Drug Repurposing Analysis Research Articles

Proteasome Inhibitors Induce Apoptosis in Ex Vivo Cells of T-Cell Prolymphocytic Leukemia

Finding an effective treatment for T-PLL patients remains a significant challenge. Alemtuzumab, currently the gold standard, is insufficient in managing the aggressiveness of the disease in the long term. Consequently, numerous efforts are underway to address this unmet clinical need. The rarity of the disease limits the ability to conduct robust clinical trials, making in silico, ex vivo, and in vivo drug screenings essential for designing new therapeutic strategies for T-PLL. We conducted a drug repurposing analysis based on T-PLL gene expression data and identified proteasome inhibitors (PIs) as a promising new class of compounds capable of reversing the T-PLL phenotype. Treatment of ex vivo T-PLL cells with Bortezomib and Carfilzomib, two PI compounds, supported this hypothesis by demonstrating increased apoptosis in leukemic cells. The current lack of a suitable in vitro model for the study of T-PLL prompted us to perform similar experiments in the SUP-T11 cell line, validating its potential by showing an increased apoptotic rate. Taken together, these findings open new avenues for investigating the molecular mechanisms underlying the efficacy of PI in T-PLL and expand the spectrum of potential therapeutic strategies for this highly aggressive disease.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics. We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups. We examined two high-risk omics-defined groups in non-overlapping test sets (n=1133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "high activity" (low PRS, high TRS) and "severe risk" (high PRS, high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signalling processes compared to a low-risk (low PRS, low TRS) subgroup. "High activity" but not "severe risk" participants had greater prospective FEV1 decline (COPDGene:-51mL/year; ECLIPSE:-40mL/year) and proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection. National Institutes of Health.

Transcriptomic imputation identifies tissue-specific genes associated with cervical myelopathy

BACKGROUND CONTEXTDegenerative cervical myelopathy (DCM) is a progressive spinal condition that can lead to severe neurological dysfunction. Despite its degenerative pathophysiology, family history has shown to be a largely important factor in incidence and progression, suggesting that inherent genetic predisposition may play a role in pathophysiology. PURPOSETo determine the tissue-specific, functional genetic basis of hereditary predisposition to cervical myelopathy. STUDY DESIGNRetrospective case-control study using patient genetics and matched EHR from the Mount Sinai BioMe Biobank. METHODSIn a large, diverse, urban biobank of 32,031 individuals, with 558 individuals with cervical myopathy, we applied transcriptomic imputation to identify genetically regulated gene expression signatures associated with DCM. We performed drug-repurposing analysis using the CMAP database to identify candidate therapeutic interventions to reverse the cervical myelopathy-associated gene signature. RESULTSWe identified 16 genes significantly associated with DCM across 5 different tissues, suggesting tissue-specific manifestations of inherited genetic risk (upregulated: HES6, PI16, TMEM183A, BDH2, LINC00937, CLEC4D, USP43, SPATA1; downregulated: TTC12, CDK5, PAFAH1B2, RCSD1, KLHL29, PTPRG, RP11-620J15.3, C1RL). Drug repurposing identified 22 compounds with the potential to reverse the DCM-associated signature, suggesting points of therapeutic intervention. CONCLUSIONSThe inherited genetic risk for cervical myelopathy is functionally associated with genes involved in tissue-specific nociceptive and proliferative processes. These signatures may be reversed by candidate therapeutics with nociceptive, calcium channel modulating, and antiproliferative effects. CLINICAL SIGNIFICANCEUnderstanding the genetic basis of DCM provides critical insights into the hereditary factors contributing to the disease, allowing for more personalized and targeted therapeutic approaches. The identification of candidate drugs through transcriptomic imputation and drug repurposing analysis offers potential new treatments that could significantly improve patient outcomes and quality of life by addressing the underlying genetic mechanisms of DCM.

