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.

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.

Identification of key genes and small molecule drugs in osteoarthritis by integrated bioinformatics analysis.

Background: Osteoarthritis (OA) is a common joint degenerative disease that can affect multiple joints. Genetic events may play an important regulatory role in the early stages of the disease, but the specific mechanisms have not yet been fully elucidated. The main purpose of this study was to screen for disease-causing hub genes and effective small molecule drugs to reveal the pathogenesis of OA and to develop novel drugs for treatment. Methods: Two gene expression profile datasets, GSE55235 and GSE55457, were integrated and further analyzed. The consistently differentially expressed genes (DEGs) were identified, and functional annotation and pathway analysis of these genes were performed with GO and KEGG. A protein-protein interaction network (PPI) of the DEGs was generated using STRING, and potential small molecule drug screening was performed on the connectivity map (CMap). Results: A total of 158 consistently differentially expressed genes were identified from the two profile datasets. The functions of these DEGs are mainly related to the TNF signaling pathway, osteoclast differentiation, MAPK signaling pathway and so on. The PPI network contains 127 nodes and 1802 edges, and the ten hub genes were interleukin 6 (IL6), vascular endothelial growth factor A (VEGFA)and so on. 7 small molecule drugs were identified as potential interactors with these hubs. Conclusions: This study explains the disorder of expression in the pathological process of OA at transcriptome, which will help to understand the pathogenesis of OA.

In silico Drug Screening Approach Using L1000-Based Connectivity Map and Its Application to COVID-19.

Conventional drug screening methods search for a limited number of small molecules that directly interact with the target protein. This process can be slow, cumbersome and has driven the need for developing new drug screening approaches to counter rapidly emerging diseases such as COVID-19. We propose a pipeline for drug repurposing combining in silico drug candidate identification followed by in vitro characterization of these candidates. We first identified a gene target of interest, the entry receptor for the SARS-CoV-2 virus, angiotensin converting enzyme 2 (ACE2). Next, we employed a gene expression profile database, L1000-based Connectivity Map to query gene expression patterns in lung epithelial cells, which act as the primary site of SARS-CoV-2 infection. Using gene expression profiles from 5 different lung epithelial cell lines, we computationally identified 17 small molecules that were predicted to decrease ACE2 expression. We further performed a streamlined validation in the normal human epithelial cell line BEAS-2B to demonstrate that these compounds can indeed decrease ACE2 surface expression and to profile cell health and viability upon drug treatment. This proposed pipeline combining in silico drug compound identification and in vitro expression and viability characterization in relevant cell types can aid in the repurposing of FDA-approved drugs to combat rapidly emerging diseases.

Applicability of Anticancer Drugs for the Triple-Negative Breast Cancer Based on Homologous Recombination Repair Deficiency.

Triple-negative breast cancer (TNBC) is a highly aggressive disease with historically poor outcomes, primarily due to the lack of effective targeted therapies. Here, we established a drug sensitivity prediction model based on the homologous recombination deficiency (HRD) using 83 TNBC patients from TCGA. Through analyzing the effect of HRD status on response efficacy of anticancer drugs and elucidating its related mechanisms of action, we found rucaparib (PARP inhibitor) and doxorubicin (anthracycline) sensitive in HR-deficient patients, while paclitaxel sensitive in the HR-proficient. Further, we identified a HRD signature based on gene expression data and constructed a transcriptomic HRD score, for analyzing the functional association between anticancer drug perturbation and HRD. The results revealed that CHIR99021 (GSK3 inhibitor) and doxorubicin have similar expression perturbation patterns with HRD, and talazoparib (PARP inhibitor) could kill tumor cells by reversing the HRD activity. Genomic characteristics indicated that doxorubicin inhibited tumor cells growth by hindering the process of DNA damage repair, while the resistance of cisplatin was related to the activation of angiogenesis and epithelial-mesenchymal transition. The negative correlation of HRD signature score could interpret the association of doxorubicin pIC50 with worse chemotherapy response and shorter survival of TNBC patients. In summary, these findings explain the applicability of anticancer drugs in TNBC and underscore the importance of HRD in promoting personalized treatment development.

Identification of novel candidate biomarkers for pancreatic adenocarcinoma based on TCGA cohort.

Pancreatic adenocarcinoma (PAAD) is the most serious solid tumor type throughout the world. The present study aimed to identify novel biomarkers and potential efficacious small drugs in PAAD using integrated bioinformatics analyses. A total of 4777 differentially expressed genes (DEGs) were filtered, 2536 upregulated DEGs and 2241 downregulated DEGs. Weighted gene co-expression network analysis was then used and identified 12 modules, of which, blue module with the most significant enrichment result was selected. KEGG and GO enrichment analyses showed that all DEGs of blue module were enriched in EMT and PI3K/Akt pathway. Three hub genes (ITGB1, ITGB5, and OSMR) were determined as key genes with higher expression levels, significant prognostic value and excellent diagnostic efficiency for PAAD. Additionally, some small molecule drugs that possess the potential to treat PAAD were screened out, including thapsigargin (TG). Functional in vitro experiments revealed that TG repressed cell viability via inactivating the PI3K/Akt pathway in PAAD cells. Totally, our findings identified three key genes implicated in PAAD and screened out several potential small drugs to treat PAAD.

