containing 0.01 mg/mL. The extracts were sterilized using a 0.22 µm filter. The composition of each extract is shown in The stocks were prepared weighing 3–6 mg of crude extract into a micro centrifuge tube. The crude extract was dissolved in DMSO (dimethyl sulfoxide anhydrous from Life technologies cat # D12345) to reach a 60 mg/mL final concentration and stored at −20 °C. For the assay, a different amount of stock material (from 1 to 20 µL) was added to 21 mL of medium to make a working extract. Different concentrations were tested for cell/tissue toxicity and one concentration was chosen for further work (data not shown). Specifically, to obtain “low” (0.007 mg/mL) and “high” (0.015 mg/mL) functional concentrations, 2.45 µL or 5.25 µL of stock extract (60 mg/mL) were added to 21 mL of medium, respectively. The final concentration of DMSO was 0.012% and 0.025% in the “low” and “high” extract concentrations, respectively. The extracts were sterilized using a 0.22 µm filter.U) had DMSO added to the media instead of extracts. The control (C) sample had not been exposed to UVC. All samples were collected 24 h after the extracts were added and were used for mRNA extraction. All samples were done in triplicate.Human EpiDermFT 3D skin tissues  were exposed to 7000 ergs UVC to induce inflammation and then, 24 h after exposure, treated with extracts of several cannabis cultivars  via their addition to the tissue growth media and incubated for another 24 h. Untreated sample  according to the manufacturer’s instructions. The cDNA fragment libraries were prepared using the TruSeq Stranded mRNA library preparation kit  with polyA selection, as described in the manual. The high-throughput gene expression profiles were obtained using the Illumina mRNA next-generation sequencing platform NextSeq500. All of the library preparations and sequencing runs were completed using the same protocol, by the same technician, and on the same sequencing instrument.Human MatTek’s 3D EpiOral tissues  were equilibrated for 24 h, then the culture medium was replaced and the tissues incubated for another 24 h. Tissues were then exposed for 24 h to TNFα (40 ng/mL) to promote inflammation or to DMSO only. Tissues were then treated with various cannabis extracts (#1–#9) that were added to the media for 24 h. The control sample was exposed to DMSO only. Samples were then harvested for mRNA extraction and sequenced as in Example #1. All samples were done in triplicate.Human MatTek’s 3D EpiIntestinal tissues  were equilibrated for 24 h, then the culture medium was replaced, and the tissues incubated for another 24 h. Tissues were then exposed for 24 h to TNFα (40 ng/mL) or to DMSO only. Tissues were then treated with various cannabis extracts  that were added to the media for 24 h. The control sample was exposed to DMSO only. Samples were then harvested for mRNA extraction. Sequencing and data analysis were performed as in Example #2. All samples were done in triplicate.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The base qualities were over 30 on the Phred scale across all of the samples and no adapter contamination was noted. The reads were mapped to the human genome  downloaded from the Illumina iGenomes repository (https://support.illumina.com/sequencing/sequencing_software/igenome.html). The mapping was done using HISAT v2.0.5 [hisat2-q—rna-strandness R—phred33-p 20—known-splicesite <known_splice_sites> -x <index> -U <fastq> -S <sam>”. The number of reads mapping to features (genes) was calculated using FeatureCounts v.1.6.1 [featureCounts -T 20 -s 2 -a <genes.gtf> -o <counts> <sam>”.The bioinformatics workflow for the CDEI calculation is shown in T v2.0.5  with theThe gene-level read counts were loaded into the R v.3.6.1 statistical language environment. The raw count data were normalized using statistical methods implemented in the DESeq2 Bioconductor package v1.22.0 . The DESThe CDEI software was tested on several datasets, explained below in several examples. We used the transcriptomic data from three different experiments. In the first experiment, we have used the data from human EpiDermFT 3D skin tissues exposed to UVC to induce inflammation and then treated with extracts of several cannabis cultivars. In the second experiment, human EpiOral tissues were treated with TNFα to induce inflammation and then treated with several different extracts. In the third experiment, human EpiIntestinal tissues were treated with TNFα and then treated with several different extracts.The total number of reads calculated from the three experiments was in the range of 17,700,978 to 45,054,688 with a median of 24,613,834.5 reads per sample. The minimum mapping rate, calculated as a fraction of reads with at least one match to the genome, was at least 93.80% with a median of 97.65%. The fraction of mapped reads assigned to features (genes) was in the range of 72.93–76.00% with a median of 74.86%.In the first experiment, we have induced inflammation in human EpiDermFT 3D skin tissues by exposing it to UVC. Analysis of the samples from the first experiment revealed that Extract #8 is the most efficient extract in restoring the transcriptome response after the UVC exposure . ExtractIn the second experiment, inflammation in 3D EpiOral tissues was established using exposure to TNFα (40 ng/mL) . The effRanking of the CDEI scores revealed that Extract #3 was the most efficient; it has restored the TNF-induced transcriptome nearly completely—with a CDEI score of 0.98. Extracts #5, #9 and #2 were also quite efficient, with CDEI scores of 0.90, 0.88 and 0.87, respectively. Extracts #8 and #4 were not very efficient, with CDEI scores of 0.10 and 0.16, respectively.Finally, in the 3rd experiment, EpiIntestinal tissues were exposed to TNFα and several extracts were used to reverse the transcriptome changes . The CDEExamples of heatmaps, with the differentially expressed genes for Samples #4, #8, #13 and #15, are shown in These experiments revealed that different extracts were efficient for different tissues. For example, a comparison of extracts for the reduction of inflammation in EpiOral and EpiIntestinal tissues showed that Extract #115 was equally and highly effective, while Extract #167 was not . At the To analyze whether there is a correlation between the cannabinoid content and the activity of the extracts, we have analyzed the amount of THC, CBD, CBG, CBN, total cannabinoids and CBD to THC ratio . The corWe developed a methodology to compute a cannabis drug efficiency index (CDEI) across the 241 pathways that contain the genes responding to the cannabis drug. The advantages of our methodology can be summarized as follows.CDEI