Connectivity Map Database Research Articles

Age-related genes affecting the immune cell infiltration in ulcerative colitis revealed by weighted correlation network analysis and machine learning.

The crosstalk between age and immunity in the context of ulcerative colitis (UC) remains incompletely understood. Our objective is to elucidate the specific age-associated genetic factors that modulate immune cell infiltration in UC, with the aim of identifying innovative therapeutic targets for the treatment of this disease. Potential batch effects between samples were removed by R package "inSilicoMerging". Unsupervised clustering analysis via the "ConsensusClusterPlus" R package was utilized to perform consensus molecular subtyping of immune subtypes in UC. The construction of a heat map was accomplished through the utilization of the R package "pheatmap", while functional enrichment analysis was executed by means of the Metascape database. The identification of the age-related gene module was achieved by performing weighted gene co-expression network analysis (WGCNA) analysis using the R package "WGCNA". The support vector machine (SVM), least absolute shrinkage and selector operation (LASSO), and random forest algorithms were performed via the "e1071", "glmnet" and "randomForest" packages in R, respectively. The diagnostic performance of the parameter was assessed using the receiver operating characteristic (ROC) curve. Correlation analysis was performed by Spearman correlation. The "XSum" package in R was employed to identify potential small-molecule drugs for UC utilizing the Connectivity Map (CMap) database. Molecular docking was performed with Autodock Vina molecular docking software. A significantly greater frequency of UC patients aged below 40 years was observed in the group with extensive disease extent as compared to those with non-extensive disease extent (70% vs. 47%; Chi-square test, p = 0.02). The application of unsupervised clustering analysis allowed for the stratification of UC patients into two distinct immune subtypes, namely cluster C1 and cluster C2. The distribution of immune subtypes was significantly different between different age categories (Chi-square test, p = 0.00219). The UC samples that were grouped under cluster C1 were distinguished by a higher abundance of macrophages and an elevated number of neutrophils relative to those in cluster C2. Based on both WGCNA and Limma analysis, 146 age-related genes were identified, which exhibited a predominant enrichment in the biological process of cellular senescence. Two age-related genes (MIDN, and PLD6) affecting the immune cell infiltration in UC were identified based on machine learning algorithms (SVM, LASSO, and random forest). The diagnostic performance of MIDN (AUC = 0.93) and PLD6 (AUC = 0.90) in discerning UC patients belonging to cluster C1 was found to be satisfactory, as demonstrated by ROC curve analysis. MIDN demonstrated a positive correlation (r = 0.50, p < 0.0001) with Neutrophil, while PLD6 exhibited a negative correlation (r = -0.52, p < 0.0001) with Neutrophil levels. The "XSum" algorithm revealed that Entinostat has therapeutic potential for UC. The docking glide score between Entinostat and MIDN, and PLD6 protein was -8.9 kcal/mol and -6.8 kcal/mol, respectively. We have identified two age-related genes, MIDN and PLD6, that are involved in immune cell infiltration in patients with ulcerative colitis. Furthermore, a small molecule drug (Entinostat) with potential therapeutic effects for UC was screened out. This study presented new perspectives on personalized clinical management and therapy research for UC.

Expression analysis, clinical significance and potential function of PLXNB2 in acute myeloid leukaemia.

The overall survival (OS) rate of adult patients suffering from acute myeloid leukaemia (AML) remains unsatisfactory at less than 40%. Current risk stratification systems fail to provide accurate guidelines for precise treatment. Novel biomarkers for predicting prognosis are urgently needed. Plexin B2 (PLXNB2), a functional receptor of angiogenin (ANG), has been found to be aberrantly expressed in multitudinous tumours. We detected overexpression of PLXNB2 mRNA in AML via transcriptome microarray analysis. This study aims to explore the potential role of PLXNB2 as a biomarker of prognosis and a prospective target of AML. qRT‒PCR was conducted to verify the expression of PLXNB2 mRNA in bone marrow mononuclear cells from AML patients. Immunohistochemical and immunofluorescence staining were performed and confirmed increased expression of PLXNB2 protein in AML bone marrow tissues. Data on PLXNB2 expression, prognosis and clinical features were accessed from multiple bioinformatic databases, including The Cancer Genome Atlas (TCGA). Genes coexpressed and correlated with PLXNB2 were identified and analysed in the TCGA AML cohort. Metascape was applied for functional and pathway enrichment analysis of genes related to PLXNB2. Small molecular agents and traditional Chinese medicines potentially targeting genes related to PLXNB2 were screened via the Connectivity Map, TCMSP and HIT databases. PLXNB2 mRNA and protein levels are higher in AML samples than in normal controls. Overexpression of PLXNB2 is associated with worse OS in AML. Patients with high PLXNB2 expression might benefit more from haematopoietic stem cell transplantation (HSCT) (indicated by prolonged OS) than those with only chemotherapy treatment. Differentially expressed genes between the high and low PLXNB2 expression groups were overlapped with PLXNB2-coexpressed genes, and genes that overlapped were enriched in immune functions, endothelial cell regulation and cell interaction gene sets, indicating the potential function of PLXNB2 in AML. A total of 36 hub genes were identified from the differentially expressed genes, and MRC1, IL10, CD163 and CCL22 had significant prognostic value for AML. Analysis of the connectivity map and traditional agents revealed that honokiol, morphines, triptolide and paeoniflorin could be potential treatment regimens. The overexpression of PLXNB2 is an adverse prognostic factor in adult AML patients and could be used as a potential biomarker. PLXNB2 might exert an oncogenic role by modulating immune functions, endothelial cell functions and cell interactions. AML patients with high PLXNB2 expression could benefit more from HSCT.