Identification of hub genes and compounds controlling ovarian cancer stem cell characteristics via stemness indices analysis.

BACKGROUND: Ovarian cancer (OC) is the most lethal gynecological malignancy. It has been reported that cancer stem cells (CSCs) play a crucial role in disseminated metastases in abdominal cavity and chemotherapy resistance of high-grade serous OC. However, the overall gene expression features of OC stem cells have not been clarified. METHODS: Expression datasets of 379 OC samples and 88 normal tissues were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype Tissue Expression (GTEx) project. Differentially expressed genes (DEGs) were screened using the "limma" package in R software. Among the DEGs, modules and hub genes that were highly related to messenger RNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi indices were identified via weighted gene co-expression network analysis (WGCNA). These hub genes were considered to be associated with OC stem cells. The Gene Ontology (GO) project and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the main biological processes that hub genes participated in. Finally, Connectivity Map (CMap) was used to predict compounds that disturb the hub genes. RESULTS: We identified 2,253 DEGs; of these, 31 had a significantly positive correlation to mRNAsi indices and were upregulated in OC, while 41 of them had a significantly negative correlation with mRNAsi indices and were downregulated in OC. Correlation analysis indicated that hub genes from the same module composed a dense interaction network. GO and KEGG enrichment analysis demonstrated that hub genes primarily play roles in cell division and proliferation. Moreover, the compounds that may disturb hub genes were identified. Of these, 11 compounds, including MS-275, DL-thiorphan, and GW-8510, which have never been studied in OC stem cells, were screened as underlying treatments targeting OC stem cells. CONCLUSIONS: Altogether, 72 hub genes that were closely linked to OC stem cell characteristics were found to mainly participate in cell division and proliferation. Moreover, compounds that disturb these hub gens were identified and can be considered underlying targets for inhibiting OC stem cells.

Ethacrynic acid inhibits STAT3 activity through the modulation of SHP2 and PTP1B tyrosine phosphatases in DU145 prostate carcinoma cells.

To identify signal transducer and activator of transcription factor 3 (STAT3) inhibitors, we generated STAT3-dependent gene expression signature by analyzing gene expression profiles of DU145 cancer cells treated with STAT3 inhibitor, piperlongumine and 2-hydroxycinnamaldehyde. Then we explored gene expression signature-based strategies using a connectivity map database and identified several STAT3 inhibitors, including ethacrynic acid (EA). EA is currently used as a diuretic drug. EA inhibited STAT3 activation in DU145 prostate cancer cells and consequently decreased the levels of STAT3 target genes such as cyclin A and MCL-1. Furthermore, EA treatment inhibited tumor growth in mice xenografted with DU145 cells and decreased p-STAT3 expression in tumor tissues. Knockdown of Src homology region 2 domain-containing phosphatase-2 (SHP2) or Protein tyrosine phosphatase 1B (PTP1B) gene expression by siRNA suppressed the ability of EA to inhibit STAT3 activation. When EA was combined with an activator of SHP2 or PTP1B, p-STAT3 expression was synergistically decreased; when EA was combined with an inhibitor of SHP2 or PTP1B, p-STAT3 expression was rescued. By using an affinity pulldown assay with biotinyl-EA, EA was shown to associate with SHP2 and PTP1B in vitro. Additionally, the drug affinity responsive target stability (DARTS) assay confirmed the direct binding of EA to SHP2 and PTP1B. SHP2 is activated by EA through active phosphorylation at Y580 and direct binding to SHP2. Collectively, our results suggest that EA inhibits STAT3 activity through the modulation of phosphatases such as SHP2 and PTP1B and may be a potential anticancer drug to target STAT3 in cancer progression.

In silico and in vitro identification of inhibitory activities of sorafenib on histone deacetylases in hepatocellular carcinoma cells.

Although sorafenib has been approved for treating hepatocellular carcinoma (HCC), clinical results are not satisfactory. Polypharmacology (one drug with multiple molecular targets) is viewed as an attractive strategy for identifying novel mechanisms of a drug and then rationally designing more-effective next-generation therapeutic agents. In this study, a polypharmacological study of sorafenib was performed by mining the next-generation Connectivity Map (CMap) database, CLUE (https://clue.io/). We found that sorafenib may act as a histone deacetylase (HDAC) inhibitor based on similar gene expression profiles. In vitro experimental analyses demonstrated that sorafenib indirectly inhibited HDAC activity in both sorafenib-sensitive and -resistant HCC cells. A cancer genomics analysis using the cBioPortal online tool showed the frequent upregulation of HDAC mRNAs. Furthermore, HCC patients with higher expressions of HDAC1 and HDAC2 had worse overall survival. Taken together, our study suggests that inhibition of HDAC by sorafenib may provide clinical benefits against HCC, and enhancement of HDAC-inhibitory activity of sorafenib may improve its therapeutic efficacy. In addition, our study also provides a novel strategy to study polypharmacology.