explicitly calculates the drug efficiency of cannabis on diseases, using the expression values of the genes. As far as we understand, this is a novel methodology and there is no other methodology or software similar to CDEI to do it.CDEI evaluates the enrichment scores of the individual pathways in the control, untreated and treated cases. Then CDEI statistically integrates and compares the results from all samples. Finally, CDEI measures the overall drug efficiency of cannabis on diseases.An important characteristic of the CDEI calculation is that CDEI is not affected by the properties of the input data chosen. CDEI purely measures the efficacy of the cannabis drug by statistically comparing the cannabis-treated and untreated cases with the control.With the emergence of large-scale methods in genomics, the gene-level transcription changes became an obvious target in the search for biomarkers in complex diseases. Yet, in over a decade of research effort, only a handful of gene or protein biomarkers made its way into clinical practice. For example, as of 2017, 26 gene-based predictive and diagnostic biomarkers were used in cancer medicine . BiomarkConsidering the importance of pathway analysis, multiple methods were developed to address the task. These methods can be roughly classified as non-topology-based and topology-based . Non-topRecognizing the importance of pathway topology, CannSelect uses the SPIA algorithm to calculate pathway activation scores based on gene expression profiles. Previously, SPIA was shown to outperform other pathway analysis methods in terms of data aggregation  and in tEstimation of pathway-level activities opens a possibility of selecting drugs or other bioactive compounds that could alter the physiological state of the system in the desired direction by changing the activities of perturbed pathways.This idea is at the core of several computational methods, including Oncofinder , GeroScoIn theory, the CDEI approach mechanistically resembles Oncofinder, Geroscope, or iPanda; however, it differs from them in terms of pathway score and drug score algorithms and, more importantly, it is designed with a different purpose in mind. Oncofinder and other similar methods require two conditions—disease and normal state—and serve to facilitate the selection of drugs that could act on pathways altered between these two conditions. The data can be obtained experimentally or downloaded from public repositories. CDEI, on the other hand, requires three conditions—an untreated control , pathological state, and pathological state treated with a drug of interest. The output of CDEI is a numerical measure of the ability of a drug to reverse gene expression changes in a pathological state to mimic those in the healthy control. In contrast to Oncofinder, CDEI is useful as a computational step that follows laboratory testing of panels of drugs or other bioactive compounds to select prospective candidates for further investigation.There are different methodologies to analyze the enrichment of individual pathways. GSEA  finds the enrichment of pathways by focusing on gene sets . SPIA meAs detailed in the accompanying manual of the software, CDEI provides a convenient graphical user interface software available to users. The CDEI software works on both Windows and Linux systems. It validates the user inputs of the gene expression values and computes the scores of the individual pathways as an intermediate output. Then the software generates the drug efficiency index of cannabis on diseases as the final output.One of the recent studies using the SPIA method was the report by Franco et al. (2019) . The autPathway analysis has the potential to improve performance by applying machine learning methods. For example, the representative genes that represent a pathway can be selected by feature selection methods and pathways can be further ranked or weighted based on the representative genes using classification methods .To conclude, the CDEI algorithm represents a multistage process that includes the specific application of a drug screen, integration of pathway databases, weighted summarization of the pathway activation scores and identification of the most efficient drugs/extracts. The bioinformatics part of the CDEI process is wrapped into user-friendly graphical interface software. Currently, the CDEI algorithm was only used for the analysis of the efficacy of cannabis extracts in the reduction of inflammation. It remains to be shown whether it would also be able to rank the cannabis extracts  by their efficacy for other molecular processes, diseases, or conditions. The CDEI algorithm may also be an efficient tool to stratify patients in the clinical studies or clinical trials by their response to a drug, thus providing selection criteria upon transition from Phase 2 to Phase 3 or Phase 4 clinical trials. It will also be very useful in pre-clinical experiments using various cell, tissue or animal models, like recently used analysis of the efficiency of cannabis extracts for the reduction of inflammation .


To analyze axon morphology on rapid Golgi impregnated pyramidal neurons in the dorsolateral prefrontal cortex in schizophrenia.t test.Postmortem brain tissue from five subjects diagnosed with schizophrenia and five control subjects without neuropathological findings was processed with the rapid Golgi method. Layer III and layer V pyramidal neurons from Brodmann area 9 were chosen in each brain for reconstruction with Neurolucida software. The axons and cell bodies of 136 neurons from subjects with schizophrenia and of 165 neurons from control subjects were traced. The data obtained by quantitative analysis were compared between the schizophrenia and control group with the P = 0.032). Furthermore, in the schizophrenia group more axons had visibly stained collaterals (14.7% vs 5.5%).Axon impregnation length was consistently greater in the schizophrenia group. The axon main trunk length was significantly greater in the schizophrenia than in the control group (93.7 ± 36.6 μm vs 49.8 ± 9.9 μm, Axon rapid Golgi impregnation stops at the beginning of the myelin sheath. The increased axonal staining in the schizophrenia group could, therefore, be explained by reduced axon myelination. Such a decrease in axon myelination is in line with both the disconnection hypothesis and the two-hit model of schizophrenia as a neurodevelopmental disease. Our results support that the cortical circuitry disorganization in schizophrenia might be caused by functional alterations of two major classes of principal neurons due to altered oligodendrocyte development. Schizophrenia is a psychiatric disorder