A comprehensive identification of potential molecular targets and small drugs candidate for melanoma cancer using bioinformatics and network-based screening approach

Melanoma is the third most common malignant skin tumor and has increased in morbidity and mortality over the previous decade due to its rapid spread into the bloodstream or lymphatic system. This study used integrated bioinformatics and network-based methodologies to reliably identify molecular targets and small molecular medicines that may be more successful for Melanoma diagnosis, prognosis and treatment. The statistical LIMMA approach utilized for bioinformatics analysis in this study found 246 common differentially expressed genes (cDEGs) between case and control samples from two microarray gene-expression datasets (GSE130244 and GSE15605). Protein–protein interaction network study revealed 15 cDEGs (PTK2, STAT1, PNO1, CXCR4, WASL, FN1, RUNX2, SOCS3, ITGA4, GNG2, CDK6, BRAF, AGO2, GTF2H1 and AR) to be critical in the development of melanoma (KGs). According to regulatory network analysis, the most important transcriptional and post-transcriptional regulators of DEGs and hub-DEGs are ten transcription factors and three miRNAs. We discovered the pathogenetic mechanisms of MC by studying DEGs’ biological processes, molecular function, cellular components and KEGG pathways. We used molecular docking and dynamics modeling to select the four most expressed genes responsible for melanoma malignancy to identify therapeutic candidates. Then, utilizing the Connectivity Map (CMap) database, we analyzed the top 4-hub-DEGs-guided repurposable drugs. We validated four melanoma cancer drugs (Fisetin, Epicatechin Gallate, 1237586-97-8 and PF 431396) using molecular dynamics simulation with their target proteins. As a result, the results of this study may provide resources to researchers and medical professionals for the wet-lab validation of MC diagnosis, prognosis and treatments. Communicated by Ramaswamy H. Sarma

An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer.

Epithelial-mesenchymal transition (EMT) is an important biological process in tumor invasion and metastasis, and thus a potential indicator of the progression and drug resistance of breast cancer. This study comprehensively analyzed EMT-related genes in triple-negative breast cancer (TNBC) to develop an EMT-related prognostic gene signature. With the application of The Cancer Genome Atlas (TCGA) database, Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Genotype-Tissue Expression (GTEx) database, we identified EMT-related signature genes (EMGs) by Cox univariate regression and LASSO regression analysis. Risk scores were calculated and used to divide patients with TNBC into high-risk group and low-risk groups by the median value. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were applied for model validation. Independent prognostic predictors were used to develop nomograms. Then, we assessed the risk model in terms of the immune microenvironment, genetic alteration and DNA methylation effects on prognosis, the probability of response to immunotherapy and chemotherapy, and small molecule drugs predicted by The Connectivity Map (Cmap) database. Thirteen EMT-related genes with independent prognostic value were identified and used to stratify the patients with TNBC into high- and low-risk groups. The survival analysis revealed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group. Populations of immune cells, including CD4 memory resting Tcells, CD4 memory activated Tcells, and activated dendritic cells, significantly differed between the high- and low-risk groups. Moreover, some therapeutic drugs to which the high-risk group might show sensitivity were identified. Our research identified the significant impact of EMGs on prognosis in TNBC, providing new strategies for personalizing TNBC treatment and improving clinical outcomes.

Mechanistic insights into super-enhancer-driven genes as prognostic signatures in patients with glioblastoma.

Glioblastoma (GBM) is one of the most common malignant brain tumors in adults and is characterized by high aggressiveness and rapid progression, poor treatment, high recurrence rate, and poor prognosis. Although super-enhancer (SE)-driven genes haven been recognized as prognostic markers for several cancers, whether it can be served as effective prognostic markers for patients with GBM has not been evaluated. We first combined histone modification data with transcriptome data to identify SE-driven genes associated with prognosis in patients with GBM. Second, we developed a SE-driven differentially expressed genes (SEDEGs) risk score prognostic model by univariate Cox analysis, KM survival analysis, multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression. Its reliability in predicting was verified by two external data sets. Third, through mutation analysis, immune infiltration, we explored the molecular mechanisms of prognostic genes. Next, Genomics of Drug Sensitivity in Cancer (GDSC) and the Connectivity Map (cMap) database were employed to assess different sensitivities to chemotherapeutic agents and small-molecule drug candidates between high- and low-risk patients. Finally, SEanalysis database was chosen to identify SE-driven transcription factors (TFs) regulating prognostic markers which will reveal a potential SE-driven transcriptional regulatory network. First, we developed a 11-gene risk score prognostic model (NCF2, MTHFS, DUSP6, G6PC3, HOXB2, EN2, DLEU1, LBH, ZEB1-AS1, LINC01265, and AGAP2-AS1) selected from 1,154 SEDEGs, which is not only an independent prognostic factor for patients, but also can effectively predict the survival rate of patients. The model can effectively predict 1-, 2- and 3-year survival of patients and was validated in external Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) datasets. Second, the risk score was positively correlated with the infiltration of regulatory T cell, CD4 memory activated T cell, activated NK cell, neutrophil, resting mast cell, M0 macrophage, and memory B cell. Third, we found that high-risk patients showed higher sensitivity than low-risk patients to both 27 chemotherapeutic agents and 4 small-molecule drug candidates which might benefit further precision therapy for GBM patients. Finally, 13 potential SE-driven TFs imply how SE regulates GBM patient's prognosis. The SEDEG risk model not only helps to elucidate the impact of SEs on the course of GBM, but also provides a bright future for prognosis determination and choice of treatment for GBM patients.

