Adult bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stem cells that can differentiate into three lineages. They are suitable sources for cell-based therapy and regenerative medicine applications. This study aims to evaluate the hub genes and key pathways of differentially expressed genes (DEGs) related to osteogenesis by bioinformatics analysis in three different days. The DEGs were derived from the three different days compared with day 0.Gene expression profiles of GSE37558 were obtained from the Gene Expression Omnibus (GEO) database. A total of 4076 DEGs were acquired on days 8, 12, and 25. Gene ontology (GO) enrichment analysis showed that the non-canonical Wnt signaling pathway and lipopolysaccharide (LPS)-mediated signaling pathway were commonly upregulated DEGs for all 3 days. KEGG pathway analysis indicated that the PI3K-Akt and focal adhesion were also commonly upregulated DEGs for all 3 days. Ten hub genes were identified by CytoHubba on days 8, 12, and 25. Then, we focused on the association of these hub genes with the Wnt pathways that had been enriched from the protein-protein interaction (PPI) by the Cytoscape plugin MCODE.These findings suggested further insights into the roles of the PI3K/AKT and Wnt pathways and their association with osteogenesis. In addition, the stem cell microenvironment via growth factors, extracellular matrix (ECM), IGF1, IGF2, LPS, and Wnt most likely affect osteogenesis by PI3K/AKT. Mesenchymal stem cells (MSCs) are multipotent and nonhematopoietic stromal cells that have the ability for self-renewal . MSCs arFor this reason, several hypotheses have been considered in this study as follows: the first hypothesis is that NF-κB is an important mediator in osteogenesis promotion by toll-like receptor 4 via the BMP2 pathway. Since TLR4 from the lipopolysaccharide (LPS)-mediated pathway plays an important role in osteogenesis. The TLR4 promotes osteogenic differentiation upon stimulation by its ligand LPS . StimulaThe second hypothesis is that the genes in focal adhesion signaling promote osteogenic differentiation, since the extracellular matrix (ECM) can affect osteogenic differentiation via focal adhesion kinase (FAK). Upon binding of the ligand to integrins, FAK becomes activated . ActivatThe third hypothesis is that stem-cell niche and microenvironment mediate osteogenesis through the PI3K/AKT signaling pathway, since it seems that the PI3K/AKT is the central pathway in differentiating the MSCs into the osteoblast.Furthermore, as the role of the Wnt pathway in the differentiation of MSCs into osteoblasts , 15, theTo test these four hypotheses, we evaluated the gene expression profiling microarray data (GSE37558) at different time points  during osteogenesis of MSC and focused on the main biological processes and KEGG pathway enrichment of the differentially expressed genes (DEGs) which were derived from the three different days compared with day 0. The highlighted pathways in our study were including LPS-mediated signaling pathway, focal adhesion, PI3K/AKT, and Wnt pathway which play important regulatory functions during the osteogenesis of hMSCs.P values of < 0.01 and |logFC| > 0.5  analysis showed that angiogenesis, the non-canonical Wnt signaling pathway, and lipopolysaccharide (LPS)-mediated signaling pathway were commonly upregulated DEGs on days 8, 12, and 25 . The modules were extracted using MCODE according to the number of nodes that were > 4 and a node score of > 4. Enrichment analyses of the BPs and the KEGG pathway of modules were performed (Supplementary Table 1). Supplementary Table 1 list the modules of DEGs related to osteogenesis from BP and KEGG pathway for days 8, 12, and 25. Of note, the cell cycle and MAPK were enriched for all of the assessed days, whereas the Wnt pathway was enriched only on days 8 and 25. Supplementary Table 2 provides a list of the top 10 hub proteins identified by CytoHubba from Cytoscape for each of the days. The association of the hub genes with the Wnt pathway was investigated. We focused on the CTNNB1(β-catenin) as the hub protein involved in the Wnt signaling in upregulated DEGs for days 8 and 25. CTNNB1 was shown in module 4, day 8  have the capability to adhere to plastic surfaces; (ii) test positive for CD90, CD73, and CD105 and negative for CD79, CD19, CD45, CD34, CD14 or CD11b, and HLA-DR; and (iii) have the potential to differentiate into osteoblasts, chondrocytes, and adipocytes in vitro . In thisFRZB, FZD4, SFRP, and FZD1) in the non-canonical Wnt pathway. This pathway is frequently upregulated on days 8, 12, and 25 . TLR4 and CD14 from this pathway are two genes that are involved in the TLR4 signaling pathway. Three accessory proteins, including MD2, LBP, and CD14, have roles in TLR4 activation. LBP and CD14 facilitate the transfer of LPS to the TLR4/MD2 complex [LPS-mediated signaling pathway was another BP that was upregulated for all 3 days Fig. a. There  complex . A study complex . Taken t complex .Fig. 4MPDGFD and VEGFB, the receptor tyrosine kinases PDGFRA and PDGFRB were activated, followed by upregulation of mitogen-activated protein kinase 2 (MAP2K2) of MAPK  and phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) that belongs to the PI3K pathway  is affected by prostaglandin E2 that activates PI3K/AKT signaling, resulting in osteogenic differentiation induced by BMP . InteresMukherjee et al. have demonstrated that AKT played a role in all stages of osteogenic differentiation. The results of a study indicated that AKT2, but not AKT1, was important in osteogenic differentiation through BMP-2 that AKT2 stimulated expression of the Runx2 gene [nx2 gene . OverallIn our study, MAP2K2 from the PI3K-AKT pathway (on day 3) and MAPK3 (on day 8) as a hub gene were upregulated. MAP2Ks, including MEK1 (MAP2K1) and MEK2 (MAP2K2), activate ERK1 (MAPK3) and ERK2 (MAPK1) . StudiesIt has been recently demonstrated that JNK1 is a negative regulator of osteogenesis through BMP-2 by Runx2 phosphorylation . JNK2 isITGA1, ITGA10, ITGB5, CTNNB1, MYL9, VEGFB, LAMA2, COMP, ITGAV, PDGFRA, PDGFRB, PDGFD, COL11A1, and PIK3R1) involved in the focal adhesion pathway that upregulated on days 8–25. We have observed the upregulation of integrin subunit alpha 5 (ITGA5) on days 8 and 12. Hamidouche et al. noted that FAK/ERK1/2-MAPKs and PI3K signaling pathways promoted osteogenic differentiation through induction of ITGA5 hMSCs [ITGA11 and insulin-like growth factor binding protein 7 (IGFBP7) also upregulated on day 25. Integrin α11 (ITGA11) is a receptor for osteolectin that actives the Wnt pathway and promotes osteogenesis [IGFBP7 gene promoted osteogenic differentiation of hBM-MSCs by upregulation of the β-catenin pathway [There were 14 genes (A5 hMSCs . It has A5 hMSCs . In anotA5 hMSCs  that canA5 hMSCs . The intA5 hMSCs . TherefoA5 hMSCs . We haveA5 hMSCs . Our datogenesis . Recentl pathway .The role of important hub genes including interleukin (IL)-6, AKT1, VEGFA, CDK1, PLK1, CDC20, CCNA2, MAPK3 (on day 8), and CTNNB1 (on days 8 and 25) in the Wnt pathway was studied (Supplementary Table 2).The inhibitory effects of IL-6 in osteoblast differentiation in rheumatoid arthritis are due to its negative interaction with the Wnt pathway . In a stAKT1 is an important gene in the PI3K/AKT pathway. Studies have been conducted about the cross-talk between Wnt/β-catenin and PI3K/AKT signaling pathways , 64. HanVEGFA plays a pivotal role in angiogenesis. Numerous studies have assessed the role played by VEGFA in linking osteogenesis and angiogenesis , 67. In CDK1, PLK1, CDC20, and CCNA2 are genes involved in the cell cycle. Proliferation and differentiation have opposite connections . In agreMAPK3 is related to the MAPK pathway. The results showed that the ERK interacts with the Wnt/β-catenin signaling pathway. It has also been shown that the ERK pathway is involved in the differentiation of osteoblasts through the regulation of RUNX2, β-catenin, and ATF4 . The WntThe role of β-catenin was implicated in both canonical Wnt pathways and cell-cell adhesion , 76. In The interaction between integrin-related signaling molecules and the Wnt pathway suggested that integrin receptors are associated with integrin-linked kinase (ILK) . GSK3β iIn summary, PI3K and Wnt signaling are important pathways in osteogenic differentiation. IGF2 links integrin, PI3K/AKT, and MAPK (JNK1). The extracellular environment can affect osteogenic differentiation PI3K/AKT mediated via the following: (1) LPS by TLR4; (2) integrins from ECM by the binding of substances such as osteolectin and osteocalcin, and activation of the FAK/MAPK pathway; (3) IGF1 and IGF2 through IGFRs; (4) growth factors via RTK; and (5) beta-catenin via Wnt pathway. Whether PI3K/AKT promotes osteogenesis through BMP2 directly or via NF-κB should be investigated. Taken together, this study provides further insight into the role of signaling pathways and their interaction in determining the fate of mesenchymal stem cells into osteoblast.http://www.ncbi.nlm.nih.gov/geo/) database. GPL6947 platforms (Illumina HumanHT-12 v3.0 Gene Expression beadchip) were used for the gene expression profiles. In this study, we selected 13 samples from human BM-derived MSCs (hBM-MSCs) that had been cultured in osteogenic differentiation medium at four-time points . The control group comprised four replicates from day 0, and the differentiated groups included three replicates for each time point . Sample accession (GSM) of GSE37558 related to this study in four different days are shown in Table Raw data related to expression profiling of GSE37558  were takhttp://www.ncbi.nlm.nih.gov/geo/geo2r/) was used to identify the DEGs between the control group (day 0) and differentiated groups . The DEGs were identified with adjusted P values of < 0.01 and |logFC| > 0.5.GEO2R (https://david.ncifcrf.gov) for up- and downregulated DEGs. P values of less than 0.05 were considered to be the criteria. DAVID was also used to analyze both GO ontology and the KEGG pathway of the modules. Venn diagrams (http://bioinfogp.cnb.csic.es/tools/venny/index.html) were used to identify up- and downregulated DEGs for different days.Gene ontology (GO) for biological processes (BP), molecular function (MF), and cellular component (CC) in addition to KEGG pathway enrichment analyses of differential gene expressions were obtained using the Database for Annotation Visualization and Integrated Discovery  database to analyze the interactions among the DEGs. A cutoff value of greater than 0.4 was used to evaluate the protein-protein interaction (PPI) network. Visualization of the PPI networks was performed by Cytoscape . The Cytoscape StringApp was used to retrieve the functional enrichment of up- and downregulated DEGs at days 8, 12, and 25. The top 10 proteins were ranked by topological analysis methods using CytoHubba. We found