DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds.

Pathway genes functionally participate in the same biological process. They typically act cooperatively, and none is considered dispensable. The dominant paradigm in drug discovery is the one-to-one strategy, which aims to find the most sensitive drug to act on an individual target. However, many complex diseases, such as cancer, are caused by dysfunction among multiple-gene pathways, not just one. Therefore, identifying pathway genes that are responsive to synthetic compounds in a global physiological environment may be more effective in drug discovery. The high redundancy of crosstalk between biological pathways, though, hints that the covariance matrix, which only connects genes with strong marginal correlations, may miss higher-level interactions, such as group interactions. We herein report the development of DPADM-a Drug-Pathway association Detection Model that infers pathways responsive to specific drugs. This model elucidates higher-level gene-gene interactions by evaluating the conditional dependencies between genes under different drug treatments. The advantage of the proposed method is demonstrated using simulation studies by comparing with another two methods. We applied this model to the Connectivity Map data set (CMap), and demonstrated that DPADM is able to identify many drug-pathway associations, such as mitoxantrone (MTX)- PI3K/AKT association, which targets the topological conditions of DNA transcription. Surprisingly, apart from identifying pathways corresponding to specific drugs, our methodology also revealed new drug-related pathways with functions similarly to those of seed genes.

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.

Exploration of small molecule compounds targeting abdominal aortic aneurysm based on CMap database and molecular dynamics simulation.

OBJECTIVE: The mitigation of abdominal aortic aneurysm (AAA) growth through pharmaceutical intervention offers the potential to avert the perils associated with AAA rupture and the subsequent need for surgical intervention. Nevertheless, the existing effective drugs for AAA treatment are limited, necessitating a pressing exploration for novel therapeutic medications. METHODS: AAA-related transcriptome data were downloaded from GEO, and differentially expressed genes (DEGs) in AAA tissue were screened for GO and KEGG enrichment analyses. Small molecule compounds and their target proteins with negative connectivity to the AAA expression profile were predicted in the Connectivity Map (CMap) database. Molecular docking and molecular dynamics simulation were performed to predict the binding of the target protein to the small molecule compound, and the MM/GBSA method was used to calculate the binding free energy. Cluster analysis was performed using the cluster tool in the GROMACS package. An AAA cell-free model was built, and CETSA experiments were used to demonstrate the binding ability of small molecules to the target protein in cells. RESULTS: A total of 2244 DEGs in AAA were obtained through differential analysis, and the DEGs were mainly enriched in the tubulin binding biological function and cell cycle pathway. The CMap results showed that Apicidin had a potential therapeutic effect on AAA with a connectivity score of -97.74, and HDAC4 was the target protein of Apicidin. Based on literature, HDAC4-Apicidin was selected as the subsequent research object. The lowest affinity of Apicidin-HDAC4 molecular docking was -8.218 kcal/mol. Molecular dynamics simulation results indicated that Apicidin-HDAC4 could form a stable complex. MM/GBSA analysis showed a total binding free energy of -55.40 ± 0.79 kcal/mol, and cluster analysis showed that there were two main conformational clusters during the binding process, accounting for 22.4% and 57.8%, respectively. Apicidin could form hydrogen bonds with surrounding residues for stable binding. CETSA experiment proved the stable binding ability of Apicidin and HDAC4. CONCLUSION: Apicidin inhibited HDAC4 in AAA and exhibited favorable protein-ligand interactions and stability, making it a potential candidate drug for treating AAA.

Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders.

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.

Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts.