Cancer-testis antigen CEP55 serves as a prognostic biomarker and is correlated with immune infiltration and immunotherapy efficacy in pan-cancer.

Background: Centrosomal Protein 55 (CEP55) was initially described as a main participant in the final stage of cytokinesis. Further research identified CEP55 as a cancer-testis antigen (CTA) that is aberrantly expressed in different malignancies and a cancer vaccination candidate. The current study aimed to disclose the complete expression of CEP55, its effect on various malignancy prognoses, and its role in the tumor microenvironment. Methods: Transcriptional information regarding tumor and normal tissues, as well as externally validated and protein expression data were gathered from the Cancer Genome Atlas, Genotype-Tissue Expression project, Gene Expression Omnibus, and Human Protein Atlas. We examined the effect of CEP55 on tumor prognosis using Kaplan-Meier (KM) and univariate Cox regression analyses. In addition, we investigated the connections between CEP55 expression and hallmark cancer pathways, immune cell infiltration, and immune regulator expression across malignancies. We constructed and validated a CEP55-related risk model for hepatocellular carcinoma (HCC) and explored the correlations between CEP55 expression and HCC molecular subtypes. Finally, we investigated putative small-molecule drugs targeting CEP55 using a connectivity map (CMap) database and validated them using molecular docking analysis. Findings: CEP55 was aberrantly expressed in most cancers and revealed a prognostic value for several malignancies. Cancers with high CEP55 expression showed significantly enhanced cell cycle, proliferation, and immune-related pathways. For most malignancies, elevated CEP55 expression was associated with the infiltration of myeloid-derived suppressor cells (MDSCs) and Th2 cells. In addition, CEP55 expression was linked to immunomodulators and the potential prediction of immune checkpoint inhibitor (ICI) responses, and strongly associated with distinct molecular HCC subtypes, whereby the CEP55-based nomogram performed well in predicting short- and long-term HCC survival. Finally, we used connectivity map (CMap) and molecular docking analyses to discover three candidate small-molecule drugs that could directly bind to CEP55. Conclusion: CEP55 affected the occurrence and development of various cancers and possibly the regulation of the tumor immune microenvironment. Our findings suggest that CEP55 is a potential biomarker for prognosis and a powerful biomarker for ICI efficacy prediction.

LncRNAs associated with vascular mimicry establish a novel molecular subtype and prognostic model for pancreatic cancer.

Vascular mimicry (VM) epitomizes an innovative tumor angiogenesis pathway, potentially serving as an alternate conduit under the assumption of traditional tumor angiogenesis pathway inhibition. The role of VM in pancreatic cancer (PC), however, remains unexplored. Using differential analysis and Spearman correlation, we identified key long non-coding RNAs (lncRNAs) signatures in PC from the collected set of VM-associated genes in the literature. We identified optimal clusters using the non-negative matrix decomposition (NMF) algorithm, and then compared clinicopathological features and prognostic differences between clusters. We also assessed tumor microenvironmental (TME) differences between clusters using multiple algorithms. Using univariate Cox regression analyses as well as lasso regression, we constructed and validated new lncRNA prognostic risk models for PC. We used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze model-enriched functions and pathways. Nomograms were then developed to predict patient survival in association with clinicopathological factors. In addition, single-cell RNA-sequencing (scRNA-seq) analysis was used to analyze the expression patterns of VM-related genes and lncRNAs in the PC of TME. Finally, we used the Connectivity Map (cMap) database to predict local anaesthetics that could modify the VM of PC. In this study, we developed a novel three-cluster molecular subtype using the identified VM-associated lncRNA signatures of PC. The different subtypes have significantly different clinical characteristics and prognostic value, and also show differential treatment response and TME. Following an in-depth analysis, we constructed and validated a novel prognostic risk model for PC based on the VM-associated lncRNA signatures. Enrichment analysis suggested that high riskscores were significantly associated with functions and pathways, including extracellular matrix remodeling, et al. In addition, we predicted eight local anaesthetics that could modulate VM in PC. Finally, we discovered differential expression of VM-related genes and lncRNAs across various cell types within pancreatic cancer. VM has a critical role in PC. This study pioneers the development of a VM-based molecular subtype that demonstrates substantial differentiation in PC populations. Furthermore, we highlighted the significance of VM within the immune microenvironment of PC. Moreover, VM might contribute to PC tumorigenesis through its mediation of mesenchymal remodeling and endothelial transdifferentiation-related pathways, which offers a new perspective on its role in PC.

