Revisiting Connectivity Map from a gene co-expression network analysis

Low-dose naltrexone extends healthspan and lifespan in C. elegans via SKN-1 activation

As the global aging population rises, finding effective interventions to improve aging health is crucial. Drug repurposing, utilizing existing drugs for new purposes, presents a promising strategy for rapid implementation. We explored naltrexone from the Library of Integrated Network-based Cellular Signatures (LINCS) based on several selection criteria. Low-dose naltrexone (LDN) has gained attention for treating various diseases, yet its impact on longevity remains underexplored. Our study on C. elegans demonstrated that a low dose, but not high dose, of naltrexone extended the healthspan and lifespan. This effect was mediated through SKN-1 (NRF2 in mammals) signaling, influencing innate immune gene expression and upregulating oxidative stress responses. With LDN's low side effects profile, our findings underscore its potential as a geroprotector, suggesting further exploration for promoting healthy aging in humans is warranted.

Network-based drug repurposing for schizophrenia

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

A gene network database for the identification of key genes for diagnosis, prognosis, and treatment in sepsis

Sepsis and sepsis-related diseases cause a high rate of mortality worldwide. The molecular and cellular mechanisms of sepsis are still unclear. We aim to identify key genes in sepsis and reveal potential disease mechanisms. Six sepsis-related blood transcriptome datasets were collected and analyzed by weighted gene co-expression network analysis (WGCNA). Functional annotation was performed in the gProfiler tool. DSigDB was used for drug signature enrichment analysis. The proportion of immune cells was estimated by the CIBERSORT tool. The relationships between modules, immune cells, and survival were identified by correlation analysis and survival analysis. A total of 37 stable co-expressed gene modules were identified. These modules were associated with the critical biology process in sepsis. Four modules can independently separate patients with long and short survival. Three modules can recurrently separate sepsis and normal patients with high accuracy. Some modules can separate bacterial pneumonia, influenza pneumonia, mixed bacterial and influenza A pneumonia, and non-infective systemic inflammatory response syndrome (SIRS). Drug signature analysis identified drugs associated with sepsis, such as testosterone, phytoestrogens, ibuprofen, urea, dichlorvos, potassium persulfate, and vitamin B12. Finally, a gene co-expression network database was constructed ( https://liqs.shinyapps.io/sepsis/ ). The recurrent modules in sepsis may facilitate disease diagnosis, prognosis, and treatment.

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

Targeting macrophage TFEB-14-3-3 epsilon Interface by naringenin inhibits abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a lethal cardiovascular disease, and there is no proven drug treatment for this condition. In this study, by using the Connectivity Map (CMap) approach, we explored naringenin, a naturally occurring citrus flavonoid, as a putative agent for inhibiting AAA. We then validated the prediction with two independent mouse models of AAA, calcium phosphate (CaPO₄)-induced C57BL/6J mice and angiotensin II-infused ApoE^−/− mice. Naringenin effectively blocked the formation of AAAs and the progression of established AAAs. Transcription factor EB (TFEB) is the master regulator of lysosome biogenesis. Intriguingly, the protective role of naringenin on AAA was abolished by macrophage-specific TFEB depletion in mice. Unbiased interactomics, combined with isothermal titration calorimetry (ITC) and cellular thermal shift assays (CETSAs), further revealed that naringenin is directly bound to 14-3-3 epsilon blocked the TFEB-14-3-3 epsilon interaction, and therefore promoted TFEB nuclear translocation and activation. On one hand, naringenin activated lysosome-dependent inhibition of the NLRP3 inflammasome and repressed aneurysmal inflammation. On the other hand, naringenin induced TFEB-dependent transcriptional activation of GATA3, IRF4, and STAT6 and therefore promoted reparative M2 macrophage polarization. In summary, naturally derived naringenin or macrophage TFEB activation shows promising efficacy for the treatment of AAA.

Gene Network Analysis of Hepatocellular Carcinoma Identifies Modules Associated with Disease Progression, Survival, and Chemo Drug Resistance

Background

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. HCC transcriptome has been extensively studied; however, the progress in disease mechanisms, prognosis, and treatment is still slow.

Methods

A rank-based module-centric workflow was introduced to analyze important modules associated with HCC development, prognosis, and drug resistance. The currently largest HCC cell line RNA-Seq dataset from the LIMORE database was used to construct the reference modules by weighted gene co-expression network analysis.

Results

Thirteen reference modules were identified with validated reproducibility. These modules were all associated with specific biological functions. Differentially expressed module analysis revealed the crucial modules during HCC development. Modules and hub genes are indicative of patient survival. Modules can differentiate patients in different HCC stages. Furthermore, drug resistance was revealed by drug-module association analysis. Based on differentially expressed modules and hub genes, six candidate drugs were screened. The hub genes of those modules merit further investigation.

