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Bruedigam C, van Leeuwen JPTM, van de Peppel J. Gene Expression Analyses in Models of Rosiglitazone-Induced Physiological and Pathological Mineralization Identify Novel Targets to Improve Bone and Vascular Health. Cells 2023; 12:2462. [PMID: 37887306 PMCID: PMC10605243 DOI: 10.3390/cells12202462] [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: 09/04/2023] [Revised: 09/25/2023] [Accepted: 09/30/2023] [Indexed: 10/28/2023] Open
Abstract
Clinical studies revealed detrimental skeletal and vascular effects of the insulin sensitizer rosiglitazone. We have shown earlier that rosiglitazone accelerates osteoblast differentiation from human mesenchymal stem cells (hMSC) at the expense of increased oxidative stress and cell death. In calcifying human vascular cells, rosiglitazone stimulates pathological mineralization, an effect diminished by the antioxidant resveratrol. Here, we aimed to elucidate transcriptional networks underlying the rosiglitazone-enhanced mineralization phenotype. We performed genome-wide transcriptional profiling of osteogenic hMSCs treated with rosiglitazone for short-term periods of 1 up to 48 h during the first two days of differentiation, a phase that we show is sufficient for rosiglitazone stimulation of mineralization. Microarray-based mRNA expression analysis revealed 190 probes that were differently expressed in at least one condition compared to vehicle-treated control. This rosiglitazone gene signature contained well-known primary PPAR targets and was also endogenously regulated during osteogenic hMSC differentiation and osteoblast-like differentiation of vascular smooth muscle cells (VSMCs) into calcifying vascular cells (CVCs). Comparative analysis revealed rosiglitazone targets that were commonly enriched in osteoblasts and CVCs or specifically enriched in either osteoblasts or CVCs. Finally, we compared expression patterns of CVC-specific genes with patient expression data from carotid plaque versus intact adjacent tissue, and identified five rosiglitazone targets to be differentially regulated in CVCs and carotid plaque but not osteoblasts when compared to their non-mineralizing counterparts. These targets, i.e., PDK4, SDC4, SPRY4, TCF4 and DACT1, may specifically control extracellular matrix mineralization in vascular cells, and hence provide target candidates for further investigations to improve vascular health.
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Kusebauch U, Lorenzetti APR, Campbell DS, Pan M, Shteynberg D, Kapil C, Midha MK, López García de Lomana A, Baliga NS, Moritz RL. A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS. Sci Data 2023; 10:697. [PMID: 37833331 PMCID: PMC10575869 DOI: 10.1038/s41597-023-02590-5] [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: 06/23/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).
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Affiliation(s)
- Ulrike Kusebauch
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | | | - David S Campbell
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Min Pan
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - David Shteynberg
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Charu Kapil
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Mukul K Midha
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Adrián López García de Lomana
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
| | - Nitin S Baliga
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
- Departments of Biology and Microbiology, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Lawrence Berkeley National Lab, Berkeley, CA, USA
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA.
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3
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Cüvitoğlu A, Isik Z. Network neighborhood operates as a drug repositioning method for cancer treatment. PeerJ 2023; 11:e15624. [PMID: 37456868 PMCID: PMC10340098 DOI: 10.7717/peerj.15624] [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: 10/31/2022] [Accepted: 06/01/2023] [Indexed: 07/18/2023] Open
Abstract
Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.
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Affiliation(s)
- Ali Cüvitoğlu
- The Graduate School of Natural and Applied Sciences, Dokuz Eylül University, Izmir, Turkiye
| | - Zerrin Isik
- Computer Engineering Department, Engineering Faculty, Dokuz Eylül University, Izmir, Turkiye
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4
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Ünsal Ü, Cüvitoğlu A, Turhan K, Işık Z. NMSDR: Drug repurposing approach based on transcriptome data and network module similarity. Mol Inform 2023; 42:e2200077. [PMID: 36411244 DOI: 10.1002/minf.202200077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 09/19/2022] [Accepted: 11/21/2022] [Indexed: 11/23/2022]
Abstract
Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level. The overlap of the disease network and each compound-specific network is calculated based on a shortest-path similarity of networks by accounting for all protein pairs between networks. A higher similarity score indicates a significant potential of a compound. The approach was validated for breast and lung cancers. When all compounds are sorted by their normalized-similarity scores, 36 and 16 drugs are proposed as new candidates for breast and lung cancer treatment, respectively. A literature survey on candidate compounds revealed that some of our predictions have been clinically investigated in phase II/III trials for the treatment of two cancer types. As a summary, the proposed approach has provided promising initial results by modeling biochemical cell responses in a network-level data representation.