Prostate cancer stemness (PCS) cells have been reported to drive tumor progression, recurrence and drug resistance. However, there is lacking systematical assessment of stemlike indices and associations with immunological properties in prostate adenocarcinoma (PRAD). We thus collected 7 PRAD cohorts with 1465 men and calculated the stemlike indices for each sample using one-class logistic regression machine learning algorithm. We selected the mRNAsi to quantify the stemlike indices that correlated significantly with prognosis and accordingly identified 21 PCS-related CpG loci and 13 pivotal signature. The 13-gene based PCS model possessed high predictive significance for progression-free survival (PFS) that was trained and validated in 7 independent cohorts. Meanwhile, we conducted consensus clustering and classified the total cohorts into 5 PCS clusters with distinct outcomes. Samples in PCScluster5 possessed the highest stemness fractions and suffered from the worst prognosis. Additionally, we implemented the CIBERSORT algorithm to infer the differential abundance across 5 PCS clusters. The activated immune cells (CD8+ T cell and dendritic cells) infiltrated significantly less in PCScluster5 than other clusters, supporting the negative regulations between stemlike indices and anticancer immunity. High mRNAsi was also found to be associated with up-regulation of immunosuppressive checkpoints, like PDL1. Lastly, we used the Connectivity Map (CMap) resource to screen potential compounds for targeting PRAD stemness, including the top hits of cell cycle inhibitor and FOXM1 inhibitor. Taken together, our study comprehensively evaluated the PRAD stemlike indices based on large cohorts and established a 13-gene based classifier for predicting prognosis or potential strategies for stemness treatment.

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

BACKGROUND: Uveitis and posterior scleritis are sight-threatening diseases with undefined pathogenesis and accurate diagnosis remains challenging. METHODS: Two 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. RESULTS: In 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. CONCLUSIONS: This 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.

Repositioning the existing drugs for neuroinflammation: a fusion of computational approach and biological validation to counter the Parkinson's disease progression.

Parkinson's disease is caused by the deficiency of striatal dopamine and the accumulation of aggregated α-synuclein in the substantia nigra pars compacta (SNpc). Neuroinflammation associated with oxidative stress is a key factor contributing to the death of dopaminergic neurons in SNpc and advancement of Parkinson's disease. Two molecular targets, i.e., nuclear factor kappa-light-chain-enhancer (NF-kB) and α-synuclein play a substantial role in neuroinflammation progression. Therefore, the compounds targeting these neuroinflammatory targets hold a great potential to combat Parkinson's disease. Thereby, in this study, molecular docking and Connectivity Map (CMap) based gene expression profiling was utilized to reposition the approved drugs as neuroprotective agents for Parkinson's disease. With in silico screening, two drugs namely theophylline and propylthiouracil were selected for anti-neuroinflammatory activity evaluation in in vivo models of chronic neuroinflammation. The neuroinflammatory effect of the identified compounds was confirmed by quantifying the expression of three important neuroinflammatory mediators, i.e. IL-6, TNF-alpha, and IL-1 beta on brain tissue using ELISA assay. The ELISA experiment demonstrated that both compounds significantly decreased the expression of neuroinflammatory mediators, highlighting the compounds' potential in neuroinflammation management. Furthermore, the drug and disease interaction network of the two identified drugs and diseases (neuroinflammation and Parkinson's disease) suggested that the two drugs might interact with various targets namely adenosine receptors, Poly [ADP-ribose] polymerase-1, myeloperoxidase (MPO) and thyroid peroxidase through multiple pathways associated with neuroinflammation and Parkinson's disease. Computational studies suggest that a particular drug may be effective in managing Parkinson's disease associated with neuroinflammation. However, further research is needed to confirm this in biological experiments.

Signature-Based Computational Drug Repurposing for Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disease characterized by progressive loss of the upper and lower motor neurons. There are currently limited approved drugs for the disorder, and for this reason the strategy of repositioning already approved therapeutics could exhibit a successful outcome. Herein, we used CMAP and L1000CDS2 databases which include gene expression profiles datasets (genomic signatures) to identify potent compounds and classes of compounds which reverse disease's signature which could in turn reverse its phenotype. ALS signature was obtained by comparing gene expression of muscle biopsy specimens between diseased and healthy individuals. Statistical analysis was conducted to explore differentially transcripts in patients' samples. Then, the list of upregulated and downregulated genes was used to query both databases in order to determine molecules which downregulate the genes which are upregulated by ALS and vice versa. These compounds, based on their chemical structure along with known treatments, were clustered to reveal drugs with novel and potentially more effective mode of action with most of them predicted to affect pathways heavily involved in ALS. This evidence suggests that these compounds are strong candidates for moving to the next phase of the drug repurposing pipeline which is in vitro and in vivo experimental evaluation.

Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy.

BACKGROUND: In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them. METHODS: In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses. RESULTS: We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined. CONCLUSION: Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents.

