Candidate Small Molecules Research Articles

Induction of ß Cell Replication by Small Molecule-Mediated Menin Inhibition and Combined PKC Activation and TGF‑ß Inhibition as Revealed by A Refined Primary Culture Screening.

Objective Pancreatic β cells are recognized as central players in the pathogenesis of types 1 and 2 diabetes. Efficient and robust primary culture methods are required to interrogate β cell biology and screen potential anti-diabetic therapeutics. The aim of this study was to refine monolayer culture of beta cells and to investigate potential inducers of beta cell proliferation. Materials and Methods In this experimental study, we compared different culture methods to optimize conditions required for a monolayer culture of rat pancreatic islet cells in order to facilitate image analysis-based assays. We also used the refined culture method to screen a group of rationally selected candidate small molecules and their combinations to determine their potential proliferative effects on the β cells. Results Ham’s F10 medium supplemented with 2% foetal bovine serum (FBS) in the absence of any surface coating provided a superior monolayer β cell culture, while other conditions induced fibroblast-like cell growth or multilayer cell aggregation over two weeks. Evaluation of candidate small molecules showed that a menin inhibitor MI-2 and a combination of transforming growth factor-β (TGF-β) inhibitor SB481542 and protein kinase C (PKC) activator indolactam V (IndV) significantly induced replication of pancreatic β cells. ConclusionOverall, our optimized culture condition provided a convenient approach to study the cultured pancreatic islet cells and enabled us to detect the proliferative effect of menin inhibition and combined TGF-β inhibition and PKC activation, which could be considered as potential strategies for inducing β cell proliferation and regeneration.

Identification of prognostic long non-coding RNA signature with potential drugs in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the primary malignancy in the liver with high rate of death and recurrence. Novel prognostic model would be crucial for early diagnosis and improved clinical decision. The study aims to provide an effective lncRNA-based signature to predict survival time and tumor recurrence for HCC. Based on public database, lncRNA-based classifiers for overall survival and tumor recurrence were built with regression analysis and cross validation strategy. According to the risk-score of the classifiers, the whole cohorts were divided into groups with high and low risk. Afterwards, the efficiency of the lncRNA-based classifiers was evaluated and compared with other clinical factors. Finally, candidate small molecules for high risk groups were further screened using drug response databases to explore potential drugs for HCC treatment.

A Bioluminescent Biosensor for Quantifying the Interaction of SARS-CoV-2 and Its Receptor ACE2 in Cells and In Vitro.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is currently spreading and mutating with increasing speed worldwide. Therefore, there is an urgent need for a simple, sensitive, and high-throughput (HTP) assay to quantify virus–host interactions in order to quickly evaluate the infectious ability of mutant viruses and to develop or validate virus-inhibiting drugs. Here, we developed an ultrasensitive bioluminescent biosensor to evaluate virus–cell interactions by quantifying the interaction between the SARS-CoV-2 receptor binding domain (RBD) and its cellular receptor angiotensin-converting enzyme 2 (ACE2) both in living cells and in vitro. We have successfully used this novel biosensor to analyze SARS-CoV-2 RBD mutants and evaluated candidate small molecules (SMs), antibodies, and peptides that may block RBD:ACE2 interaction. This simple, rapid, and HTP biosensor tool will significantly expedite the detection of viral mutants and the anti-COVID-19 drug discovery process.

Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the most common subtype of head and neck cancer; however, its pathogenesis and potential therapeutic targets remain largely unknown. In the present study, we analyzed three gene expression profiles and screened differentially expressed genes (DEGs) between HNSCC and normal tissues. The DEGs were subjected to gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), protein–protein interaction (PPI), and survival analyses, while the connectivity map (CMap) database was used to predict candidate small molecules that may reverse the biological state of HNSCC. Finally, we measured the expression of the most relevant core gene in vitro and examined the effect of the top predicted potential drug against the proliferation of HNSCC cell lines. Among the 208 DEGs and ten hub genes identified, CDK1 and CDC45 were associated with unfavorable HNSCC prognosis, and three potential small molecule drugs for treating HNSCC were identified. Increased CDK1 expression was confirmed in HNSCC cells, and menadione, the top predicted potential drug, exerted significant inhibitory effects against HNSCC cell proliferation and markedly reversed CDK1 expression. Together, the findings of the present study suggest that the ten hub genes and pathways identified may be closely related to HNSCC pathogenesis. In particular, CDK1 and CDC45 overexpression could be reliable biomarkers for predicting unfavorable prognosis in patients with HNSCC, while the new candidate small molecules identified by CMap analysis provide new avenues for the development of potential drugs to treat HNSCC.

