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Li X, Xue C, Zhu Z, Yu X, Yang Q, Cui L, Li M. Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis. Mol Psychiatry 2024:10.1038/s41380-024-02660-z. [PMID: 39003413 DOI: 10.1038/s41380-024-02660-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 07/15/2024]
Abstract
Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partially due to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs' selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the "one drug, multiple targets" paradigm for the drug development of common psychiatric disorders.
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Affiliation(s)
- Xiangyi Li
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, Guangdong, China
| | - Chao Xue
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Zheng Zhu
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Xuegao Yu
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Qi Yang
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Liqian Cui
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
- Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, Guangzhou, 510080, Guangdong, China.
- National Key Clinical Department and Key Discipline of Neurology, Guangzhou, 510080, Guangdong, China.
| | - Miaoxin Li
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, Guangdong, China.
- Research Center of Medical Sciences, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong, Guangzhou, 510080, China.
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000, Guangdong, China.
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Chen S, Gao N, Li C, Zhai F, Jiang X, Zhang P, Guan J, Li K, Xiang R, Ling G. DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases. J Chem Inf Model 2024; 64:5317-5327. [PMID: 38900583 DOI: 10.1021/acs.jcim.4c00296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.
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Affiliation(s)
- Siqi Chen
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Nan Gao
- Wuya College of Innovation, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Chunzhi Li
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Fei Zhai
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Xiwei Jiang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Peng Zhang
- Wuya College of Innovation, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Jibin Guan
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Kefeng Li
- Center for Artificial Intelligence-Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SR 999708, China
| | - Rongwu Xiang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
- Liaoning Medical Big Data and Artificial Intelligence Engineering Technology Research Center, Shenyang 110016, China
| | - Guixia Ling
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
- Wuya College of Innovation, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
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Brownjohn PW, Zoufir A, O’Donovan DJ, Sudhahar S, Syme A, Huckvale R, Porter JR, Bange H, Brennan J, Thompson NT. Computational drug discovery approaches identify mebendazole as a candidate treatment for autosomal dominant polycystic kidney disease. Front Pharmacol 2024; 15:1397864. [PMID: 38846086 PMCID: PMC11154008 DOI: 10.3389/fphar.2024.1397864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024] Open
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is a rare genetic disorder characterised by numerous renal cysts, the progressive expansion of which can impact kidney function and lead eventually to renal failure. Tolvaptan is the only disease-modifying drug approved for the treatment of ADPKD, however its poor side effect and safety profile necessitates the need for the development of new therapeutics in this area. Using a combination of transcriptomic and machine learning computational drug discovery tools, we predicted that a number of existing drugs could have utility in the treatment of ADPKD, and subsequently validated several of these drug predictions in established models of disease. We determined that the anthelmintic mebendazole was a potent anti-cystic agent in human cellular and in vivo models of ADPKD, and is likely acting through the inhibition of microtubule polymerisation and protein kinase activity. These findings demonstrate the utility of combining computational approaches to identify and understand potential new treatments for traditionally underserved rare diseases.
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Affiliation(s)
| | | | | | | | | | | | | | - Hester Bange
- Crown Bioscience Netherlands B.V., Biopartner Center Leiden JH, Leiden, Netherlands
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Truong TTT, Liu ZSJ, Panizzutti B, Kim JH, Dean OM, Berk M, Walder K. Network-based drug repurposing for schizophrenia. Neuropsychopharmacology 2024; 49:983-992. [PMID: 38321095 PMCID: PMC11039639 DOI: 10.1038/s41386-024-01805-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 02/08/2024]
Abstract
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.
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Affiliation(s)
- Trang T T Truong
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Zoe S J Liu
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Bruna Panizzutti
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Jee Hyun Kim
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Olivia M Dean
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Michael Berk
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
- Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, The Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, University of Melbourne, Parkville, 3010, Australia
| | - Ken Walder
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia.
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Zhang Y, Ji L, Yang D, Wu J, Yang F. Decoding cardiovascular risks: analyzing type 2 diabetes mellitus and ASCVD gene expression. Front Endocrinol (Lausanne) 2024; 15:1383772. [PMID: 38715799 PMCID: PMC11075663 DOI: 10.3389/fendo.2024.1383772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/09/2024] [Indexed: 06/04/2024] Open
Abstract
Background ASCVD is the primary cause of mortality in individuals with T2DM. A potential link between ASCVD and T2DM has been suggested, prompting further investigation. Methods We utilized linear and multivariate logistic regression, Wilcoxon test, and Spearman's correlation toanalyzethe interrelation between ASCVD and T2DM in NHANES data from 2001-2018.The Gene Expression Omnibus (GEO) database and Weighted Gene Co-expression Network Analysis (WGCNA) wereconducted to identify co-expression networks between ASCVD and T2DM. Hub genes were identified using LASSO regression analysis and further validated in two additional cohorts. Bioinformatics methods were employed for gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, along with the prediction of candidate small molecules. Results Our analysis of the NHANES dataset indicated a significant impact of blood glucose on lipid levels within diabetic cohort, suggesting that abnormal lipid metabolism is a critical factor in ASCVD development. Cross-phenotyping analysis revealed two pivotal genes, ABCC5 and WDR7, associated with both T2DM and ASCVD. Enrichment analyses demonstrated the intertwining of lipid metabolism in both conditions, encompassing adipocytokine signaling pathway, fatty acid degradation and metabolism, and the regulation of adipocyte lipolysis. Immune infiltration analysis underscored the involvement of immune processes in both diseases. Notably, RITA, ON-01910, doxercalciferol, and topiramate emerged as potential therapeutic agents for both T2DM and ASCVD, indicating their possible clinical significance. Conclusion Our findings pinpoint ABCC5 and WDR7 as new target genes between T2DM and ASCVD, with RITA, ON-01910, doxercalciferol, and topiramate highlighted as promising therapeutic agents.
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Affiliation(s)
- Youqi Zhang
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Key Laboratory of Myocardial Ischemia, Ministry of Education, Harbin, China
| | - Liu Ji
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Key Laboratory of Myocardial Ischemia, Ministry of Education, Harbin, China
| | - Daiwei Yang
- Department of Orthopedics, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jianjun Wu
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Fan Yang
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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Gonzalez G, Herath I, Veselkov K, Bronstein M, Zitnik M. Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.573985. [PMID: 38260532 PMCID: PMC10802439 DOI: 10.1101/2024.01.03.573985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
As an alternative to target-driven drug discovery, phenotype-driven approaches identify compounds that counteract the overall disease effects by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network model designed to predict arbitrary perturbagens - sets of therapeutic targets - capable of reversing disease effects. Unlike existing methods that learn responses to perturbations, PDGrapher solves the inverse problem, which is to infer the perturbagens necessary to achieve a specific response - i.e., directly predicting perturbagens by learning which perturbations elicit a desired response. Experiments across eight datasets of genetic and chemical perturbations show that PDGrapher successfully predicted effective perturbagens in up to 9% additional test samples and ranked therapeutic targets up to 35% higher than competing methods. A key innovation of PDGrapher is its direct prediction capability, which contrasts with the indirect, computationally intensive models traditionally used in phenotypedriven drug discovery that only predict changes in phenotypes due to perturbations. The direct approach enables PDGrapher to train up to 30 times faster, representing a significant leap in efficiency. Our results suggest that PDGrapher can advance phenotype-driven drug discovery, offering a fast and comprehensive approach to identifying therapeutically useful perturbations.
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Affiliation(s)
- Guadalupe Gonzalez
- Imperial College London, London, UK
- Prescient Design, Genentech, South San Francisco, CA, USA
- F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Isuru Herath
- Merck & Co., South San Francisco, CA, USA
- Cornell University, Ithaca, NY, USA
| | | | | | - Marinka Zitnik
- Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
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Kawaguchi T, Okamoto K, Fujimoto S, Bando M, Wada H, Miyamoto H, Sato Y, Muguruma N, Horimoto K, Takayama T. Lansoprazole inhibits the development of sessile serrated lesions by inducing G1 arrest via Skp2/p27 signaling pathway. J Gastroenterol 2024; 59:11-23. [PMID: 37989907 DOI: 10.1007/s00535-023-02052-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 10/07/2023] [Indexed: 11/23/2023]
Abstract
BACKGROUND Although the serrated-neoplasia pathway reportedly accounts for 15-30% of colorectal cancer (CRC), no studies on chemoprevention of sessile serrated lesions (SSLs) have been reported. We searched for effective compounds comprehensively from a large series of compounds by employing Connectivity Map (CMAP) analysis of SSL-specific gene expression profiles coupled with in vitro screening using SSL patient-derived organoids (PDOs), and validated their efficacy using a xenograft mouse model of SSL. METHODS We generated SSL-specific gene signatures based on DNA microarray data, and applied them to CMAP analysis with 1309 FDA-approved compounds to select candidate compounds. We evaluated their inhibitory effects on SSL-PDOs using a cell viability assay. SSL-PDOs were orthotopically transplanted into NOG mice for in vivo evaluation. The signal transduction pathway was evaluated by gene expression profile and protein expression analysis. RESULTS We identified 221 compounds by employing CMAP analysis of SSL-specific signatures, which should cancel the gene signatures, and narrowed them down to 17 compounds. Cell viability assay using SSL-PDOs identified lansoprazole as having the lowest IC50 value (47 µM) among 17 compounds. When SSL-PDO was orthotopically transplanted into murine intestinal tract, the tumor grew gradually. Administration of lansoprazole to mice inhibited the growth of SSL xenograft whereas the tumor in control mice treated with vehicle alone grew gradually over time. The Ki67 index in xenograft lesions from the lansoprazole group was significantly lower compared with the control group. Cell cycle analysis of SSL-PDOs treated with lansoprazole exhibited a significant increase in G1 phase cell population. Microarray and protein analysis revealed that lansoprazole downregulated Skp2 expression and upregulated p27 expression in SSL-PDOs. CONCLUSIONS Our data strongly suggest that lansoprazole is the most effective chemopreventive agent against SSL, and that lansoprazole induces G1 cell cycle arrest by downregulating Skp2 and upregulating p27 in SSL cells.