Conclusion

We proposed a reference module-based analysis of the HCC transcriptome. The identified modules are associated with HCC development, survival, and drug resistance. M3 and M6 may play important roles during HCV to HCC development. M1, M3, M5, and M7 are associated with HCC survival. High M4, high M9, low M1, and low M3 may be associated with dasatinib, doxorubicin, CD532, and simvastatin resistance. Our analysis provides useful information for HCC diagnosis and treatment.

Agent Clustering Strategy Based on Metabolic Flux Distribution and Transcriptome Expression for Novel Drug Development

The network module-based method has been used for drug repositioning. The traditional drug repositioning method only uses the gene characteristics of the drug but ignores the drug-triggered metabolic changes. The metabolic network systematically characterizes the connection between genes, proteins, and metabolic reactions. The differential metabolic flux distribution, as drug metabolism characteristics, was employed to cluster the agents with similar MoAs (mechanism of action). In this study, agents with the same pharmacology were clustered into one group, and a total of 1309 agents from the CMap database were clustered into 98 groups based on differential metabolic flux distribution. Transcription factor (TF) enrichment analysis revealed the agents in the same group (such as group 7 and group 26) were confirmed to have similar MoAs. Through this agent clustering strategy, the candidate drugs which can inhibit (Japanese encephalitis virus) JEV infection were identified. This study provides new insights into drug repositioning and their MoAs.

Reference Module-Based Analysis of Ovarian Cancer Transcriptome Identifies Important Modules and Potential Drugs

Ovarian cancer (OVC) is often diagnosed at the advanced stage resulting in a poor overall outcome for the patient. The disease mechanisms, prognosis, and treatment require imperative elucidation. A rank-based module-centric framework was proposed to analyze the key modules related to the development, prognosis, and treatment of OVC. The ovarian cancer cell line microarray dataset GSE43765 from the Gene Expression Omnibus database was used to construct the reference modules by weighted gene correlation network analysis. Twenty-three reference modules were tested for stability and functionally annotated. Furthermore, to demonstrate the utility of reference modules, two more OVC datasets were collected, and their gene expression profiles were projected to the reference modules to generate a module-level expression. An epithelial-mesenchymal transition module was activated in OVC compared to the normal epithelium, and a pluripotency module was activated in ovarian cancer stroma compared to ovarian cancer epithelium. Seven differentially expressed modules were identified in OVC compared to the normal ovarian epithelium, with five up-regulated, and two down-regulated. One module was identified to be predictive of patient overall survival. Four modules were enriched with SNP signals. Based on differentially expressed modules and hub genes, five candidate drugs were screened. The hub genes of those modules merit further investigation. We firstly propose the reference module-based analysis of OVC. The utility of the analysis framework can be extended to transcriptome data of other kinds of diseases.

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.

Glucocorticoids improve severe or critical COVID-19 by activating ACE2 and reducing IL-6 levels

COVID-19 is a public health emergency that has rapidly spread to over 200 countries and regions, and no effective treatment has been established to date. Severe and critical cases have been associated with higher mortality due to acute respiratory distress syndrome (ARDS) and cytokine storm. Based on the novelty and recent emergence of COVID-19, no effective treatment regimen has been identified, thus prompting clinicians to engage in drug repurposing to address the immediate therapeutic need. This study focused on the molecular target angiotensin-converting enzyme 2 (ACE2) of SARS-CoV-2 and screened a group of ACE2 agonists by bioinformatics. Glucocorticoids are a type of ACE2 activator. We verified the efficacy of nine chemicals on regulating ACE2 expression in human GES-1, an upper digestive tract epithelial cell line, and THP-1, a human monocyte cell line, and found that several glucocorticoids imparted activating effects on ACE2 in both cell lines. The drugs triciribine and kinetin riboside activate ACE2 expression or inhibit IL-6 production in macrophages to some extent. In addition, we compared the efficacies of several glucocorticoids. Hydrocortisone showed the strongest effect on ACE2 activation, followed by prednisolone, dexamethasone, and methylprednisolone. We retrospectively analyzed the therapeutic efficacy of nine severe or critical patients from a cohort of 90 COVID-19 cases, who received medium to small doses of glucocorticoids from our integrated medical team in Wuhan. Seven out of nine patients revealed significant improvement in clinical parameters and chest CT images. This study provides experimental and clinical evidence that medium-to-low-dose glucocorticoids may play a protective role in the respiratory and digestive systems by activating ACE2 and suppressing cytokine storm.