Exploring hub pyroptosis-related genes, molecular subtypes, and potential drugs in ankylosing spondylitis by comprehensive bioinformatics analysis and molecular docking

BackgroundAnkylosing spondylitis (AS) is a chronic inflammatory autoimmune disease, and the diagnosis and treatment of AS have been limited because its pathogenesis is still unclear. Pyroptosis is a proinflammatory type of cell death that plays an important role in the immune system. However, the relationship between pyroptosis genes and AS has never been elucidated.MethodsGSE73754, GSE25101, and GSE221786 datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis-related genes (DE-PRGs) were identified by R software. Machine learning and PPI networks were used to screen key genes to construct a diagnostic model of AS. AS patients were clustered into different pyroptosis subtypes according to DE-PRGs using consensus cluster analysis and validated using principal component analysis (PCA). WGCNA was used for screening hub gene modules between two subtypes. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for enrichment analysis to elucidate underlying mechanisms. The ESTIMATE and CIBERSORT algorithms were used to reveal immune signatures. The connectivity map (CMAP) database was used to predict potential drugs for the treatment of AS. Molecular docking was used to calculate the binding affinity between potential drugs and the hub gene.ResultsSixteen DE-PRGs were detected in AS compared to healthy controls, and some of these genes showed a significant correlation with immune cells such as neutrophils, CD8 + T cells, and resting NK cells. Enrichment analysis showed that DE-PRGs were mainly related to pyroptosis, IL-1β, and TNF signaling pathways. The key genes (TNF, NLRC4, and GZMB) screened by machine learning and the protein–protein interaction (PPI) network were used to establish the diagnostic model of AS. ROC analysis showed that the diagnostic model had good diagnostic properties in GSE73754 (AUC: 0.881), GSE25101 (AUC: 0.797), and GSE221786 (AUC: 0.713). Using 16 DE-PRGs, AS patients were divided into C1 and C2 subtypes, and these two subtypes showed significant differences in immune infiltration. A key gene module was identified from the two subtypes using WGCNA, and enrichment analysis suggested that the module was mainly related to immune function. Three potential drugs, including ascorbic acid, RO 90–7501, and celastrol, were selected based on CMAP analysis. Cytoscape showed GZMB as the highest-scoring hub gene. Finally, molecular docking results showed that GZMB and ascorbic acid formed three hydrogen bonds, including ARG-41, LYS-40, and HIS-57 (affinity: -5.3 kcal/mol). GZMB and RO-90–7501 formed one hydrogen bond, including CYS-136 (affinity: -8.8 kcal/mol). GZMB and celastrol formed three hydrogen bonds, including TYR-94, HIS-57, and LYS-40 (affinity: -9.4 kcal/mol).ConclusionsOur research systematically analyzed the relationship between pyroptosis and AS. Pyroptosis may play an essential role in the immune microenvironment of AS. Our findings will contribute to a further understanding of the pathogenesis of AS.

P-335 Nervous system drugs are predicted as potential repurposing candidates for treatment in endometrial failure

Abstract Study question Is there a most represented group of approved drugs that can revert the impaired endometrial expression pattern associated with endometrial failure to improve reproductive outcomes? Summary answer Transcriptomics-based drug repurposing strategy identifies drugs currently used for nervous system diseases as repurposing candidates for endometrial therapies in infertility. What is known already Endometrium is a dynamic tissue with a key role in human reproduction whose alterations lead to infertility problems. Transcriptomic studies have been performed to understand molecular bases of endometrial failure, but effective treatments remain unknown. Transcriptomics-based drug repurposing strategy identifies approved drugs with a reversed disease gene expression profile, where genes up-regulated in a condition are down-regulated with the drug therapy and vice-versa, having a predicted therapeutic effect. Encouraging results for conditions such as preterm birth or cancer have already been shown using this methodology. In this study, we applied this approach to obtain treatment predictions for endometrial failure. Study design, size, duration A prospective multicenter study was performed between January 2019 and December 2021. A total of 192 women (18-50 years old) undergoing IVF with good-quality embryos were included and an endometrial sample in mid-secretory phase was collected to study their gene expression. Through artificial intelligence algorithms, we identified transcriptomic patterns with different endometrial prognoses. Expression changes among good or poor prognosis profiles were used to identify potential approved therapies to treat infertility leveraging drug expression databases. Participants/materials, setting, methods Gene signatures associated with poor prognosis were identified (FDR&amp;lt;0.05) and queried against the Connectivity Map database, containing gene expression profiles for 1,309 drugs in 5 different cell lines. Statistical analyses were performed to evaluate the drugs selected and obtain a score based on the drug expression reversal of the disease signatures. Significant drugs (adj. p-value) inversely associated with the gene signatures were highlighted. Finally, approved drugs were classified according to Anatomical Therapeutic Chemical codes. Main results and the role of chance We identified four transcriptomic profiles significantly (p-value&amp;lt;0.05) associated with different reproductive outcomes in the first embryo transfer after biopsy collection. Two poor prognosis profiles, one of them more related to clinical miscarriage, P1 (implantation rate = 30.4%, live birth rate = 42.9%, clinical miscarriage rate = 50%), and another to biochemical miscarriage, P2 (implantation rate = 20.0%, live birth rate = 33.3%, biochemical miscarriage rate = 66.7%) were compared with the best prognosis profile (implantation rate = 64.6%, live birth rate = 95.2%, biochemical miscarriage rate = 0.0%, clinical miscarriage rate = 4.8%), obtaining a total of 439 and 4,984 differential expressed genes, respectively for P1 and P2. After applying the drug repurposing methodology, we selected 50 and 32 significant approved drugs (adj. p-value&amp;lt;0.05) for P1 and P2. A total of 14 and 11 different drugs categories were obtained for each poor prognosis profile highlighting Nervous System (26%) and Alimentary tract and metabolism (12%) for P1, and Nervous System (23%), Antineoplastic and Immune modulating agents (15%) and Antiparasitic products (15%) for P2. Moreover, when both nervous system categories were compared, a total of 15 drugs were found in common, making possible the use of a unique drug for both profiles. Limitations, reasons for caution Since the Connectivity Map database does not include endometrial tissue, the action of these drugs should be validated experimentally in endometrial cell culture. Afterwards, to prioritize the best drug, side effects and approved doses reported in drug databases should be taken into consideration in further analyses. Wider implications of the findings The use of drug repurposing generates hypotheses for finding suitable drugs for endometrial failure, not requiring preclinical trials and going directly to Phase II clinical trials. These potential treatments will improve reproductive outcomes. Nervous system drugs seem to have a considerable effect on endometrium revealing possible causes of endometrial failure. Trial registration number Not Applicable