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Affiliation(s)
- Tomoyuki Kawaguchi
- Department of Gastroenterology and Oncology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Koichi Okamoto
- Department of Gastroenterology and Oncology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Shota Fujimoto
- Department of Gastroenterology and Oncology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Masahiro Bando
- Department of Gastroenterology and Oncology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hironori Wada
- Department of Gastroenterology and Oncology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hiroshi Miyamoto
- Department of Gastroenterology and Oncology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yasushi Sato
- Department of Community Medicine for Gastroenterology and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Naoki Muguruma
- Department of Gastroenterology and Oncology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Katsuhisa Horimoto
- Molecular Profiling Research Center for Drug Discovery (Molprof) National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan
- SOCIUM Inc, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan
| | - Tetsuji Takayama
- Department of Gastroenterology and Oncology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.
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Cioce M, Fumagalli MR, Donzelli S, Goeman F, Canu V, Rutigliano D, Orlandi G, Sacconi A, Pulito C, Palcau AC, Fanciulli M, Morrone A, Diodoro MG, Caricato M, Crescenzi A, Verri M, Fazio VM, Zapperi S, Levrero M, Strano S, Grazi GL, La Porta C, Blandino G. Interrogating colorectal cancer metastasis to liver: a search for clinically viable compounds and mechanistic insights in colorectal cancer Patient Derived Organoids. J Exp Clin Cancer Res 2023; 42:170. [PMID: 37460938 DOI: 10.1186/s13046-023-02754-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/07/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Approximately 20-50% of patients presenting with localized colorectal cancer progress to stage IV metastatic disease (mCRC) following initial treatment and this is a major prognostic determinant. Here, we have interrogated a heterogeneous set of primary colorectal cancer (CRC), liver CRC metastases and adjacent liver tissue to identify molecular determinants of the colon to liver spreading. Screening Food and Drug Administration (FDA) approved drugs for their ability to interfere with an identified colon to liver metastasis signature may help filling an unmet therapeutic need. METHODS RNA sequencing of primary colorectal cancer specimens vs adjacent liver tissue vs synchronous and asynchronous liver metastases. Pathways enrichment analyses. The Library of Integrated Network-based Cellular Signatures (LINCS)-based and Connectivity Map (CMAP)-mediated identification of FDA-approved compounds capable to interfere with a 22 gene signature from primary CRC and liver metastases. Testing the identified compounds on CRC-Patient Derived Organoid (PDO) cultures. Microscopy and Fluorescence Activated Cell Sorting (FACS) based analysis of the treated PDOs. RESULTS We have found that liver metastases acquire features of the adjacent liver tissue while partially losing those of the primary tumors they derived from. We have identified a 22-gene signature differentially expressed among primary tumors and metastases and validated in public databases. A pharmacogenomic screening for FDA-approved compounds capable of interfering with this signature has been performed. We have validated some of the identified representative compounds in CRC-Patient Derived Organoid cultures (PDOs) and found that pentoxyfilline and, to a minor extent, dexketoprofen and desloratadine, can variably interfere with number, size and viability of the CRC -PDOs in a patient-specific way. We explored the pentoxifylline mechanism of action and found that pentoxifylline treatment attenuated the 5-FU elicited increase of ALDHhigh cells by attenuating the IL-6 mediated STAT3 (tyr705) phosphorylation. CONCLUSIONS Pentoxifylline synergizes with 5-Fluorouracil (5-FU) in attenuating organoid formation. It does so by interfering with an IL-6-STAT3 axis leading to the emergence of chemoresistant ALDHhigh cell subpopulations in 5-FU treated PDOs. A larger cohort of CRC-PDOs will be required to validate and expand on the findings of this proof-of-concept study.
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Affiliation(s)
- Mario Cioce
- Department of Medicine, Laboratory of Molecular Medicine and Biotechnology, University Campus Bio-Medico of Rome, Rome, Italy.
- Institute of Translational Pharmacology, National Research Council of Italy (CNR), Rome, Italy.
| | - Maria Rita Fumagalli
- Center for Complexity and Biosystems, Department of Environmental Science and Policy, University of Milan, Via Celoria 26, 20133, Milano, Italy
- CNR - Consiglio Nazionale Delle Ricerche, Biophysics Institute, Via De Marini 6, 16149, Genoa, Italy
| | - Sara Donzelli
- Translational Oncology Research Unit, Department of Research, Advanced Diagnostic and Technological Innovation, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Frauke Goeman
- Department of Research, Diagnosis and Innovative Technologies, UOSD SAFU, Translational Research Area, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Valeria Canu
- Translational Oncology Research Unit, Department of Research, Advanced Diagnostic and Technological Innovation, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Daniela Rutigliano
- Department of Medicine, Laboratory of Molecular Medicine and Biotechnology, University Campus Bio-Medico of Rome, Rome, Italy
- Translational Oncology Research Unit, Department of Research, Advanced Diagnostic and Technological Innovation, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Giulia Orlandi
- Scientific Direction, IRCCS San Gallicano Dermatological Institute, Rome, Italy
| | - Andrea Sacconi
- Clinical Trial Center, Biostatistics and Bioinformatics Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Claudio Pulito
- Translational Oncology Research Unit, Department of Research, Advanced Diagnostic and Technological Innovation, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Alina Catalina Palcau
- Translational Oncology Research Unit, Department of Research, Advanced Diagnostic and Technological Innovation, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Maurizio Fanciulli
- Department of Research, Diagnosis and Innovative Technologies, UOSD SAFU, Translational Research Area, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Aldo Morrone
- Scientific Direction, IRCCS San Gallicano Dermatological Institute, Rome, Italy
| | - Maria Grazia Diodoro
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Marco Caricato
- Colorectal Surgery Unit, Fondazione Policlinico Universitario Campus Bio-Medico, Università Campus Bio-Medico, Rome, Italy
| | - Anna Crescenzi
- Department of Medicine, Laboratory of Molecular Medicine and Biotechnology, University Campus Bio-Medico of Rome, Rome, Italy
- Unit of Endocrine Organs and Neuromuscular Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Martina Verri
- Unit of Endocrine Organs and Neuromuscular Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Vito Michele Fazio
- Department of Medicine, Laboratory of Molecular Medicine and Biotechnology, University Campus Bio-Medico of Rome, Rome, Italy
- Institute of Translational Pharmacology, National Research Council of Italy (CNR), Rome, Italy
| | - Stefano Zapperi
- Center for Complexity and Biosystems, Department of Physics, University of Milan, Via Celoria 16, 20133, Milano, Italy
- Istituto Di Chimica Della Materia Condensata E Di Tecnologie Per L'Energia, CNR - Consiglio Nazionale Delle Ricerche, Via R. Cozzi 53, 20125, Milano, Italy
| | - Massimo Levrero
- Cancer Research Center of Lyon (CRCL), UMR Inserm, CNRS 5286 Mixte CLB, Université de Lyon, 1 (UCBL1), 69003, Lyon, France
| | - Sabrina Strano
- Department of Research, Diagnosis and Innovative Technologies, UOSD SAFU, Translational Research Area, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Gian Luca Grazi
- Department of Experimental and Clinical Medicine, Hepato-Biliary Pancreatic Surgery, University of Florence, Florence, Italy
| | - Caterina La Porta
- Center for Complexity and Biosystems, Department of Environmental Science and Policy, University of Milan, Via Celoria 26, 20133, Milano, Italy
- CNR - Consiglio Nazionale Delle Ricerche, Istituto Di Biofisica, Via Celoria 26, 20133, Milano, Italy
| | - Giovanni Blandino
- Translational Oncology Research Unit, Department of Research, Advanced Diagnostic and Technological Innovation, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy.
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Liu S, Wang S, Wang Z. Identification of genetic mechanisms underlying lipid metabolism-mediated tumor immunity in head and neck squamous cell carcinoma. BMC Med Genomics 2023; 16:110. [PMID: 37210507 DOI: 10.1186/s12920-023-01543-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 05/13/2023] [Indexed: 05/22/2023] Open
Abstract
OBJECTIVE To identify the genetic mechanisms underlying lipid metabolism-mediated tumor immunity in head and neck squamous carcinoma (HNSC). MATERIALS AND METHODS RNA sequencing data and clinical characteristics of HNSC patients were procured from The Cancer Genome Atlas (TCGA) database. Lipid metabolism-related genes were collected from KEGG and MSigDB databases. Immune cells and immune-related genes were obtained from the TISIDB database. The differentially expressed genes (DEGs) in HNSC were identified and weighted correlation network analysis (WGCNA) was performed to identify the significant gene modules. Lasso regression analysis was performed to identify hub genes. The differential gene expression pattern, diagnostic values, relationships with clinical features, prognostic values, relationships with tumor mutation burden (TMB), and signaling pathways involved, were each investigated. RESULTS One thousand six hundred sixty-eight DEGs were identified as dysregulated between HNSC tumor samples and healthy control head and neck samples. WGCNA analysis and Lasso regression analysis identified 8 hub genes, including 3 immune-related genes (PLA2G2D, TNFAIP8L2 and CYP27A1) and 5 lipid metabolism-related genes (FOXP3, IL21R, ITGAL, TRAF1 and WIPF1). Except CYP27A1, the other hub genes were upregulated in HNSC as compared with healthy control samples, and a low expression of these hub genes indicated a higher risk of death in HNSC. Except PLA2G2D, all other hub genes were significantly and negatively related with TMB in HNSC. The hub genes were implicated in several immune-related signaling pathways including T cell receptor signaling, Th17 cell differentiation, and natural killer (NK) cell mediated cytotoxicity. CONCLUSION Three immune genes (PLA2G2D, TNFAIP8L2, and CYP27A1) and immune-related pathways (T cell receptor signaling, Th17 cell differentiation, and natural killer (NK) cell mediated cytotoxicity) were predicted to play significant roles in the lipid metabolism-mediated tumor immunity in HNSC.
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Affiliation(s)
- Shaokun Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Xuanwu Hospital Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Shuning Wang
- Capital Medical University, No.10 Xitou Tiao, You'an Menwai, Fengtai District, Beijing, 10069, China
| | - Zhenlin Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Xuanwu Hospital Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
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10
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Zhao T, Zhu G, Dubey HV, Flaherty P. Identification of significant gene expression changes in multiple perturbation experiments using knockoffs. Brief Bioinform 2023; 24:bbad084. [PMID: 36892174 PMCID: PMC10025447 DOI: 10.1093/bib/bbad084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/20/2023] [Accepted: 02/13/2023] [Indexed: 03/10/2023] Open
Abstract
Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.