Signaling Pathways in Clear Cell Renal Cell Carcinoma and Candidate Drugs Unveiled through Transcriptomic Network Analysis of Hub Genes

Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer. It advances quickly and often metastasizes, making the prognosis for patients challenging. This study used weighted gene co-expression network analysis (WGCNA) to study gene expression data of different stages of ccRCC obtained in the GEO database. The analysis identified three significant highly preserved gene modules across the datasets: GSE53757, GSE22541, GSE66272, and GSE73731. Functional annotation and pathway enrichment analysis using DAVID revealed inflammatory pathways (e.g., NF-kB, Hippo, and HIF-1 pathways) that may drive ccRCC development and progression. The study also introduced the involvement of viral infections associated with the disease in the metabolic reprogramming of ccRCC. A drug repurposing analysis was also conducted to identify potential drug candidates for ccRCC using the upregulated and downregulated hub genes. The top candidates are ziprasidone (dopamine and serotonin receptor antagonist) and fentiazac (cyclooxygenase inhibitor). Other drug candidates were also obtained, such as phosphodiesterase/DNA methyltransferase/ATM kinase inhibitors, acetylcholine antagonists, and NAD precursors. Overall, the study’s findings suggest that identifying several genes and signaling pathways related to ccRCC may uncover new targets, biomarkers, and even drugs that can be repurposed, which can help develop new and effective treatments for the disease.

Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses

The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins.

Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders.

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.

Risk loci involved in giant cell arteritis susceptibility: a genome-wide association study

Giant cell arteritis is an age-related vasculitis that mainly affects the aorta and its branches in individuals aged 50 years and older. Current options for diagnosis and treatment are scarce, highlighting the need to better understand its underlying pathogenesis. Genome-wide association studies (GWAS) have emerged as a powerful tool for unravelling the pathogenic mechanisms involved in complex diseases. We aimed to characterise the genetic basis of giant cell arteritis by performing the largest GWAS of this vasculitis to date and to assess the functional consequences and clinical implications of identified risk loci. We collected and meta-analysed genomic data from patients with giant cell arteritis and healthy controls of European ancestry from ten cohorts across Europe and North America. Eligible patients required confirmation of giant cell arteritis diagnosis by positive temporal artery biopsy, positive temporal artery doppler ultrasonography, or imaging techniques confirming large-vessel vasculitis. We assessed the functional consequences of loci associated with giant cell arteritis using cell enrichment analysis, fine-mapping, and causal gene prioritisation. We also performed a drug repurposing analysis and developed a polygenic risk score to explore the clinical implications of our findings. We included a total of 3498 patients with giant cell arteritis and 15 550 controls. We identified three novel loci associated with risk of giant cell arteritis. Two loci, MFGE8 (rs8029053; p=4·96 × 10-8; OR 1·19 [95% CI 1·12-1·26]) and VTN (rs704; p=2·75 × 10-9; OR 0·84 [0·79-0·89]), were related to angiogenesis pathways and the third locus, CCDC25 (rs11782624; p=1·28 × 10-8; OR 1·18 [1·12-1·25]), was related to neutrophil extracellular traps (NETs). We also found an association between this vasculitis and HLA region and PLG. Variants associated with giant cell arteritis seemed to fulfil a specific regulatory role in crucial immune cell types. Furthermore, we identified several drugs that could represent promising candidates for treatment of this disease. The polygenic risk score model was able to identify individuals at increased risk of developing giant cell arteritis (90th percentile OR 2·87 [95% CI 2·15-3·82]; p=1·73 × 10-13). We have found several additional loci associated with giant cell arteritis, highlighting the crucial role of angiogenesis in disease susceptibility. Our study represents a step forward in the translation of genomic findings to clinical practice in giant cell arteritis, proposing new treatments and a method to measure genetic predisposition to this vasculitis. Institute of Health Carlos III, Spanish Ministry of Science and Innovation, UK Medical Research Council, and National Institute for Health and Care Research.

Abstract 6150: MMP14 and MKLN1 as colorectal cancer susceptibility genes and a drug repurposing candidate from genome-wide association study in South Korea

