Gene-Set Local Hierarchical Clustering (GSLHC)—A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups

Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.     

In-silico drug screening and potential target identification for hepatocellular carcinoma using Support Vector Machines based on drug screening result

Hepatocellular carcinoma (HCC) is a severe liver malignancy with few drug treatment options. In finding an effective treatment for HCC, screening drugs that are already FDA-approved will fast track the clinical trial and drug approval process. Connectivity Map (CMap), a large repository of chemical-induced gene expression profiles, provides the opportunity to analyze drug properties on the basis of gene expression. Support Vector Machines (SVM) were utilized to classify the effectiveness of drugs against HCC using gene expression profiles in CMap. The results of this classification will help us (1) identify genes that are chemically sensitive, and (2) predict the effectiveness of remaining chemicals in CMap in the treatment of HCC and provide a prioritized list of possible HCC drugs for biological verification.     

Gene expression-based chemical genomics identifies potential therapeutic drugs in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an aggressive tumor with a poor prognosis. Currently, only sorafenib is approved by the FDA for advanced HCC treatment; therefore, there is an urgent need to discover candidate therapeutic drugs for HCC. We hypothesized that if a drug signature could reverse, at least in part, the gene expression signature of HCC, it might have the potential to inhibit HCC-related pathways and thereby treat HCC. To test this hypothesis, we first built an integrative platform, the "Encyclopedia of Hepatocellular Carcinoma genes Online 2", dubbed EHCO2, to systematically collect, organize and compare the publicly available data from HCC studies. The resulting collection includes a total of 4,020 genes. To systematically query the Connectivity Map (CMap), which includes 6,100 drug-mediated expression profiles, we further designed various gene signature selection and enrichment methods, including a randomization technique, majority vote, and clique analysis. Subsequently, 28 out of 50 prioritized drugs, including tanespimycin, trichostatin A, thioguanosine, and several anti-psychotic drugs with anti-tumor activities, were validated via MTT cell viability assays and clonogenic assays in HCC cell lines. To accelerate their future clinical use, possibly through drug-repurposing, we selected two well-established drugs to test in mice, chlorpromazine and trifluoperazine. Both drugs inhibited orthotopic liver tumor growth. In conclusion, we successfully discovered and validated existing drugs for potential HCC therapeutic use with the pipeline of Connectivity Map analysis and lab verification, thereby suggesting the usefulness of this procedure to accelerate drug repurposing for HCC treatment.     

Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.     

Gene expression signature-based approach identifies a pro-resolving mechanism of action for histone deacetylase inhibitors

Despite several therapies being currently available to treat inflammatory diseases, new drugs to treat chronic conditions with less side effects and lower production costs are still needed. An innovative approach to drug discovery, the Connectivity Map (CMap), shows how integrating genome-wide gene expression data of drugs and diseases can accelerate this process. Comparison of genome-wide gene expression data generated with annexin A1 (AnxA1) with the CMap revealed significant alignment with gene profiles elicited by histone deacetylase inhibitors (HDACIs), what made us to hypothesize that AnxA1 might mediate the anti-inflammatory actions of HDACIs. Addition of HDACIs (valproic acid, sodium butyrate and thricostatin A) to mouse macrophages caused externalization of AnxA1 with concomitant inhibition of cytokine gene expression and release, events that occurred independently as this inhibition was retained in AnxA1 null macrophages. In contrast, novel AnxA1-mediated functions for HDACIs could be unveiled, including promotion of neutrophil apoptosis and macrophage phagocytosis, both steps crucial for effective resolution of inflammation. In a model of acute resolving inflammation, administration of valproic acid and sodium butyrate to mice at the peak of disease accelerated resolution processes in wild type, but much more modestly in AnxA1 null mice. Deeper analyses revealed a role for endogenous AnxA1 in the induction of neutrophil death in vivo by HDACIs. In summary, interrogation of the CMap revealed an unexpected association between HDACIs and AnxA1 that translated in mechanistic findings with particular impact on the processes that regulate the resolution of inflammation. We propose non-genomic modulation of AnxA1 in immune cells as a novel mechanism of action for HDACIs, which may underlie their reported efficacy in models of chronic inflammatory pathologies.     

Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning

Although genome-wide association studies have identified many risk loci associated with colorectal cancer, the molecular basis of these associations are still unclear. We aimed to infer biological insights and highlight candidate genes of interest within GWAS risk loci. We used an in silico pipeline based on functional annotation, quantitative trait loci mapping of cis-acting gene, PubMed text-mining, protein-protein interaction studies, genetic overlaps with cancer somatic mutations and knockout mouse phenotypes, and functional enrichment analysis to prioritize the candidate genes at the colorectal cancer risk loci. Based on these analyses, we observed that these genes were the targets of approved therapies for colorectal cancer, and suggested that drugs approved for other indications may be repurposed for the treatment of colorectal cancer. This study highlights the use of publicly available data as a cost effective solution to derive biological insights, and provides an empirical evidence that the molecular basis of colorectal cancer can provide important leads for the discovery of new drugs.     