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is the metabolic disorder that appears during pregnancy. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non-GDM samples were analyzed. Functional enrichment analysis were performed using ToppGene. Then we constructed the protein–protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA–hub gene network and TF–hub gene regulatory network. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up-regulated and 430 down-regulated genes. Functional enrichment analysis showed these DEGs were mainly enriched in reproduction, cell adhesion, cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3 and PRKCA were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. This investigation identified hub genes, signal pathways and therapeutic agents, which might help us, enhance our understanding of the mechanisms of GDM and find some novel therapeutic agents for GDM.

A novel fungal metabolite inhibits Plasmodium falciparum transmission and infection

BackgroundMalaria transmission depends on infected mosquitoes and can be controlled by transmission-blocking drugs. The recently discovered FREP1-mediated malaria transmission pathway is an excellent target to screen drugs for limiting transmission.MethodsTo identify candidate small molecules, we used an ELISA-based approach to analyze extracts from a fungal library for inhibition of the FREP1–parasite interaction. We isolated and determined one active compound by chromatography and crystallography, respectively. We measured the effects of the bioactive compound on malaria transmission to mosquitoes through standard membrane-feeding assays (SMFA) and on parasite proliferation in blood by culturing.ResultsWe discovered the ethyl acetate extract of the fungus Purpureocillium lilacinum that inhibited Plasmodium falciparum transmission to mosquitoes. Pre-exposure to the extract rendered Anopheles gambiae resistant to Plasmodium infection. Furthermore, we isolated one novel active compound from the extract and identified it as 3-amino-7,9-dihydroxy-1-methyl-6H-benzo[c]chromen-6-one, or “pulixin.” Pulixin prevented FREP1 from binding to P. falciparum-infected cell lysate. Pulixin blocked the transmission of the parasite to mosquitoes with an EC50 (the concentration that gave half-maximal response) of 11 µM based on SMFA. Notably, pulixin also inhibited the proliferation of asexual-stage P. falciparum with an EC50 of 47 nM. The compound did not show cytotoxic effects at a concentration of 116 µM or lower.ConclusionBy targeting the FREP1–Plasmodium interaction, we discovered that Purpureocillium lilacinum extract blocked malaria transmission. We isolated and identified the bioactive agent pulixin as a new compound capable of stopping malaria transmission to mosquitoes and inhibiting parasite proliferation in blood culture.Graphical

Development and Validation of Epigenetic Signature Predict Survival for Patients with Laryngeal Squamous Cell Carcinoma.

Establishing epigenetic signature to improve the accuracy of survival prediction and optimize therapeutic strategies for laryngeal squamous cell carcinoma (LSCC) by a genome-wide integrated analysis of methylation and the transcriptome. LSCC DNA methylation datasets and RNA sequencing datasets were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs), which developed an epigenetic signature. The predictive accuracy and clinical value of the epigenetic signature were evaluated by receiver operating characteristic and decision curve analysis, and compared with tumor-node-metastasis (TNM) stage system. In addition, prognostic value of the epigenetic signature was validated by external Gene Expression Omnibus (GEO) database. According to five MDGs of epigenetic signature, the candidate small molecules for LSCC were screen out by the CMap database. A total of 88 DNA MDGs were identified, five of which (MAGEB2, SUSD1, ZNF382, ZNF418, and ZNF732) were chosen to construct an epigenetic signature. The epigenetic signature can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.8 (5-year overall survival [OS]) and AUC of 0.745 (3-year OS). Stratification analysis affirmed that the epigenetic signature was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated that the efficacy of epigenetic signature appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the epigenetic signature was superior to traditional TNM stage. In addition, the epigenetic signature was confirmed in external LSCC cohorts from GEO. Finally, CMap matched the 10 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. An epigenetic signature, with five DNA MDGs, was identified and validated in LSCC patients by integrating multidimensional genomic data, which may offer novel research directions and prospects for individualized treatment of patients with LSCC.