that manifests as various symptoms: positive , negative (flattened affect and apathy), and cognitive  . Its etiThe disconnection hypothesis is of particular interest and is in line with the two-hit model of schizophrenia as a neurodevelopmental disease ,9,10. ItRecent studies have revealed that abnormalities in oligodendrocytes lead to altered myelination in schizophrenia -23. MyelIt is well-established that the degree of myelination affects axon impregnation in Golgi staining ,26. ThisQuantitative Golgi analysis was performed on the brain samples of ten human subjects aged from 30 to 86 years with a postmortem delay of 6 to 24 hours .Five subjects had no medical history of neurological or psychiatric disorders and no neuropathological deviations in the brain on autopsy. Another five samples were taken from patients with diagnosed schizophrenia during life according to DSM III and ICD-9 criteria. Relevant medical history was obtained from both autopsy reports and medical records. All analyzed subjects died without preagonal state, and the postmortem delay represents the actual interval of neuron death. The brain tissue is a part of the Zagreb Neuroembryological Collection ,29. It wThe brain tissue was cut into blocks following Talairach coordinates . Tissue The tissue was processed with the classical chrome-osmium rapid Golgi staining method ,33-36. IThe success of Golgi impregnation was determined in accordance with relevant literature ,34,37,382) from layers III and V of Brodmann area 9 were selected for three-dimensional reconstruction with Neurolucida 4 software  using a 60 × air objective of an Olympus BX50 microscope connected to a Hitachi 3CCD color video camera HV-C20M. The neurons were selected for reconstruction as follows: slides of brain tissue from Brodmann area 9 were numbered and randomly chosen for analysis. Subsequently, pyramidal neurons from a region of 3 mm pial length that encompassed all cortical layers meeting the following criteria were selected: the neurons had to be positioned in the middle of the section and the axons should not be cut on the slide edge. Another criterion was that the neurons had fully impregnated somata as well as four or more basal dendrites with at least third-order dendritic branching, in order to ensure adequate impregnation . The nd axons .Area was analyzed. Area refers to the surface within the boundary of the cell body in μm2. For axons, the parameters Length, Surface, Volume, Tortuosity, Base Diameter, and Average Diameter were analyzed. Length refers to the total length of line segments used to trace a stained axon segment. Surface and Volume refer to the total surface area and total volume of a stained axon segment, respectively, and are computed by modeling the pieces of the segment as truncated circular cones. Tortuosity is the ratio between the length of a stained axon segment and the distance (straight line) between the endpoints of the segment. A straight axon segment would have tortuosity of 1, and tortuosity increases with the complexity of the path of the axon segment. Base Diameter represents the diameter of the axon segment at the start of the segment, while Average Diameter is a length-weighted mean of the diameter along the stained axon segment.For cell bodies, the parameter Surface, Volume, Tortuosity, Base Diameter, and Average Diameter were evaluated only for the axon main trunk, ie, for first order axon segments .The length of the axon main trunk and of the axon collaterals were analyzed separately as “axon main trunk length” and “axon collateral length.” “Axon main trunk length” refers to the average stained length of the main trunk of the axon, ie, the average length of first order axon segments. The parameter “axon collateral length” refers to the average stained length of the axon collaterals, ie, the average length of second order axon segments. Quantitative data analysis was performed with GraphPad Prism, version 8.3.0  and SPSS, version 26 .P < 0.05 was considered statistically significant. Both groups contained three male and two female subjects (To ensure that the schizophrenia and control groups differ in as few parameters as possible (except in neuropathology), the two groups were compared for age and post-mortem time using the Mann-Whitney test. subjects , which eThe analyzed parameters  pertaining to neurons from each brain were shown to likely be sampled from a log-normal distribution. The distribution of the parameters was evaluated using GraphPad Prism’s in-built software, which runs four normality and four log-normality tests. The software fits a normal or log-normal distribution using the maximum likelihood method and compares the two likelihoods. Since the logarithmic distribution is characterized by the presence of extremely high values, to which the arithmetic mean is very sensitive, a more appropriate measure of central tendency to describe such data sets is the geometric mean. Therefore, the data for individual brains as well as for all pooled data are presented as geometric mean ×  ÷ geometric standard deviation factor (GSDF) .For every analyzed parameter pertaining to the axon main trunk, the geometric mean was calculated for each individual brain. These geometric means were used to calculate the means and standard deviations for the schizophrenia and control groups. For parameters pertaining to axon collaterals, only pooled data are presented.t-test was used. P < 0.05 was considered statistically significant.For comparison between the schizophrenia and control groups, Since the data are obtained from a hierarchical model, in order to verify the validity of the comparison between the groups, we also addressed different levels of variability by modeling the data using a generalized linear mixed model (GLMM). The advantage of a GLMM is that it can account for non-normal distributions as well as for a multilevel  data structure with both random and fixed effects -43. In pArea, age, and postmortem delay) and “axon main trunk length” was determined. Spearman’s rank correlation coefficient ρ was used as a measure for the strength of the association   than in the control group (49.0 μm ×  ÷ 2.0) . This wa= 0.032) . The axoThe axon collateral length was greater in the schizophrenia group (33.1 μm ×  ÷ 3.0) than in the control group (22.3 μm ×  ÷ 2.7). Furthermore, the neurons in the schizophrenia group had an average number of collaterals per axon of 0.22, compared with 0.07 in the control group. More axons had visibly stained collaterals in the schizophrenia group (14.7%) than in the control group (5.5%).The GLMM analysis revealed that for all the analyzed parameters, the random effects were