the most similar proteins in the following methods: MNC, degree, EPC, and closeness. Key modules in the PPI network were identified by using the Cytoscape plugin Molecular Complex Detection . The criteria were both a node score and node number greater than 4.We used the Search Tool for the Retrieval of Interacting Genes  for days 8, 12, and 25. The media was replaced every 3 days, and the characterization of osteogenic was performed by alizarin red and Oil Red O staining .Human BM-MSCs were obtained from the Royan Stem Cell Bank. The study protocol was approved by the Royan Institute ethical committee board. All the experiments were designed in three biological replicates. The cells were cultured in α-MEM  with 10%FBS , 100 U/ml penicillin , and 100 mg/ml streptomycin. After 24 h, the cells were induced with osteogenic medium containing DMEM supplemented with 10%FBS, 50 μg/mL ascorbic acid , 10 mM β-glycerophosphate , and 1 × 10The real-time PCR was performed for the validation of microarray data. The expression level of mRNAs for CTNNB1, MAPK3, ITGAV, ITGA5, CCNB1, and TLR4 were evaluated by a real-time PCR System. We isolated total RNA by TRI reagent  from cultured MSCs in osteogenic medium on days 8, 12, and 25. cDNA was synthesized from 1 μg total RNA using the PrimeScriptTMRT reagent Kit . All reactions were carried out duplicate by using StepOnePlus Real-time PCR System . GAPDH was used as a reference gene to normalize the expression of all target genes. All tested groups compared to the control group (day 0). The △△Ct method was used to analyze Q-RT PCR data for quantitative analysis. The specific primers sequence for each gene is shown in Table Statistical analyses were performed using Prism statistical software (version 8). The statistical significance of the differences between groups at days 8, 12, and 25 was performed by the Kruskal-Wallis test.Additional file 1:. Supplementary Figure 1. Venn diagram of molecular function (MF) and cellular component (CC) on days 8, 12, and 25. a Upregulated differentially expressed genes (DEG). b Downregulated DEGs. Supplementary Table 2. Hub genes for up- and downregulated genes ranked in CytoHubba. Supplementary Figure 2. Protein-protein interaction (PPI) network (STRING). Supplementary Figure 3. The ability of MSCs to differentiate into osteoblast and adipocyteAdditional file 2:. Supplementary Table 1. Biological process (BP) and KEGG enrichment analyses of the differentially expressed genes in the Modules. (XLS 42 kb)


Mesial temporal lobe epilepsy (MTLE) is a chronic neurological disorder affecting almost 40% of adult patients with epilepsy. Hippocampal sclerosis (HS) is a common histopathological abnormality found in patients with MTLE. HS is characterised by extensive neuronal loss in different hippocampus sub-regions. In this study, we used laser microdissection-based microproteomics to determine the protein abundances in different regions and layers of the hippocampus dentate gyrus (DG) in an electric stimulation rodent model which displays classical HS damage similar to that found in patients with MTLE. Our results indicate that there are differences in the proteomic profiles of different layers (granule cell and molecular), as well as different regions, of the DG . We have identified new signalling pathways and proteins present in specific layers and regions of the DG, such as PARK7, RACK1, and connexin 31/gap junction. We also found two major signalling pathways that are common to all layers and regions: inflammation and energy metabolism. Finally, our results highlight the utility of high-throughput microproteomics and spatial-limited isolation of tissues in the study of complex disorders to fully appreciate the large biological heterogeneity present in different cell populations within the central nervous system. Laser microdissection-based microproteomics allows for the study of the proteome of enriched specific cell types of interest obtained from a small amount of tissue (100 μg or less)3.Currently, it is well recognised that proteins and their biological roles are multifaceted and complex since all biological mechanisms rely on correct protein function. However, a major part of protein cellular machinery remains unknown, especially in disease-related processes. Therefore, studying proteins, specifically their presence and abundance, is relevant when addressing complex mechanisms leading to disease27/02/2020 18:22:00. It presents a wide variety of clinic manifestations, aetiologies, severities, and prognoses. However, the occurrence of an epileptic seizure, caused by abnormal neuronal discharges, is the common feature of all types of epilepsy4. Mesial temporal lobe epilepsy (MTLE) is the most frequent form in adults, present in 40% of patients with epilepsy, many of whom do not respond to clinical treatment8.Epilepsy is a chronic neurological disorder affecting around 2% of the world population10, which can be divided into three types according to the International League Against Epilepsy (ILAE) classification. Type 1 is the most common, and it is characterised by neuronal loss that mainly affects the CA1 and CA3 regions (Cornu Ammonis) of the hippocampus14. The hippocampus is a medial temporal lobe structure that is involved in cognitive functions, such as recent memory formation and spatial navigation. The dentate gyrus (DG) is an integral part of the hippocampal formation and may be subdivided into dorsal and ventral regions in a longitudinal disposition in rodents and posterior and anterior regions in humans20. Despite their similar neuronal compositions, the dorsal and ventral portions of the DG have different connections with cortical and subcortical areas, which indicate a variation in the structure and function throughout the dorsal-ventral axis21. Furthermore, the DG has three layers: the molecular layer (ML) is formed mostly by dendrites of granule cells and axons from the perforant pathway, the granule cell layer (GL) is comprised of granule cell bodies, and the polymorphic layer contains a number of different cell types23. Considering these characteristics of the hippocampus and that it is the most affected structure in patients with MTLE due to the HS, the precise isolation of different cell populations and cell compartments, such as the DG, may improve the specificity of the information obtained using high-throughput techniques, such as microproteomics; thus, leading to the identification of unique mechanisms that take place in precise anatomic regions and cell types22.The most frequent histopathological characteristic found in MTLE is hippocampal sclerosis (HS)In this study, for the first time, we present the proteomic profile from different regions and layers of the DG, obtained by laser microdissection-based microproteomics of an epilepsy rodent model displaying a HS phenotype similar to the classical observed in patients with MTLE.24. Animals were euthanised 15 days after the last stimulation session, and brains were collected and processed for laser microdissection. The time point of 15 days for the collection of tissue was chosen because it is part of the ‘silent phase’ of the model as described previously24. At this time point, an extensive neuronal loss is observed, especially at the CA1, CA2, and CA3 regions of the hippocampus of stimulated rats, which likely occurs due to the electrical stimulation. Therefore, the 15-day time-point fairly reflects the molecular and cellular mechanisms that underly the epileptogenesis process that occurs in the perforant pathway stimulation (PPS) model. We have used the term ‘epileptogenesis’ in the present work, according to the new terminology revised by the ILAE25.Electrical stimulation was performed one week after electrode placement. First, the animals were stimulated for 30 minutes on 2 consecutive days and for 8 hours on the third dayOverall, we found that more than 50% of the proteins differentially expressed at p < 0.05 were uniquely expressed in specific layers and regions of the hippocampus. Specifically, there were 36 out of 58 proteins expressed solely in the granule cell layer of the dorsal dentate gyrus (GL-dDG), 11 out of 16 in the molecular layer of the dorsal dentate gyrus (ML-dDG), 50 out of 75 in the granule cell layer of the ventral dentate gyrus (GL-vDG), and 21 out of 34 in the molecular layer of the ventral dentate gyrus (ML-vDG) , of which 32 were downregulated and 26 were upregulated Fig. . The mosWhen comparing the ML-dDG of epileptic rats with sham controls, we identified a total of 448 proteins. We found that 16 of them were differentially expressed p < 0.05), where 11 were downregulated and 5 upregulated . In total, 52 were downregulated and 23 were upregulated, as shown in Fig. In the final comparison of the ML-vDG of epileptic rats with sham controls, we identified 534 proteins, of which, 34 were differentially expressed at p < 0.05. Of these, 29 were downregulated and 5 were upregulated Fig. . The mosOverall, there were two main pathways present in all layers and regions of the DG: inflammation and energy metabolism. We identified the following inflammatory pathways in the GL-dDG: role of integrins in NK cells cytotoxicity, PD-1-induced metabolic changes in T cells, antigen presentation by MHC class I, classical pathway and the effect of INDO on T cell metabolism. The enriched inflammatory pathways in the GL-vDG were CRTH2 signalling in Th2 cells, MIF (the neuroendocrine-macrophage connector), function of MEF2 in T lymphocytes, IL-16 signalling pathway, reactive oxygen species (ROS) in IL-4 signalling, IL-3 signalling via ERK and PI3K, regulatory role of C1q in platelet activation, MIF-JAB1 signalling, and NETosis in SLE. In contrast, the ML-dDG did not present any enriched inflammatory pathways; and the ML-vDG presented the T cell receptor signalling pathway. In addition, we found the following energy metabolism pathways: oxidative phosphorylation (in the GL-dDG), glycolysis, gluconeogenesis (in the GL-dDG and ML-vDG), glycogen metabolism (in the ML-vDG), pyruvate metabolism (in the GL-dDG), tricarbonic acid cycle , and mitochondrial dysfunction in neurodegenerative diseases .