Comprehensive profiling of extracellular vesicles in uveitis and scleritis enables biomarker discovery and mechanism exploration

BackgroundUveitis and posterior scleritis are sight-threatening diseases with undefined pathogenesis and accurate diagnosis remains challenging.MethodsTwo plasma-derived extracellular vesicle (EV) subpopulations, small and large EVs, obtained from patients with ankylosing spondylitis-related uveitis, Behcet's disease uveitis, Vogt-Koyanagi-Harada syndrome, and posterior scleritis were subjected to proteomics analysis alongside plasma using SWATH-MS. A comprehensive bioinformatics analysis was performed on the proteomic profiles of sEVs, lEVs, and plasma. Candidate biomarkers were validated in a new cohort using ELISA. Pearson correlation analysis was performed to analyze the relationship between clinical parameters and proteomic data. Connectivity map database was used to predict therapeutic agents.ResultsIn total, 3,668 proteins were identified and over 3000 proteins were quantified from 278 samples. When comparing diseased group to healthy control, the proteomic profiles of the two EV subgroups were more correlated with disease than plasma. Comprehensive bioinformatics analysis highlighted potential pathogenic mechanisms for these diseases. Potential biomarker panels for four diseases were identified and validated. We found a negative correlation between plasma endothelin-converting enzyme 1 level and mean retinal thickness. Potential therapeutic drugs were proposed, and their targets were identified.ConclusionsThis study provides a proteomic landscape of plasma and EVs involved in ankylosing spondylitis-related uveitis, Behcet's disease uveitis, Vogt-Koyanagi-Harada syndrome, and posterior scleritis, offers insights into disease pathogenesis, identifies valuable biomarker candidates, and proposes promising therapeutic agents.

Exploring the molecular mechanism of comorbidity of autism spectrum disorder and inflammatory bowel disease by combining multiple data sets

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder that is difficult to diagnose. Inflammatory bowel disease (IBD) is a common chronic digestive disease. Previous studies have shown a potential correlation between ASD and IBD, but the pathophysiological mechanism remains unclear. The purpose of this research was to examine the biological mechanisms underlying the differentially expressed genes (DEGs) of ASD and IBD using bioinformatics tools.MethodsLimma software was used to evaluate the DEGs between ASD and IBD. The GSE3365, GSE18123, and GSE150115 microarray data sets were acquired from the Gene Expression Omnibus (GEO) database. We then performed 6 analyses, namely, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation; weighted gene coexpression network analysis; correlation analysis of hub genes with autophagy, ferroptosis and immunity; transcriptional regulation analysis of hub genes; single-cell sequencing analysis; and potential therapeutic drug prediction.ResultsA total of 505 DEGs associated with ASD and 616 DEGs associated with IBD were identified, and 7 genes overlapped between these sets. GO and KEGG analyses revealed several pathways enriched in both diseases. A total of 98 common genes related to ASD and IBD were identified by weighted gene coexpression network analysis (WGCNA), and 4 hub genes were obtained by intersection with the 7 intersecting DEGs, which were PDGFC, CA2, GUCY1B3 and SDPR. We also found that 4 hub genes in the two diseases were related to autophagy, ferroptosis or immune factors. In addition, motif–TF annotation analysis showed that cisbp__M0080 was the most relevant motif. We also used the Connectivity Map (CMap) database to identify 4 potential therapeutic agents.ConclusionThis research reveals the shared pathogenesis of ASD and IBD. In the future, these common hub genes may provide new targets for further mechanistic research as well as new therapies for patients with ASD and IBD.