Ileal Derived Organoids From Crohn’s Disease Patients Show Unique Transcriptomic and Secretomic Signatures

Background & AimsWe used patient-derived organoids (PDOs) to study the epithelial-specific transcriptional and secretome signatures of the ileum during Crohn’s disease (CD) with varying phenotypes to screen for disease profiles and potential druggable targets.MethodsRNA sequencing was performed on isolated intestinal crypts and 3-week-old PDOs derived from ileal biopsies of CD patients (n = 8 B1, inflammatory; n = 8 B2, stricturing disease) and non-inflammatory bowel disease (IBD) controls (n = 13). Differentially expressed (DE) genes were identified by comparing CD vs control, B1 vs B2, and inflamed vs non-inflamed. DE genes were used for computational screening to find candidate small molecules that could potentially reverse B1and B2 gene signatures. The secretome of a second cohort (n = 6 non-IBD controls, n = 7 CD, 5 non-inflamed, 2 inflamed) was tested by Luminex using cultured organoid conditioned medium.ResultsWe found 90% similarity in both the identity and abundance of protein coding genes between PDOs and intestinal crypts (15,554 transcripts of 19,900 genes). DE analysis identified 814 genes among disease group (CD vs non-IBD control), 470 genes different between the CD phenotypes, and 5 false discovery rate correction significant genes between inflamed and non-inflamed CD. The PDOs showed both similarity and diversity in the levels and types of soluble cytokines and growth factors they released. Perturbagen analysis revealed potential candidate compounds to reverse B2 disease phenotype to B1 in PDOs.ConclusionsPDOs are similar at the transcriptome level with the in vivo epithelium and retain disease-specific gene expression for which we have identified secretome products, druggable targets, and corresponding pharmacologic agents. Targeting the epithelium could reverse a stricturing phenotype and improve outcomes.

Identification of novel biomarkers and candidate small molecule drugs in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis of high-throughput data.

Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA.Methods: The gene expression profile of GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in GSE55235 and GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA‐ and RA‐specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database.Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA‐ and OA‐specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression.Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.

Unraveling Targetable Systemic and Cell-Type-Specific Molecular Phenotypes of Alzheimer's and Parkinson's Brains With Digital Cytometry.

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the two most common neurodegenerative disorders worldwide, with age being their major risk factor. The increasing worldwide life expectancy, together with the scarcity of available treatment choices, makes it thus pressing to find the molecular basis of AD and PD so that the causing mechanisms can be targeted. To study these mechanisms, gene expression profiles have been compared between diseased and control brain tissues. However, this approach is limited by mRNA expression profiles derived for brain tissues highly reflecting their degeneration in cellular composition but not necessarily disease-related molecular states. We therefore propose to account for cell type composition when comparing transcriptomes of healthy and diseased brain samples, so that the loss of neurons can be decoupled from pathology-associated molecular effects. This approach allowed us to identify genes and pathways putatively altered systemically and in a cell-type-dependent manner in AD and PD brains. Moreover, using chemical perturbagen data, we computationally identified candidate small molecules for specifically targeting the profiled AD/PD-associated molecular alterations. Our approach therefore not only brings new insights into the disease-specific and common molecular etiologies of AD and PD but also, in these realms, foster the discovery of more specific targets for functional and therapeutic exploration.

Identification and validation of methylation-driven genes prognostic signature for recurrence of laryngeal squamous cell carcinoma by integrated bioinformatics analysis

BackgroundRecurrence remains a major obstacle to long-term survival of laryngeal squamous cell carcinoma (LSCC). We conducted a genome-wide integrated analysis of methylation and the transcriptome to establish methylation-driven genes prognostic signature (MDGPS) to precisely predict recurrence probability and optimize therapeutic strategies for LSCC.MethodsLSCC DNA methylation datasets and RNA sequencing (RNA-seq) dataset were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs). By univariate and multivariate Cox regression analyses, five genes of DNA MDGs was developed a recurrence-free survival (RFS)-related MDGPS. The predictive accuracy and clinical value of the MDGPS were evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA), and compared with TNM stage system. Additionally, prognostic value of MDGPS was validated by external Gene Expression Omnibus (GEO) database. According to 5 MDGs, the candidate small molecules for LSCC were screen out by the CMap database. To strengthen the bioinformatics analysis results, 30 pairs of clinical samples were evaluated by digoxigenin-labeled chromogenic in situ hybridization (CISH).ResultsA total of 88 DNA MDGs were identified, and five RFS-related MDGs (LINC01354, CCDC8, PHYHD1, MAGEB2 and ZNF732) were chosen to construct a MDGPS. The MDGPS can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.738 (5-year RFS) and AUC of 0.74 (3-year RFS). Stratification analysis affirmed that the MDGPS was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated the efficacy of MDGPS appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the MDGPS was superior to traditional TNM stage. Additionally, the MDGPS was confirmed in external LSCC cohorts from GEO. CMap matched the 9 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. Finally, CISH analysis in 30 LSCC tissues and paired adjacent normal tissues revealed that MAGEB2 has significantly higher expression of LSCC compared to adjacent non-neoplastic tissues; LINC01354, CCDC8, PHYHD1, and ZNF732 have significantly lower expression of LSCC compared to adjacent non-neoplastic tissues, which were in line with bioinformatics analysis results.ConclusionA MDGPS, with five DNA MDGs, was identified and validated in LSCC patients by combining transcriptome and methylation datasets analysis. Compared TNM stage alone, it generates more accurate estimations of the recurrence prediction and maybe offer novel research directions and prospects for individualized treatment of patients with LSCC.