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Affiliation(s)
- Tingting Zhao
- Department of Information Systems and Analytics, College of Business, Bryant University, Smithfield, 02917, RI, USA
- Center for Health and Behavioral Sciences, Bryant University, Smithfield, 02917, RI, USA
| | - Guangyu Zhu
- Department of Computer Science and Statistics, University of Rhode Island, Kingston, 02881, RI, USA
| | - Harsh Vardhan Dubey
- Department of Mathematics & Statistics, University of Massachusetts Amherst, Amherst, 01003, MA, USA
| | - Patrick Flaherty
- Department of Mathematics & Statistics, University of Massachusetts Amherst, Amherst, 01003, MA, USA
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11
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Zhang H, Kong W, Xie Y, Zhao X, Luo D, Chen S, Pan Z. Telomere-related genes as potential biomarkers to predict endometriosis and immune response: Development of a machine learning-based risk model. Front Med (Lausanne) 2023; 10:1132676. [PMID: 36968845 PMCID: PMC10034389 DOI: 10.3389/fmed.2023.1132676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/20/2023] [Indexed: 03/11/2023] Open
Abstract
IntroductionEndometriosis (EM) is an aggressive, pleomorphic, and common gynecological disease. Its clinical presentation includes abnormal menstruation, dysmenorrhea, and infertility, which seriously affect the patient's quality of life. However, the pathogenesis underlying EM and associated regulatory genes are unknown.MethodsTelomere-related genes (TRGs) were uploaded from TelNet. RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to identify the pathways involved in the action of the signature genes. Finally, the CAMP database was used to screen drugs for potential use in EM treatment.ResultsFifteen total genes were screened as EM–telomere differentially expressed genes. Further screening by machine learning obtained six genes as characteristic predictive of EM. Immuno-infiltration analysis of the telomeric genes showed that expressions including macrophages and natural killer cells were significantly higher in cluster A. Further enrichment analysis showed that the differential genes were mainly enriched in biological pathways like cell cycle and extracellular matrix. Finally, the Connective Map database was used to screen 11 potential drugs for EM treatment.DiscussionTRGs play a crucial role in EM development, and are associated with immune infiltration and act on multiple pathways, including the cell cycle. Telomere signature genes can be valuable predictive markers for EM.
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12
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom,
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,
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13
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Li P, Bai C, Zhan L, Zhang H, Zhang Y, Zhang W, Wang Y, Zhao J. Specific gene module pair-based target identification and drug discovery. Front Pharmacol 2023; 13:1089217. [PMID: 36726786 PMCID: PMC9886283 DOI: 10.3389/fphar.2022.1089217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
Identification of the biological targets of a compound is of paramount importance for the exploration of the mechanism of action of drugs and for the development of novel drugs. A concept of the Connectivity Map (CMap) was previously proposed to connect genes, drugs, and disease states based on the common gene-expression signatures. For a new query compound, the CMap-based method can infer its potential targets by searching similar drugs with known targets (reference drugs) and measuring the similarities into their specific transcriptional responses between the query compound and those reference drugs. However, the available methods are often inefficient due to the requirement of the reference drugs as a medium to link the query agent and targets. Here, we developed a general procedure to extract target-induced consensus gene modules from the transcriptional profiles induced by the treatment of perturbagens of a target. A specific transcriptional gene module pair (GMP) was automatically identified for each target and could be used as a direct target signature. Based on the GMPs, we built the target network and identified some target gene clusters with similar biological mechanisms. Moreover, a gene module pair-based target identification (GMPTI) approach was proposed to predict novel compound-target interactions. Using this method, we have discovered novel inhibitors for three PI3K pathway proteins PI3Kα/β/δ, including PU-H71, alvespimycin, reversine, astemizole, raloxifene HCl, and tamoxifen.
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Affiliation(s)
- Peng Li
- Shanxi key lab for modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Jinzhong, Shanxi, China,*Correspondence: Peng Li,
| | - Chujie Bai
- Department of Orthopedic Oncology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China
| | - Lingmin Zhan
- Shanxi key lab for modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Jinzhong, Shanxi, China
| | - Haoran Zhang
- Shanxi key lab for modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Jinzhong, Shanxi, China
| | - Yuanyuan Zhang
- Shanxi key lab for modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Jinzhong, Shanxi, China
| | - Wuxia Zhang
- Shanxi key lab for modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Jinzhong, Shanxi, China
| | - Yingdong Wang
- Shanxi key lab for modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Jinzhong, Shanxi, China
| | - Jinzhong Zhao
- Shanxi key lab for modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Jinzhong, Shanxi, China
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14
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Lang X, Liu J, Zhang G, Feng X, Dan W. Knowledge Mapping of Drug Repositioning's Theme and Development. Drug Des Devel Ther 2023; 17:1157-1174. [PMID: 37096060 PMCID: PMC10122475 DOI: 10.2147/dddt.s405906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023] Open
Abstract
Background In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.
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Affiliation(s)
- Xiaona Lang
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Jinlei Liu
- Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China
| | - Guangzhong Zhang
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
| | - Xin Feng
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Wenchao Dan
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
- Correspondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email
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15
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Mugiyanto E, Adikusuma W, Irham LM, Huang WC, Chang WC, Kuo CN. Integrated genomic analysis to identify druggable targets for pancreatic cancer. Front Oncol 2022; 12:989077. [PMID: 36531045 PMCID: PMC9752886 DOI: 10.3389/fonc.2022.989077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 10/19/2022] [Indexed: 03/31/2024] Open
Abstract
According to the National Comprehensive Cancer Network and the American Society of Clinical Oncology, the standard treatment for pancreatic cancer (PC) is gemcitabine and fluorouracil. Other chemotherapeutic agents have been widely combined. However, drug resistance remains a huge challenge, leading to the ineffectiveness of cancer therapy. Therefore, we are trying to discover new treatments for PC by utilizing genomic information to identify PC-associated genes as well as drug target genes for drug repurposing. Genomic information from a public database, the cBio Cancer Genomics Portal, was employed to retrieve the somatic mutation genes of PC. Five functional annotations were applied to prioritize the PC risk genes: Kyoto Encyclopedia of Genes and Genomes; biological process; knockout mouse; Gene List Automatically Derived For You; and Gene Expression Omnibus Dataset. DrugBank database was utilized to extract PC drug targets. To narrow down the most promising drugs for PC, CMap Touchstone analysis was applied. Finally, ClinicalTrials.gov and a literature review were used to screen the potential drugs under clinical and preclinical investigation. Here, we extracted 895 PC-associated genes according to the cBioPortal database and prioritized them by using five functional annotations; 318 genes were assigned as biological PC risk genes. Further, 216 genes were druggable according to the DrugBank database. CMap Touchstone analysis indicated 13 candidate drugs for PC. Among those 13 drugs, 8 drugs are in the clinical trials, 2 drugs were supported by the preclinical studies, and 3 drugs are with no evidence status for PC. Importantly, we found that midostaurin (targeted PRKA) and fulvestrant (targeted ESR1) are promising candidate drugs for PC treatment based on the genomic-driven drug repurposing pipelines. In short, integrated analysis using a genomic information database demonstrated the viability for drug repurposing. We proposed two drugs (midostaurin and fulvestrant) as promising drugs for PC.
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Affiliation(s)
- Eko Mugiyanto
- PhD Program in Clinical Drug Development of Herbal Medicine, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Department of Pharmacy, Faculty of Health Science, University of Muhammadiyah Pekajangan Pekalongan, Pekalongan, Indonesia
| | - Wirawan Adikusuma
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Department of Pharmacy, Faculty of Health Science, University of Muhammadiyah Mataram, Mataram, Indonesia
| | | | - Wan-Chen Huang
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan
| | - Wei-Chiao Chang
- PhD Program in Clinical Drug Development of Herbal Medicine, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Integrative Research Center for Critical Care, Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Chun-Nan Kuo
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
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16
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Fukunishi Y, Higo J, Kasahara K. Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles. Biophys Rev 2022; 14:1423-1447. [PMID: 36465086 PMCID: PMC9703445 DOI: 10.1007/s12551-022-01015-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/06/2022] [Indexed: 11/29/2022] Open
Abstract
Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.
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Affiliation(s)
- Yoshifumi Fukunishi
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Junichi Higo
- Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima Minamimachi, Chuo-Ku, Kobe, Hyogo 650-0047 Japan ,Research Organization of Science and Technology, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
| | - Kota Kasahara
- College of Life Sciences, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
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17
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Lu Y, Wang D, Zhu Y, Du Y, Zhang J, Yang H. A risk model developed based on necroptosis to assess progression for ischemic cardiomyopathy and identify possible therapeutic drugs. Front Pharmacol 2022; 13:1039857. [PMID: 36518671 PMCID: PMC9744324 DOI: 10.3389/fphar.2022.1039857] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/28/2022] [Indexed: 09/26/2023] Open
Abstract
Object: Ischemic cardiomyopathy (ICM), with high morbidity and mortality, is the most common cause of heart failure. Cardiovascular remodeling secondary to chronic myocardial ischemia is the main cause of its progression. A recently identified type of programmed cell death called necroptosis is crucial in the development of various cardiovascular diseases. However, the function role of necroptosis in cardiac remodeling of ICM has not been elucidated. Our study aimed to screen for genes associated with necroptosis and construct a risk score to assess the progression and evaluate the prognosis of ICM patients, and further to search for potentially therapeutic drugs. Methods: The gene expression profiling was obtained from the GEO database. LASSO regression analysis was used to construct necroptosis-related gene signatures associated with ICM progression and prognosis. TF-gene and miRNA-gene networks were constructed to identify the regulatory targets of potential necroptosis-related signature genes. Pathway alterations in patients with high necroptosis-related score (NRS) were analyzed by GO, KEGG, GSEA analysis, and immune cell infiltration was estimated by ImmuCellAI analysis. CMap analysis was performed to screen potential small molecule compounds targeting patients with high NRS. Independent risk analyses were performed using nomograms. Results: Six necroptosis-related signature genes (STAT4, TNFSF10, CHMP5, CHMP18, JAK1, and CFLAR) were used to define the NRS, with areas under the ROC curves of 0.833, 0.765, and 0.75 for training test, test set, and validation set, respectively. Transcription factors FOXC1 and hsa-miR-124-3p miRNA may be regulators of signature genes. Patients with higher NRS have pathway enriched in fibrosis and metabolism and elevated nTreg cells. AZD-7762 may be an effective drug to improve the prognosis of patients with high NRS. A feature-based nomogram was constructed from which patients could derive clinical benefit. Conclusion: Our results reveal 6 necroptosis gene signatures that can evaluate the progression and prognosis of ICM with high clinical value, and identify potential targets that could help improve cardiovascular remodeling.