not significant. The fixed effect (neuropathology) was significant for axon main trunk length, axon main trunk surface, and axon main trunk volume, while it was not significant for tortuosity, base diameter, and average diameter .P = 0.632) (P = 0.155) (Area and axon main trunk length was weak (ρ = 0.183) but significant (P = 0.001) (P > 0.999) and postmortem delay (P = 0.476).There was a weak negative correlation between age and axon main trunk length (ρ = - 0.176), which was not significant (= 0.632)  and a me= 0.155) . The cor= 0.001) . There wIn this study, the staining of the axon main trunk was on average significantly longer than in the control group. This was supported by the results of the GLMM analysis, which suggest that the differences between the two groups could be mainly attributed to the differences in neuropathology.The rapid Golgi method stains the axon up to the beginning of the myelin sheath ,26, and Based on the neurodevelopmental schizophrenia model, structural lesions in the perinatal period can interact with the process of synaptic pruning in associative circuitry during late adolescence and early adulthood -57. It iOur findings suggest that not all projection neurons are affected by demyelination equally. On average, there was a clear overall shift toward a greater axon impregnation length in schizophrenia subjects, and the average values of all schizophrenia subjects were higher than those of the control group. In schizophrenia subjects, at least half of the neurons had an axon impregnation length of over 100 μm, whereas in control subjects such length was present in only 10%-20% of neurons. In addition, four schizophrenia subjects had several neurons with a substantially greater axon impregnation length, while this was observed in only one control subject. It is worth noting that the variability in axon staining within a single brain was quite high and was particularly pronounced in subjects CO195 and CO246. These subjects also had the highest mean values in the schizophrenia and control groups, respectively. Our data strongly suggest an overall slight decrease in the level of axon myelination of pyramidal neurons in schizophrenia, which appears to highly affect only a certain proportion of neurons. Neurons with a high level of demyelination may have important functional alteration. Due to the central integrative role of deep layer III neurons in the cortico-cortical network and of large layer V neurons in guiding cortico-striatal executive actions, even demyelination affecting a small proportion of these neurons could cause global changes in cortico-cortical and cortico-subcortical processing ,12. In tArea) was weakly associated with the length of axon staining. We also demonstrated that the subjects’ age at death and postmortem delay on axon staining were unlikely to contribute to the observed differences between the schizophrenia and control groups. Age-related changes in the PFC have previously been described regarding the dendritic field, dendritic spine density, and synapse density (The analysis of potential confounding factors revealed that the size of the neuron cell body (cell body  density , however density . Our anaDue to the limitations of the rapid Golgi method ,39 and tIn conclusion, neurodevelopmental alterations during infancy and early childhood could present an opportunity for early detection of subjects at risk for developing schizophrenia . Therefo


Prunella vulgaris (PV) has a long history of application in traditional Chinese and Western medicine as a remedy for the treatment of subacute thyroiditis (SAT). This study applied network pharmacology to elucidate the mechanism of the effects of PV against SAT. Components of the potential therapeutic targets of PV and SAT-related targets were retrieved from databases. To construct a protein-protein interaction (PPI) network, the intersection of SAT-related targets and PV-related targets was input into the STRING platform. Gene ontology (GO) analysis and KEGG pathway enrichment analysis were carried out using the DAVID database. Networks were constructed by Cytoscape for visualization. The results showed that a total of 11 compounds were identified according to the pharmacokinetic parameters of ADME. A total of 126 PV-related targets and 2207 SAT-related targets were collected, and 83 overlapping targets were subsequently obtained. The results of the KEGG pathway and compound-target-pathway (C-T-P) network analysis suggested that the anti-SAT effect of PV mainly occurs through quercetin, luteolin, kaempferol, and beta-sitosterol and is most closely associated with their regulation of inflammation and apoptosis by targeting the PIK3CG, MAPK1, MAPK14, TNF, and PTGS2 proteins and the PI3K-Akt and TNF signaling pathways. The study demonstrated that quercetin, luteolin, kaempferol, and beta-sitosterol in PV may play a major role in the treatment of SAT, which was associated with the regulation of inflammation and apoptosis, by targeting the PI3K-Akt and TNF signaling pathways. Subacute thyroiditis (SAT), which is also called subacute granulomatous thyroiditis, de Quervain's thyroiditis, or giant-cell thyroiditis, is the most common cause of thyroid pain. SAT is a self-limited inflammatory thyroid disease possibly related to viral infection that usually presents as a prodrome of low-grade fever, fatigue, goitre, and pharyngitis symptoms . There iPrunella vulgaris (PV) has the functions of clearing fire and dispersing knots and swelling, and it has been confirmed that PV suppresses inflammation via several signal transduction pathways [pathways . In recepathways . Initialpathways . HoweverNetwork pharmacology, which elucidates the synergistic effects and the underlying mechanism of multiple components and multitargets, has been proven to be a powerful tool for the exploration of Traditional Chinese Medicine . In receIn the present study, network pharmacology analysis, including the identification of active ingredients, prediction of ingredient-related targets and SAT-related targets, construction of a protein-protein interaction (PPI) network, and gene ontology (GO) and KEGG pathway analysis, was subsequently performed to clarify the biological processes related to the target protein involved in the PV-mediated treatment of SAT. The workflow is as follows .http://lsp.nwsuaf.edu.cn/tcmsp.php) based on the term “Xiaokucao” to search for the effective components of PV. Then, according to the pharmacokinetic parameters of ADME, components meeting the criteria of both an OB  ≥30% and a DL (Drug-Likeness) ≥0.18 were defined as active ingredients [The PV-related targets were retrieved from the