−6. For the ventral DG, the data showed 82 nodes and 167 edges, while the expected number of edges was 49 and the PPI enrichment p-value was 1 × 10−16. For the reactome pathways in the dorsal DG, we found two enriched pathways: metabolism and membrane trafficking. While for the ventral DG, we found 24 enriched pathways, the most significant were L1CAM interactions, transmission across chemical synapses, recycling pathway of L1, and axon guidance. The protein networks are shown in Fig. Remarkably, we found 49 proteins that have never been reported as possibly being involved in epilepsy in an animal model or human tissue. These proteins are listed in Table 24  in the two sub-regions  of the hippocampus DG in animals submitted to the PPS protocol. The granule cell layer is mainly composed of granule cells, which is the only type of cell that gives rise to axons that innervate other hippocampal areas. There is also the mossy cell, whose axons leave the DG from one side to innervate the contralateral DG. The granule cell layer also contains numerous types of interneurons, mainly inhibitory. The molecular layer is primarily composed of dendrites of the granule cells, as well as axons from the enthorrinal cortex and other sources24. Therefore, to make the comparison of the two genomic strategies meaningful, we chose to use the same tissue samples as in the previous study, thus eliminating biological variability and taking advantage of the RNA-sequencing data as a validation strategy for the current work. However, we acknowledge that by using a spatially defined cell population of interest, we are not able to apply more traditional validation techniques, such as western blot, due to the limited amount of protein obtained. Nonetheless, we are confident in the biological validity of the results presented here since the technique we used, mass spectrometry, is highly sensitive and accurate. Furthermore, consistent with our previous report on the transcriptome of the PPS animal model24, we identified a different profile of protein expression in the dorsal and ventral portions of the DG in the PPS model of MTLE.An important strength of the present work is the availability of previously published RNA-sequencing data24. Thus, inflammation-related pathways seem to be activated in the PPS model, and the most prevalent pathways and biological processes identified were related to immune response, suggesting either an active phagocytosis process or infiltration of immune system cells in the DG24. Different inflammatory pathways were present in both regions of the DG, as also reported in the transcriptomics study24. Considering the PPI analysis, our results also point to enrichment of inflammation-related pathways in the ventral DG. In adult rodents, the generation of recurrent seizures, either by chemoconvulsants or electrical stimulation, triggers the rapid induction of inflammatory mediators in brain regions related to seizure activity onset and propagation26. Therefore, inflammation may be a consequence, as well as a cause of epilepsy. Brain inflammation can occur in types of epilepsy that are not linked to immunological dysfunction, indicating that chronic inflammation might be linked to some epilepsies, independent of the presence of initial insults or causes28. Seizures can induce brain inflammation that may become chronic, leading to progressive cell loss and inducing additional seizures, which can contribute further to inflammation31. Indeed, the PPS model used in the present study presents severe neuronal loss due to the induction of epilepsy, indicating the occurrence of an active process of inflammation during epileptogenesis. Similarly to what we found in the PPS model, pilocarpine and kainic acid-induced epilepsy models, as well as tissue from patients with MTLE share common neuroinflammatory response in brain areas affected by the injury caused either by the presence of a precipitant insult  or by recurrent seizures31. Furthermore, we found that some of the identified enriched inflammatory pathways  had PKC (in the GL-vDG), cPKC (in the GL-vDG), and the 14-3-3 protein (in the GL-vDG) as the main proteins abnormally regulated. These are known to be involved in signal transduction, and PKC, in particular, is responsible for phosphorylating many classes of proteins inside the cells (UniProt database). Therefore, as these proteins are downregulated, we suggest that these changes may be due to a compensatory mechanism aiming to decrease the inflammatory processes during epileptogenesis in the PPS model.Yet, we found two important pathways present in all layers and regions of the DG: inflammation and energy metabolism. These two pathways have been linked to many neurological disorders, and this has also been found in our transcriptomics results). Remarkably, in all energy-related enriched pathways, we found downregulation of the identified proteins. Mitochondrial oxidative phosphorylation is the primary source of ATP in neurons, and patients with HS can have mitochondrial ultra-structural abnormalities in DG neurons32. The combination of mitochondrial dysfunction and high neuronal excitability leads to an intracellular decrease in ATP levels and abnormal calcium homeostasis33. Animals with epilepsy induced by kainic acid have deficits in mitochondrial function34. Oxidative phosphorylation is also responsible for maintaining cell membrane potential using the Na++/K+ pump ATPase35. Therefore, changes in energy metabolism can be directly linked to epileptogenesis.The second major pathway enriched in both layers and regions of the DG was energy metabolism. This result was also seen in the PPI analysis, and the main enriched pathways were metabolism (dDG and vDG), the citric acid cycle (vDG), gluconeogenesis (vDG), and amino acid metabolism  and vimentin (in the GL-dDG). These changes indicate an active process of astrogliosis and tissue damage, probably induced by neuronal death resulting from the stimulation performed in PPS animals.36, and it has been implicated in neuronal repair as a response to injury caused by ischemia, seizures, and other types of damage to the nervous system37. Therefore, the upregulation of Cys-C in the PPS model is likely to represent a defence mechanism against neuronal injury. Furthermore, the enrichment of the cell adhesion-gap junction pathway in the ML-vDG was represented mainly by the dysregulation of the structural protein tubulin alpha, tubulin (in microtubules), and tubulin beta. We also found that the transcript of connexin 31 is downregulated exclusively in the ML-vDG. The tubulin proteins and connexin 31 are present in gap junctions, and connexin 31 is responsible for the electrical coupling of cells, having an essential role in the synchronisation of neuronal activity39. Therefore, changes in proteins related to gap junctions, mainly connexin 31, is a new molecular mechanism to be further investigated in epilepsy. In addition, the downregulation of proteins involved in electrical coupling could alter the ionic balance in the neurons.It is noteworthy that cystatin-C (Cys-C) was differentially expressed in a specific layer and region of the DG, and it was upregulated in the GL-dDG of the PPS rats. Cys-C has been found to be abnormally expressed in an animal model of chronic epilepsy induced by the stimulation of the amygdala41. RACK1 has been associated with synapse plasticity, neurogenesis, and neuronal migration, and its decreased expression is known to result in seizures and epilepsy42. Furthermore, RACK1 is a scaffolding protein that is responsible for activating PKC, which in turn is associated with some of the enriched pathways found in our study, besides inflammation, such as activity-dependent synaptic AMPA receptor removal, constitutive and regulated NMDA receptor trafficking and signal transduction , in the GL-vDG. In addition, RACK1 could be involved in the regulation of SCN1A, a gene that encodes the sodium voltage-gated channel alpha subunit 1 protein. SCN1A mutations cause Dravet syndrome, an epileptic encephalopathy, and genetic epilepsy with febrile seizures plus (GEFS+)43.Interestingly, we found that a given protein could be regulated in the opposite direction in different regions of the DG in PPS animals. RACK1 was upregulated in the GL-dDG and downregulated in the GL-vDG. RACK1 is associated with various cell signalling pathways, especially those involving the cell membrane46. Furthermore, in disease models where mTORC1 is overactive, PARK7 expression is also increased, suggesting that PARK7 regulates mTORC146. In turn, mTORC1 activity can induce rapid and dramatic remodelling of the synaptic proteome by regulating the synthesis of specific proteins and by altering the local expression of synaptic proteins46. Proteins in the mTORC1 signalling pathway seem to control the synthesis of GAP-43, which was found to be downregulated in the GL-vDG of PPS animals46. All these changes, together with the enrichment of the mTORC2 downstream signalling pathway, suggest that abnormal expression of PARK7 and GAP-43 might be a result of the dysregulation of the mTOR pathway in the PPS model. Indeed, the mTOR pathway has been shown to be involved in many neurological disorders, such as autism spectrum disorders48, Alzheimer’s disease, tuberous sclerosis complex50, focal cortical dysplasia52 and, more recently, intractable epilepsy58. The mTORC1 and mTORC2 complexes are also upregulated in tissue from patients with MTLE59. Therefore, our findings strengthen the relevance of the mTOR pathway in the mechanisms underlying MTLE60.We also identified that the PARK7 protein was downregulated in the GL-vDG and upregulated in the GL-dDG. PARK7 has been involved in some types of Parkinson´s disease and other neurological disorders62. However, the correlation between transcripts and their protein products has been described as surprisingly low and very dependent on the tissue species analysed65. Indeed, this correlation has been reported to be modest for human and chimpanzee brain tissue 67. Moreover, this correlation varies in different cell types in the brain69. The discrepancies between the abundances of specific transcripts and proteins may be caused by the complexity and specificity of the transcription and translation mechanisms and are likely to involve variations in degradation rates, molecular abundances, alternative splicing, the occurrence of post-translation modification, and, especially, differences between transcription and translation rates70. In addition, we also found heterogeneity in the degree of correlation between transcripts and proteins in the different areas examined in our study and this correlation was lower in the dorsal DG.One may expect a high correlation between the expression levels of RNA and a proteinIn conclusion, we report the proteomic profile of an animal model of MTLE, displaying the typical histopathological features of HS, as seen in patients with MTLE. Altogether, our results indicate that there are differences in the proteomic profile of differentially regulated proteins between different layers (granular and molecular layers) and different regions of the DG . However, we also identified two major signalling pathways that are enriched in all layers and regions examined: inflammation and energy metabolism, both of which have been previously implicated in the mechanisms leading to epilepsy. In addition, we identified new signalling pathways and proteins present in specific layers and regions of the