Resveratrol inhibits development of colorectal adenoma via suppression of LEF1; comprehensive analysis with connectivity map.

Although many chemopreventive studies on colorectal tumors have been reported, no effective and safe preventive agent is currently available. We searched for candidate preventive compounds against colorectal tumor comprehensively from United States Food and Drug Administration (FDA)-approved compounds by using connectivity map (CMAP) analysis coupled with in vitro screening with colorectal adenoma (CRA) patient-derived organoids (PDOs). We generated CRA-specific gene signatures based on the DNA microarray analysis of CRA and normal epithelial specimens, applied them to CMAP analysis with 1309 FDA-approved compounds, and identified 121 candidate compounds that should cancel the gene signatures. We narrowed them down to 15 compounds, and evaluated their inhibitory effects on the growth of CRA-PDOs in vitro. We finally identified resveratrol, one of the polyphenolic phytochemicals, as a compound showing the strongest inhibitory effect on the growth of CRA-PDOs compared with normal epithelial PDOs. When resveratrol was administered to ApcMin/+ mice at 15 or 30 mg/kg, the number of polyps (adenomas) was significantly reduced in both groups compared with control mice. Similarly, the number of polyps (adenomas) was significantly reduced in azoxymethane-injected rats treated with 10 or 100 mg/resveratrol compared with control rats. Microarray analysis of adenomas from resveratrol-treated rats revealed the highest change (downregulation) in expression of LEF1, a key molecule in the Wnt signaling pathway. Treatment with resveratrol significantly downregulated the Wnt-target gene (MYC) in CRA-PDOs. Our data demonstrated that resveratrol can be the most effective compound for chemoprevention of colorectal tumors, the efficacy of which is mediated through suppression of LEF1 expression in the Wnt signaling pathway.

A comprehensive evaluation of connectivity methods for L1000 data.

The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, using the drug-drug similarities from the Drug Repurposing Hub database as a benchmark standard, we evaluated six popular published methods for the prediction performance of drug-drug relationships based on the partial area under the receiver operating characteristic (ROC) curve at false positive rates of 0.001, 0.005 and 0.01 (AUC0.001, AUC0.005 and AUC0.01). The similarity evaluating algorithm called ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200. Further, we tested these methods with an experimentally derived gene signature related to estrogen in breast cancer cells, and the results confirmed that ZhangScore was more accurate than other methods. Moreover, based on scoring results of ZhangScore for the gene signature of TOP2A knockdown, in addition to well-known TOP2A inhibitors, we identified a number of potential inhibitors and at least two of them were the subject of previous investigation. Our studies provide potential guidelines for researchers to choose the suitable connectivity method. The six connectivity methods used in this report have been implemented in R package (https://github.com/Jasonlinchina/RCSM).

Repurposing of ingenol mebutate for treating human colorectal cancer by targeting S100 calcium-binding protein A4 (S100A4).

Colorectal cancer (CRC) is the world's second most common cause of cancer-related death. Novel treatments are still urgently needed. S100 calcium-binding protein A4 (S100A4) was demonstrated to be an anticancer therapeutic target. Herein, we found that higher S100A4 expression was associated with a poorer prognosis in publicly available cohorts and a Taiwanese CRC patient cohort. To identify repurposed S100A4 inhibitors, we mined the Connectivity Map (CMap) database for clinical drugs mimicking the S100A4-knockdown gene signature. Ingenol mebutate, derived from the sap of the plant Euphorbia peplus, is approved as a topical treatment for actinic keratosis. The CMap analysis predicted ingenol mebutate as a potent S100A4 inhibitor. Indeed, both messenger RNA and protein levels of S100A4 were attenuated by ingenol mebutate in human CRC cells. In addition, CRC cells with higher S100A4 expressions and/or the wild-type p53 gene were more sensitive to ingenol mebutate, and their migration and invasion were inhibited by ingenol mebutate. Therefore, our results suggest the repurposing of ingenol mebutate for treating CRC by targeting S100A4.

A Novel Based-Network Strategy to Identify Phytochemicals from Radix Salviae Miltiorrhizae (Danshen) for Treating Alzheimer's Disease.