Pharmacology Database and Analysis Platform  and OMIM database (http://www.omim.org/) was performed. The keywords, “subacute thyroiditis,” “subacute granulomatous thyroiditis,” “de Quervain's thyroiditis,” and “giant-cell thyroiditis,” were used, and the targets were the human genes identified in this study. The intersection of compound targets and disease targets is considered to be therapeutic targets.Screening of SAT-related genes from the GeneCards database (Compound-target (C-T), compound-overlapping target (C-OT), and compound-target-pathway (C-T-P) networks were constructed by Cytoscape 3.7.2. The topological parameters of the nodes were calculated by a “network analyzer,” including the degree centrality (DC) and betweenness centrality (BC). Degree refers to the number of adjacent proteins. BC is another standard measure of node centrality in a network, which measures the total number of nonredundant shortest paths going through a certain node or edge; both parameters are considered to be great predictors of essentiality in the interaction network , 13. In https://string-db.org/) to construct a PPI network, with the species limited to “Homo sapiens” and high confidence (minimum required interaction score ≥0.7). Then, the network data were downloaded and input into Cytoscape 3.7.2, and the topological parameters of the nodes were calculated by a “network analyzer.” The nodes with DC scores and BC scores greater than 2 times the median were considered as important nodes in the network, namely, as hub targets [To clarify the direct or indirect regulatory relationship between therapeutic targets, we used the STRING platform  terms (Table S3), 36 molecular function (MF) terms (Table S4) and 25 cellular component (CC) terms (Table S5). We screened the top 10 significantly enriched terms in the BP, MF, and CC categories, as shown in P value < 0.05 , and we screened the top 20 pathways after removing uncorrelated pathways such as “bladder cancer” and “Chagas disease” according to a e < 0.05 . The resA C-T-P network was constructed based on the top 20 pathways and involved targets and the corresponding compounds by Cytoscape 3.7.2 to further illustrate the molecular biological process of PV treatment of SAT . A totalIn the present study, we studied the molecular mechanism of the effects of PV on the treatment of SAT with network pharmacology. A total of 11 components were eventually retrieved, which mainly included flavones and nonsteroids according to the criteria of OB ≥ 30% and DL ≥ 0.18. Quercetin, kaempferol, and luteolin, which are plant flavonoids, exhibited high topological values in the C-OT network, suggesting that these three components play an important role in treating SAT. Quercetin, exhibiting the highest degree value and a relatively high OB, has been demonstrated to improve thyroid function in vivo. It has been demonstrated that quercetin has a therapeutic effect on hyperthyroidism and could be used to protect against experimental hyperthyroidism-induced liver damage via the MAPK/Nrf2 pathway . Additioβ, PTGS2, APP, EGFR, ESR1, CCL2, MAPK14, AR, HMOX1, and NOS, which can be regarded as the hub targets of PV in the treatment of SAT. These genes are associated with inflammation, hypoxia, apoptotic processes, immune responses, oxidative stress, angiogenesis, and other processes. For example, IL6, together with tumor necrosis factor-alpha (TNF-α), and IL-1β are proinflammatory cytokines that serve as biomarkers for monitoring disease activity and predicting disease severity [β. JunB-independent AP-1 family members mediate the promotion of endothelial proliferation by VEGF, whereas induction of JunB expression primarily mediates VEGF-induced endothelial migration [Through the topological analysis of the PPI network, we identified 18 hub genes, including TP53, IL6, JUN, MAPK1, MAPK8, TNF, VEGFA, EGF, IL-1severity , 21. Junigration . VEGFA iigration . EGFR isigration , reduce igration . EGFR trα, and IL-6, which modulate thyroid hormonogenesis, were produced [Through the GO enrichment analysis of the therapeutic targets, one interesting phenomenon was observed: the therapeutic effect is mainly observed on the cell surface and in the extracellular space, and it is observed to a lesser degree in the cytosol. This result can be explained by the pathogenesis of SAT. This disease is thought to be caused by a viral infection or a postviral inflammatory process, and double-stranded RNA (dsRNA) formed intracellularly in thyrocytes may be a cause of thyroid dysfunction; therefore, autoimmunity may play a secondary role in pathogenesis , 27. dsRproduced , 30. In α is a multifunctional cytokine with proinflammatory and anti-inflammatory characteristics that plays a critical role in almost every type of inflammatory pathogenesis [α and other cytokines. Both quercetin and luteolin at 25 μM effectively decreased TNF-α, IL-6, IFN-γ, and IL-1β production in human whole blood incubated with LPS [κB and AP-1 are important transcriptional factors involved in the modulation of proinflammatory mediators. Quercetin (100 μM) significantly decreased NF-κB and AP-1 activity in high glucose-induced rat aortic endothelial cells. Luteolin (1–10 μM) significantly inhibited TNF-α and IL-6 production and MAPK (JNK and p38) and transcriptional factor (NF-κB and AP-1) activation in SW982 cells [By integrating the KEGG pathway analysis with the C-T-P network, we speculate that PV exerts its therapeutic effects against SAT mainly through quercetin, luteolin, kaempferol, and beta-sitosterol and is most closely associated with their regulation of inflammation and apoptosis by targeting the PIK3CG, MAPK1, MAPK14, TNF, and PTGS2 proteins and the PI3K-Akt and TNF signaling pathways, according to the topological parameters. The PI3K/Akt pathway functions in the transmission of cell signaling through transduction systems to the cell nucleus, where it plays an important role in cell growth, proliferation, apoptosis, migration, and differentiation . Activatogenesis . It has with LPS . Moreove82 cells . These rIn conclusion, a total of 11 components and 83 overlapping targets between subacute SAT-related and PV-related targets, as well as 18 hub genes and 90 pathways, were retrieved by network analysis. The results of the KEGG pathway and C-T-P network analysis suggested that the active components of PV, including quercetin, luteolin, kaempferol, and beta-sitosterol, may play a critical role in the treatment of SAT by targeting the PIK3CG, MAPK1, MAPK14, TNF, and PTGS2 proteins and the PI3K-Akt and TNF signaling pathways. The present study provides evidence to support the further study of PV for the treatment of SAT.


