Recovering drug-induced apoptosis subnetwork from Connectivity Map data

Multi-omics analysis to explore the molecular mechanisms related to keloid

BACKGROUND

Keloid is a benign skin tumor that result from abnormal wound healing and excessive collagen deposition. The pathogenesis is believed to be linked to genetic predisposition and immune imbalance, although the precise mechanisms remain poorly understood. Current therapeutic approaches may not consistently yield satisfactory outcomes and are often accompanied by potential side effects and risks. The high recurrence rate and refractory nature of keloid nodules present significant challenges and uncertainties in their management. Given the lack of effective treatment strategies, it is essential to identify key molecular pathways and potential therapeutic targets for keloid.

OBJECTIVE

This study aimed to identify the potential pathogenic mechanisms, hub genes, and immune cell involvement in keloid formation, with the goal of providing novel insights for targeted therapies.

METHODS

We utilized a combination of bulk RNA sequencing to analyze gene expression profiles in keloid tissues. Differentially expressed genes (DEGs) were identified and subjected to pathway enrichment analysis to reveal key biological processes involved in keloid pathogenesis. Mendelian randomization was performed to investigate the causal relationship between genetic factors and keloid formation, identifying potential hub genes. Immune infiltration analysis was conducted to determine the role of specific immune cells in keloid development. Subsequently, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to investigate the functional pathways associated with the hub genes. Network analysis was employed to identify transcription factors, miRNAs, and potential drugs in the Connectivity Map associated with the hub genes. Single-cell RNA sequencing was also used to identify cell-specific expression patterns of these genes.

RESULTS

Pathway enrichment analysis highlighted the association of keloid pathogenesis with cell proliferation and division, providing insights into the molecular processes involved. Mendelian randomization revealed that DUSP1 acts as an inhibitor of keloid formation, while HOXA5 promotes keloid pathogenesis. Immune infiltration analysis suggested that mast cells and macrophages play critical roles in the disease's progression. Based on hub gene analysis, the IL17 signaling pathway emerged as a key pathway implicated in keloid development. Further drug prediction models identified 9-methyl-5H-6-thia-4, 5-diaza-chrysene-6, 6-dioxide, zebularine, temozolomide and valproic acid targeting these hub genes.

CONCLUSION

DUSP1 and HOXA5 are hub genes in keloid pathogenesis, with DUSP1 acting as an inhibitor and HOXA5 as a promoter of disease progression. Targeting the regulatory networks associated with these genes could provide novel therapeutic strategies. Mast cells and macrophages are identified as critical immune cell types involved in the disease process. Additionally, the IL17 signaling pathway plays a crucial role in keloid development, highlighting its potential as a therapeutic target. These findings suggest that a multi-target approach focusing on these pathways could offer effective treatment options for keloid patients.

Three novel piperidones exhibit tumor-selective cytotoxicity on leukemia cells via protein degradation and stress-mediated mechanisms

Background

Cancer is an ongoing worldwide health problem. Although chemotherapy remains the mainstay therapy for cancer, it is not always effective and has detrimental side effects. Here, we present piperidone compounds P3, P4, and P5 that selectively target cancer cells via protein- and stress-mediated mechanisms.

Methods

We assessed typical apoptotic markers including phosphatidylserine externalization, caspase-3 activation, and DNA fragmentation through flow cytometry. Then, specific markers of the intrinsic pathway of apoptosis including the depolarization of the mitochondria and the generation of reactive oxygen species (ROS) were investigated. Finally, we utilized western blot techniques, RT-qPCR, and observed the cell cycle profile after compound treatment to evaluate the possible behavior of these compounds as proteasome inhibitors. For statistical analyses, we employed the one-way ANOVA followed by Bonferroni post hoc test.

Results

P3, P4, and P5 induce cytotoxic effects towards tumorigenic cells, as opposed to non-cancerous cells, at the low micromolar range. Compound treatment leads to the activation of the intrinsic pathway of apoptosis. The accumulation of poly-ubiquitinated proteins and the pro-apoptotic protein Noxa, both typically observed after proteasome inhibition, occurs after P3, P4, and P5 treatment. The stress-related genes PMAIP1, ATF3, CHAC1, MYC, and HMOX-1 were differentially regulated to contribute to the cytotoxic activity of P3–P5. Finally, compound P5 causes cell cycle arrest at the G₂/M phase.