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Affiliation(s)
- Yang Lu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dashuai Wang
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yaoxi Zhu
- Department of Cardiovascular Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yimei Du
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinying Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Han Yang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Shah I, Bundy J, Chambers B, Everett LJ, Haggard D, Harrill J, Judson RS, Nyffeler J, Patlewicz G. Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities. Chem Res Toxicol 2022; 35:1929-1949. [PMID: 36301716 PMCID: PMC10483698 DOI: 10.1021/acs.chemrestox.2c00245] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.
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Affiliation(s)
- Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Joseph Bundy
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Bryant Chambers
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Logan J. Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Derik Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Richard S. Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Johanna Nyffeler
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
- Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Fellow, Oak Ridge, Tennessee, 37831, US
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
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Hsieh CY, Tu CC, Hung JH. Estimating intraclonal heterogeneity and subpopulation changes from bulk expression profiles in CMap. Life Sci Alliance 2022; 5:5/10/e202101299. [PMID: 35688486 PMCID: PMC9187873 DOI: 10.26508/lsa.202101299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 11/24/2022] Open
Abstract
Premnas is a computational framework that provides a new perspective to interpret perturbational data in LINC L1000 CMap by learning an ad hoc subpopulation representation from scRNA-seq and performing the digital cytometry to estimate the abundance of undetermined subpopulations. The connectivity among signatures upon perturbations curated in the CMap library provides a valuable resource for understanding therapeutic pathways and biological processes associated with the drugs and diseases. However, because of the nature of bulk-level expression profiling by the L1000 assay, intraclonal heterogeneity and subpopulation compositional change that could contribute to the responses to perturbations are largely neglected, hampering the interpretability and reproducibility of the connections. In this work, we proposed a computational framework, Premnas, to estimate the abundance of undetermined subpopulations from L1000 profiles in CMap directly according to an ad hoc subpopulation representation learned from a well-normalized batch of single-cell RNA-seq datasets by the archetypal analysis. By recovering the information of subpopulation changes upon perturbation, the potentials of drug-resistant/susceptible subpopulations with CMap L1000 were further explored and examined. The proposed framework enables a new perspective to understand the connectivity among cellular signatures and expands the scope of the CMAP and other similar perturbation datasets limited by the bulk profiling technology.
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Affiliation(s)
- Chiao-Yu Hsieh
- Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Ching-Chih Tu
- Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Jui-Hung Hung
- Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Lung Cancer. Int J Mol Sci 2022; 23:ijms231810933. [PMID: 36142846 PMCID: PMC9504879 DOI: 10.3390/ijms231810933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 09/13/2022] [Indexed: 11/24/2022] Open
Abstract
Despite the significant progress made towards comprehending the deregulated signatures in lung cancer, these vary from study to study. We reanalyzed 25 studies from the Gene Expression Omnibus (GEO) to detect and annotate co-deregulated signatures in lung cancer and in single-gene or single-drug perturbation experiments. We aimed to decipher the networks that these co-deregulated genes (co-DEGs) form along with their upstream regulators. Differential expression and upstream regulators were computed using Characteristic Direction and Systems Biology tools, including GEO2Enrichr and X2K. Co-deregulated gene expression profiles were further validated across different molecular and immune subtypes in lung adenocarcinoma (TCGA-LUAD) and lung adenocarcinoma (TCGA-LUSC) datasets, as well as using immunohistochemistry data from the Human Protein Atlas, before being subjected to subsequent GO and KEGG enrichment analysis. The functional alterations of the co-upregulated genes in lung cancer were mostly related to immune response regulating the cell surface signaling pathway, in contrast to the co-downregulated genes, which were related to S-nitrosylation. Networks of hub proteins across the co-DEGs consisted of overlapping TFs (SOX2, MYC, KAT2A) and kinases (MAPK14, CSNK2A1 and CDKs). Furthermore, using Connectivity Map we highlighted putative repurposing drugs, including valproic acid, betonicine and astemizole. Similarly, we analyzed the co-DEG signatures in single-gene and single-drug perturbation experiments in lung cancer cell lines. In summary, we identified critical co-DEGs in lung cancer providing an innovative framework for their potential use in developing personalized therapeutic strategies.
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21
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Zeng Y, Cai Y, Chai P, Mao Y, Chen Y, Wang L, Zeng K, Zhan Z, Xie Y, Li C, Zhan H, Zhao L, Chen X, Zhu X, Liu Y, Chen M, Song Y, Zhou A. Optimization of cancer immunotherapy through pyroptosis: A pyroptosis-related signature predicts survival benefit and potential synergy for immunotherapy in glioma. Front Immunol 2022; 13:961933. [PMID: 35990696 PMCID: PMC9382657 DOI: 10.3389/fimmu.2022.961933] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 07/11/2022] [Indexed: 12/03/2022] Open
Abstract
Background Pyroptosis is a critical type of programmed cell death that is strongly associated with the regulation of tumor and immune cell functions. However, the role of pyroptosis in tumor progression and remodeling of the tumor microenvironment in gliomas has not been extensively studied. Thus, in this study, we aimed to establish a comprehensive pyroptosis-related signature and uncover its potential clinical application in gliomas. Methods The TCGA glioma cohort was obtained and divided into training and internal validation cohorts, while the CGGA glioma cohort was used as an external validation cohort. Unsupervised consensus clustering was performed to identify pyroptosis-related expression patterns. A Cox regression analysis was performed to establish a pyroptosis-related risk signature. Real-time quantitative PCR was performed to analyze the expression of signature genes in glioma tissues. Immune infiltration was analyzed and validated by immunohistochemical staining. The expression patterns of signature genes in different cell types were analyzed using single-cell RNA sequencing data. Finally, therapeutic responses to chemotherapy, immunotherapy, and potential small-molecule inhibitors were investigated. Results Patients with glioma were stratified into clusters 1 and 2 based on the expression patterns of pyroptosis-related genes. Cluster 2 showed a longer overall (P<0.001) and progression-free survival time (P<0.001) than Cluster 1. CD8+ T cell enrichment was observed in Cluster 1. A pyroptosis-related risk signature (PRRS) was then established. The high PRRS group showed a significantly poorer prognosis than the low PRRS group in the training cohort (P<0.001), with validation in the internal and external validation cohorts. Immunohistochemical staining demonstrated that CD8+ T cells were enriched in high PRRS glioma tissues. PRRS genes also showed cell-specific expression in tumor and immune cells. Moreover, the high PRRS risk group showed higher temozolomide sensitivity and increased response to anti-PD1 treatment in a glioblastoma immunotherapy cohort. Finally, Bcl-2 inhibitors were screened as candidates for adjunct immunotherapy of gliomas. Conclusion The pyroptosis-related signature established in this study can be used to reliably predict clinical outcomes and immunotherapy responses in glioma patients. The correlation between the pyroptosis signature and the tumor immune microenvironment may be used to further guide the sensitization of glioma patients to immunotherapy.
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Affiliation(s)
- Yu Zeng
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Yonghua Cai
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Peng Chai
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yangqi Mao
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanwen Chen
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Li Wang
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Kunlin Zeng
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Ziling Zhan
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Yuxin Xie
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Cuiying Li
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Hongchao Zhan
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Liqian Zhao
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaoxia Chen
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Xiaoxia Zhu
- Department of Radiation Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yu Liu
- Department of Neurosurgery, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Chen
- Department of Neurosurgery, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Aidong Zhou, ; Ye Song, ; Ming Chen,
| | - Ye Song
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Neurosurgery, Ganzhou People’s Hospital, Ganzhou, China
- *Correspondence: Aidong Zhou, ; Ye Song, ; Ming Chen,
| | - Aidong Zhou
- Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
- Department of Radiation Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, Guangzhou, China
- *Correspondence: Aidong Zhou, ; Ye Song, ; Ming Chen,
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22
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Khunsriraksakul C, McGuire D, Sauteraud R, Chen F, Yang L, Wang L, Hughey J, Eckert S, Dylan Weissenkampen J, Shenoy G, Marx O, Carrel L, Jiang B, Liu DJ. Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies. Nat Commun 2022; 13:3258. [PMID: 35672318 PMCID: PMC9171100 DOI: 10.1038/s41467-022-30956-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 05/25/2022] [Indexed: 02/08/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) are popular approaches to test for association between imputed gene expression levels and traits of interest. Here, we propose an integrative method PUMICE (Prediction Using Models Informed by Chromatin conformations and Epigenomics) to integrate 3D genomic and epigenomic data with expression quantitative trait loci (eQTL) to more accurately predict gene expressions. PUMICE helps define and prioritize regions that harbor cis-regulatory variants, which outperforms competing methods. We further describe an extension to our method PUMICE +, which jointly combines TWAS results from single- and multi-tissue models. Across 79 traits, PUMICE + identifies 22% more independent novel genes and increases median chi-square statistics values at known loci by 35% compared to the second-best method, as well as achieves the narrowest credible interval size. Lastly, we perform computational drug repurposing and confirm that PUMICE + outperforms other TWAS methods.