AIMP2-DX2, an exon 2-deleted splice variant of AIMP2 , is highly expressed in lung cancer and involved in tumor progression in vivo. Oncogenic function of AIMP2-DX2 and its correlation with poor prognosis of cancer patients have been well established; however, the application of this potentially important biomarker to cancer research and diagnosis has been hampered by a lack of antibodies specific for the splice variant, possibly due to the poor immunogenicity and/or stability of AIMP2-DX2. In this study a monoclonal antibody, H5, that specifically recognizes AIMP2-DX2 and its isoforms was generated via rabbit immunization and phage display techniques, using a short peptide corresponding to the exon 1/3 junction sequence as an antigen. Furthermore, based on mutagenesis, limited cleavage, and mass spectrometry studies, it is also suggested that the endogenous isoform of AIMP2-DX2 recognized by H5 is produced by proteolytic cleavage of 33 amino acids from N-terminus and is capable of inducing cell proliferation similarly to the uncleaved protein. H5 monoclonal antibody is applicable to enzyme-linked immunosorbent assay, immunoblot, immunofluorescence, and immunohistochemistry, and expected to be a valuable tool for detecting AIMP2-DX2 with high sensitivity and specificity for research and diagnostic purposes. EGFR) and for the anaplastic lymphoma kinase gene (ALK) rearrangements have opened new therapeutic opportunities for EGFR-positive solid tumors and ALK-positive non-small cell lung cancer, respectively; however, despite the recent attempts to discover novel targets from the uncharacterized genes of human genome, there are still over 43% cancer patients for whom no biomarkers or targeted therapies are available . We. We3-251g frames A. Accord35-251 as revealed by immunoblotting using rH5 mAb (34-251) could be the endogenous DX2 isoform detected by rH5 mAb. To further investigate whether DX234-251 is the dominant isoform, we cloned the cleavage-defective DX2 R33A mutant, transfected it into cells, and compared the levels of isoform generation. While the cells transfected with DX2-F wild type (WT) showed clear band of DX2 isoform, the cells with the mutant form revealed only a faint band  was fully identified  but the  rH5 mAb C, suggesint band D, implyi34-251 exhibited oncogenic function similar to DX2-F, and observed that the isoform efficiently enhanced cell proliferation similarly to DX2-F  value of hH5 mAb to the epitope peptide, DX2-F and DX234-251 were measured at 22.5 nM, 9.42 nM, and 3.23 nM, respectively  staining to investigate whether H5 mAb is applicable for IF. Strep-DX2 or -AIMP2 was overexpressed in CHO-K1 cells and stained with rH5 mAb. While we did not observe any clear signal from AIMP2-expressing cells A, left, Previous studies have shown that DX2 compromises the tumor suppressive activity of AIMP2 and promotes tumor progression . It is a7 clones as deduced from the number of E. coli transformants. While this number is somewhat low for an antibody library, the immunized antibody repertoire was already enriched with target binders and prominent binding clones could be isolated, even with VH-VL shuffling that might considerably dilute native VH-VL pairings in the original immune repertoire. The library was panned against the exon 1/3 junction sequence peptide conjugated to BSA, and multiple peptide binders were identified by ELISA screening of the panning output clones. Of these, only one clone could detect the endogenous DX2 isoform protein in H460 cell lysate in immunoblot assay, possibly due to the difference between the conformations of the BSA-conjugated peptide and the corresponding sequence in SDS-denatured protein, or because some of the clones recognized a part of the linker structure that conjugated the peptide to BSA. The conformational dependence of the antibody binding to the exon 1/3 junction sequence may also be the reason for the dependence of H5 binding to DX2 on the N-terminal sequence of the antigen, possibly via flexibility-derived conformational differences.In this study, we immunized rabbits with a short peptide, GHVQDYGALKD, specific to exon 1/3 junction of DX2. Remarkably, one of the two immunized rabbits yielded a polyserum that showed a strong and specific immunoblotting band roughly corresponding to the molecular weight of DX2. We generated a phage Fab library from the splenic cDNA of the rabbit, from which H5 mAb was isolated. The size of the Fab library prepared for this study was estimated to be 7.2 × 1084-225 which includes parts of exons 2 and 4 and the entire exon 3 [34-251, which could be generated by cleavage between Arg33 and Ser34 by proteases, such as trypsin . Taken together, these results suggested that DX234-251 could be used as a surrogate marker for DX2-F and sensitively detected by H5. The development of H5 mAb expanded the limitation of clone #324, which is only applicable to immunoblotting because the antibody detects both AIMP2 and DX2. In the era of precision medicine for cancer therapy, it is expected that the H5 antibody has the potential to be a useful research tool as well as a diagnostic agent for several solid tumors associated with DX2 overexpression, in IHC, cytology staining, or point-of-care testing kits. H5 mAb is currently being tested in multiple studies for the diagnosis of cancers including hematological and colorectal cancers, with increased numbers of patients to ensure proper clinical validation of DX2 and H5 (manuscripts submitted or in preparation).While DX2-F is detected much more weakly by H5 than by clone #324, DX2feration F. The leAIMP2-DX2 is selectively overexpressed in many cancers, and its expression level is correlated with the clinical outcome of the treatment, making it an ideal cancer diagnostic marker and a potential cancer treatment target. However, the generation of AIMP2-DX2 specific antibodies has been hampered by the poor stability and low immunogenicity of the splice variant, which in turn significantly delayed the biological characterization and the development of diagnosis for this important cancer marker. The novel monoclonal antibody H5 specifically recognizes AIMP2-DX2 with a preference for the isoform(s) with N-terminal truncation; our study suggests that the predominant isoform of AIMP2-DX2 is likely to have the first 33 amino acids cleaved by one or more intracellular proteases, the identity and the significance of which requiring further research efforts. Nonetheless, the AIMP2-DX2 isoform detected by H5 mAb is a viable surrogate for the full-length AIMP2-DX2 (DX2-F), and H5 mAb is expected to be a useful capture and detection agent for research and diagnosis of AIMP2-DX2-expressing cancers.The work reported in this manuscript has been applied for patents in the United States, the European Union, China, Japan, and Korea.