Conclusion

Taken together, compounds P3, P4, and P5 exhibit strong potential as anticancer drug candidates as shown by strong cytotoxic potential, activation of the intrinsic pathway of apoptosis, and show typical proteasome inhibitor characteristics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43440-021-00322-3.

The Advantages of Connectivity Map Applied in Traditional Chinese Medicine

Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.

Identification of crucial genes and prediction of small molecules for multidrug resistance of Hodgkin's lymphomas

BACKGROUND

Multidrug resistance of Hodgkin's lymphoma (HL) often results in recurrence. Thus, we aimed to explore the underlying molecular mechanisms of multidrug resistance using bioinformatics strategies.

METHODS

The gene expression profile was obtained from GEO database. Then, the differentially expressed genes were screened out, and their functional annotations were carried out. Then, gene-signal interaction network was constructed and Connectivity Map (CMAP) analysis was performed.

RESULTS

A total of 1425 dysregulated genes were screened out, which were mainly enriched in biological items, such as small molecule metabolic, signal transduction, and cell apoptosis. Some survival-related pathways, such as MAPK pathways, apoptosis, and P53 pathway, and several hub genes, such as PRKCA, ACTN1, PIP5K1B, PRKACB, and JAK2, might play key roles in the development of multidrug resistance. Interestingly, felodipine was predicted to be a potential agent overcoming the multidrug resistance.

CONCLUSIONS

The present study offered new insights into the molecular mechanisms of multidrug resistance and identified a series of important hub genes and small agents that might be critical for treatment of multidrug-resistant HL.

Identifying key genes, pathways and screening therapeutic agents for manganese-induced Alzheimer disease using bioinformatics analysis

Alzheimer disease (AD) is a progressive neurodegenerative disease, the etiology of which remains largely unknown. Accumulating evidence indicates that elevated manganese (Mn) in brain exerts toxic effects on neurons and contributes to AD development. Thus, we aimed to explore the gene and pathway variations through analysis of high through-put data in this process.To screen the differentially expressed genes (DEGs) that may play critical roles in Mn-induced AD, public microarray data regarding Mn-treated neurocytes versus controls (GSE70845), and AD versus controls (GSE48350), were downloaded and the DEGs were screened out, respectively. The intersection of the DEGs of each datasets was obtained by using Venn analysis. Then, gene ontology (GO) function analysis and KEGG pathway analysis were carried out. For screening hub genes, protein-protein interaction network was constructed. At last, DEGs were analyzed in Connectivity Map (CMAP) for identification of small molecules that overcome Mn-induced neurotoxicity or AD development.The intersection of the DEGs obtained 140 upregulated and 267 downregulated genes. The top 5 items of biological processes of GO analysis were taxis, chemotaxis, cell-cell signaling, regulation of cellular physiological process, and response to wounding. The top 5 items of KEGG pathway analysis were cytokine-cytokine receptor interaction, apoptosis, oxidative phosphorylation, Toll-like receptor signaling pathway, and insulin signaling pathway. Afterwards, several hub genes such as INSR, VEGFA, PRKACB, DLG4, and BCL2 that might play key roles in Mn-induced AD were further screened out. Interestingly, tyrphostin AG-825, an inhibitor of tyrosine phosphorylation, was predicted to be a potential agent for overcoming Mn-induced neurotoxicity or AD development.The present study provided a novel insight into the molecular mechanisms of Mn-induced neurotoxicity or AD development and screened out several small molecular candidates that might be critical for Mn neurotoxicity prevention and Mn-induced AD treatment.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer

Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine.

A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network

Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy

With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.

ksRepo: a generalized platform for computational drug repositioning

Background

Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema.

Results

ksRepo eliminates the need for a single microarray platform as input and allows for the use of a variety of drug and chemical exposure databases. We tested ksRepo’s performance on a set of five prostate cancer datasets using the Comparative Toxicogenomics Database (CTD) as our database of gene-compound interactions. ksRepo successfully predicted significance for five frontline prostate cancer therapies, representing a significant enrichment from over 7000 CTD compounds, and achieved specificity similar to other repositioning methods.

Conclusions

We present ksRepo, which enables investigators to use any data inputs for computational drug repositioning. ksRepo is implemented in a series of four functions in the R statistical environment under a BSD3 license. Source code is freely available at http://github.com/adam-sam-brown/ksRepo. A vignette is provided to aid users in performing ksRepo analysis.