Abstract Background: One of the major topics about colorectal cancer (CRC) is to find susceptible genes. Furthermore, functional interpretation of post-GWAS analysis gains attention and various approaches to find drug-repurposing candidates are used in cancer research. Our study aims to identify susceptible genes for CRC and provide drug-repurposing candidates. Methods: CRC patients in Seoul National University Hospital with prospective follow-up were recruited. From blood-derived DNA, genome-wide SNPs were genotyped using the Korea Biobank Array and 409,063 SNPs were extracted. SNP-based logistic regression model was fit for the event of CRC estimating beta values. Furthermore, post-GWAS analysis was conducted using regulatory function, multiple annotation and gene expression database. Drug-repurposing candidate was identified through the pre-trained deep neural network with susceptible genes. Results: We used 500 participants of CRC cases and 1,500 participants of healthy controls. For GWAS, statistically significant associations were mapped to MKLN1, MMP14, PTPN14 and MKL2 genes. For post-GWAS analysis, strong associations were found for MKLN1 (rs75170436, 7q32.3, Beta=-0.90, P-value=5.90×10-13) and MMP14 (rs3751489, 14q11.2, Beta=-1.91, P-value=2.31×10-12). From the drug-repurposing analysis, PPARδ/β agonist (GW0742; Binding score=3.79(MKLN1), 12.06(MMP14)) was identified for both genes with the highest score. Conclusions: We revealed MKLN1 and MMP14 genes as susceptible genes associated with CRC development. Additionally, we identified PPARδ/β agonist (GW0742) as a drug-repurposing candidate for the treatment of CRC. These findings can promote the understanding of CRC development and provide a novel therapeutic candidate for CRC patients in the future. Citation Format: Dabin Yun, Jung-Ho Yang, Sun-Seong Kweon, Jin-ah Sim, Minjung Kim, Ji Won Park, Seung Yong Jeong, Aesun Shin, Nan Song. MMP14 and MKLN1 as colorectal cancer susceptibility genes and a drug repurposing candidate from genome-wide association study in South Korea [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6150.

Basal–epithelial subpopulations underlie and predict chemotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, characterized by extensive intratumoral heterogeneity, high metastasis, and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of these aggressive behaviors remains poorly understood. Using single-cell and spatial transcriptome analysis, here we discovered basal epithelial subpopulations located within the stroma that exhibit chemoresistance characteristics. The subpopulations are defined by distinct signature genes that show a frequent gain in copy number and exhibit an activated epithelial-to-mesenchymal transition program. A subset of these genes can accurately predict chemotherapy response and are associated with poor prognosis. Interestingly, among these genes, elevated ITGB1 participates in enhancing intercellular signaling while ACTN1 confers a survival advantage to foster chemoresistance. Furthermore, by subjecting the transcriptional signatures to drug repurposing analysis, we find that chemoresistant tumors may benefit from distinct inhibitors in treatment-naive versus post-NAC patients. These findings shed light on the mechanistic basis of chemoresistance while providing the best-in-class biomarker to predict chemotherapy response and alternate therapeutic avenues for improved management of TNBC patients resistant to chemotherapy.

Bioinformatics and systems biology analysis revealed PMID26394986-Compound-10 as potential repurposable drug against covid-19

The global health pandemic known as COVID-19, which stems from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a significant concern worldwide. Several treatment methods exist for COVID-19; however, there is an urgent demand for previously established drugs and vaccines to effectively combat the disease. Since, discovering new drugs poses a significant challenge, making the repurposing of existing drugs can potentially reduce time and costs compared to developing entirely new drugs from scratch. The objective of this study is to identify hub genes and associated repurposed drugs targeting them. We analyzed differentially expressed genes (DEGs) by analyzing RNA-seq transcriptomic datasets and integrated with genes associated with COVID-19 present in different databases. We detected 173 Covid-19 associated genes for the construction of a protein-protein interaction (PPI) network which resulted in the identification of the top 10 hub genes/proteins (STAT1, IRF7, MX1, IRF9, ISG15, OAS3, OAS2, OAS1, IRF3, and IRF1). Hub genes were subjected to GO functional and KEGG pathway enrichment analyses, which indicated some key roles and signaling pathways that were strongly related to SARS-CoV-2 infections. We conducted drug repurposing analysis using CMap, TTD, and DrugBank databases with these 10 hub genes, leading to the identification of Piceatannol, CKD-712, and PMID26394986-Compound-10 as top-ranked candidate drugs. Finally, drug–gene interactions analysis through molecular docking and validated via molecular dynamic simulation for 80 ns suggests PMID26394986-Compound-10 as the only potential drug. Our research demonstrates how in silico analysis might produce repurposing candidates to help respond faster to new disease outbreaks. Communicated by Ramaswamy H. Sarma