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Affiliation(s)
- Chachrit Khunsriraksakul
- grid.29857.310000 0001 2097 4281Bioinformatics and Genomics Graduate Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Daniel McGuire
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Renan Sauteraud
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Fang Chen
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Lina Yang
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Lida Wang
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Jordan Hughey
- grid.29857.310000 0001 2097 4281Bioinformatics and Genomics Graduate Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Scott Eckert
- grid.29857.310000 0001 2097 4281Bioinformatics and Genomics Graduate Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - J. Dylan Weissenkampen
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Ganesh Shenoy
- grid.29857.310000 0001 2097 4281Department of Neurosurgery, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Olivia Marx
- grid.29857.310000 0001 2097 4281Biomedical Science Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Laura Carrel
- grid.29857.310000 0001 2097 4281Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Bibo Jiang
- grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Dajiang J. Liu
- grid.29857.310000 0001 2097 4281Bioinformatics and Genomics Graduate Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
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Zhang Y, Heylen L, Partoens M, Mills JD, Kaminski RM, Godard P, Gillard M, de Witte PAM, Siekierska A. Connectivity Mapping Using a Novel sv2a Loss-of-Function Zebrafish Epilepsy Model as a Powerful Strategy for Anti-epileptic Drug Discovery. Front Mol Neurosci 2022; 15:881933. [PMID: 35686059 PMCID: PMC9172968 DOI: 10.3389/fnmol.2022.881933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/08/2022] [Indexed: 12/03/2022] Open
Abstract
Synaptic vesicle glycoprotein 2A (SV2A) regulates action potential-dependent neurotransmitter release and is commonly known as the primary binding site of an approved anti-epileptic drug, levetiracetam. Although several rodent knockout models have demonstrated the importance of SV2A for functional neurotransmission, its precise physiological function and role in epilepsy pathophysiology remains to be elucidated. Here, we present a novel sv2a knockout model in zebrafish, a vertebrate with complementary advantages to rodents. We demonstrated that 6 days post fertilization homozygous sv2a–/– mutant zebrafish larvae, but not sv2a+/– and sv2a+/+ larvae, displayed locomotor hyperactivity and spontaneous epileptiform discharges, however, no major brain malformations could be observed. A partial rescue of this epileptiform brain activity could be observed after treatment with two commonly used anti-epileptic drugs, valproic acid and, surprisingly, levetiracetam. This observation indicated that additional targets, besides Sv2a, maybe are involved in the protective effects of levetiracetam against epileptic seizures. Furthermore, a transcriptome analysis provided insights into the neuropathological processes underlying the observed epileptic phenotype. While gene expression profiling revealed only one differentially expressed gene (DEG) between wildtype and sv2a+/– larvae, there were 4386 and 3535 DEGs between wildtype and sv2a–/–, and sv2a+/– and sv2a–/– larvae, respectively. Pathway and gene ontology (GO) enrichment analysis between wildtype and sv2a–/– larvae revealed several pathways and GO terms enriched amongst up- and down-regulated genes, including MAPK signaling, synaptic vesicle cycle, and extracellular matrix organization, all known to be involved in epileptogenesis and epilepsy. Importantly, we used the Connectivity map database to identify compounds with opposing gene signatures compared to the one observed in sv2a–/– larvae, to finally rescue the epileptic phenotype. Two out of three selected compounds rescued electrographic discharges in sv2a–/– larvae, while negative controls did not. Taken together, our results demonstrate that sv2a deficiency leads to increased seizure vulnerability and provide valuable insight into the functional importance of sv2a in the brain in general. Furthermore, we provided evidence that the concept of connectivity mapping represents an attractive and powerful approach in the discovery of novel compounds against epilepsy.
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Affiliation(s)
- Yifan Zhang
- Laboratory for Molecular Biodiscovery, KU Leuven, Leuven, Belgium
| | - Lise Heylen
- Laboratory for Molecular Biodiscovery, KU Leuven, Leuven, Belgium
| | - Michèle Partoens
- Laboratory for Molecular Biodiscovery, KU Leuven, Leuven, Belgium
| | - James D. Mills
- Department of Neuropathology, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
- Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom
| | - Rafal M. Kaminski
- Department of Medicinal Chemistry, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland
- UCB Pharma, Braine-l’Alleud, Belgium
| | | | | | - Peter A. M. de Witte
- Laboratory for Molecular Biodiscovery, KU Leuven, Leuven, Belgium
- *Correspondence: Peter A. M. de Witte,
| | - Aleksandra Siekierska
- Laboratory for Molecular Biodiscovery, KU Leuven, Leuven, Belgium
- Aleksandra Siekierska,
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24
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Asano T, Chelvanambi S, Decano JL, Whelan MC, Aikawa E, Aikawa M. In silico Drug Screening Approach Using L1000-Based Connectivity Map and Its Application to COVID-19. Front Cardiovasc Med 2022; 9:842641. [PMID: 35402570 PMCID: PMC8989014 DOI: 10.3389/fcvm.2022.842641] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/14/2022] [Indexed: 01/25/2023] Open
Abstract
Conventional drug screening methods search for a limited number of small molecules that directly interact with the target protein. This process can be slow, cumbersome and has driven the need for developing new drug screening approaches to counter rapidly emerging diseases such as COVID-19. We propose a pipeline for drug repurposing combining in silico drug candidate identification followed by in vitro characterization of these candidates. We first identified a gene target of interest, the entry receptor for the SARS-CoV-2 virus, angiotensin converting enzyme 2 (ACE2). Next, we employed a gene expression profile database, L1000-based Connectivity Map to query gene expression patterns in lung epithelial cells, which act as the primary site of SARS-CoV-2 infection. Using gene expression profiles from 5 different lung epithelial cell lines, we computationally identified 17 small molecules that were predicted to decrease ACE2 expression. We further performed a streamlined validation in the normal human epithelial cell line BEAS-2B to demonstrate that these compounds can indeed decrease ACE2 surface expression and to profile cell health and viability upon drug treatment. This proposed pipeline combining in silico drug compound identification and in vitro expression and viability characterization in relevant cell types can aid in the repurposing of FDA-approved drugs to combat rapidly emerging diseases.
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Affiliation(s)
- Takaharu Asano
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Sarvesh Chelvanambi
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Julius L. Decano
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Mary C. Whelan
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Department of Human Pathology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health, Moscow, Russia
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Department of Human Pathology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health, Moscow, Russia
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
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25
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Computational Methods for Drug Repurposing. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:119-141. [PMID: 35230686 DOI: 10.1007/978-3-030-91836-1_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.
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26
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Li C, Tian C, Zeng Y, Liang J, Yang Q, Gu F, Hu Y, Liu L. Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma. Front Cell Dev Biol 2022; 10:769711. [PMID: 35211471 PMCID: PMC8861524 DOI: 10.3389/fcell.2022.769711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/19/2022] [Indexed: 12/24/2022] Open
Abstract
Lung adenocarcinoma (LUAD) is a frequently diagnosed cancer type, and many patients have already reached an advanced stage when diagnosed. Thus, it is crucial to develop a novel and efficient approach to diagnose and classify lung adenocarcinoma at an early stage. In our study, we combined in silico analysis and machine learning to develop a new five-gene–based diagnosis strategy, which was further verified in independent cohorts and in vitro experiments. Considering the heterogeneity in cancer, we used the MATH (mutant-allele tumor heterogeneity) algorithm to divide patients with early-stage LUAD into two groups (C1 and C2). Specifically, patients in C2 had lower intratumor heterogeneity and higher abundance of immune cells (including B cell, CD4 T cell, CD8 T cell, macrophage, dendritic cell, and neutrophil). In addition, patients in C2 had a higher likelihood of immunotherapy response and overall survival advantage than patients in C1. Combined drug sensitivity analysis (CTRP/PRISM/CMap/GDSC) revealed that BI-2536 might serve as a new therapeutic compound for patients in C1. In order to realize the application value of our study, we constructed the classifier (to classify early-stage LUAD patients into C1 or C2 groups) with multiple machine learning and bioinformatic analyses. The 21-gene–based classification model showed high accuracy and strong generalization ability, and it was verified in four independent validation cohorts. In summary, our research provided a new strategy for clinicians to make a quick preliminary assisting diagnosis of early-stage LUAD and make patient classification at the intratumor heterogeneity level. All data, codes, and study processes have been deposited to Github and are available online.
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Affiliation(s)
- Chang Li
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chen Tian
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yulan Zeng
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinyan Liang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qifan Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feifei Gu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yue Hu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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27
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Liao G, Yang Y, Xie A, Jiang Z, Liao J, Yan M, Zhou Y, Zhu J, Hu J, Zhang Y, Xiao Y, Li X. Applicability of Anticancer Drugs for the Triple-Negative Breast Cancer Based on Homologous Recombination Repair Deficiency. Front Cell Dev Biol 2022; 10:845950. [PMID: 35281113 PMCID: PMC8913497 DOI: 10.3389/fcell.2022.845950] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/14/2022] [Indexed: 12/14/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is a highly aggressive disease with historically poor outcomes, primarily due to the lack of effective targeted therapies. Here, we established a drug sensitivity prediction model based on the homologous recombination deficiency (HRD) using 83 TNBC patients from TCGA. Through analyzing the effect of HRD status on response efficacy of anticancer drugs and elucidating its related mechanisms of action, we found rucaparib (PARP inhibitor) and doxorubicin (anthracycline) sensitive in HR-deficient patients, while paclitaxel sensitive in the HR-proficient. Further, we identified a HRD signature based on gene expression data and constructed a transcriptomic HRD score, for analyzing the functional association between anticancer drug perturbation and HRD. The results revealed that CHIR99021 (GSK3 inhibitor) and doxorubicin have similar expression perturbation patterns with HRD, and talazoparib (PARP inhibitor) could kill tumor cells by reversing the HRD activity. Genomic characteristics indicated that doxorubicin inhibited tumor cells growth by hindering the process of DNA damage repair, while the resistance of cisplatin was related to the activation of angiogenesis and epithelial-mesenchymal transition. The negative correlation of HRD signature score could interpret the association of doxorubicin pIC50 with worse chemotherapy response and shorter survival of TNBC patients. In summary, these findings explain the applicability of anticancer drugs in TNBC and underscore the importance of HRD in promoting personalized treatment development.
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Affiliation(s)
- Gaoming Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yiran Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Aimin Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zedong Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jianlong Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Min Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yao Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jiali Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jing Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- *Correspondence: Yunpeng Zhang, ; Yun Xiao, ; Xia Li,
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, China
- *Correspondence: Yunpeng Zhang, ; Yun Xiao, ; Xia Li,
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, China
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China
- *Correspondence: Yunpeng Zhang, ; Yun Xiao, ; Xia Li,
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28
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Yang C, Zhang H, Chen M, Wang S, Qian R, Zhang L, Huang X, Wang J, Liu Z, Qin W, Wang C, Hang H, Wang H. A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer. eLife 2022; 11:71880. [PMID: 35191375 PMCID: PMC8893721 DOI: 10.7554/elife.71880] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 02/16/2022] [Indexed: 12/24/2022] Open
Abstract
Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL4)-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.
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Affiliation(s)
- Chen Yang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Hailin Zhang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Mengnuo Chen
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Siying Wang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Ruolan Qian
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Linmeng Zhang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaowen Huang
- Division of Gastroenterology and Hepatology, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Wang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Zhicheng Liu
- Hepatic Surgery Center, Huazhong University of Science and Technology, Wuhan, China
| | - Wenxin Qin
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Cun Wang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Hualian Hang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Wang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
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Trapotsi MA, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data, methods and integration. RSC Chem Biol 2022; 3:170-200. [PMID: 35360890 PMCID: PMC8827085 DOI: 10.1039/d1cb00069a] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022] Open
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
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Affiliation(s)
- Maria-Anna Trapotsi
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
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30
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Transcription Factor Activation Profiles (TFAP) identify compounds promoting differentiation of Acute Myeloid Leukemia cell lines. Cell Death Dis 2022; 8:16. [PMID: 35013135 PMCID: PMC8748454 DOI: 10.1038/s41420-021-00811-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/22/2021] [Accepted: 12/13/2021] [Indexed: 11/26/2022]
Abstract
Repurposing of drugs for new therapeutic use has received considerable attention for its potential to limit time and cost of drug development. Here we present a new strategy to identify chemicals that are likely to promote a desired phenotype. We used data from the Connectivity Map (CMap) to produce a ranked list of drugs according to their potential to activate transcription factors that mediate myeloid differentiation of leukemic progenitor cells. To validate our strategy, we tested the in vitro differentiation potential of candidate compounds using the HL-60 human cell line as a myeloid differentiation model. Ten out of 22 compounds, which were ranked high in the inferred list, were confirmed to promote significant differentiation of HL-60. These compounds may be considered candidate for differentiation therapy. The method that we have developed is versatile and it can be adapted to different drug repurposing projects.