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.     

Helveticoside is a biologically active component of the seed extract of Descurainia sophia and induces reciprocal gene regulation in A549 human lung cancer cells

Background

Although the pharmacological activities of the seed extract of Descurainia sophia have been proven to be useful against cough, asthma, and edema, the biologically active components, particularly at the molecular level, remain elusive. Therefore, we aimed to identify the active component of an ethanol extract of D. sophia seeds (EEDS) by applying a systematic genomic approach.

Results

After treatment with EEDS, the dose-dependently expressed genes in A549 cells were used to query the Connectivity map to determine which small molecules could closely mimic EEDS in terms of whole gene expression. Gene ontology and pathway analyses were also performed to identify the functional involvement of the drug responsive genes. In addition, interaction network and enrichment map assays were implemented to measure the functional network structure of the drug-responsive genes. A Connectivity map analysis of differentially expressed genes resulted in the discovery of helveticoside as a candidate drug that induces a similar gene expression pattern to EEDS. We identified the presence of helveticoside in EEDS and determined that helveticoside was responsible for the dose-dependent gene expression induced by EEDS. Gene ontology and pathway analyses revealed that the metabolism and signaling processes in A549 cells were reciprocally regulated by helveticoside and inter-connected as functional modules. Additionally, in an ontological network analysis, diverse cancer type-related genes were found to be associated with the biological functions regulated by helveticoside.

Conclusions

Using bioinformatic analyses, we confirmed that helveticoside is a biologically active component of EEDS that induces reciprocal regulation of metabolism and signaling processes. Our approach may provide novel insights to the herbal research field for identifying biologically active components from extracts.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1918-1) contains supplementary material, which is available to authorized users.     

Identification of thioridazine,an antipsychotic drug,as an antiglioblastoma and anticancer stem cell agent using public gene expression data

Glioblastoma (GBM) is a common and malignant tumor with a poor prognosis. Glioblastoma stem cells (GSCs) have been reported to be involved in tumorigenesis, tumor maintenance and therapeutic resistance. Thus, to discover novel candidate therapeutic drugs for anti-GBM and anti-GSCs is an urgent need. We hypothesized that if treatment with a drug could reverse, at least in part, the gene expression signature of GBM and GSCs, this drug may have the potential to inhibit pathways essential in the formation of GBM and thereby treat GBM. Here, we collected 356 GBM gene signatures from public databases and queried the Connectivity Map. We systematically evaluated the in vitro antitumor effects of 79 drugs in GBM cell lines. Of the drugs screened, thioridazine was selected for further characterization because it has potent anti-GBM and anti-GSCs properties. When investigating the mechanisms underlying the cytocidal effects of thioridazine, we found that thioridazine induces autophagy in GBM cell lines, and upregulates AMPK activity. Moreover, LC3-II was upregulated in U87MG sphere cells treated with thioridazine. In addition, thioridazine suppressed GBM tumorigenesis and induced autophagy in vivo. We not only repurposed the antipsychotic drug thioridazine as a potent anti-GBM and anti-GSCs agent, but also provided a new strategy to search for drugs with anticancer and anticancer stem cell properties.Glioblastomas (GBM), the most common and most aggressive primary brain tumors in adults, are classified as grade IV astrocytomas by the World Health Organization and account for 54% of all gliomas.¹ Surgery is typically followed by radiation therapy and chemotherapy with temozolomide (TMZ), which has been in clinical use since 2005.^{2, 3} Despite this multimodal approach, the median survival time of GBM patients is ~14.6 months.³ Therefore, a large number of new drugs are in development for GBM treatment.Instead of focusing on a single drug target, using a batch of genes to query the Connectivity Map (Cmap, http://www.broad.mit.edu/cmap/) may not only allow multiple targets to be considered simultaneously, but it may also identify potential new drugs. Cmap is a database that provides ~7000 microarray expression profiles (conducted on Affymetrix HG-U133A arrays) from four different cancer cell lines treated with 1309 molecular drugs. Of the 1309 drugs included in Cmap, most are currently used in clinical treatment or are well-developed drugs; thus, we can rapidly identify potential drugs and proceed to clinical trial.Thioridazine is an antipsychotic drug and is widely used to treat schizophrenia and psychosis. Recently, it has been shown that patients with schizophrenia have a lower risk of getting cancer (1.93%) than patients without schizophrenia (2.97%).⁴ In addition, inverse cancer comorbidity has been reported in people with certain CNS disorders, and pharmacological treatments is one of possible explanations.⁵Using in silico drug screening via Cmap followed by empirical validations, we discovered that thioridazine can reduce the viability of GBM cells and GBM stem cells, induce autophagy and affect the expressions of related proteins in GBM cells. Thus, thioridazine has potential to treat GBM.     