MULTIPLATFORM MOLECULAR ANALYSIS OF VESTIBULAR SCHWANNOMA REVEALS TWO ROBUST SUBGROUPS WITH DISTINCT MICROENVIRONMENT

Abstract BTFC travel award recipient Vestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle and poses a significant morbidity for patients. While many exhibit benign behaviour, others have a more aggressive nature and pattern of growth. Predicting who will fall into which category consistently remains uncertain. There is a need for a better understanding of the molecular landscape, and important subgroups therein, of this disease. METHODS: We select all vestibular schwannomas from our tumour bank with both methylation and RNA profiling available. Unsupervised clustering methods were used to define two distinct molecular subgroups of VS which were explored using computational techniques including bulk deconvolution analysis, gene pathway enrichment analysis, and drug repurposing analysis. Methylation data from two other cohorts were used to validate our findings, given a paucity of external samples with available multi-omic data. RESULTS: A total of 75 tumours were analyzed. Consensus clustering and similarity network fusion defined two subgroups ("immunogenic" and "proliferative”) with significant differences in immune, stroma, and tumour cell abundance (p<0.05). Gene network analysis and computational drug repurposing found critical differences in targets of immune checkpoint inhibition PD-1 and CTLA-4, the MEK pathway, and the epithelial to mesenchymal transition program, suggesting a need for subgroup-specific targeted treatment/trial design in the future. CONCLUSIONS: We leverage computational tools with multi-omic molecular data to define two robust subgroups of vestibular schwannoma with differences in microenvironment and therapeutic vulnerabilities.

F.2 Multiplatform molecular analysis of vestibular schwannoma reveals two robust subgroups with distinct microenvironment

Background: Vestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle and poses a significant morbidity for patients. While many exhibit benign behaviour, others have a more aggressive nature. There is a need for a better understanding of the molecular landscape, and important subgroups therein, of this disease. Methods: We select all VS from our tumour bank with both methylation and RNA profiling. Unsupervised clustering methods were used to define two distinct molecular subgroups of VS which were explored using computational techniques including bulk deconvolution analysis, gene pathway enrichment analysis, and drug repurposing analysis. Methylation data from two other cohorts were used to validate our findings. Results: A total of 75 tumours were analyzed. Consensus clustering and similarity network fusion defined two subgroups (“immunogenic” and “proliferative”) with significant differences in immune, stroma, and tumour cell abundance. Gene network analysis and computational drug repurposing found critical differences in targets of immune checkpoint inhibition PD-1 and CTLA-4, the MEK pathway, and the epithelial-to-mesenchymal transition program with associated candidate drug targets, suggesting a need for subgroup-specific treatment/trial design in the future. Conclusions: We leverage computational tools with multi-omic molecular data to define two robust subgroups of vestibular schwannoma with differences in microenvironment and therapeutic vulnerabilities.

Multiplatform molecular analysis of vestibular schwannoma reveals two robust subgroups with distinct microenvironment

BackgroundVestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle and poses a significant morbidity for patients. While many exhibit benign behaviour, others have a more aggressive nature and pattern of growth. Predicting who will fall into which category consistently remains uncertain. There is a need for a better understanding of the molecular landscape, and important subgroups therein, of this disease.MethodsWe select all vestibular schwannomas from our tumour bank with both methylation and RNA profiling available. Unsupervised clustering methods were used to define two distinct molecular subgroups of VS which were explored using computational techniques including bulk deconvolution analysis, gene pathway enrichment analysis, and drug repurposing analysis. Methylation data from two other cohorts were used to validate our findings, given a paucity of external samples with available multi-omic data.ResultsA total of 75 tumours were analyzed. Consensus clustering and similarity network fusion defined two subgroups (“immunogenic” and “proliferative”) with significant differences in immune, stroma, and tumour cell abundance (p < 0.05). Gene network analysis and computational drug repurposing found critical differences in targets of immune checkpoint inhibition PD-1 and CTLA-4, the MEK pathway, and the epithelial to mesenchymal transition program, suggesting a need for subgroup-specific targeted treatment/trial design in the future.ConclusionsWe leverage computational tools with multi-omic molecular data to define two robust subgroups of vestibular schwannoma with differences in microenvironment and therapeutic vulnerabilities.

Drug repositioning via host-pathogen protein-protein interactions for the treatment of cervical cancer.