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31
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Dai D, Guo Y, Shui Y, Li J, Jiang B, Wei Q. Combination of Radiosensitivity Gene Signature and PD-L1 Status Predicts Clinical Outcome of Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma: A Study Based on The Cancer Genome Atlas Dataset. Front Mol Biosci 2022; 8:775562. [PMID: 34970597 PMCID: PMC8712874 DOI: 10.3389/fmolb.2021.775562] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/22/2021] [Indexed: 12/24/2022] Open
Abstract
Aim: The aim of our study was to investigate the potential predictive value of the combination of radiosensitivity gene signature and PD-L1 expression for the prognosis of locally advanced head and neck squamous cell carcinoma (HNSCC). Methods: The cohort was selected from The Cancer Genome Atlas (TCGA) and classified into the radiosensitive (RS) group and radioresistant (RR) group by a radiosensitivity-related gene signature. The cohort was also grouped as PD-L1-high or PD-L1-low based on PD-L1 mRNA expression. The least absolute shrinkage and selection operator (lasso)-based Cox model was used to select hub survival genes. An independent validation cohort was obtained from the Gene Expression Omnibus (GEO) database. Results: We selected 288 locally advanced HNSCC patients from TCGA. The Kaplan–Meier method found that the RR and PD-L1-high group had a worse survival than others (p = 0.033). The differentially expressed gene (DEG) analysis identified 553 upregulated genes and 486 downregulated genes (p < 0.05, fold change >2) between the RR and PD-L1-high group and others. The univariate Cox analysis of each DEG and subsequent lasso-based Cox model revealed five hub survival genes (POU4F1, IL34, HLF, CBS, and RNF165). A further hub survival gene-based risk score model was constructed, which was validated by an external cohort. We observed that a higher risk score predicted a worse prognosis (p = 0.0013). The area under the receiver operating characteristic curve (AUC) plots showed that this risk score model had good prediction value (1-year AUC = 0.684, 2-year AUC = 0.702, and 3-year AUC = 0.688). Five different deconvolution methods all showed that the B cells were lower in the RR and PD-L1-high group (p < 0.05). Finally, connectivity mapping analysis showed that the histone deacetylase (HDAC) inhibitor trichostatin A might have the potential to reverse the phenotype of RR and PD-L1-high in locally advanced HNSCC (p < 0.05, false discovery rate <0.1). Conclusion: The combination of 31-gene signature and the PD-L1 mRNA expression had a potential predictive value for the prognosis of locally advanced HNSCC who had RT. The B cells were lower in the RR and PD-L1-high group. The identified risk gene signature of locally advanced HNSCC and the potential therapeutic drug trichostatin A for the RR and PD-L1-high group are worth being further studied in a prospective homogenous cohort.
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Affiliation(s)
- Dongjun Dai
- Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yinglu Guo
- Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yongjie Shui
- Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jinfan Li
- Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Biao Jiang
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qichun Wei
- Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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32
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Cakir A, Tuncer M, Taymaz-Nikerel H, Ulucan O. Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection. THE PHARMACOGENOMICS JOURNAL 2021; 21:673-681. [PMID: 34155353 DOI: 10.1038/s41397-021-00246-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 05/28/2021] [Accepted: 06/10/2021] [Indexed: 02/06/2023]
Abstract
One in every ten drug candidates fail in clinical trials mainly due to efficacy and safety related issues, despite in-depth preclinical testing. Even some of the approved drugs such as chemotherapeutics are notorious for their side effects that are burdensome on patients. In order to pave the way for new therapeutics with more tolerable side effects, the mechanisms underlying side effects need to be fully elucidated. In this work, we addressed the common side effects of chemotherapeutics, namely alopecia, diarrhea and edema. A strategy based on Random Forest algorithm unveiled an expression signature involving 40 genes that predicted these side effects with an accuracy of 89%. We further characterized the resulting signature and its association with the side effects using functional enrichment analysis and protein-protein interaction networks. This work contributes to the ongoing efforts in drug development for early identification of side effects to use the resources more effectively.
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Affiliation(s)
- Arzu Cakir
- Department of Genetics and Bioengineering, Istanbul Bilgi University, Istanbul, Eyupsultan, Turkey
| | - Melisa Tuncer
- Department of Genetics and Bioengineering, Istanbul Bilgi University, Istanbul, Eyupsultan, Turkey
| | - Hilal Taymaz-Nikerel
- Department of Genetics and Bioengineering, Istanbul Bilgi University, Istanbul, Eyupsultan, Turkey
| | - Ozlem Ulucan
- Department of Genetics and Bioengineering, Istanbul Bilgi University, Istanbul, Eyupsultan, Turkey.
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33
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Arora C, Kaur D, Naorem LD, Raghava GPS. Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway. PLoS One 2021; 16:e0259534. [PMID: 34767591 PMCID: PMC8589158 DOI: 10.1371/journal.pone.0259534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 10/20/2021] [Indexed: 12/12/2022] Open
Abstract
Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10-4, C = 0.84, logrank-p = 3.8x10-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10−4 (C = 0.88, logrank-p = 1.44x10-9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.
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Affiliation(s)
- Chakit Arora
- Indraprastha Institute of Information Technology-Delhi, Department of Computational Biology, New Delhi, India
| | - Dilraj Kaur
- Indraprastha Institute of Information Technology-Delhi, Department of Computational Biology, New Delhi, India
| | - Leimarembi Devi Naorem
- Indraprastha Institute of Information Technology-Delhi, Department of Computational Biology, New Delhi, India
| | - Gajendra P. S. Raghava
- Indraprastha Institute of Information Technology-Delhi, Department of Computational Biology, New Delhi, India
- * E-mail:
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34
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Drug target inference by mining transcriptional data using a novel graph convolutional network framework. Protein Cell 2021; 13:281-301. [PMID: 34677780 PMCID: PMC8532448 DOI: 10.1007/s13238-021-00885-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 09/08/2021] [Indexed: 12/14/2022] Open
Abstract
A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
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35
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Investigating Pathogenetic Mechanisms of Alzheimer's Disease by Systems Biology Approaches for Drug Discovery. Int J Mol Sci 2021; 22:ijms222011280. [PMID: 34681938 PMCID: PMC8540696 DOI: 10.3390/ijms222011280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 12/29/2022] Open
Abstract
Alzheimer's disease (AD) is the most common cause of dementia, characterized by progressive cognitive decline and neurodegenerative disorder. Abnormal aggregations of intracellular neurofibrillary tangles (NFTs) and unusual accumulations of extracellular amyloid-β (Aβ) peptides are two important pathological features in AD brains. However, in spite of large-scale clinical studies and computational simulations, the molecular mechanisms of AD development and progression are still unclear. In this study, we divided all of the samples into two groups: early stage (Braak score I-III) and later stage (Braak score IV-VI). By big database mining, the candidate genetic and epigenetic networks (GEN) have been constructed. In order to find out the real GENs for two stages of AD, we performed systems identification and system order detection scheme to prune false positives with the help of corresponding microarray data. Applying the principal network projection (PNP) method, core GENs were extracted from real GENs based on the projection values. By the annotation of KEGG pathway, we could obtain core pathways from core GENs and investigate pathogenetic mechanisms for the early and later stage of AD, respectively. Consequently, according to pathogenetic mechanisms, several potential biomarkers are identified as drug targets for multiple-molecule drug design in the treatment of AD.
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36
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Reay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet 2021; 22:658-671. [PMID: 34302145 DOI: 10.1038/s41576-021-00387-z] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 02/07/2023]
Abstract
Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.
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Affiliation(s)
- William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia. .,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, New South Wales, Australia.
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37
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Taukulis IA, Olszewski RT, Korrapati S, Fernandez KA, Boger ET, Fitzgerald TS, Morell RJ, Cunningham LL, Hoa M. Single-Cell RNA-Seq of Cisplatin-Treated Adult Stria Vascularis Identifies Cell Type-Specific Regulatory Networks and Novel Therapeutic Gene Targets. Front Mol Neurosci 2021; 14:718241. [PMID: 34566577 PMCID: PMC8458580 DOI: 10.3389/fnmol.2021.718241] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/17/2021] [Indexed: 11/21/2022] Open
Abstract
The endocochlear potential (EP) generated by the stria vascularis (SV) is necessary for hair cell mechanotransduction in the mammalian cochlea. We sought to create a model of EP dysfunction for the purposes of transcriptional analysis and treatment testing. By administering a single dose of cisplatin, a commonly prescribed cancer treatment drug with ototoxic side effects, to the adult mouse, we acutely disrupt EP generation. By combining these data with single cell RNA-sequencing findings, we identify transcriptional changes induced by cisplatin exposure, and by extension transcriptional changes accompanying EP reduction, in the major cell types of the SV. We use these data to identify gene regulatory networks unique to cisplatin treated SV, as well as the differentially expressed and druggable gene targets within those networks. Our results reconstruct transcriptional responses that occur in gene expression on the cellular level while identifying possible targets for interventions not only in cisplatin ototoxicity but also in EP dysfunction.