INVESTIGATING NEW THERAPEUTIC OPPORTUNITIES FOR HYPERTENSION THROUGH TRANSCRIPTOMIC AND PROTEOMIC ANALYSIS OF THE KIDNEY RENIN-ANGIOTENSIN SYSTEM

Objective: We systematically characterised the expression of all renin-angiotensin system (RAS) genes in the human kidney and searched in silico for new pharmacological therapeutic opportunities to modify RAS. Design and method: We compiled data from open access resources to characterise all components of the RAS pathway. We used transcriptomic profiles of 73 human kidney tissues from the Genotype-Tissue Expression Project, integrated genome-wide DNA genotypes and RNA-sequencing-derived transcriptome profiles of 478 human kidneys from Human Kidney Tissue Resource and, kidney tissue proteomics from 72 samples recruited by Clinical Proteomic Tumour Analysis Consortium. Additionally, we used an independent single-cell RNA sequencing data and gene expression datasets curated from the Kidney Precision Medicine Project (KPMP) and Enrichr (web-based enrichment analysis tool). Lastly, we integrated the Connectivity Map and Drug Gene Interaction Database to identify drug repurposing opportunities for treatment of hypertension targeting the RAS genes. Results: We identified 37 genes encoding components of the RAS pathway with measurable expression in human kidney tissue. 15% of these gene-pairs demonstrated a positive (r&gt;0.5) correlation in kidney expression most consistent with a functionally integrated network of interacting molecules. Angiotensinogen (AGT), the key component of the pathway - showed the highest level of mRNA expression in the liver, and we demonstrated that its hepatic mRNA expression correlates most strongly with its mRNA expression in the kidney (r = 0.71, P = 2.60e-06). Expression levels of mRNA for 92% of RAS genes correlated positively with their protein abundance in the kidney, where renin had the strongest positive correlation (r = 0.64, P&lt;0.01). Enrichr and KPMP revealed that RAS gene expression was enriched in mesangial and epithelial cell types. Existing genetic evidence for correlation between glutamyl aminopeptidase (ENPEP) and systolic blood pressure might relate to its capacity to generate angiotensin III acting on dilator angiotensin II type 2 receptors. This raises potential repurposing opportunities for ENPEP inhibitors like firibastat and tosedostat for the treatment of hypertension. Conclusions: Collectively, this study confirmed synchronized expression of RAS genes in the kidney at both mRNA and protein levels and identified new therapeutic opportunities for treatment of hypertension, through drug repurposing.

Transcription‐based drug repurposing in Alzheimer disease psychosis

AbstractBackgroundThere are at least 50 million people with Alzheimer’s Disease (AD) or other dementias worldwide. About 50% of people with AD may develop psychotic symptoms (delusions and hallucinations). However, there is no effective pharmacological therapy for psychosis in AD and all current treatments are based on antipsychotics originally designed for other disorders like schizophrenia. In this study, we identify the molecular signature of psychosis in AD in post‐mortem brain samples and then use the connectivity map (CMAP) and library of network‐based cellular signatures (LINC) to predict candidate drugs based on the disease transcriptional signature.MethodWe generated RNA‐Seq data (Illumina Stranded mRNA Prep) and compared the gene expression levels across 4 groups: Control, AD with no psychosis (AD), AD with Delusions (ADD), and AD with both Delusions and Hallucinations (ADDH). The final identified differentially expressed up and down‐regulated genes (DEGs) were used as query signatures in the CMAP and LINC databases to identify drugs that could potentially reverse the gene expression signatures in disease samples. To investigate the disease mechanisms and the effect of repurposed drugs on it, a drug‐target interaction network was reconstructed and combined with the human interactome. Enrichment analysis of this network identifies AD and psychosis‐related pathways and the potential effect the nominated drugs have on them.ResultAfter filtering out the common DEGs, 357, 171, and 66 unique genes remained in the AD, ADD, and ADDH signature sets, respectively. The signatureSearch package was used to search transcriptome signatures against CMAP and LINC. The top ranked drugs, based on negative connectivity scores, were selected as final results. Gene set enrichment analysis of the drug target genes presented some key pathways and activities related to psychiatric disorders. For example, serotonin receptor signalling pathway is one of the most over‐represented pathway of target genes in both ADD and ADDH groups.ConclusionThis study can improve our understanding of the molecular mechanisms underlying AD psychosis. The candidate drugs we have identified could be repurposed for clinical trials of psychosis in AD and provide valuable knowledge for future pharmacological research.

Comprehensive analysis of endoplasmic reticulum stress-associated genes signature of ulcerative colitis.

Endoplasmic reticulum stress (ERS) is a critical factor in the development of ulcerative colitis (UC); however, the underlying molecular mechanisms remain unclear. This study aims to identify pivotal molecular mechanisms related to ERS in UC pathogenesis and provide novel therapeutic targets for UC. Colon tissue gene expression profiles and clinical information of UC patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database, and the ERS-related gene set was downloaded from GeneCards for analysis. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were utilized to identify pivotal modules and genes associated with UC. A consensus clustering algorithm was used to classify UC patients. The CIBERSORT algorithm was employed to evaluate the immune cell infiltration. Gene Set Variation Analysis (GSVA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore potential biological mechanisms. The external sets were used to validate and identify the relationship of ERS-related genes with biologics. Small molecule compounds were predicted using the Connectivity Map (CMap) database. Molecular docking was performed to simulate the binding conformation of small molecule compounds and key targets. The study identified 915 differentially expressed genes (DEGs) and 11 ERS-related genes (ERSRGs) from the colonic mucosa of UC patients and healthy controls, and these genes had good diagnostic value and were highly correlated. Five potential small-molecule drugs sharing tubulin inhibitors were identified, including albendazole, fenbendazole, flubendazole, griseofulvin, and noscapine, among which noscapine exhibited the highest correlation with a high binding affinity to the targets. Active UC and 10 ERSRGs were associated with a large number of immune cells, and ERS was also associated with colon mucosal invasion of active UC. Significant differences in gene expression patterns and immune cell infiltration abundance were observed among ERS-related subtypes. The results suggest that ERS plays a vital role in UC pathogenesis, and noscapine may be a promising therapeutic agent for UC by affecting ERS.

Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer’s disease patients

BackgroundDespite numerous clinical trials and decades of endeavour, there is still no effective cure for Alzheimer's disease. Computational drug repositioning approaches may be employed for the development of new treatment strategies for Alzheimer’s patients since an extensive amount of omics data has been generated during pre-clinical and clinical studies. However, targeting the most critical pathophysiological mechanisms and determining drugs with proper pharmacodynamics and good efficacy are equally crucial in drug repurposing and often imbalanced in Alzheimer’s studies.MethodsHere, we investigated central co-expressed genes upregulated in Alzheimer’s disease to determine a proper therapeutic target. We backed our reasoning by checking the target gene’s estimated non-essentiality for survival in multiple human tissues. We screened transcriptome profiles of various human cell lines perturbed by drug induction (for 6798 compounds) and gene knockout using data available in the Connectivity Map database. Then, we applied a profile-based drug repositioning approach to discover drugs targeting the target gene based on the correlations between these transcriptome profiles. We evaluated the bioavailability, functional enrichment profiles and drug-protein interactions of these repurposed agents and evidenced their cellular viability and efficacy in glial cell culture by experimental assays and Western blotting. Finally, we evaluated their pharmacokinetics to anticipate to which degree their efficacy can be improved.ResultsWe identified glutaminase as a promising drug target. Glutaminase overexpression may fuel the glutamate excitotoxicity in neurons, leading to mitochondrial dysfunction and other neurodegeneration hallmark processes. The computational drug repurposing revealed eight drugs: mitoxantrone, bortezomib, parbendazole, crizotinib, withaferin-a, SA-25547 and two unstudied compounds. We demonstrated that the proposed drugs could effectively suppress glutaminase and reduce glutamate production in the diseased brain through multiple neurodegeneration-associated mechanisms, including cytoskeleton and proteostasis. We also estimated the human blood–brain barrier permeability of parbendazole and SA-25547 using the SwissADME tool.ConclusionsThis study method effectively identified an Alzheimer’s disease marker and compounds targeting the marker and interconnected biological processes by use of multiple computational approaches. Our results highlight the importance of synaptic glutamate signalling in Alzheimer’s disease progression. We suggest repurposable drugs (like parbendazole) with well-evidenced activities that we linked to glutamate synthesis hereby and novel molecules (SA-25547) with estimated mechanisms for the treatment of Alzheimer’s patients.

BKM120 inhibits malignant rhabdoid tumor of the kidney through induction of apoptosis and G0/G1 phase arrest

Malignant rhabdoid tumor of the kidney (MRTK) has an inferior prognosis and is insensitive to radiotherapy and chemotherapy. Search for novel, potent medicinal agents is urgent. Herein, data on the gene expression and clinical characteristics of malignant rhabdoid tumors (MRT) were retrieved from the TARGET database. Prognosis-related genes were identified by differential analysis and one-way cox regression analysis, and prognosis-related signalling pathways were identified by enrichment analysis. The prognosis-related genes were imported into the Connectivity Map database for query, and BKM120 was predicted and screened as a potential therapeutic agent for MRTK. A combination of high-throughput RNA sequencing and Western blot verified that the PI3K/Akt signaling pathway is associated with MRTK prognosis and is overactivated in MRTK. Our results outlined that BKM120 inhibited the proliferation, migration, and invasion ability of G401 cells and induced apoptosis and cell cycle G0/G1 phase arrest. In vivo, BKM120 inhibited tumor growth and had no significant toxic side effects. Western blot and immunofluorescence results confirmed that BKM120 could reduce the expression of PI3K and p-AKT, critical proteins of the PI3K/Akt signaling pathway. BKM120 inhibits MRTK by inhibiting PI3K/Akt signalling pathway to induce apoptosis and cell cycle G0/G1 phase arrest, which is anticipated to give the clinical treatment of MRTK a new direction.

Cinchonine, a Potential Oral Small-Molecule Glucagon-Like Peptide-1 Receptor Agonist, Lowers Blood Glucose and Ameliorates Non-Alcoholic Steatohepatitis.