Integrating Differential Gene Expression Analysis with Perturbagen-Response Signatures May Identify Novel Therapies for Thyroid-Associated Orbitopathy.

PurposeTo evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify small molecules that revert pathologic gene signature and alter disease phenotype in orbital adipose stem cells (OASCs) derived from patients with thyroid-associated orbitopathy (TAO).MethodsDifferentially expressed genes identified via RNA sequencing were inputted into LINCS L1000 Characteristic Direction Signature Search Engine (L1000CDS2) to predict candidate small molecules to reverse pathologic gene expression. TAO OASC cell lines were treated in vitro with six identified small molecules (Torin-2, PX12, withaferin A, isoliquiritigenin, mitoxantrone, and MLN8054), and expression of key adipogenic and differentially expressed genes was measured with quantitative polymerase chain reaction after 7 days of treatment. OASCs were differentiated into adipocytes, treated for 15 days, and stained with Oil Red O (OD 490 nm) to evaluate adipogenic changes.ResultsThe expression of key differentially expressed genes (IRX1, HOXB2, S100B, and KCNA4) and adipogenic genes (peroxisome proliferator activated receptor-γ, FABP4) was significantly decreased in TAO OASCs after treatment (P < .05). In treated TAO adipocytes (n = 3), all six tested small molecules yielded significant decrease (P < .05) in Oil Red O staining. In treated non-TAO adipocytes (n = 3), only three of the drugs yielded a significant decrease in Oil Red O staining.ConclusionsCombining disease expression signatures with LINCS small molecule prediction software can identify promising preclinical drug candidates for TAO.Translational RelevanceThese findings may offer insight into future potential therapeutic options for TAO and demonstrate a streamlined model to predict drug candidates for other diseases.

Teaching of Biopharmaceutics in a Drug Design Course: Use of GastroPlus as Educational Software

Drug design and development requires collaboration among scientists with diverse expertise. In early-stage drug design, establishment of promising drug candidates requires integration of structure-based molecular design with biopharmaceutics and ADMET (absorption, distribution, metabolism, excretion, and toxicity) principles. Much of this work is increasingly computational. Thus, students in a drug design course should learn the basis for and effects of candidate small molecules binding to therapeutic targets and the importance of simultaneous understanding of ADMET characteristics that will largely determine if the molecule can achieve a therapeutically relevant concentration at the target site and become an orally deliverable drug. This second aspect of education in drug design has been relatively overlooked compared to structure-based design. Here, we describe a course using sophisticated simulation and modeling software, GastroPlus and ADMET Predictor, which are commonly used in the pharmaceutical industry. Our teaching approach has evolved over a decade in early graduate and advanced undergraduate settings. We use a combination of short lectures, software demonstration, and hands-on use, which allows students to “design a drug” with incorporation of structural and ADMET considerations. Student feedback indicated that the use of the software and the course design were helpful in enhancing their understanding of the importance of biopharmaceutics properties and ADMET in drug design. GastroPlus and ADMET Predictor have recently become more accessible to educators, and our experience using this software in an educational setting may be helpful for instructors who wish to develop a similar course.

Induction of Lysosome Membrane Permeabilization as a Therapeutic Strategy to Target Pancreatic Cancer Stem Cells.