DG, such as PARK7, connexin 31/gap junction differential regulation in the vDG, and RACK1, which has opposite differential regulation in the dDG and vDG. These findings can now be further explored in additional studies. Finally, our results highlight the utility of high-throughput OMICS approaches in the study of disorders with complex and intricate mechanisms, such as epilepsy, and the importance of obtaining tissue from more circumscribed anatomic areas to fully appreciate the large biological heterogeneity present in the central nervous system.24.The experimental protocol was approved by the University of Campinas (UNICAMP) animal research ethics committee, which evaluates experimental protocols according to currently accepted ethical practices and legislation regarding animal research in Brazil [Brazilian federal law 11.794 (10/08/2008)]. We used the same animals  and procedures described in a previously published study aimed at reporting the transcriptome profile of PPS animalsThe frozen tissue, with 60-μm thickness, was serially sectioned in a Leica cryostat at −15 °C, covering the whole hippocampus. The tissue was mounted in PEN membrane-coated slides (Life Technologies), and they were immediately stained with Cresyl violet and dehydrated with ethanol. Microdissection was performed with a Palm Zeiss System. Samples were then manually collected using a forceps in 1.5-mL Axygen microtubes under surgical microscopy, where one tissue section was separated for microproteomics and one for transcriptomics.2 , proteins were digested with 0.6 μg of trypsin  by incubation for 16 h at room temperature. The reaction was stopped by adding TFA  to a final concentration of 0.4%. Samples were centrifuged at 2500 g for 10 minutes at room temperature, and proteins were desalted using SepPack columns  with acetonitrile , dried in SpeedVac vacuum concentrator (Eppendorf), and stored at −20 °C.Tissue samples were homogenised with 50 μL of urea (8 M) and incubated for 1.5 h at room temperature. Samples were subsequently centrifuged at 2000 g for 5 minutes, and the supernatant was quantified using the Qubit Protein Assay kit . We used 30 μg of protein for each sample, which was reduced with 5 mM DTT  at 56 °C for 25 minutes and alkylated with 14 mM iodoacetamide  at room temperature for 30 minutes in the dark. After the addition of 1 mM CaCl−1 for 85 minutes. The nanoelectrospray voltage was set to 2.2 kV, and the source temperature was 275 °C. All instrumental methods were set up in the data-dependent acquisition mode (DDA). Full scan MS spectra (300–1600 m/z) were acquired in the Orbitrap analyser after accumulation to a target value of 1 × 106. The resolution in the Orbitrap was set to r = 60.000, and the 20 most intense peptide ions with charge state ≥2 were sequentially isolated to a target value of 5.000 and fragmented in the linear ion trap using low-energy CID . The signal threshold for triggering an MS/MS event was set to 1.000 counts. Dynamic exclusion was enabled with an exclusion size list of 500, an exclusion duration of 60 s, and a repeat count of 1. An activation of q = 0.25 and an activation time of 10 ms were used71.Samples were reconstituted in 67.5 µL of 0.1% formic acid. An aliquot of 4.5 μL was analysed on an ETD enabled Orbitrap Velos mass spectrometer  connected to an EASY-nLC system  through a Proxeon nanoelectrospray ion source. Peptides were separated by a 2–90% acetonitrile gradient in 0.1% formic acid using a PicoFrit analytical column  at a flow rate of 300 nL min72. We did not consider any cut-off for fold change values since we believed that biological significance could be lost when this was done. To obtain enriched pathways, gene ontology, and networks involving the identified proteins, we used Metacore® software (Thomson Reuters). Multiple comparison correction was performed through the false discovery rate (FDR) at a level of 0.05, but it did not result in any significant protein (p.adjust). Therefore, we considered statistically significant results for which p-values were <0.0573. The PPI analyses were performed using the STRING database, and for pathway enrichment, we considered the Reactome database inside the STRING74.We performed the statistical modelling and inference by fitting linear mixed models as implemented in the MSstats R/Bioconductor packageA picture of one hippocampus from one naïve rat was obtained in an optical microscope, and the image was processed using Adobe Photoshop to build the schematic representation of the DG subfields.Legend for supplementary material.Supplementary Dataset 1.Supplementary Dataset 2.Supplementary Dataset 3.Supplementary Dataset 4.Supplementary Dataset 5.Supplementary Dataset 6.Supplementary Dataset 7.Supplementary Dataset 8.Supplementary Dataset 9.Supplementary Dataset 10.Supplementary Dataset 11.Supplementary Dataset 12.Supplementary Dataset 13.


Developing effective drugs for Alzheimer’s disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood–brain barrier-permeable FDA-approved drugs. Our study provides a strategy for precision medicine through the convergence of mathematical modeling and a miniature pathological brain model using iCOs. Developing effective drugs for Alzheimer’s disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, the authors report an efficient network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of human cerebral organoids. Symptoms include memory deterioration and cognitive impairment, resulting from neuronal loss, hippocampal atrophy, and brain inflammation2. On the molecular level, the pathology is characterized mainly by the accumulation of amyloid plaques, neurofibrillary tangles, and dystrophic neurites consisting of hyper-phosphorylated tau protein3, which can be identified by Pittsburgh compound B (PiB)-positron emission tomography (PET) and tau-PET, respectively4. The difficulty of acquiring human brain samples and the lack of a disease model that adequately recapitulates the pathological hallmarks pose significant challenges in the field.Alzheimer’s disease (AD) is the most common form of dementia, afflicting more than 40 million individuals worldwide5. Moreover, AD phenotypes do not appear spontaneously with aging in non-transgenic mice, casting doubt on the existence of disease-initiating molecular pathways in these species6. Cellular models, such as iPSC-derived neurons, offer the advantage over animal models in having a human genetic background8, but conventional monolayer culture, which lacks the interstitial space, fails to show the extracellular amyloid aggregate deposition that is a major hallmark of AD10. These limitations could explain the consistent clinical failure of medications found to be effective in pre-clinical models12. A physiologically relevant human-derived in vitro model for drug screening is urgently needed to enable the successful translation of AD drug candidates from bench to bedside.Numerous animal and cellular models have been developed to tackle this issue, but each has unresolved limitations. Animal models provide valuable insights into disease mechanisms, but the commonly used transgenic rodent models carry familial AD mutations that account for 5% of all AD cases, although the most common type of AD is sporadic cases (>95%) in which apolipoprotein E (ApoE) ε4 allele is the major genetic risk factor for sporadic AD14. Several groups have demonstrated the applicability of cerebral organoids in neurodegenerative studies by integrating genetic mutations that are definite causes of the disease15. These studies have shown that using cerebral organoids as a three-dimensional (3D) in vitro model can provide aspects of and insights into pathological conditions that could not be recapitulated in conventional monolayer culture. These include extracellular deposition of amyloidogenic peptides, propagation of protein through complex cell-to-cell interactions in a spatial context, and the impaired interplay of diverse cellular subtypes16. The guided formation of cerebral organoids by the timed supplementation of cells with defined growth factors has been shown to yield multiple neuronal subtypes at consistent proportions17. Using this method, we were able to produce a massive number of homogeneous organoids suitable for high-content screening (HCS) system.In recent years, the development of cerebral organoids has opened up a previously unknown realm of the human brain that could not be explored due to limited accessibility20. Several subtypes of AD exist according to their different onset mechanisms that depend on causal risk factors, which signifies the need to identify optimal drug target for each risk factor22.AD is a multifactorial disease that is caused by malfunctions in its complex regulatory processes, such as vesicle trafficking, endocytosis, lipid metabolism, and immunity23.Given the complexity arising from the diverse risk factors and multi-step pathogenic processes of AD, it is difficult to identify a disease-modifying target for each patient by conventional single pathway analysis. Hence, an integrated system-level approach is required to determine an optimal drug target with the consideration of the existing genetic factors and their effects on the complex molecular landscapeIn this study, we show that iPSC-derived cerebral organoids (iCOs) developed from sporadic AD patients who are predisposed for an increased brain burden of both amyloid and tau, recapitulate the pathological features of the disease. Mass production of iCOs that are uniform in size and homogeneous in cell composition enabled us to perform drug screening using HCS system on a physiologically relevant platform. Mathematical modeling considering a network of molecular pathways and relevant genetic factors was employed to identify several FDA-approved drugs as candidates for drug repositioning. In sum, by integrating mathematical modeling and pathological features of brain organoids, we herein developed a drug-screening platform that can be expanded for use in precision medicine.