The glucagon-like peptide-1 receptor (GLP-1R) is an effective therapeutic target for type 2 diabetes mellitus (T2DM) and non-alcoholic steatohepatitis (NASH). Research has focused on small-molecule GLP-1R agonists because of their ease of use in oral formulations and improved patient compliance. However, no small-molecule GLP-1R agonists are currently available in the market. We aimed to screen for a potential oral small-molecule GLP-1R agonist and evaluated its effect on blood glucose and NASH. The Connectivity map database was used to screen for candidate small-molecule compounds. Molecular docking was performed using SYBYL software. Rat pancreatic islets were incubated in different concentrations glucose solutions, with cinchonine or Exendin (9-39) added to determine insulin secretion levels. C57BL/6 mice, GLP-1R-/- mice and hGLP-1R mice were used to conduct oral glucose tolerance test. In addition, we fed ob/ob mice with the GAN diet to induce the NASH model. Cinchonine (50 mg/kg or 100 mg/kg) was administered orally twice daily to the mice. Serum liver enzymes were measured using biochemical analysis. Liver tissues were examined using Hematoxylin-eosin staining, Oil Red O staining and Sirius Red staining. Based on the small intestinal transcriptome of geniposide, a recognized small-molecule GLP-1R agonist, we identified that cinchonine exerted GLP-1R agonist-like effects. Cinchonine had a good binding affinity for GLP-1R. Cinchonine promoted glucose-dependent insulin secretion, which could be attenuated significantly by Exendin (9-39), a specific GLP-1R antagonist. Moreover, cinchonine could reduce blood glucose in C57BL/6 and hGLP-1R mice, an effect that could be inhibited with GLP-1R knockout. In addition, cinchonine reduced body weight gain and food intake in ob/ob-GAN NASH mice dose-dependently. 100 mg/kg cinchonine significantly improved liver function by reducing the ALT, ALP and LDH levels. Importantly, 100 mg/kg cinchonine ameliorated hepatic steatosis and fibrosis in NASH mice. Cinchonine, a potential oral small-molecule GLP-1R agonist, could reduce blood glucose and ameliorate NASH, providing a strategy for developing small-molecule GLP-1R agonists.

Identification of Novel Immunosuppressive Drug Targets for Liver Transplantation Using Genome-Scale Metabolic Modeling

Abstract The global database on donation and transplantation reported 129,681 worldwide organ transplants in 2020, with kidneys and livers being the most commonly transplanted organs. Rejection of the transplanted organ by the host's immune system is a common complication and a major reason for graft failure and patient mortality. To address these limitations, we developed genome-scale metabolic models of liver and CD4+ T cells using the pipeline for MetAbolic Drug Repurposing IDentification-MADRID and integrating our data analysis results from RNA-seq, proteomics, and microarray human sample datasets (step 1). Drug targets obtained from the ConnectivityMap database are mapped to metabolic genes in the model where systematic knockouts are performed on each gene which leads to identifying differential fluxes (step 2). Our human-cell-specific models identified novel immunosuppressive drug targets by integrating differentially expressed genes in post-liver transplanted non-tolerant versus healthy control individuals and differential fluxes (step 3). We identified 193, 217, and 196 potential metabolic gene targets for T-helper 1, 2, and 17, respectively, against patients with a non-tolerant phenotype after liver transplantation. This genome-scale metabolic modeling approach for liver transplantation can assist in personalized treatment by using any individual’s sample data at step 1 and 2. It can also save time and resources in preclinical drug development studies by predicting more precise drug targets and drug repurposing based on patients’ omics data. By using this approach, we aim to improve the success rates of liver transplantations and reduce the complications associated with organ rejection. The work was supported by University of Nebraska collaboration initiatives.

TIP60 is required for tumorigenesis in non-small cell lung cancer.

Histone modifications play crucial roles in transcriptional activation, and aberrant epigenetic changes are associated with oncogenesis. Lysine (K) acetyltransferases 5 (TIP60, also known as KAT5) is reportedly implicated in cancer development and maintenance, although its function in lung cancer remains controversial. Here we demonstrate that TIP60 knockdown in non-small cell lung cancer cell lines decreased tumor cell growth, migration, and invasion. Furthermore, analysis of a mouse lung cancer model with lung-specific conditional Tip60 knockout revealed suppressed tumor formation relative to controls, but no apparent effects on normal lung homeostasis. RNA-seq and ChIP-seq analyses of inducible TIP60 knockdown H1975 cells relative to controls revealed transglutaminase enzyme (TGM5) as downstream of TIP60. Investigation of a connectivity map database identified several candidate compounds that decrease TIP60 mRNA, one that suppressed tumor growth in cell culture and in vivo. In addition, TH1834, a TIP60 acetyltransferase inhibitor, showed comparable anti-tumor effects in cell culture and in vivo. Taken together, suppression of TIP60 activity shows tumor-specific efficacy against lung cancer, with no overt effect on normal tissues. Our work suggests that targeting TIP60 could be a promising approach in treating lung cancer.

Decoding Connectivity Map-based drug repurposing for oncotherapy

The rising global burden of cancer has driven considerable efforts into the research and development of effective anti-cancer agents. Fortunately, with impressive advances in transcriptome profiling technology, the Connectivity Map (CMap) database has emerged as a promising and powerful drug repurposing approach. It provides an important platform for systematically discovering of the associations among genes, small-molecule compounds and diseases, and elucidating the mechanism of action of drug, contributing toward efficient anti-cancer pharmacotherapy. Moreover, CMap-based computational drug repurposing is gaining attention because of its potential to overcome the bottleneck constraints faced by traditional drug discovery in terms of cost, time and risk. Herein, we provide a comprehensive review of the applications of drug repurposing for anti-cancer drug discovery and summarize approaches for computational drug repurposing. We focus on the principle of the CMap database and novel CMap-based software/algorithms as well as their progress achieved for drug repurposing in the field of oncotherapy. This article is expected to illuminate the emerging potential of CMap in discovering effective anti-cancer drugs, thereby promoting efficient healthcare for cancer patients.