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Affiliation(s)
- Ian A. Taukulis
- Auditory Development and Restoration Program, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - Rafal T. Olszewski
- Auditory Development and Restoration Program, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - Soumya Korrapati
- Auditory Development and Restoration Program, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - Katharine A. Fernandez
- Laboratory of Hearing Biology and Therapeutics, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - Erich T. Boger
- Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - Tracy S. Fitzgerald
- Mouse Auditory Testing Core Facility, National Institutes of Health, Bethesda, MD, United States
| | - Robert J. Morell
- Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - Lisa L. Cunningham
- Laboratory of Hearing Biology and Therapeutics, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - Michael Hoa
- Auditory Development and Restoration Program, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
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38
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Evaluation of connectivity map shows limited reproducibility in drug repositioning. Sci Rep 2021; 11:17624. [PMID: 34475469 PMCID: PMC8413422 DOI: 10.1038/s41598-021-97005-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 08/19/2021] [Indexed: 12/29/2022] Open
Abstract
The Connectivity Map (CMap) is a popular resource designed for data-driven drug repositioning using a large transcriptomic compendium. However, evaluations of its performance are limited. We used two iterations of CMap (CMap 1 and 2) to assess their comparability and reliability. We queried CMap 2 with CMap 1-derived signatures, expecting CMap 2 would highly prioritize the queried compounds; the success rate was 17%. Analysis of previously published prioritizations yielded similar results. Low recall is caused by low differential expression (DE) reproducibility both between CMaps and within each CMap. DE strength was predictive of reproducibility, and is influenced by compound concentration and cell-line responsiveness. Reproducibility of CMap 2 sample expression levels was also lower than expected. We attempted to identify the "better" CMap by comparison with a third dataset, but they were mutually discordant. Our findings have implications for CMap usage and we suggest steps for investigators to limit false positives.
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39
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Lee F, Shah I, Soong YT, Xing J, Ng IC, Tasnim F, Yu H. Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment. Curr Res Toxicol 2021; 2:282-295. [PMID: 34467220 PMCID: PMC8384775 DOI: 10.1016/j.crtox.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/15/2021] [Accepted: 07/31/2021] [Indexed: 11/06/2022] Open
Abstract
TempO-Seq assays of rat hepatocytes in collagen sandwich are highly reproducible. Gene expression analysis shows S1500+ is representative of the whole transcriptome. Connectivity mapping shows consistency between TempO-Seq and Affymetrix data. Gene set enrichment shows consistency between S1500+ and the whole transcriptome. Gene set enrichment using hallmark gene sets informs hepatotoxicity.
Cell-based in vitro models coupled with high-throughput transcriptomics (HTTr) are increasingly utilized as alternative methods to animal-based toxicity testing. Here, using a panel of 14 chemicals with different risks of human drug-induced liver injury (DILI) and two dosing concentrations, we evaluated an HTTr platform comprised of collagen sandwich primary rat hepatocyte culture and the TempO-Seq surrogate S1500+ (ST) assay. First, the HTTr platform was found to exhibit high reproducibility between technical and biological replicates (r greater than 0.85). Connectivity mapping analysis further demonstrated a high level of inter-platform reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project. Second, the TempO-Seq ST assay was shown to be a robust surrogate to the whole transcriptome (WT) assay in capturing chemical-induced changes in gene expression, as evident from correlation analysis, PCA and unsupervised hierarchical clustering. Gene set enrichment analysis (GSEA) using the Hallmark gene set collection also demonstrated consistency in enrichment scores between ST and WT assays. Lastly, unsupervised hierarchical clustering of hallmark enrichment scores broadly divided the samples into hepatotoxic, intermediate, and non-hepatotoxic groups. Xenobiotic metabolism, bile acid metabolism, apoptosis, p53 pathway, and coagulation were found to be the key hallmarks driving the clustering. Taken together, our results established the reproducibility and performance of collagen sandwich culture in combination with TempO-Seq S1500+ assay, and demonstrated the utility of GSEA using the hallmark gene set collection to identify potential hepatotoxicants for further validation.
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Affiliation(s)
- Fan Lee
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Imran Shah
- Center for Computational Toxicology & Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, United States
| | - Yun Ting Soong
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Jiangwa Xing
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Inn Chuan Ng
- Department of Physiology and Mechanobiology Institute, National University of Singapore, Singapore
| | - Farah Tasnim
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Hanry Yu
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore.,Department of Physiology and Mechanobiology Institute, National University of Singapore, Singapore.,Critical Analytics for Manufacturing Personalized-Medicine, Singapore-MIT Alliance for Research and Technology, Singapore
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40
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The Complex Structure of the Pharmacological Drug-Disease Network. ENTROPY 2021; 23:e23091139. [PMID: 34573762 PMCID: PMC8466955 DOI: 10.3390/e23091139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 12/29/2022]
Abstract
The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. Here, we construct a bipartite graph in terms of active ingredients and diseases based on thoroughly classified data from a recognized pharmacological website. We find that the connectivities between drugs (outgoing links) and diseases (incoming links) follow approximately a stretched-exponential function with different fitting parameters; for drugs, it is between exponential and power law functions, while for diseases, the behavior is purely exponential. The network projections, onto either drugs or diseases, reveal that the co-ocurrence of drugs (diseases) in common target diseases (drugs) lead to the appearance of connected components, which varies as the threshold number of common target diseases (drugs) is increased. The corresponding projections built from randomized versions of the original bipartite networks are considered to evaluate the differences. The heterogeneity of association at group level between active ingredients and diseases is evaluated in terms of the Shannon entropy and algorithmic complexity, revealing that higher levels of diversity are present for diseases compared to drugs. Finally, the robustness of the original bipartite network is evaluated in terms of most-connected nodes removal (direct attack) and random removal (random failures).
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41
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Jang G, Park S, Lee S, Kim S, Park S, Kang J. Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding. Bioinformatics 2021; 37:i376-i382. [PMID: 34252937 PMCID: PMC8275331 DOI: 10.1093/bioinformatics/btab275] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2021] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by compounds. However, these approaches fail to reveal MoAs in the absence of actual compound signatures. RESULTS We present MoAble, which predicts MoAs without requiring compound signatures. We train a deep learning-based coembedding model to map compound signatures and compound structure into the same embedding space. The model generates low-dimensional compound signature representation from the compound structures. To predict MoAs, pathway enrichment analysis is performed based on the connectivity between embedding vectors of compounds and those of genetic perturbation. Results show that MoAble is comparable to the methods that use actual compound signatures. We demonstrate that MoAble can be used to reveal MoAs of novel compounds without measuring compound signatures with the same prediction accuracy as that with measuring them. AVAILABILITY AND IMPLEMENTATION MoAble is available at https://github.com/dmis-lab/moable. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gwanghoon Jang
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Sungjoon Park
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Sanghoon Lee
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Sunkyu Kim
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Sejeong Park
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea.,Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Republic of Korea
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42
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Kaur D, Arora C, Raghava GPS. Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer. Mol Diagn Ther 2021; 25:629-646. [PMID: 34155607 DOI: 10.1007/s40291-021-00539-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/27/2021] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs. OBJECTIVES The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature. METHODS This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes. RESULTS Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86. CONCLUSION We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.
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Affiliation(s)
- Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India
| | - Chakit Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India
| | - Gajendra Pal Singh Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India.
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Transcriptional drug repositioning and cheminformatics approach for differentiation therapy of leukaemia cells. Sci Rep 2021; 11:12537. [PMID: 34131166 PMCID: PMC8206077 DOI: 10.1038/s41598-021-91629-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 05/21/2021] [Indexed: 02/05/2023] Open
Abstract
Differentiation therapy is attracting increasing interest in cancer as it can be more specific than conventional chemotherapy approaches, and it has offered new treatment options for some cancer types, such as treating acute promyelocytic leukaemia (APL) by retinoic acid. However, there is a pressing need to identify additional molecules which act in this way, both in leukaemia and other cancer types. In this work, we hence developed a novel transcriptional drug repositioning approach, based on both bioinformatics and cheminformatics components, that enables selecting such compounds in a more informed manner. We have validated the approach for leukaemia cells, and retrospectively retinoic acid was successfully identified using our method. Prospectively, the anti-parasitic compound fenbendazole was tested in leukaemia cells, and we were able to show that it can induce the differentiation of leukaemia cells to granulocytes in low concentrations of 0.1 μM and within as short a time period as 3 days. This work hence provides a systematic and validated approach for identifying small molecules for differentiation therapy in cancer.
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44
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Le BL, Andreoletti G, Oskotsky T, Vallejo-Gracia A, Rosales R, Yu K, Kosti I, Leon KE, Bunis DG, Li C, Kumar GR, White KM, García-Sastre A, Ott M, Sirota M. Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19. Sci Rep 2021; 11:12310. [PMID: 34112877 PMCID: PMC8192542 DOI: 10.1038/s41598-021-91625-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/17/2021] [Indexed: 12/15/2022] Open
Abstract
The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated 16 of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.
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Affiliation(s)
- Brian L Le
- Department of Pediatrics, UCSF, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
| | - Gaia Andreoletti
- Department of Pediatrics, UCSF, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
| | - Tomiko Oskotsky
- Department of Pediatrics, UCSF, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
| | | | - Romel Rosales
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Katharine Yu
- Department of Pediatrics, UCSF, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, UCSF, San Francisco, CA, USA
| | - Idit Kosti
- Department of Pediatrics, UCSF, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
| | - Kristoffer E Leon
- Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA, USA
| | - Daniel G Bunis
- Department of Pediatrics, UCSF, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, UCSF, San Francisco, CA, USA
| | - Christine Li
- Department of Pediatrics, UCSF, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Shanghai American School, Shanghai, China
| | - G Renuka Kumar
- Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA, USA
| | - Kris M White
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Melanie Ott
- Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA, USA
- Department of Medicine, UCSF, San Francisco, CA, USA
| | - Marina Sirota
- Department of Pediatrics, UCSF, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.
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Uva P, Bosco MC, Eva A, Conte M, Garaventa A, Amoroso L, Cangelosi D. Connectivity Map Analysis Indicates PI3K/Akt/mTOR Inhibitors as Potential Anti-Hypoxia Drugs in Neuroblastoma. Cancers (Basel) 2021; 13:cancers13112809. [PMID: 34199959 PMCID: PMC8200206 DOI: 10.3390/cancers13112809] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/17/2021] [Accepted: 06/01/2021] [Indexed: 11/16/2022] Open
Abstract
Neuroblastoma (NB) is one of the deadliest pediatric cancers, accounting for 15% of deaths in childhood. Hypoxia is a condition of low oxygen tension occurring in solid tumors and has an unfavorable prognostic factor for NB. In the present study, we aimed to identify novel promising drugs for NB treatment. Connectivity Map (CMap), an online resource for drug repurposing, was used to identify connections between hypoxia-modulated genes in NB tumors and compounds. Two sets of 34 and 21 genes up- and down-regulated between hypoxic and normoxic primary NB tumors, respectively, were analyzed with CMap. The analysis reported a significant negative connectivity score across nine cell lines for 19 compounds mainly belonging to the class of PI3K/Akt/mTOR inhibitors. The gene expression profiles of NB cells cultured under hypoxic conditions and treated with the mTORC complex inhibitor PP242, referred to as the Mohlin dataset, was used to validate the CMap findings. A heat map representation of hypoxia-modulated genes in the Mohlin dataset and the gene set enrichment analysis (GSEA) showed an opposite regulation of these genes in the set of NB cells treated with the mTORC inhibitor PP242. In conclusion, our analysis identified inhibitors of the PI3K/Akt/mTOR signaling pathway as novel candidate compounds to treat NB patients with hypoxic tumors and a poor prognosis.