Alzheimer's disease (AD) is a common age-related neurodegenerative disease that strikes millions worldwide. Herein, we demonstrate a new approach based on network target to identify anti-AD compounds from Danshen. Network pharmacology and molecular docking were employed to establish the DS-AD network, which mainly involved apoptosis of neuron cells. Then network scoring was confirmed via Connectivity Map analysis. M308 (Danshenxinkun D) was an anti-AD candidate with a high score (p < 0.01). Furthermore, we conducted ex vivo experiments with H2O2-treated PC12 cells to verify the neuroprotective effect of Salvia miltiorrhiza-containing plasma (SMP), and UPLC-Q-TOF/MS and RT-qPCR were performed to demonstrate the anti-AD activity of M308 from SMP. Results revealed that SMP could enhance cell viability and level of acetylcholine. AO/EB staining and Mitochondrial membrane potential (MMP) analysis showed that SMP significantly suppressed apoptosis, which may be due to anti-oxidative stress activity. Moreover, the effects of M308 and SMP on expressions of PSEN1, DRD2, and APP mRNA were consistent, and M308 can significantly reverse the expression of PSEN1 and DRD2 mRNA in H2O2-treated PC12 cells. The strategy based on the network could be employed to identify anti-AD compounds from Chinese herbs. Notably, M308 stands out as a promising anti-AD candidate for development.

Analysis of long non-coding RNA expression profiles in disuse osteoporosis using microarray and bioinformatics.

Disuse osteoporosis (DOP) is one of the major consequences of long space flights. DOP also occurs in patients with spinal cord injuries and prolonged bedridden states that can have a severe impact on human health. Bone marrow mesenchymal stem cells (BMSCs) are multipotent stromal cells that play an important role in bone homeostasis. Long non-coding RNAs (lncRNAs) are involved in regulating osteogenic differentiation of BMSCs, and their abnormal expression might lead to the formation of orthopedic diseases. However, the specific mechanism of DOP has not yet been elucidated. All sequencing data were obtained from Gene Expression Omnibus (GEO) datasets. The limma package of R was applied to identify DEmRNAs and DElncRNAs. Pearson correlation coefficients (PCC) between DElncRNADEmRNA expression levels were calculated. Functional annotation was performed for DEmRNAs coexpressed with DElncRNAs. In addition, the Cytohubba plug-in in Cytoscape was applied to determine the top 10 hub genes. Finally, connectivity map (CMap) analysis was used to identify potential therapeutic drugs for DOP. The gene expression data, GSE100930 and GSE17696, were retrieved from the GEO database. A total of 2,212 differentially expressed mRNAs (DEmRNAs) and 22 differentially expressed lncRNAs (DElncRNAs) were obtained. Gene ontology (GO) functional terms, Kyoto Encyclopedia of Genes, and Genomes (KEGG) pathway enrichment analysis reveal 30 significant GO terms and 13 significant pathways. A coding-non-coding gene co-expression (CNC) network was constructed to study the potential role of hub-DElncRNAs and their co-expressed DEmRNAs in DOP. The lncRNAs, GSNAS1, SNHG12, and EPB41LA4A-AS1, were significant in the CNC network and potential regulators of DOP development. Three bioactive compounds (scoulerine, kinetin riboside, dexanabinol) with potential therapeutic significance for DOP were obtained through the Connectivity Map (CMAP) analysis. Our study revealed a new mechanism for a lineage shift of bone marrow mesenchymal stem cells under microgravity, and linked the function of protein-coding mRNAs with ncRNAs, which may contribute to the development of new therapies for DOP.

Screening Novel Drug Candidates for Kidney Renal Clear Cell Carcinoma Treatment: A Study on Differentially Expressed Genes through the Connectivity Map Database.