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Affiliation(s)
- Ülkü Ünsal
- Department of Biostatistics and Medical Informatics, Karadeniz Technical University, 61080, Trabzon, Türkiye.,Department of Health Management, Karadeniz Technical University, 61080, Trabzon, Türkiye
| | - Ali Cüvitoğlu
- Department of Computer Engineering, Dokuz Eylul University, 35390, İzmir, Türkiye
| | - Kemal Turhan
- Department of Biostatistics and Medical Informatics, Karadeniz Technical University, 61080, Trabzon, Türkiye
| | - Zerrin Işık
- Department of Computer Engineering, Dokuz Eylul University, 35390, İzmir, Türkiye
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5
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DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes. PLoS One 2022; 17:e0276253. [PMID: 36260602 PMCID: PMC9581374 DOI: 10.1371/journal.pone.0276253] [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: 03/16/2022] [Accepted: 10/03/2022] [Indexed: 11/19/2022] Open
Abstract
Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. DISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to evaluate patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against the overall space. Results confirm the possibility to soundly extend discriminative criteria towards numerical outcomes without the drawbacks well-associated with discretization procedures. Results from four case studies confirm the validity and relevance of the proposed methods, further unveiling critical directions for research on biotechnology and biomedicine. Availability: DISA is freely available at https://github.com/JupitersMight/DISA under the MIT license.
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Ding Q, Wu X, Li X, Wang H. Vorinostat Corrects Cognitive and Non-Cognitive Symptoms in a Mouse Model of Fragile X Syndrome. Int J Neuropsychopharmacol 2022; 25:147-159. [PMID: 34791268 PMCID: PMC8832232 DOI: 10.1093/ijnp/pyab081] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 11/04/2021] [Accepted: 11/12/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Fragile X syndrome (FXS) is caused by mutations in the FMR1 gene. It is a form of heritable intellectual disability and autism. Despite recent advance in elucidating disease mechanisms, there is no efficacious medication. Because de novo drug development is a lengthy process, repurposing the existing FDA-approved drugs offers an opportunity to advance clinical intervention for FXS. Our previous study with transcriptome analysis predicts potential therapeutic effects of vorinostat on FXS. METHODS We analyzed the vorinostat-induced transcriptome changes and confirmed its similarity to that induced by trifluoperazine, which was previously shown to correct pathological outcomes associated with FXS. To validate the therapeutic efficacy, we examined vorinostat's effect on correcting the key behavioral and cellular symptoms in a mouse model of FXS. RESULTS We found that vorinostat restores object location memory and passive avoidance memory in the Fmr1 knockout mice. For the non-cognitive behavioral symptoms, vorinostat corrected the autism-associated alterations, including repetitive behavior and social interaction deficits. In the open field test, vorinostat dampened hyperactivity in the center area of the arena. Surprisingly, vorinostat did not correct the abnormally elevated protein synthesis in cultured Fmr1 knockout hippocampal neurons, suggesting that different aspects of pathological outcomes may respond differently to a specific therapeutic intervention. CONCLUSIONS We used the drug-induced transcriptome signature to predict new application of existing drugs. Our data reveal the therapeutic effects of the FDA-approved drug vorinostat in a mouse model of FXS.