− iCOs and PiB+ iCOs). iCOs from CRISPR-Cas9-edited apolipoprotein E (ApoE) ɛ4 isogenic iPSC lines were also used . The process of making sAD organoids is summarized in Fig. The overall scheme of this study is presented in Fig. + #1 iPSCs, which showed triple X syndrome, and were thus excluded from the comparative analyses of PiB− iCOs vs PiB+ iCOs. We also quality-checked the generated iCOs. On day 60, the iCOs showed some regions of Sox2+ neuroepithelial budding zones and had NeuN+ or beta III-tubulin+ neuronal cells  positivity  and E4 isogenic (E4iso) iPSCs, and compared them. Similar to the above-described results, E4iso iCOs exhibited higher levels of the examined pathogenic proteins, compared to E3par iCOs  plot showed that there was ApoE ɛ type-dependent separation  in the mRNA expression pattern  variant  in lipoprotein lipase (LPL) 1-42, Aβ1-40, total tau, and phosphorylated tau (p-tau), secreted to conditioned media. As expected, PiBCOs Fig. , upper, COs Fig. . Interesers Fig. , lower. COs Fig. , with thCOs Fig. . This suern Fig. . An excesis Fig. . Furthersis Fig. . Interessis Fig. . In addisis Fig. . Togethe26  confocal microscopy imaging  suppresses tau hyperphosphorylation and apoptotic signaling. Meanwhile, AKT activates mTORC1 to inhibit autophagy, such that activated AKT subsequently causes accumulation of Aβ34. In this way, numerous signaling pathways are involved in the regulatory process of AD through complicated interactions. Such complexity makes it difficult to intuitively understand how perturbing a given gene or protein will affect the accumulation of pathological processes.In order to understand the complex dynamics of molecular interactions in our iCOs with pathological features of AD, we need to construct a molecular regulatory network model using systems biology approach. For example, the MAPK signaling pathway is activated in carriers of the ApoE ɛ4 subtype, which is reflected by increases in Aβ productionWe herein developed a relevant mathematical model of the neuronal molecular regulatory network for AD, with the goal of enabling researchers to gain a better mechanistic understanding of AD pathological dynamics at a molecular-regulation level and systematically investigate candidate molecular targets for their ability to alter the levels of pathogenic proteins. The procedure of constructing our network model is described in the Methods. In our network model, the neuronal intra-cellular molecular pathways are mainly composed of MAPK signaling pathway, WNT signaling pathway, and PI3K-AKT signaling pathway Fig. . Genes a35  and LPL SNP (LPLA288T SNP), which showed different pathological features in the previous experiments, and to identify the regulation of candidate targets which can reduce the abundance of pathological proteins.We conducted in silico perturbation analysis to understand the dynamic behavior of network models for ApoE ɛ4 allele  expression. Moreover, the autophagy-related signaling pathway is down-regulated through suppressed TFEB, leading to the accumulation of Aβ and p-tau. The non-canonical WNT pathway is up-regulated by increased Aβ, which forms a vicious cycle that increases Aβ production through BACE1 activation. These are consistent with previous biological observationsIn order to identify the optimal candidate targets for lowering the abundance of Aβ and p-tau, we performed in silico perturbation analysis Fig. . For thiFrom the simulation results, we selected those targets that have high phenotype scores as far as FDA-approved drugs are available for drug repositioning to inhibit them, and further analyzed the alteration of signaling pathways by perturbation of single targets or double-target combinations Fig. . For insIn sum, we could propose candidate drugs based on systems analysis of the dynamical network model with detailed regulatory mechanisms. The original usage of the FDA-approved drugs that were suggested from this analysis and expected to be targeted in sAD iCOs are listed in Supplementary Table iso iCOs and PiB+ iCOs with ApoE e4 allele (considering ApoE-related pathways); some targeted PiB+ #3 iCOs, which had the LPLA288T SNP (considering LPL-related pathways).The selection of candidate drugs was done according to the following steps Fig. : (i) outGiven the many reports that iCOs exhibit size variations during their growth, we next sought to minimize the possible variation to improve their potential utility as drug-treatment targets. We performed three quality control (QC) steps, and obtained well-shaped and evenly sized iCOs  ɛ4 isogenic iPSC lines (E3par and E4iso iCOs)  Fig. . Also no50 for cancer cells, there were no mathematical models for the molecular regulatory interactions in the neuron, which also have complex dynamics that are difficult to intuitively understand52. Therefore, to understand the functional role of each molecular component and identify mechanism-based control targets, we needed to investigate the interactions of the components within the interaction network considering the dynamics of the molecular network. For these reasons, we developed and analyzed the neuronal molecular regulatory network and presented the mathematical model of a molecular regulatory network considering dynamics in the neuron made by integrating all available experimental evidence. Second, many researchers have claimed that the homogeneity of testing samples is important for a highly controlled drug-screening platform54. Since there have been many reports that point out the sample-to-sample variability of human brain organoids58, especially on their size variations58, the first thing we focused was the way to control the quality of our iCOs. We performed several steps of QCs and finally got well-shaped and even-sized iCOs. As shown in Supplementary Fig. 60. Although one paper showed the HCS system using human iCOs, they did not check pathological hallmark proteins such as Aβ or tau deposition or focus on the AD61. Finally, we tried to apply FDA-approved drugs on our drug screening model to show the possibilities of drug repositioning and simplify the drug approval process in preparation for the practical use  and mTOR, ULK1 (related to autophagy pathway) have to be further validated.Our current study has several limitations worth noting. This network model assumes an initial state with little neuronal loss. Therefore, the adjustment of network model by disease stage is necessary. In addition, since it is a network model that consist of limited and only observable experimental information, considering the quality of data that will be developed in the future, it will be possible to create a more complex, emergent decision network that can be analyzed in an advanced manner. Next, even though we generated iCOs from iPSCs as a biomimetic mini-brain, microglial population cannot emerge embryologically during the developmental process of iCOs because their origin has been known as yolk-sac while it is a matter of debateIn conclusion, we provide a powerful tool for the development of the AD drugs by our network-based drug-screening platform through the integration of mathematical modeling and the pathological features of human iCOs, which are derived from the sporadic AD patients. With further generation of the iCOs from various types of AD patients, our approach may propose strategies for the precision medicine therapy.11C-Pittsburgh compound B positron emission tomography (PiB-PET) and MRI using a 3.0 T PET-MR scanner  for brain amyloid imaging. The methods used to obtain and process the patients’ imaging data are described in our previous paper, in which the characteristics of our recruited participants were further summarized64.For the generation of induced pluripotent stem cells (iPSCs) and iCOs, 10 participants were recruited. Detailed information on the participants is presented in Supplementary Table Detailed methods for generation, maintenance, and characterization of both iPSCs and iCOs are provided in the Supplementary Methods.26 with minor adjustments for use with a 96-well plate. Detailed methods for ECi 3D tissue clearing for iCOs are provided in the Supplementary Methods.We followed a previously published protocolImages were acquired using an ImageXpress Micro Confocal High-Content Imaging System (Molecular Devices) with a 4X Plan Apo objective. A total of 25 planes were acquired in 50-μm intervals that covered the whole organoid. All images and their maximum projection images were used for quantification of fluorescence. Images were analyzed using the MetaXpress High-Content Image Acquisition and Analysis Software (Molecular Devices). The following organoid modules were used to characterize each sample: (i) Source, Open Close with the circle filter shape; (ii) Adaptive Threshold: TexasRed Segmentation; and (iii) approximate width from 300 to 3000 μm. Samples that did not meet the predefined standards were excluded from further analyses. The iCOs were further quality-controlled for HCS acquisition and analysis (see below).For drug treatment, detailed information on concentrations is shown in Fig. 2. Supplementary Fig. First, high-quality embryoid bodies (EBs) were selected on Day 7 and seeded to the ultra-low-attachment 96-well plates. Second, on Day 60, high-quality (less size-variation) iCOs were selected and re-seeded. Third, during the HCS imaging, automatic QCs were performed with the following exclusion criteria: (i) shape factor score <0.7; (ii) diameter length <1000 μm or >1400 μm; and (iii) average area of iCOs < 700,000 μmDetailed methods for immunocytochemistry and immunohistochemistry for both iPSCs and iCOs are provided in the Supplementary Methods.65. In detail, 0.9 mg/ml of MTT  in opti-MEM was treated and the plates were incubated for 2 h at 37 °C. After that, medium was fully removed and isopropanol was added and incubated again for 2 h at 37 °C. When formazan crystals were dissolved in the isopropanol solution, absorbance was measured at 540 nm.To check cell viability for iCOs, we performed 3--2,5-diphenyltetrazolium bromide (MTT) assay for iCOs with minor modification of the method from our previous report2 for 12 h. The culture medium was collected and centrifuged at 3000g for 10 min at 4 °C, and the supernatant was collected and stored at −80 °C. The proteins levels of Aβ1-40 , Aβ1-42 , phospho-tau (pT181) , and total-tau  were measured by enzyme-linked immunosorbent assay (ELISA) according to manufacturer’s instructions. BCA analysis was performed with the same samples, and the data were normalized by the total protein content. Levels of Aβs secreted from E3par and E4iso iCOs were further measured using xMAP technology (Bioplex 200 systems). The utilized protocol is also described in our previous paper66.To analyze protein secretion under the serum-deprived condition, the culture medium was changed to Opti-MEM and samples were incubated at 37 °C under 5% CODetailed methods for RT-qPCR are provided in the Supplementary Methods.Detailed methods for FLIPR Calcium 6 assay are provided in the Supplementary Methods.PRJNA678865. To analyze public transcriptome data 68, GEO2R analyzer (https://www.ncbi.nlm.nih.gov/geo/geo2r) was used.Detailed methods for RNA sequencing, DEGs, and GO analysis are provided in the Supplementary Methods. RNA sequencing data is available at NCBI under SRA accession number 69 and AlzPathway70, and also by an extensive survey of relevant experimental data on neuronal cells. In our study, we focused on APOE ɛ4 allele, LPL-related signaling pathways in line with experimental results of this study. Through a literature-based investigation, we constructed a model network mainly from MAPK, WNT, and