OBJECTIVE: Kidney renal clear cell carcinoma (KIRC) is a common cancer with high morbidity and mortality in renal cancer. Thus, the transcriptome data of KIRC patients in The Cancer Genome Atlas (TCGA) database were analyzed and drug candidates for the treatment of KIRC were explored through the connectivity map (CMap) database. METHODS: The transcriptome data of KIRC patients were downloaded from TCGA database, and KIRC-associated hub genes were screened out through differential analysis and protein-protein interaction (PPI) network analysis. Afterward, the CMap database was used to select drug candidates for KIRC treatment, and the drug-targeted genes were obtained through the STITCH database. A PPI network was constructed by combining drug-targeted genes with hub genes that affected the pathogenesis of KIRC to obtain final hub genes. Finally, combining hub genes and KIRC-associated hub genes, the pathways affected by drugs were explored by pathway enrichment analysis. RESULTS: A total of 2,312 differentially expressed genes were found in patients, which were concentrated in immune cell activity, cytokine, and chemokine secretion pathways. Drug screening disclosed 5 drug candidates for KIRC treatment: fedratinib, Ly344864, geldanamycin, AS-605240, and luminespib. Based on drug-targeted genes and KIRC-associated hub genes, 16 hub genes were screened out. Pathway enrichment analysis revealed that drugs mainly affected pathways such as neuroactive ligand pathways, cell adhesion, and chemokines. CONCLUSION: The above results indicated that fedratinib, LY 344864, geldanamycin, AS-605240, and luminespib could be used as candidates for KIRC therapy. The findings from this study will make contributions to the treatment of KIRC in the future.

Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases.

BACKGROUND: Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. RESULTS: In this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR CONCLUSIONS: The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.

NBM-BMX, an HDAC8 Inhibitor, Overcomes Temozolomide Resistance in Glioblastoma Multiforme by Downregulating the ß-Catenin/c-Myc/SOX2 Pathway and Upregulating p53-Mediated MGMT Inhibition.

Although histone deacetylase 8 (HDAC8) plays a role in glioblastoma multiforme (GBM), whether its inhibition facilitates the treatment of temozolomide (TMZ)-resistant GBM (GBM-R) remains unclear. By assessing the gene expression profiles from short hairpin RNA of HDAC8 in the new version of Connectivity Map (CLUE) and cells treated by NBM-BMX (BMX)-, an HDAC8 inhibitor, data analysis reveals that the Wnt signaling pathway and apoptosis might be the underlying mechanisms in BMX-elicited treatment. This study evaluated the efficacy of cotreatment with BMX and TMZ in GBM-R cells. We observed that cotreatment with BMX and TMZ could overcome resistance in GBM-R cells and inhibit cell viability, markedly inhibit cell proliferation, and then induce cell cycle arrest and apoptosis. In addition, the expression level of ß-catenin was reversed by proteasome inhibitor via the ß-catenin/ GSK3ß signaling pathway to reduce the expression level of c-Myc and cyclin D1 in GBM-R cells. BMX and TMZ cotreatment also upregulated WT-p53 mediated MGMT inhibition, thereby triggering the activation of caspase-3 and eventually leading to apoptosis in GBM-R cells. Moreover, BMX and TMZ attenuated the expression of CD133, CD44, and SOX2 in GBM-R cells. In conclusion, BMX overcomes TMZ resistance by enhancing TMZ-mediated cytotoxic effect by downregulating the ß-catenin/c-Myc/SOX2 signaling pathway and upregulating WT-p53 mediated MGMT inhibition. These findings indicate a promising drug combination for precision personal treating of TMZ-resistant WT-p53 GBM cells.

Transcriptional Modulation of the Hippo Signaling Pathway by Drugs Used to Treat Bipolar Disorder and Schizophrenia.

Recent reports suggest a link between positive regulation of the Hippo pathway with bipolar disorder (BD), and the Hippo pathway is known to interact with multiple other signaling pathways previously associated with BD and other psychiatric disorders. In this study, neuronal-like NT2 cells were treated with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), valproate (0.5 mM), or vehicle control for 24 h. Genome-wide mRNA expression was quantified and analyzed using gene set enrichment analysis (GSEA), with genes belonging to Hippo, Wnt, Notch, TGF- ß, and Hedgehog retrieved from the KEGG database. Five of the eight drugs downregulated the genes of the Hippo pathway and modulated several genes involved in the interacting pathways. We speculate that the regulation of these genes, especially by aripiprazole, clozapine, and quetiapine, results in a reduction of MAPK and NFκB pro-inflammatory signaling through modulation of Hippo, Wnt, and TGF-ß pathways. We also employed connectivity map analysis to identify compounds that act on these pathways in a similar manner to the known psychiatric drugs. Thirty-six compounds were identified. The presence of antidepressants and antipsychotics validates our approach and reveals possible new targets for drug repurposing.