Despite significant efforts to improve pancreatic ductal adenocarcinoma (PDAC) clinical outcomes, overall survival remains dismal. The poor response to current therapies is partly due to the existence of pancreatic cancer stem cells (PaCSCs), which are efficient drivers of PDAC tumorigenesis, metastasis and relapse. To find new therapeutic agents that could efficiently kill PaCSCs, we screened a chemical library of 680 compounds for candidate small molecules with anti-CSC activity, and identified two compounds of a specific chemical series with potent activity in vitro and in vivo against patient-derived xenograft (PDX) cultures. The anti-CSC mechanism of action of this specific chemical series was found to rely on induction of lysosomal membrane permeabilization (LMP), which is likely associated with the increased lysosomal mass observed in PaCSCs. Using the well characterized LMP-inducer siramesine as a tool molecule, we show elimination of the PaCSC population in mice implanted with tumors from two PDX models. Collectively, our approach identified lysosomal disruption as a promising anti-CSC therapeutic strategy for PDAC.

Rank Order of Small Molecule Induced Hypoxiamimesis to Prevent Retinopathy of Prematurity.

Here we rank order small molecule inhibitors of hypoxia inducible factor (HIF) prolyl hydroxylases (PHDs) using severity of oxygen induced retinopathy (OIR) as an outcome measure. Dose response analyses in cell cultures of hepatoma (Hep3B), retinal Müller cells (MIO-M1) and primary retinal endothelial cells were conducted to evaluate potency by comparing dose to HIF-1,2 protein levels by western blotting. In vivo dose response was determined using the luciferase-transgene HIF reporter (luc-ODD). Each compound was placed in rank order by their ability to reduce neovascularization and capillary drop out in the OIR mouse model. An Epas1 KO confined to retinal Müller cells was used to determine whether successful protection by HIF stabilization requires HIF-2. Two candidate small molecules can prevent OIR by stabilizing HIF-1 to prevent oxygen induced growth attenuation and vascular obliteration. Müller cell HIF-2, the mediator of pathologic retinal angiogenesis, is not required for protection. The lack of dependence on Müller cell HIF-2 predicts that inhibition of HIF PHD will not drive pathological angiogenesis.

A Loop-Based and AGO-Incorporated Virtual Screening Model Targeting AGO-Mediated miRNA-mRNA Interactions for Drug Discovery to Rescue Bone Phenotype in Genetically Modified Mice.

Several virtual screening models are proposed to screen small molecules only targeting primary miRNAs without selectivity. Few attempts have been made to develop virtual screening strategies for discovering small molecules targeting mature miRNAs. Mature miRNAs and their specific target mRNA can form unique functional loops during argonaute (AGO)‐mediated miRNA–mRNA interactions, which may serve as potential targets for small‐molecule drug discovery. Thus, a loop‐based and AGO‐incorporated virtual screening model is constructed for targeting the loops. The previously published studies have found that miR‐214 can target ATF4 to inhibit osteoblastic bone formation, whereas miR‐214 can target TRAF3 to promote osteoclast activity. By using the virtual model, the top ten candidate small molecules targeting miR‐214‐ATF4 mRNA interactions and top ten candidate small molecules targeting miR‐214‐TRAF3 mRNA interactions are selected, respectively. Based on both in vitro and in vivo data, one small molecule can target miR‐214‐ATF4 mRNA to promote ATF4 protein expression and enhance osteogenic potential, whereas one small molecule can target miR‐214‐TRAF3 mRNA to promote TRAF3 protein expression and inhibit osteoclast activity. These data indicate that the loop‐based and AGO‐incorporated virtual screening model can help to obtain small molecules specifically targeting miRNA–mRNA interactions to rescue bone phenotype in genetically modified mice.

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets.

Background The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. Methods The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. Results By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. Conclusion The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

Identification of Key Genes in Association with Progression and Prognosis in Cervical Squamous Cell Carcinoma.