Integrating interaction data with biological knowledge can be a critical approach for drug development or drug repurposing. In this context, host-pathogen-protein-protein interaction (HP-PPI) networks are useful instrument to uncover the phenomena underlying therapeutic effects in infectious diseases, including cervical cancer, which is almost exclusively due to human papillomavirus (HPV) infections. Cervical cancer is one of the second leading causes of death, and HPV16 and HPV18 are the most common subtypes worldwide. Given the limitations of traditionally used virus-directed drug therapies for infectious diseases and, at the same time, recent cancer statistics for cervical cancer cases, the need for innovative treatments becomes clear. Accordingly, in this study, we emphasize the potential of host proteins as drug targets and identify promising host protein candidates for cervical cancer by considering potential differences between HPV subtypes (i.e., HPV16 and HPV18) within a novel bioinformatics framework that we have developed. Subsequently, subtype-specific HP-PPI networks were constructed to obtain host proteins. Using this framework, we next selected biologically significant host proteins. Using these prominent host proteins, we performed drug repurposing analysis. Finally, by following our framework we identify the most promising host-oriented drug candidates for cervical cancer. As a result of this framework, we discovered both previously associated and novel drug candidates, including interferon alfacon-1, pimecrolimus, and hyaluronan specifically for HPV16 and HPV18 subtypes, respectively. Consequently, with this study, we have provided valuable data for further experimental and clinical efforts and presented a novel bioinformatics framework that can be applied to any infectious disease.

Targeting mucin hypersecretion in COVID-19 therapy.

Targeting mucin hypersecretion in COVID-19 therapy.

TRANSCRIPTOME-WIDE ASSOCIATION STUDY OF TREATMENT-RESISTANT DEPRESSION AND DEPRESSION SUBTYPES FOR DRUG REPURPOSING

Major depressive disorder (MDD) is the single largest contributor to global disability and up to 20-30% of patients do not respond to at least two antidepressants (treatment-resistant depression, TRD). This study leveraged imputed gene expression in TRD to perform a drug repurposing analysis. Among those with MDD, we defined TRD as having at least two antidepressant switches according to primary care records in UK Biobank (UKB). We performed a transcriptome-wide association study (TWAS) of TRD (n = 2165) vs healthy controls (n = 11,188) using FUSION and gene expression levels from 21 tissues. We identified compounds with opposite gene expression signatures (ConnectivityMap data) compared to our TWAS results using the Kolmogorov-Smirnov test, Spearman and Pearson correlation. As symptom patterns are routinely assessed in clinical practice and could be used to provide targeted treatments, we identified MDD subtypes associated with TRD in UKB and analysed them using the same pipeline described for TRD. Anxious MDD (n = 14,954) and MDD with weight gain (n = 4697) were associated with TRD. In the TWAS, two genes were significantly dysregulated (TMEM106B and ATP2A1 for anxious and weight gain MDD, respectively). A muscarinic receptor antagonist was identified as top candidate for repurposing in TRD; inhibition of heat shock protein 90 was the main mechanism of action identified for anxious MDD, while modulators of metabolism such as troglitazone showed promising results for MDD with weight gain. This was the first TWAS of TRD and associated MDD subtypes. Our results shed light on possible pharmacological approaches in individuals with difficult-to-treat depression.

Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing

Major depressive disorder (MDD) is the single largest contributor to global disability and up to 20–30% of patients do not respond to at least two antidepressants (treatment-resistant depression, TRD). This study leveraged imputed gene expression in TRD to perform a drug repurposing analysis. Among those with MDD, we defined TRD as having at least two antidepressant switches according to primary care records in UK Biobank (UKB). We performed a transcriptome-wide association study (TWAS) of TRD (n = 2165) vs healthy controls (n = 11,188) using FUSION and gene expression levels from 21 tissues. We identified compounds with opposite gene expression signatures (ConnectivityMap data) compared to our TWAS results using the Kolmogorov-Smirnov test, Spearman and Pearson correlation. As symptom patterns are routinely assessed in clinical practice and could be used to provide targeted treatments, we identified MDD subtypes associated with TRD in UKB and analysed them using the same pipeline described for TRD. Anxious MDD (n = 14,954) and MDD with weight gain (n = 4697) were associated with TRD. In the TWAS, two genes were significantly dysregulated (TMEM106B and ATP2A1 for anxious and weight gain MDD, respectively). A muscarinic receptor antagonist was identified as top candidate for repurposing in TRD; inhibition of heat shock protein 90 was the main mechanism of action identified for anxious MDD, while modulators of metabolism such as troglitazone showed promising results for MDD with weight gain. This was the first TWAS of TRD and associated MDD subtypes. Our results shed light on possible pharmacological approaches in individuals with difficult-to-treat depression.