GADD45 proteins: central players in tumorigenesis

The Growth Arrest and DNA Damage-inducible 45 (GADD45) proteins have been implicated in regulation of many cellular functions including DNA repair, cell cycle control, senescence and genotoxic stress. However, the pro-apoptotic activities have also positioned GADD45 as an essential player in oncogenesis. Emerging functional evidence implies that GADD45 proteins serve as tumor suppressors in response to diverse stimuli, connecting multiple cell signaling modules. Defects in the GADD45 pathway can be related to the initiation and progression of malignancies. Moreover, induction of GADD45 expression is an essential step for mediating anti-cancer activity of multiple chemotherapeutic drugs and the absence of GADD45 might abrogate their effects in cancer cells. In this review, we present a comprehensive discussion of the functions of GADD45 proteins, linking their regulation to effectors of cell cycle arrest, DNA repair and apoptosis. The ramifications regarding their roles as essential and central players in tumor growth suppression are also examined. We also extensively review recent literature to clarify how different chemotherapeutic drugs induce GADD45 gene expression and how its up-regulation and interaction with different molecular partners may benefit cancer chemotherapy and facilitate novel drug discovery.     

生物信息技术加速开发旧药新用途

传统的技术路线研发新药,不仅周期很长而且耗资巨大,开发已获批准药物新的治疗用途,又称为药物重定位,比传统的新药研发具有明显的优势．基于芯片的基因表达谱分析,已常规地广泛用于各种人类疾病的临床研究,提供了在全基因组水平描述疾病状态的特征信号．同时,基因芯片也广泛地用于对比药物处理前后细胞基因表达模式的变化,这也提供了反映药物效应的高质量信号．最近出版的Science Translational Medicine杂志同时发表了一个研究组的两篇论文,为我们展示了如何利用生物信息学手段重新解析和比较全基因组基因表达谱数据,以高效地预测药物的新用途．这两篇论文使用了公共数据库中的100种疾病基因表达谱数据,以及164种药物处理前后细胞基因表达谱数据,通过比较和配对疾病与药物基因表达谱,得到了一些可以逆转疾病异常表达基因的药物,其中证实了一些已知的药物-疾病组合,也预测了一些新的药物-疾病组合．最后通过实验验证了抗溃疡药可用于治疗肺癌,而抗癫痫药可治疗炎症性肠道疾病,进一步证实了他们所采用研究策略的正确性．于是,肺癌和炎性肠道疾病这两种临床上难治的疾病有了新的候选治疗药物,我们也有了一种挖掘已有数据快速发现药物新用途的思路和方法．     

Nanoscale Reaction Vessels Designed for Synthesis of Copper-Drug Complexes Suitable for Preclinical Development

The development of copper-drug complexes (CDCs) is hindered due to their very poor aqueous solubility. Diethyldithiocarbamate (DDC) is the primary metabolite of disulfiram, an approved drug for alcoholism that is being repurposed for cancer. The anticancer activity of DDC is dependent on complexation with copper to form copper bis-diethyldithiocarbamate (Cu(DDC)₂), a highly insoluble complex that has not been possible to develop for indications requiring parenteral administration. We have resolved this issue by synthesizing Cu(DDC)₂ inside liposomes. DDC crosses the liposomal lipid bilayer, reacting with the entrapped copper; a reaction that can be observed through a colour change as the solution goes from a light blue to dark brown. This method is successfully applied to other CDCs including the anti-parasitic drug clioquinol, the natural product quercetin and the novel targeted agent CX-5461. Our method provides a simple, transformative solution enabling, for the first time, the development of CDCs as viable candidate anticancer drugs; drugs that would represent a brand new class of therapeutics for cancer patients.     

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.     

Gene expression‐based drug repurposing to target aging

Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target‐centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug‐perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.     

Identification and validation of a six-lncRNA prognostic signature with its ceRNA networks and candidate drugs in lower-grade gliomas

Gliomas account for 75% of the primary malignant brain tumors and a majority of lower-grade gliomas (LGG) inevitably develop into glioblastoma. The dysregulation of lncRNAs play a crucial role in LGG. In the present study, we first screened out six differentially expressed lncRNAs (AC021739.2, AL031722.1, AL354740.1, FGD5-AS1, LINC00844, and NEAT1) based on TCGA and GTEx RNA-seq databases. LncRNA prognostic signature was then established by Kaplan–Meier and multivariate Cox proportional hazards regression, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. After lncRNA-miRNA-mRNA regulatory networks were established by Cytoscape 3.7.2, Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, with results enriched in various malignancy-related functions and pathways. Finally, six putative drugs (irinotecan, camptothecin, mitoxantrone, azacitidine, mestranol, and enilconazole) were predicted by Connectivity Map. In conclusion, we identified a 6-lncRNA prognostic signature with its ceRNA networks, and six candidate drugs against LGG.     

Drug Repositioning for Diabetes Based on 'Omics' Data Mining

Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical ‘omics’ data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that ‘omics’ data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer’s disease.