Sorghum bicolor (SB) is rich in protective phytoconstituents with health benefits and regarded as a promising source of natural anti-diabetic substance. However, its comprehensive bioactive compound(s) and mechanism(s) against type-2 diabetes mellitus (T2DM) have not been exposed. Hence, we implemented network pharmacology to identify its key compounds and mechanism(s) against T2DM.Compounds in SB were explored through GC-MS and screened by Lipinski’s rule. Genes associated with the selected compounds or T2DM were extracted from public databases, and the overlapping genes between SB-compound related genes and T2DM target genes were identified using Venn diagram. Then, the networking between selected compounds and overlapping genes was constructed, visualized, and analyzed by RStudio. Finally, affinity between compounds and genes was evaluated via molecular docking.GC-MS analysis of SB detected a total of 20 compounds which were accepted by the Lipinski’s rule. A total number of 16 compounds-related genes and T2DM-related genes  were identified, and 81 overlapping genes between them were selected. Gene set enrichment analysis exhibited that the mechanisms of SB against T2DM were associated with 12 signaling pathways, and the key mechanism might be to control blood glucose level by activating PPAR signaling pathway. Furthermore, the highest affinities were noted between four main compounds and six genes .Our study overall suggests that the four key compounds detected in SB might ameliorate T2DM severity by activating the PPAR signaling pathway. Type 2 diabetes mellitus (T2DM) is a metabolic disease triggered by the complex interaction between genetic and/or environmental associations. Patients with T2DM have either insulin resistance (sugar transfer dysregulation into cells) or lack of optimal insulin secretion to sustain natural glucose levels . T2DM caSorghum bicolor (L.) Moench) are the potential alternatives to ameliorate T2DM symptoms [Sorghum bicolor (SB) contains a number of secondary metabolites which are reported to be effective in preventing various metabolic diseases, such as cancers, T2DM, obesity, and hyperglycemia [For the past few years, a report has shown that wholegrains including sorghum  was purchased from Chuncheon local market, Korea. The collected SB was dried and powdered using electric blender. Approximately 500 g of SB powder was soaked in 500 mL of 100% methanol  for 3 days and repeated for 3 times to collect extraction. The solvent extract was collected, filtered, and evaporated using a vacuum evaporator . The evaporated sample was dried under a boiling water bath  at 40°C to obtain yield.The Agilent 7890A was used to carry out GC-MS analysis. GC was equipped with a DB-5 (30m×0.25mm×0.25μm) capillary column. Initially, the instrument was maintained at a temperature of 100°C for 2.1 minutes. The temperature was risen to 300°C at the rate of 25°C/min and maintained for 20 minutes. Injection port temperature and helium flow rate were ensured as 250°C and 1.5 ml/min, respectively. The ionization voltage was 70 eV. The samples injected in split mode at 5:1. MS scan range was set at 35–550 (m/z). The fragmentation patterns of mass spectra were compared with those stored in the using W8N05ST Library MS database. The percentage of each compound was calculated from the relative peak area of each compound in the chromatogram. The concept of integration used the ChemStation integrater algorithms.http://www.swissadme.ch/) to identify “Drug-likeness” property and oral bioavailability score. The PubChem (https://pubchem.ncbi.nlm.nih.gov/) was utilized for identification of the SMILES (Simplified Molecular Input Line Entry System) of compounds.The information of chemical compounds from SB was identified by utilizing GC-MS analysis which were filtered according to the Lipinski’s rule through SwissADME (http://sea.bkslab.org/) and Swiss Target Prediction (STP) (http://www.swisstargetprediction.ch/) with "Homo Sapiens” mode. T2DM related genes were identified by DisGeNET (https://www.disgenet.org/search) and OMIM (https://www.ncbi.nlm.nih.gov/omim) databases. The overlapping genes between compounds of SB and T2DM target genes were identified and visualized by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/).Based on the SMILES, target genes linked to the compounds were selected through both Similarity Ensemble Approach (SEA) (https://string-db.org/) analysis, the overlapping genes were closely correlated, and the signaling pathways of overlapping genes were analyzed by RStudio bubble chart. Networking between bioactive compounds and genes of SB against T2DM identified a hub signaling pathway.Through STRING . The proteins selected as.PDB format converted into.pdbqt format via Autodock (http://autodock.scripps.edu/).Six target proteins of T2DM i.e. FABP3 (PDB ID: 5HZ9), FABP4 (PDB ID: 3P6D), NR1H3 (PDB ID: 2ACL), PPARA (PDB ID: ASP6), PPARD (PDB ID: 5U3Q), PPARG (PDB ID: 3E00) were selected on STRING via RCSB PDB (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/). After docking, ligands of lowest binding energy were selected to visualize the ligand-protein interaction in Pymol.The ligand molecules were docked with target proteins utilizing autodock4 by setting-up 4 energy range and 8 exhaustiveness as default to obtain 10 different poses of ligand molecules . The 2D A total of 20 compounds in SB were detected by the GC-MS analysis , and theBased on the SMILES, a total of 308 genes from SEA and 324 genes from STP linked to 20 compounds were extracted . The resA total of 4,736 genes related to T2DM were sorted by retrieving DisGeNET and OMIM databases . The resThe final overlapping 81 genes were closely linked to each other with 81 nodes and 347 edges . The intThe result of KEGG pathway enrichment analysis demonstrated that 81 genes were related to 12 signaling pathways . The 12 signaling pathways were directly related to the progression of T2DM and indicated that these 12 signaling pathways might be the key pathways of SB against T2DM. The description of 12 signaling pathways is shown in From the SEA and STP databases, it was revealed that FABP3 gene is associated with six compounds , FABP4 gene is related to six compounds , NR1H3 gene is involved with 8 compounds , PPARA gene is related to 7 compounds , PPARD gene is associated with 8 compounds , PPARG gene