Comparative transcriptome analysis of SARS-CoV, MERS-CoV, and SARS-CoV-2 to identify potential pathways for drug repurposing

The ongoing COVID-19 pandemic caused by the coronavirus, SARS-CoV-2, has already caused in excess of 1.25 million deaths worldwide, and the number is increasing. Knowledge of the host transcriptional response against this virus and how the pathways are activated or suppressed compared to other human coronaviruses (SARS-CoV, MERS-CoV) that caused outbreaks previously can help in the identification of potential drugs for the treatment of COVID-19. Hence, we used time point meta-analysis to investigate available SARS-CoV and MERS-CoV in-vitro transcriptome datasets in order to identify the significant genes and pathways that are dysregulated at each time point. The subsequent over-representation analysis (ORA) revealed that several pathways are significantly dysregulated at each time point after both SARS-CoV and MERS-CoV infection. We also performed gene set enrichment analyses of SARS-CoV and MERS-CoV with that of SARS-CoV-2 at the same time point and cell line, the results of which revealed that common pathways are activated and suppressed in all three coronaviruses. Furthermore, an analysis of an in-vivo transcriptomic dataset of COVID-19 patients showed that similar pathways are enriched to those identified in the earlier analyses. Based on these findings, a drug repurposing analysis was performed to identify potential drug candidates for combating COVID-19.

Host transcriptome-guided drug repurposing for COVID-19 treatment: a meta-analysis based approach.

BackgroundCoronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared a pandemic by the World Health Organization, and the identification of effective therapeutic strategy is a need of the hour to combat SARS-CoV-2 infection. In this scenario, the drug repurposing approach is widely used for the rapid identification of potential drugs against SARS-CoV-2, considering viral and host factors.MethodsWe adopted a host transcriptome-based drug repurposing strategy utilizing the publicly available high throughput gene expression data on SARS-CoV-2 and other respiratory infection viruses. Based on the consistency in expression status of host factors in different cell types and previous evidence reported in the literature, pro-viral factors of SARS-CoV-2 identified and subject to drug repurposing analysis based on DrugBank and Connectivity Map (CMap) using the web tool, CLUE.ResultsThe upregulated pro-viral factors such as TYMP, PTGS2, C1S, CFB, IFI44, XAF1, CXCL2, and CXCL3 were identified in early infection models of SARS-CoV-2. By further analysis of the drug-perturbed expression profiles in the connectivity map, 27 drugs that can reverse the expression of pro-viral factors were identified, and importantly, twelve of them reported to have anti-viral activity. The direct inhibition of the PTGS2 gene product can be considered as another therapeutic strategy for SARS-CoV-2 infection and could suggest six approved PTGS2 inhibitor drugs for the treatment of COVID-19. The computational study could propose candidate repurposable drugs against COVID-19, and further experimental studies are required for validation.

Support for phosphoinositol 3 kinase and mTOR inhibitors as treatment for lupus using in-silico drug-repurposing analysis

BackgroundSystemic lupus erythematosus (SLE) is an autoimmune disease with few treatment options. Current therapies are not fully effective and show highly variable responses. In this regard, large efforts have focused on developing more effective therapeutic strategies. Drug repurposing based on the comparison of gene expression signatures is an effective technique for the identification of new therapeutic approaches. Here we present a drug-repurposing exploratory analysis using gene expression signatures from SLE patients to discover potential new drug candidates and target genes.MethodsWe collected a compendium of gene expression signatures comprising peripheral blood cells and different separate blood cell types from SLE patients. The Lincscloud database was mined to link SLE signatures with drugs, gene knock-down, and knock-in expression signatures. The derived dataset was analyzed in order to identify compounds, genes, and pathways that were significantly correlated with SLE gene expression signatures.ResultsWe obtained a list of drugs that showed an inverse correlation with SLE gene expression signatures as well as a set of potential target genes and their associated biological pathways. The list includes drugs never or little studied in the context of SLE treatment, as well as recently studied compounds.ConclusionOur exploratory analysis provides evidence that phosphoinositol 3 kinase and mammalian target of rapamycin (mTOR) inhibitors could be potential therapeutic options in SLE worth further future testing.