mTOR signaling pathways, and also, we considered other signaling pathways such as Notch, RELN, Jak/Stat, and Ca2+. In particular, our network model assumes a normal aging state and is constructed in consideration of those pathways relevant for ApoE ɛ4 allele and LPL. Genes and signaling proteins were represented by nodes, and a physical or chemical interaction between nodes was represented by a link. Our network model consists of 77 nodes and 204 links.The network structure was constructed based on major signaling pathways related to AD by integrating information from public databases, such as Kyoto Encyclopedia of Genes and Genomes (KEGG)71 and using the regulatory information of the genes and proteins in the context of neuronal cells73. In our network model, the state values of each node can be either 0 or 1, representing ‘OFF’ or ‘ON’ state of the gene/protein activity, respectively. Each node state is updated synchronously according to the logical rule which was established based on experimental data from the literature. We used R package ‘BoolNet’ for the Boolean simulation74. The network state is determined by a set of node states. All the network states eventually converge to a stable state which is called an ‘attractor’ representing a particular biological phenotype76. The set of all initial states converging to a particular attractor is called the ‘basin of attraction’ of the attractor. Due to the high computational cost of simulating all initial states, we randomly sampled one million initial states. We confirmed that the main results were not sensitive to the randomly selected initial states through repeated sampling process. The Boolean functions of our model were written in accordance with the BoolNet format, and they were converted to SBML-qual77 format using BioLQM v0.6.178. In addition, the SBML file and R code for simulation are available at: 10.5281/zenodo.4259960.We constructed a mathematical model by employing Boolean network modelingAttractor landscape is the landscape of all attractors that can exist on a given network model and the set of initial states converging into each attractor. Using attractor landscape information, we calculate node activities by averaging attractor states weighted by their basin sizes. Each node activity represents the expression level or activity level of gene or protein, respectively. Thus, we can use these node activity calculations to estimate perturbation effects in silico. For example, if A node’s activity level is increased when B node is perturbed, we can consider the A node’s activity level change as the perturbation effect of the B node perturbation on A node.Detailed methods for input–output relationships for the network model are provided in the Supplementary Methods.t-test. The relationship between variables was determined by Pearson’s correlation analysis.MedCalc 17.2  and GraphPad Prism 8  were used for data analyses. The numerical data were tested using ANOVA with Tukey’s post hoc test or independent Approval for the study was obtained from the Institutional Review Board of Seoul National University Hospital, South Korea. Participants or their legal guardians provided written informed consent.Further information on research design is available in the Supplementary InformationSupplementary Data 1Reporting SummaryPeer Review FileDescription of Additional Supplementary Files


Sparganii rhizoma (SL) has potential therapeutic effects on gastric cancer (GC), but its main active ingredients and possible anticancer mechanism are still unclear. In this study, we used HPLC-Q-TOF–MS/MS to comprehensively analyse the chemical components of the aqueous extract of SL. On this basis, a network pharmacology method incorporating target prediction, gene function annotation, and molecular docking was performed to analyse the identified compounds, thereby determining the main active ingredients and hub genes of SL in the treatment of GC. Finally, the mRNA and protein expression levels of the hub genes of GC patients were further analysed by the Oncomine, GEPIA, and HPA databases. A total of 41 compounds were identified from the aqueous extract of SL. Through networkanalysis, we identified seven main active ingredients and ten hub genes: acacetin, sanleng acid, ferulic acid, methyl 3,6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl]benzoate, caffeic acid, adenine nucleoside, azelaic acid and PIK3R1, PIK3CA, SRC, MAPK1, AKT1, HSP90AA1, HRAS, STAT3, FYN, and RHOA. The results indicated that SL might play a role in GC treatment by controlling the PI3K-Akt and other signalling pathways to regulate biological processes such as proliferation, apoptosis, migration, and angiogenesis in tumour cells. In conclusion, this study used HPLC-Q-TOF–MS/MS combined with a network pharmacology approach to provide an essential reference for identifying the chemical components of SL and its mechanism of action in the treatment of GC. At present, surgery, chemotherapy, and other traditional therapies are the main treatments. However, the incidence of local recurrence and distant metastasis after gastric cancer surgery is high. Chemotherapy is associated with toxicity and side effects; thus, it is challenging for these treatments to mediate a long-term antitumour effect. Therefore, it is necessary to explore new strategies for the treatment of this disease. In China, traditional Chinese medicine (TCM) is widely used in the treatment of GC and has shown advantages with its multipathway, multitarget, and multilink characteristics, small side effects, and significant efficacy. Sparganii rhizoma (SL) is the dried tuber of the Sparganiaceae plant Sparganium stoloniferum (Buch.-Ham. ex Graebn.) Buch.-Ham. ex Juz., which is a traditional Chinese medicine. It has a pungent, bitter, flat attributes and enters the liver and spleen meridians. Its effects include tonifying the blood and promoting qi, removing stagnant food, and alleviating pain. It is included in the Pharmacopoeia of the People's Republic of China (2015 Edition)2. Previous experiments by our research team suggested that the “Sparganii rhizoma-Curcuma zedoary-Salvia chinensis” herb pair with SL as one of the main components had growth-inhibitory effects on both regular and resistant GC cells, and the inhibitory effect increased with increasing concentration3. Modern pharmacological studies have also shown that SL has an apparent inhibitory effect on the proliferation of GC cells and can promote tumour cell apoptosis4. In addition, some studies have found that the combination of traditional Chinese medicine preparations mainly composed of SL and chemotherapy can prolong the progression-free survival (PFS) of patients with advanced gastric cancer, improve the quality of life of patients, and reduce the adverse reactions to chemotherapy5. Although previous studies have shown that SL has potential therapeutic effects on GC, its main active ingredients and possible anticancer mechanism are still unclear.Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide, and its incidence rate is sixth among cancers6. Both positive and negative ionization modes have been used to confirm the related chemical compounds and their characteristic fragment ions according to the accurate molecular mass information of the excimer ion peaks and the fragment ions. Then, compounds are ultimately determined by comparisons with the relevant database. HPLC-Q-TOF–MS/MS is characterized by high resolution, high sensitivity, high selectivity, short response time, wide scanning range, high molecular mass accuracy, and an ability to obtain multistage mass spectrum fragment information for compounds. It can quickly analyse and identify the structures of complex substances such as TCM and is very convenient for basic research on TCM materials8. Network pharmacology is a method for predicting the pharmacological mechanism of drug treatments for diseases based on the theory of systems biology and the use of complex biological network models, starting from the integrity and systematic nature of interactions among drugs, chemical components, targets, and diseases10. Its holistic and systematic characteristics are consistent with the principles of the holistic view, syndrome differentiation and treatment of TCM, which have been widely used in the study of TCM12. For example, Yucheng Guo et al. used a network pharmacology research method to construct a multiscale mathematical model of inflammation-induced tumorigenesis, further identified the key biological molecular network and genetic interaction module from the dynamic evolution path of inflammation and cancer, and predicted the TCM ingredients that can inhibit inflammation-induced tumorigenesis. This method is of great value for the accurate prevention and treatment of cancer and the modernization of TCM14. Therefore, in this study, HPLC-Q-TOF–MS/MS was used to rapidly analyse and identify the chemical components in SL, and the mechanism of SL in the treatment of GC was explored by combining network pharmacology research methods. The specific flowchart is shown in Fig. High-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF–MS), which is a common qualitative and quantitative analysis technology combining liquid chromatography and mass spectrometry, can be used to analyse the structure of trace components in crude substances without a reference substanceWe analysed SL aquatic extract samples based on the above conditions of liquid chromatography and mass spectrometry. We used positive and negative ion mode scanning in this paper to obtain as much information as possible. The exact mass-to-charge ratio (m/z) of the compound was obtained by TOF–MS, while the second-order fragment ion of this mass number was obtained by product ion secondary mass spectrometry. By using online databases, referring to the relevant literature and considering the fragmentation rule of compounds, we qualitatively analysed the structures of SL-related compounds. Forty-one compounds were ultimately identified: nine phenylpropanoids, eight organic acids, four flavonoids, four amino acids, two alkaloids, and fourteen other compounds. The secondary mass spectra of each compound are shown in the “SwissTargetPrediction predicted a total of 1157 potential targets of the 41 compounds identified by mass spectrometry, and we obtained 471 after removing duplicate targets . We retrWe established a compound-target network with 262 GC target genes as anticancer targets Fig. . There aThe PPI network reveals the potential connection between targets. After removing the free genes, the PPI network contained 222 nodes and 1205 edges, with an average node degree of 10.9 Fig. . The sizTo elucidate the molecular mechanism underlying SL efficacy in the treatment of GC, we performed GO and KEGG pathway analyses on 262 anticancer targets  