is linked to 7 compounds  . Also, eHomo Sapiens” mode. Docking simulation of the affinity between A1-A6 and FABP3 protein in the “Homo sapiens” setting was analyzed. Based on the docking score, the order of the priority of binding energy is given: A5>A6>A2>A3>A1>A4. The six-binding energy of A1-FABP3, A2-FABP3, A3-FABP3, A4-FABP3, A5-FABP3, and A6-FABP3 demonstrated -6.6, -7.4, -6.7, -6.5, -8.4, and -7.8 kcal/mol, respectively. Interaction analysis of the best-docked compound namely “propyleneglycol monoleate” showed several hydrophobic bonds on FABP3 protein (PDB ID: 5HZ9). The hydrophobic amino acid residues are Thr-57, Phe-58, Val-33, Met-36, Gly-27, GLy-25, Phe-28, and Gln-32. The detailed information is enlisted in Homo sapiens” setting revealed promising binding affinity, and the order of the priority of binding energy is as follows: B6>B5>B2>B1>B3>B4. The six-binding energy of B1-FABP4, B2-FABP4, B3-FABP4, B4-FABP4, B5-FABP4, and B6-FABP4 revealed -4.6, -4.9, -4.5, -4.4, -5.6, and -7.2 kcal/mol, respectively. The 25-Oxo-27-norcholesterol (B6) had the strongest affinity on FABP4. Interaction analysis of best-docked compound namely “25-Oxo-27-norcholesterol” resulted one hydrogen bond (Ser-1) and seven hydrophobic bonds . The detailed information is enlisted in Homo sapiens” setting displayed promising binding affinity, and the order of the priority of binding energy is as follows: C2>C8>C3>C4>C1>C7>C6>C4. The eight-binding energy of C1-NR1H3, C2-NR1H3, C3-NR1H3, C4-NR1H3, C5-NR1H3, C6-NR1H3, C7-NR1H3, and C8-NR1H3 exhibited -7.3, -10.6, -7.9, -7.6, -4.9, -5.7, -5.9, and -8.3 kcal/mol, respectively. The campesterol (C2) has the strongest affinity on NR1H3. Interaction analysis of best- docked compound namely “campesterol” showed several hydrophobic bonds on NR1H3 protein (PDB ID: 2ACL). The hydrophobic amino acid residues are Gly-328, Arg-248, Leu-329, Gln-330, Val-331, Ile-299, Arg-302, Val-298, Asp-295, Leu-294, and Gln-429. The detailed information is enlisted in Homo sapiens” setting revealed promising binding affinity, and the order of the priority of binding energy is as follows: D6>D7>D3>D5>D2>D1>D4. The eight-binding energy of D1-PPARA, D2-PPARA, D3-PPARA, D4-PPARA, D5-PPARA, D6-PPARA, and D7-PPARA exhibited -4.9, -5.2, -6.0, -4.8, -5.8, -6.6, and -6.1 kcal/mol, respectively. The β-sitosterol (D6) has the strongest affinity on NR1H3. Interaction analysis of best- docked compound namely “β-sitosterol” displayed several hydrophobic bonds on PPARA protein (PDB ID: 3SP6). The hydrophobic amino acid residues are Glu-462, Ser-588, Leu-392, Asn-303, Val-306, Thr-307, Lys-310, Tyr-311, Gly-390, Pro-389, Arg-465, and Asp-466. The detailed information is enlisted in Homo sapiens” setting displayed promising binding affinity, and the order of the priority of binding energy is as follows: The eight-binding energy of E1-PPARD, E2-PPARD, E3-PPARD, E4-PPARD, E5-PPARD, E6-PPARD, E7-PPARD, and E8-PPARD exhibited -3.8, -5.2, -4.2, -7.3, -7.3, -4.6, -7.3, and -7.4 kcal/mol, respectively. The β-sitosterol (E8) has the strongest affinity on PPARD. Interaction analysis of best-docked compound namely “β-sitosterol” revealed one hydrogen bond (Met-440) and seven hydrophobic bonds . The detailed information is enlisted in Homo sapiens” setting displayed promising binding affinity, and the order of the priority of binding energy is as follows: The seven-binding energy of F1-PPARG, F2-PPARG, F3-PPARG, F4-PPARG, F5-PPARG, F6-PPARG, and F7-PPARG exhibited -5.2, -5.4, -5.2, -5.9, -7.9, -4.0, and -7.8 kcal/mol, respectively. Interaction analysis of best- docked compound namely “β-sitosterol” exposed several hydrophobic bonds on PPARG protein (PDB ID: 3E00). The hydrophobic amino acid residues are Tyr-169, Tyr-189, Leu-167, Thr-168, Lys-336, Arg-350, Glu-351, Lys-354, Gln-193, and Tyr-192. The β-sitosterol (F5) has the strongest affinity on PPARG. The detailed information is enlisted in The molecular docking analysis was performed to evaluate the binding energy of these six genes against their related each gene, respectively, and the docking figures are depicted in Compounds-genes networking system unveiled that therapeutic effect of SB against T2DM was related to 16 compounds out of 20 compounds detected by GC-MS, including five steroid derivatives, four fatty acyls, two fatty acid esters, one organooxygen, one prenol lipid, one pyrimidine nucleoside, one azole, and one coumarin. The proportion of steroid derivatives to 16 compounds was close to 30%, implying that steroid derivatives was the most essential than any other sorts of compounds for the amelioration effect of SB on T2DM.in vivo result was in line with in silico analysis [It was reported that some steroid derivatives have strong effect on hypoglycemic activity based on lipophilic properties . Noticeaanalysis . In addianalysis , 35. A panalysis . Anotheranalysis , 38. TheCompounds-genes networking also specified that the pharmacological effect of SB on T2DM was directly associated with 81 genes. The results of KEGG pathway enrichment analysis of 81 genes showed that 12 signaling pathways were directly linked to the occurrence and progression of T2DM, demonstrating that these signaling pathways might be the key pathways of SB against T2DM. The 12 signaling pathways with T2DM were succinctly discussed as follows.PPAR signaling pathway: PPAR ligands are the potential therapeutic candidates against T2DM, also, alleviate metabolic syndrome including obesity and insulin resistance . FurtherCollectively, this study suggests that 12 signaling pathways with 81 genes are linked to the development of T2DM. In addition, rich factor defines that the proportion of the DEGs number and the number of genes have been annotated in pathway . In otheSorghum bicolor (SB) is rich in beneficial phytoconstituents and seen as a possible source of natural antidiabetic agents. However, in this report, its comprehensive bioactive compounds and T2DM pathways were firstly investigated using the network pharmacology. The findings of this study indicate that the antidiabetic ability of the SB could be attributed to four main compounds  that were strongly related to PPAR signaling pathway. Therefore, our study suggests that the four key compounds of SB might ameliorate T2DM by activating the PPAR signaling pathway.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.