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Affiliation(s)
- Paolo Uva
- Clinical Bioinformatics Unit, Scientific Direction, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genova, Italy;
- Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy
| | - Maria Carla Bosco
- Laboratory of Molecular Biology, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genova, Italy; (M.C.B.); (A.E.)
| | - Alessandra Eva
- Laboratory of Molecular Biology, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genova, Italy; (M.C.B.); (A.E.)
| | - Massimo Conte
- UOC Oncologia, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genova, Italy; (M.C.); (A.G.); (L.A.)
| | - Alberto Garaventa
- UOC Oncologia, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genova, Italy; (M.C.); (A.G.); (L.A.)
| | - Loredana Amoroso
- UOC Oncologia, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genova, Italy; (M.C.); (A.G.); (L.A.)
| | - Davide Cangelosi
- Clinical Bioinformatics Unit, Scientific Direction, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genova, Italy;
- Correspondence:
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46
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Samart K, Tuyishime P, Krishnan A, Ravi J. Reconciling multiple connectivity scores for drug repurposing. Brief Bioinform 2021; 22:6278144. [PMID: 34013329 PMCID: PMC8597919 DOI: 10.1093/bib/bbab161] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022] Open
Abstract
The basis of several recent methods for drug repurposing is the key principle that an
efficacious drug will reverse the disease molecular ‘signature’ with minimal side effects.
This principle was defined and popularized by the influential ‘connectivity map’ study in
2006 regarding reversal relationships between disease- and drug-induced gene expression
profiles, quantified by a disease-drug ‘connectivity score.’ Over the past 15 years,
several studies have proposed variations in calculating connectivity scores toward
improving accuracy and robustness in light of massive growth in reference drug profiles.
However, these variations have been formulated inconsistently using various notations and
terminologies even though they are based on a common set of conceptual and statistical
ideas. Therefore, we present a systematic reconciliation of multiple disease-drug
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}{}$EMUDRA$\end{document}) by defining them using consistent
notation and terminology. In addition to providing clarity and deeper insights, this
coherent definition of connectivity scores and their relationships provides a unified
scheme that newer methods can adopt, enabling the computational drug-development community
to compare and investigate different approaches easily. To facilitate the continuous and
transparent integration of newer methods, this article will be available as a live
document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub
repository (https://github.com/jravilab/connectivity_scores) that any researcher can
build on and push changes to.
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Affiliation(s)
- Kewalin Samart
- Computational Mathematics, and Computational Math, Science & Engineering at Michigan State University, East Lansing, MI, USA
| | - Phoebe Tuyishime
- College of Agriculture and Natural Resources at Michigan State University, East Lansing, MI, USA
| | - Arjun Krishnan
- Departments of Computational Math, Science & Engineering, and Biochemistry & Molecular Biology at Michigan State University, East Lansing, MI, USA
| | - Janani Ravi
- Pathobiology and Diagnostic Investigation at Michigan State University, East Lansing, MI, USA
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Panayotis N, Freund PA, Marvaldi L, Shalit T, Brandis A, Mehlman T, Tsoory MM, Fainzilber M. β-sitosterol reduces anxiety and synergizes with established anxiolytic drugs in mice. Cell Rep Med 2021; 2:100281. [PMID: 34095883 PMCID: PMC8149471 DOI: 10.1016/j.xcrm.2021.100281] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/28/2021] [Accepted: 04/22/2021] [Indexed: 12/26/2022]
Abstract
Anxiety and stress-related conditions represent a significant health burden in modern society. Unfortunately, most anxiolytic drugs are prone to side effects, limiting their long-term usage. Here, we employ a bioinformatics screen to identify drugs for repurposing as anxiolytics. Comparison of drug-induced gene-expression profiles with the hippocampal transcriptome of an importin α5 mutant mouse model with reduced anxiety identifies the hypocholesterolemic agent β-sitosterol as a promising candidate. β-sitosterol activity is validated by both intraperitoneal and oral application in mice, revealing it as the only clear anxiolytic from five closely related phytosterols. β-sitosterol injection reduces the effects of restraint stress, contextual fear memory, and c-Fos activation in the prefrontal cortex and dentate gyrus. Moreover, synergistic anxiolysis is observed when combining sub-efficacious doses of β-sitosterol with the SSRI fluoxetine. These preclinical findings support further development of β-sitosterol, either as a standalone anxiolytic or in combination with low-dose SSRIs.
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Affiliation(s)
- Nicolas Panayotis
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Philip A. Freund
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Letizia Marvaldi
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Tali Shalit
- Ilana and Pascal Mantoux Institute for Bioinformatics, The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Alexander Brandis
- Life Science Core Facility, Weizmann Institute of Science, Rehovot, Israel
| | - Tevie Mehlman
- Life Science Core Facility, Weizmann Institute of Science, Rehovot, Israel
| | - Michael M. Tsoory
- Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Mike Fainzilber
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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48
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Rabben HL, Andersen GT, Ianevski A, Olsen MK, Kainov D, Grønbech JE, Wang TC, Chen D, Zhao CM. Computational Drug Repositioning and Experimental Validation of Ivermectin in Treatment of Gastric Cancer. Front Pharmacol 2021; 12:625991. [PMID: 33867984 PMCID: PMC8044519 DOI: 10.3389/fphar.2021.625991] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/10/2021] [Indexed: 12/11/2022] Open
Abstract
Objective: The aim of the present study was repositioning of ivermectin in treatment of gastric cancer (GC) by computational prediction based on gene expression profiles of human and mouse model of GC and validations with in silico, in vitro and in vivo approaches. Methods: Computational drug repositioning was performed using connectivity map (cMap) and data/pathway mining with the Ingenuity Knowledge Base. Tissue samples of GC were collected from 16 patients and 57 mice for gene expression profiling. Additional seven independent datasets of gene expression of human GC from the TCGA database were used for validation. In silico testing was performed by constructing interaction networks of ivermectin and the downstream effects in targeted signaling pathways. In vitro testing was carried out in human GC cell lines (MKN74 and KATO-III). In vivo testing was performed in a transgenic mouse model of GC (INS-GAS mice). Results: GC gene expression “signature” and data/pathway mining but not cMAP revealed nine molecular targets of ivermectin in both human and mouse GC associated with WNT/β-catenin signaling as well as cell proliferation pathways. In silico inhibition of the targets of ivermectin and concomitant activation of ivermectin led to the inhibition of WNT/β-catenin signaling pathway in “dose-depended” manner. In vitro, ivermectin inhibited cell proliferation in time- and concentration-depended manners, and cells were arrested in the G1 phase at IC50 and shifted to S phase arrest at >IC50. In vivo, ivermectin reduced the tumor size which was associated with inactivation of WNT/β-catenin signaling and cell proliferation pathways and activation of cell death signaling pathways. Conclusion: Ivermectin could be recognized as a repositioning candidate in treatment of gastric cancer.
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Affiliation(s)
- Hanne-Line Rabben
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,The Central Norway Regional Health Authority (RHA), Stjørdal, Norway
| | - Gøran Troseth Andersen
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Aleksandr Ianevski
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Magnus Kringstad Olsen
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Denis Kainov
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Jon Erik Grønbech
- Surgical Clinic, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Timothy Cragin Wang
- Division of Digestive and Liver Diseases, Columbia University College of Physicians and Surgeons, New York, NY, United States
| | - Duan Chen
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Chun-Mei Zhao
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,The Central Norway Regional Health Authority (RHA), Stjørdal, Norway
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49
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Le BL, Andreoletti G, Oskotsky T, Vallejo-Gracia A, Rosales R, Yu K, Kosti I, Leon KE, Bunis DG, Li C, Kumar GR, White KM, García-Sastre A, Ott M, Sirota M. Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19. RESEARCH SQUARE 2021:rs.3.rs-333578. [PMID: 33821262 PMCID: PMC8020993 DOI: 10.21203/rs.3.rs-333578/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated sixteen of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.
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Affiliation(s)
- Brian L. Le
- Department of Pediatrics, UCSF, SF, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, SF, CA, USA
| | - Gaia Andreoletti
- Department of Pediatrics, UCSF, SF, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, SF, CA, USA
| | - Tomiko Oskotsky
- Department of Pediatrics, UCSF, SF, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, SF, CA, USA
| | | | - Romel Rosales
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Katharine Yu
- Department of Pediatrics, UCSF, SF, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, SF, CA, USA
- Biomedical Sciences Graduate Program, UCSF, SF, CA, USA
| | - Idit Kosti
- Department of Pediatrics, UCSF, SF, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, SF, CA, USA
| | | | - Daniel G. Bunis
- Department of Pediatrics, UCSF, SF, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, SF, CA, USA
- Biomedical Sciences Graduate Program, UCSF, SF, CA, USA
| | - Christine Li
- Department of Pediatrics, UCSF, SF, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, SF, CA, USA
- Shanghai American School, Shanghai, China
| | - G. Renuka Kumar
- Gladstone Institute of Virology, Gladstone Institutes, SF, CA, USA
| | - Kris M. White
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Melanie Ott
- Gladstone Institute of Virology, Gladstone Institutes, SF, CA, USA
- Department of Medicine, UCSF, SF, CA, USA
| | - Marina Sirota
- Department of Pediatrics, UCSF, SF, CA, USA
- Bakar Computational Health Sciences Institute, UCSF, SF, CA, USA
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Adhami M, Sadeghi B, Rezapour A, Haghdoost AA, MotieGhader H. Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis. BMC Biotechnol 2021; 21:22. [PMID: 33711981 PMCID: PMC7952507 DOI: 10.1186/s12896-021-00680-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 02/24/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. RESULTS Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. CONCLUSIONS Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.
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Affiliation(s)
- Masoumeh Adhami
- Pathology and Stem Cell Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Balal Sadeghi
- Food Hygiene and Public Health Department, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Ali Rezapour
- Department of Agriculture, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Ali Akbar Haghdoost
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Habib MotieGhader
- Department of Basic sciences, Biotechnology Research Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
- Department of Computer Engineering, Gowgan Educational Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
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