ethynyl] benzoate and adenine nucleoside are the main active ingredients in GC treatment screened by us, there is no clear report on the antitumour effect at present, which deserves further study to discover the potential mechanism of action.In this study, HPLC-Q-TOF–MS/MS technology was used to rapidly and comprehensively analyse the chemical components of SL, and 41 compounds were identified. Then, the identified compounds were studied in network pharmacology. Finally, we found seven main active ingredients in the drug, including acacetin, sanleng acid, ferulic acid, methyl 3.6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl] benzoate, caffeic acid, adenine nucleoside, and azelaic acid; moreover, we identified PIK3R1, PIK3CA, SRC, MAPK1, AKT1, HSP90AA1, HRAS, STAT3, FYN, and RHOA as hub genes. Molecular docking showed that the active ingredients had good affinity for the hub gene proteins. These seven active ingredients may be the material basis for SL to exert therapeutic efficacy for GC. Modern pharmacological studies have shown that acacetin, as a natural flavonoid, can resist tumours in multiple links, pathways, and targets and is effective in most tumour cell lines. It can inhibit the proliferation of tumour cells, induce the autophagy and apoptosis of tumour cells, inhibit the invasion and migration of tumour cells and angiogenesis, regulate immunity, and reverse multidrug resistance40. The proto-oncogene c-SRC, a member of the SRC family of kinases (SFKs), is one of the earliest nonreceptor-dependent tyrosine protein kinases found to be closely related to human diseases41. Current studies have shown that SRC can promote tumour cell proliferation and tumour angiogenesis, inhibit apoptosis, participate in cancer cell adhesion and invasion, and coregulate tumour growth through the interaction of growth factor receptors and growth factors43. Mitogen-activated protein kinase 1 (MAPK1) has been confirmed as an essential oncogene in the progression of GC, and its level is elevated in GC tissues and cells, which can promote the proliferation, migration, and invasion of GC cells46. Heat shock protein 90 (HSP90) is overexpressed in many malignant tumours, and members of the HSP90 gene family are essential for cell cycle regulation, survival, and apoptosis. Studies have shown that the expression of HSP90AA1 is associated with poor prognosis in GC48. STAT3, a key transcription factor in tumorigenesis, focuses on multiple signalling pathways, such as cell proliferation, carcinogenesis, and apoptosis, which can promote the growth, proliferation, angiogenesis, metastasis, and immune response of tumour cells50. Similar to SRC, FYN is an SFK that is overexpressed in GC and is positively correlated with metastasis and may promote gastric cancer metastasis by activating STAT3-mediated epithelial-mesenchymal transition51. In addition, our study also showed that SRC, MAPK1, STAT3, HSP90AA1, PIK3R1, and FYN were overexpressed in GC patients, which may be associated with the poor prognosis of GC patients. The AKT1 signalling pathway plays a vital role in regulating the biological functions of tumour cell growth, proliferation, apoptosis, and metabolism. Its positive expression rate in GC tissues is significantly higher than that in adjacent tissues, and it participates in the occurrence and development of GC54. HRAS belongs to the RAS gene family, which regulates RAF-MEK-ERK, PI3K/AKT, and other signalling pathways related to cell survival and proliferation by binding to GTP/GDP and the RAS protein to act as a molecular switch56. HRAS mutations are closely associated with the occurrence of various tumours. The expression of RHOA, a RAS homologous family, is related to certain tumorigenesis; however, its prognostic value in GC remains controversial. Some studies have found that the RHOA signaling pathway plays a vital role in the occurrence, invasion, metastasis, immune escape, and multidrug resistance mechanisms of gastric cancer58. Nevertheless, some studies have shown that the overall prevalence of RHOA-mutant GC is low, usually offering a lower T stage and no distant metastasis59. Our external validation also showed that RHOA was expressed at a low protein level in GC tissues; therefore, further study of this gene is necessary.An increasing number of studies have shown that TCM is a multitarget drug. Among the ten hub genes identified in this study, PIK3R1 and PIK3CA were identified as PI3K/protein kinase B (Akt) signalling pathway regulators. Studies have shown that abnormal upregulation of PIK3R1 and PIK3CA expression enhances the catalytic activity of PI3K and then activates the PI3K-Akt signalling pathway, causing GC cells to overproliferate and increasing the migration and invasion abilities of GC cellsTo better understand the molecular mechanism of SL in the treatment of GC, we performed GO and KEGG pathway analyses on the targets. GO analysis results showed that the target genes were mainly related to biological processes such as positive regulation of transcription from RNA, negative regulation of the apoptotic process, positive regulation of cell proliferation, positive regulation of cell migration, angiogenesis, and similar processes. In CC, the nucleus accounted for the largest proportion. In MF, protein binding, ATP binding, and enzyme binding were the main components. KEGG pathway analysis showed that the signalling pathway of SL in the treatment of GC was most related to the PI3K-Akt signalling pathway. Additionally, it involved the Ras signalling pathway, the MAPK signalling pathway, and other signalling pathways. Most of the hub genes, such as HRAS, AKT1, HSP90AA1, PIK3CA, PIK3R1, MAPK1, and RHOA, play roles in these signalling pathways, which is consistent with the results of modern pharmacological studies.In conclusion, the analytical method based on HPLC-Q-TOF–MS/MS technology in this study can accurately identify the chemical components in SL efficiently, rapidly, and comprehensively. Simultaneously, the network pharmacology method is used to deeply excavate its potential active ingredients and the mechanism of drug treatment for GC to provide more scientific theoretical guidance for the improvement of quality control standards and clinical application of SL in the future. In our study, we found that SL is a multitarget anticancer drug. We predicted that the primary mechanism of action of SL in the treatment of GC is as follows: mediating PI3K-Akt, Ras, MAPK, and other signaling pathways to regulate the proliferation, apoptosis, migration, and angiogenesis of tumour cells, thus playing a role in the treatment of GC. However, the above results still need further experimental verification.Instruments Ultimate 3000 High-Performance Liquid Chromatograph (1000 mm × 1000 mm × 1000 mm), SIL-20A XR UFLC ; Triple TOF 5600 System-MS/MS High-Resolution Triple Quadrupole Time of Flight Mass Spectrometer ; Electronic Balance ; SHZ-D (III) Circulating Water Vacuum Pump ; KQ-500B Ultrasound Cleaner ; PST-JY-10 Puri Phil pure water machine.Materials Methanol , formic acid , and acetonitrile . SL medicinal materials were obtained from the TCM pharmacy of Jiangsu Province Hospital of Chinese Medicine and were purchased from Ma'anshan Jingquan Traditional Chinese Medicine Decoction Pieces Co., Ltd. Origin: Zhejiang, Batch No. 200601, Standard Basis: Pharmacopoeia of the People's Republic of China (2015 Edition). It was identified by associate professor Ruilian Yu, School of Pharmacy, the Nanjing University of Chinese Medicine as Sparganiaceae plant Sparganium stoloniferum (Buch.-Ham. ex Graebn.) Buch.-Ham. ex Juz. tubers. The specimens were deposited in the Central Laboratory of Jiangsu Province Hospital of Chinese Medicine.The proper amount of SL medicinal materials was crushed and sieved through 60 mesh, and 1 g of powder was precisely weighed. Then, the weighed 1 g powder was soaked ten times in double-distilled water for 30 min, refluxed and extracted twice, the first for 30 min and the second for 20 min, combined with two filtrates, evaporated by a rotary evaporator at 70 ℃, and then reconstituted with absolute ethanol to a 10-ml volumetric flask.Chromatographic conditions Hedera C18 column ; mobile phase: 0.1% formic acid water (B)—0.1% formic acid methanol (C), gradient elution ; flow rate: 1 mL/min, column temperature 30 ℃, injection volume 5 μL. The detection wavelength of DAD was 260 nm. Mass spectrometry conditions Electron spray ionization (ESI), using positive and negative ion mode scanning; mass scanning range m/z 50–1500; ion source temperature 550 ℃; air curtain gas flow rate 40 L/min; atomization airflow speed 55 L/min; auxiliary airflow speed 55 L/min; spray voltage + 5500 V/− 4500 V; decluster voltage ± 100 V. Data acquisition software: Analyst TF 1.6 software ; data processing software: Peakview 1.2 software .–6, and the compounds were preliminarily predicted. Then, it was further confirmed by comparing the retention time and the mass spectrum fragment information provided by the SciFinder database and related references to achieve the purpose of the accurate identification of compounds.According to the multistage mass spectrum fragment information and the precise relative molecular mass provided by high-resolution mass spectrometry, the molecular formula was fitted by Peakview 1.2 software with a mass deviation range (δ) ≤ 5 × 10http://www.swisstargetprediction.ch/)60 is a network tool for ligand-based target prediction of any small biologically active molecule. We transformed the compounds identified by mass spectrometry into canonical SMILES through the PubChem (https://pubchem.ncbi.nlm.nih.gov/), Chemical Book , and ChemSpider (http://www.chemspider.com/) databases. We then imported SMILES into SwissTargetPrediction to predict all potential targets of compounds. Species were selected as “Homo sapiens” with probability > 0 as the screening condition.SwissTargetPrediction (https://www.genecards.org/)61, the Online Mendelian Inheritance in Man 62, DisGeNET (Version 7.0) (https://www.disgenet.org/)63 and the Therapeutic Target Database 64 were used to collect relevant targets of GC. In this study, “score” ≥ mean value was used as the criterion for screening disease target genes.Using “Gastric Cancer” as the keyword, the human gene database 65 to construct the compound-target network. The “network analysis” is used to analyse the topological parameters of the network, where the “degree” represents the number of nodes connected with this node in the network; the greater the degree of the node is, the more critical it is in the network. The “betweenness centrality” reflects the importance of a node in transmitting information through the network, and the greater the betweenness centrality of the node is, the more critical it is in the network. The core network was screened based