Cervical cancer remains a primary cause of female death in developing countries, but its prognosis can be greatly improved if patients are diagnosed earlier. In the present study, we screened the common differentially expressed genes (DEGs) of cervical squamous cell carcinoma (CESC) from dataset GSE7803, Gene Expression Omnibus, and The Cancer Genome Atlas databases. An integrated bioinformatics analysis was performed based on these DEGs for their enrichment in functions and pathways, interaction network, prognostic signature, and candidate molecular drugs. As a result, 164 (114 upregulated and 47 downregulated) DEGs of CESC were identified for further investigation. We then conducted the gene ontology term enrichment and Kyoto Encyclopedia of Genes and Genomes Pathway analyses to reveal the underlying functions and pathways of these DEGs. In the protein-protein interaction network, hub module and hub genes were identified. Five genes of significant prognostic value-DSG2, ITM2A, CENPM, RIBC2, and MEIS2-were identified by prognostic signature analysis and used to construct a risk linear model. Further validation and investigation suggested DSG2 might be a key gene in CESC prognosis. We then identified two candidate small molecules (trichostatin A and tanespimycin) against CESC. Further validation and exploration of these hub genes are warranted for future prospect in clinical applications.

DOP64 Discovery of highly selective small-molecule oral inhibitors of integrin α4β7 for the treatment of inflammatory bowel diseases

Abstract Background Integrins play a key role in facilitating immune cell trafficking throughout the body and represent an important receptor family for therapeutic intervention. In particular, the α4β7 integrin is a clinically validated target for the treatment of inflammatory bowel diseases (IBD), as exemplified by the humanised monoclonal antibody vedolizumab, which blocks the interactions between α4β7-expressing lymphocytes and its ligand MAdCAM-1. This blockade leads to the inhibition of these circulating lymphocytes from exiting the bloodstream and entering intestinal mucosal tissues resulting in a decrease in mucosal inflammation in patients. While oral inhibitors of the α4β7 integrin are advantageous over biologics, the efforts have been impeded by challenges to achieve desired selectivity and optimal DMPK properties. The aim of this study was to develop and characterise orally bioavailable small-molecule inhibitors of the α4β7 integrin and to determine their therapeutic potential. Methods Oral small-molecule inhibitors targeting the α4β7 integrin were discovered using Morphic Integrin Technology (MInT) platform. These small-molecule inhibitors were tested for potency and selectivity against a broad panel of integrin family members in multiple biochemical and cell-based functional assays in a ligand-competitive fashion. An acute PD assay with CFSE-labelled lymphocytes was developed to evaluate the activity of the small-molecule compounds in blocking lymphocyte trafficking to gut-associated lymphoid tissues in mice. The in vivo activity was also examined through changes in circulating α4β7+ CD4+ T memory cells in a relevant non-human primate model. Results Key drug candidate small molecules demonstrated over 1000-fold selectivity in vitro against a broad panel of integrin family members, including the α4β1 integrin. These compounds effectively blocked lymphocyte trafficking to mesenteric lymph nodes and Peyer’s patches in the gut in a dose-dependent manner, similar to an α4β7-specific antibody, in an acute gut-homing assay in mice. Additionally, these inhibitors also demonstrated effective occlusion of immune trafficking in a relevant non-human primate model. The lead compound has favourable DMPK properties, good oral bioavailability and is projected to have sufficient exposure in humans to effectively block α4β7-expressing immune cells in circulation. Conclusion Potent, selective, oral small-molecule inhibitors of α4β7 integrin have been discovered that demonstrate on-target, mechanistic efficacy in two animal models relevant to human IBD. It has the potential to be an effective and safe therapeutic in monotherapy as well as serving as a backbone for combination with other IBD drugs.

DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations.

The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug-target interactions (DTIs). In this study, we propose a large-scale DTI prediction system, DEEPScreen, for early stage drug discovery, using deep convolutional neural networks. One of the main advantages of DEEPScreen is employing readily available 2-D structural representations of compounds at the input level instead of conventional descriptors that display limited performance. DEEPScreen learns complex features inherently from the 2-D representations, thus producing highly accurate predictions. The DEEPScreen system was trained for 704 target proteins (using curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against the state-of-the-art on multiple benchmark datasets to indicate the effectiveness of the proposed approach and verified selected novel predictions through molecular docking analysis and literature-based validation. Finally, JAK proteins that were predicted by DEEPScreen as new targets of a well-known drug cladribine were experimentally demonstrated in vitro on cancer cells through STAT3 phosphorylation, which is the downstream effector protein. The DEEPScreen system can be exploited in the fields of drug discovery and repurposing for in silico screening of the chemogenomic space, to provide novel DTIs which can be experimentally pursued. The source code, trained "ready-to-use" prediction models, all datasets and the results of this study are available at ; https://github.com/cansyl/DEEPscreen.