Berberine regulates the Notch1/PTEN/PI3K/AKT/mTOR pathway and acts synergistically with 17-AAG and SAHA in SW480 colon cancer cells.

CONTEXT: Berberine (BBR) is used to treat diarrhoea and gastroenteritis in the clinic. It was found to have anticolon cancer effects. OBJECTIVE: To study the anticolon cancer mechanism of BBR by connectivity map (CMAP) analysis. MATERIALS AND METHODS: CMAP based mechanistic prediction was conducted by comparing gene expression profiles of 10 µM BBR treated MCF-7 cells with that of clinical drugs such as helveticoside, ianatoside C, pyrvinium, gossypol and trifluoperazine. The treatment time was 12 h and two biological replications were performed. The DMSO-treated cells were selected as a control. The interaction between 100 µM BBR and target protein was measured by cellular thermal shift assay. The protein expression of 1-9 µM BBR treated SW480 cells were measured by WB assay. Apoptosis, cell cycle arrest, mitochondrial membrane potential (MMP) of 1-9 µM BBR treated SW480 cells were measured by flow cytometry and Hoechst 33342 staining methods. RESULTS: CMAP analysis found 14 Hsp90, HDAC, PI3K or mTOR protein inhibitors have similar functions with BBR. The experiments showed that BBR inhibited SW480 cells proliferation with IC50 of 3.436 µM, induced apoptosis, autophage, MMP depolarization and arrested G1 phase of cell cycle at 1.0 µM. BBR dose-dependently up-regulated PTEN, while inhibited Notch1, PI3K, Akt and mTOR proteins at 1.0-9.0 µM (p < 0.05). BBR also acted synergistically with Hsp90 and HDAC inhibitor (0.01 µM) in SW480 cells at 0.5 and 1.0 µM. DISCUSSION AND CONCLUSIONS: The integrative gene expression-based chemical genomic method using CMAP analysis may be applicable for mechanistic studies of other multi-targets drugs.

Construction of a circRNA-miRNA-mRNA network to explore the pathogenesis and treatment of pancreatic ductal adenocarcinoma.

BACKGROUND: Many studies focusing on circular RNAs (circRNAs) have recently been published. However, a large number of circRNAs remain to be explored. This study was designed to discover new circRNAs and investigate their potential roles in the pathogenesis of pancreatic ductal adenocarcinoma (PDAC). METHODS: A combination of gene chip analysis and bioinformatic methods was utilized to reveal new circRNAs and their possible mechanisms in PDAC. A circRNA-miRNA-mRNA network was established based on the results of differential analyses and interaction predictions. Promising drugs for treating PDAC were determined by connectivity map (CMap) analysis. RESULTS: Expression profile data were collected from the Gene Expression Omnibus database, and integration of differentially expressed circRNAs (DECs) from two gene chips using the RobustRankAggreg method revealed 10 DECs. The microRNA (miRNA) response elements of these 10 DECs were predicted. The predicted miRNAs and differentially expressed miRNAs were intersected, and 12 overlapping miRNAs were acquired. Next, 2908 miRNA target mRNAs and 1187 differentially expressed genes (DEGs) in PDAC were identified and combined, revealing 118 overlapping mRNAs. A protein-protein interaction network was constructed with the 118 mRNAs, and four hub genes (CDH1, SERPINE1, IRS1 and FYN) were identified. Using Gene Expression Profiling Interactive Analysis, survival analyses were conducted for the four hub genes, and SERPINE1 and FYN were found to be significantly associated with PDAC patient survival. Functional enrichment analysis indicated that these four hub genes are closely associated with certain cancer-related biological functions and pathways. In addition, CMap analysis based on the four hub genes was performed to screen potential therapeutic agents for PDAC, and three bioactive chemicals (celastrol, 5109870 and MG-132) were discovered. CONCLUSIONS: The results of this study further our understanding of the pathogenesis and treatment of PDAC from the perspective of the circRNA-related competing endogenous RNA network.