on the network node topological parameters “degree” and “betweenness centrality” to obtain the main active ingredients of SL for the treatment of GC.The chemical components of SL and its therapeutic targets in GC were introduced into Cytoscape (Version 3.8.0) (https://string-db.org/)66, and the correlation between target proteins was analysed. “Organism” was set as “Homo sapiens”. The PPI network was constructed with a “combined score” ≥ 0.9 as the screening condition. The visualization process was carried out with Cytoscape (Version 3.8.0), and targets with a high degree of connectivity were selected as hub genes.The targets of SL for the treatment of GC were imported into the STRING Database (Version 11.0) (https://david.ncifcrf.gov/)68 provides systematic and comprehensive biological function annotation information for a large number of genes. It can identify the most significantly enriched biological annotations. We introduced the target set of SL for GC treatment into DAVID (Version 6.8) and defined the species as “Homo sapiens” for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. To more comprehensively annotate the biological functions of genes to better understand the molecular mechanism of SL in treating GC, GO will describe the nature of genes from three terms, including cell component , molecular function (which can describe the activity at the molecular level) and biological process. P < 0.01 was used as a screening condition. Enrichment analysis bubble maps were plotted using the R language.The Database for Annotation, Visualization and Integrated Discovery (DAVID) (Version 6.8)  was used to further construct the compound-target-pathway network.https://www.rcsb.org/)70, where the best protein crystal structure was selected , and its PDB format file was downloaded. Before docking, the original crystal ligand and water molecule in the protein–ligand complex were removed using PyMol71. The protein and ingredients were then hydrogenated, charged, and subjected to other operations using AutoDockTools and converted into PDBQT format files. Auto Dock Vina72 was used to perform molecular docking between the processed ingredients and protein, and the docking results were visualized using PyMol software.To further validate the reliability of the target prediction results, molecular docking was performed on the selected active ingredients and hub genes. Active ingredients were loaded in the SDF format file of their 3D structure through the PubChem database and were then imported into Chem3D for optimization and saved in mol2 format; hub genes were kept in the Research Collaboratory for Structural Bioinformatics Protein Data Bank 73 is a cancer gene expression profile database and integrated data-mining platform designed to facilitate the discovery of genome-wide expression analysis. Through the Oncomine database, we compared the differential expression of hub genes in GC tissues and normal gastric tissues.Oncomine 4.5 (http://gepia.cancer-pku.cn/index.html)74 is a newly developed interactive web server for analysing the RNA sequencing expression data of 9736 tumours and 8587 normal samples from the TCGA and GTEx projects using a standard processing pipeline. The GEPIA database can further verify the differential expression of hub genes between GC and normal gastric tissues, and it can also analyse them according to pathological stages.Gene Expression Profiling Interactive Analysis 75 database is mainly an extensive proteome database based on immunohistochemical analysis. The protein expression levels of hub genes in GC tissues and normal gastric tissues were compared according to the staining intensity and percentage of stained cells in the tissues, and representative immunohistochemical staining pictures were obtained.The Human Protein Atlas (Version 19.3) (HPA, Supplementary Figures.Supplementary Tables.


Pulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis.By integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein–protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape.A total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-κB signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1.With bioinformatics analysis of merged datasets, biomarker candidates of pulpitis were screened and the findings may be as reference to develop a new method of pulpitis diagnosis. Dental pulp is loose connective tissue in the pulp cavity surrounded by rigid dentin and is necessary for tooth nutrition, innervation, and immunocompetency . When thDifferent therapies for pulp disease can be selected according to the evaluation of pulp inflammation. Given the importance of tooth preservation, vital pulp therapies such as pulp capping, pulpotomy, and stepwise excavation of caries may be more appropriate than root canal treatment under some circumstances . CurrentIn clinical practice, incomplete or ambiguous clinical data make it difficult to determine whether a deep carious lesion has led to the widespread infection of dental pulp (irreversible pulpitis), the infection of only a small amount of tissue near the lesion, or no infection of pulp tissue (reversible pulpitis) . Hence, Dental pulp is not isolated in the oral cavity and releases many biological products to the external environment in response to external harmful stimuli –14. At tIn this study, we focused on gene expression in pulp tissue from pulpitis patients. We selected and analysed two microarray platform datasets in the GEO database, integrated the datasets and identified DEGs between pulpitis and normal pulp tissues. Then, GO enrichment analysis, KEGG pathway analysis and GSEA were used to analyse the major biological functions of the DEGs. Ten hub genes related to pulpitis were identified by constructing a PPI network with Cytoscape. The aim of the present study was to identify candidate biomarkers for pulpitis diagnosis and prognosis based on functional and molecular analyses by evaluating DEGs in pulpitis and normal tissue.Homo sapiens” and “dental pulp”. The diagnostic criteria of normal pulp and pulpitis were in line with endodontics diagnoses system in accordance with the American Association of Endodontists (AAE) guidelines [Two microarray datasets of pulpitis from GEO database were retrieved, with the keywords: “pulpitis”, “idelines . GSE7745https://bioconductor.org/packages/sva/) was used to remove batch effects based on the standard protocol [https://bioconductor.org/packages/limma/) package in Bioconductor. Up- or downregulated DEGs between samples of pulpitis and normal pulps were identified with the cut-off criteria of adjusted p-value  < 0.05 and |fold change (FC)| > 2.After removing the probes of lncRNA in GSE92681, mRNAs of two datasets were merged into one file, and then ComBat normalization in SVA package . P < 0.05 and false discovery rate (FDR) < 5% were used as the cut-off criteria.GO enrichment and KEGG pathway analyses were used to investigate the functional progression of pulpitis. Biological process (BP), molecular function (MF), cellular component (CC) in GO analysis and potential pathways in KEGG analysis were performed in the Database for Annotation Visualization and Integrated Discovery (DAVID) (http://software.broadinstitute.org/gsea/ downloads.jsp) as a reference gene set [http://software.broadinstitute.org/gsea/index.jsp). Gene set permutations were performed 1000 times, and the pathway set list was sorted by the Normalized Enrichment Score (NES). P < 0.05 and FDR < 0.25 were considered statistically significant.To further explore the function of DEGs in inflammatory progression, GSEA was performed using h.all.v7.0.symbols.gmt (gene set . The GSEhttp://string-db.org/). PPI pairs and PPI network were visualized in the Cytoscape software (Version 3.7.1), and cytohHubba and MCODE plugin in Cytoscape were used to calculate the degrees of protein nodes and to select the significant modules. Top 10 genes were identified as hub genes.The PPI network of DEGs was constructed using the Search Tool for THE Retrieval of Interacting Genes (STRING) database , the data from two samples, GSM2434473 and GSM2434475, were excluded Fig. . Then, aGO BP, CC and MF and KEGG pathway analyses were performed for functional analysis of the DEGs. Regarding BP terms, the DEGs were enriched in inflammatory response, signal transduction, immune response and cell adhesion. Regarding CC terms, the DEGs were enriched in integral component of membrane, plasma membrane, and extracellular exosome. Regarding the MF terms, the DEGs were enriched in protein binding, calcium ion binding, and protein homodimerization activity Fig. , Table 2P = 0.008), the IL6/JAK/STAT3 signalling pathway  and inflammatory response pathways , as shown in Fig. The GSEA results revealed that the enriched biological processes mainly involved the IL2/STAT5 signalling pathway , a signalling molecule that regulates various kinds of cellular processes and plays a critical role in the immune system. PTPRC can negatively regulate cytokine receptor signalling by suppressing the JAK signalling pathway . PTPRC iDuring the treatment of deep caries and traumatic exposure of dental pulp, it is essential to assess whether the pulp inflammation is reversible. It has been reported that inflamed pulp tissue might produce some biomarkers that are secrete to the external environment , 14, makHowever, in addition to the challenges of sample collection, many difficulties in the analysis and application of biomarkers need to be overcome. Much progress remains to be made in developing an effective method for molecular diagnosis in the clinical setting, which is the one of the limitations of the current study. In addition, due to the paucity of available datasets of pulpitis in the GEO database, the sample size in this study was limited. We will increase the sample size in a future study if additional datasets can be retrieved from the database.In summary, the present study analysed merged datasets of pulpitis tissue, aiming to gain insight into methods for the diagnosis or treatment of different inflammatory levels of pulpitis. In addition to investigating the possible regulatory mechanisms of DEGs, we screened key genes as biomarker candidates for the diagnosis of pulpitis, including PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1. The use of bioinformatic methods to analyse merged datasets is effective for identifying biomarkers of diseases. The present findings require further validation in future studies.Additional file 1. Details of expression matrix from integrated datasets.Additional file 2. Details of differentially expressed genes of pulpitis.Additional file 3. Results of GO enrichment analysis of DEGs.Additional file 4. Results of KEGG pathway analysis of DEGs.Additional file 5. Results of STRING analysis of DEGs.Additional file 6. Details of PPI network.