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Affiliation(s)
- Qi Ding
- Department of Physiology Michigan State University, East Lansing, Michigan, USA
| | - Xueting Wu
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Xuan Li
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Hongbing Wang
- Department of Physiology Michigan State University, East Lansing, Michigan, USA
- Neuroscience Program Michigan State University, East Lansing, Michigan, USA
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Yin Z, Wong STC. Artificial intelligence unifies knowledge and actions in drug repositioning. Emerg Top Life Sci 2021; 5:803-813. [PMID: 34881780 PMCID: PMC8923082 DOI: 10.1042/etls20210223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022]
Abstract
Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.
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Affiliation(s)
- Zheng Yin
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center and Ting Tsung & Wei Fong Chao Center for BRAIN, Houston Methodist Research Institute, Weill Cornell Medicine, Houston, TX 77030, U.S.A
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center and Ting Tsung & Wei Fong Chao Center for BRAIN, Houston Methodist Research Institute, Weill Cornell Medicine, Houston, TX 77030, U.S.A
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Chipofya M, Tayara H, Chong KT. Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks. Pharmaceutics 2021; 13:1906. [PMID: 34834320 PMCID: PMC8622176 DOI: 10.3390/pharmaceutics13111906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/29/2021] [Accepted: 11/06/2021] [Indexed: 11/30/2022] Open
Abstract
An important stage in the process of discovering new drugs is when candidate molecules are tested of their efficacy. It is reported that testing drug efficacy empirically costs billions of dollars in the drug discovery pipeline. As a mechanism of expediting this process, researchers have resorted to using computational methods to predict the action of molecules in silico. Here, we present a way of predicting the therapeutic-use class of drugs from chemical structures only using graph convolutional networks. In comparison with existing methods which use fingerprints or images as training samples, our approach has yielded better results in all metrics under consideration. In particular, validation accuracy increased from 83-88% to 86-90% for single label tasks. Similarly, the model achieved an accuracy of over 88% on new test data. Finally, our multi-label classification model made new predictions which indicated that some of the drugs could have other therapeutic uses other than those indicated in the dataset. We performed a literature-based evaluation of these predictions and found evidence that validates them. This renders the model a potential tool to be used in search of drugs that are candidates for repurposing.
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Affiliation(s)
- Mapopa Chipofya
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
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9
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Ruan Y, Chen XH, Jiang F, Liu YG, Liang XL, Lv BM, Zhang HY, Zhang QY. Agent Clustering Strategy Based on Metabolic Flux Distribution and Transcriptome Expression for Novel Drug Development. Biomedicines 2021; 9:biomedicines9111640. [PMID: 34829869 PMCID: PMC8615746 DOI: 10.3390/biomedicines9111640] [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: 10/12/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 11/16/2022] Open
Abstract
The network module-based method has been used for drug repositioning. The traditional drug repositioning method only uses the gene characteristics of the drug but ignores the drug-triggered metabolic changes. The metabolic network systematically characterizes the connection between genes, proteins, and metabolic reactions. The differential metabolic flux distribution, as drug metabolism characteristics, was employed to cluster the agents with similar MoAs (mechanism of action). In this study, agents with the same pharmacology were clustered into one group, and a total of 1309 agents from the CMap database were clustered into 98 groups based on differential metabolic flux distribution. Transcription factor (TF) enrichment analysis revealed the agents in the same group (such as group 7 and group 26) were confirmed to have similar MoAs. Through this agent clustering strategy, the candidate drugs which can inhibit (Japanese encephalitis virus) JEV infection were identified. This study provides new insights into drug repositioning and their MoAs.
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10
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Zinni M, Pansiot J, Léger PL, El Kamouh M, Baud O. Sildenafil-Mediated Neuroprotection from Adult to Neonatal Brain Injury: Evidence, Mechanisms, and Future Translation. Cells 2021; 10:cells10102766. [PMID: 34685745 PMCID: PMC8534574 DOI: 10.3390/cells10102766] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/28/2021] [Accepted: 10/07/2021] [Indexed: 12/12/2022] Open
Abstract
Cerebral stroke, traumatic brain injury, and hypoxic ischemic encephalopathy are among the most frequently occurring brain injuries. A complex pathogenesis, characterized by a synergistic interaction between alterations of the cerebrovascular system, cell death, and inflammation, is at the basis of the brain damage that leads to behavioral and neurodevelopmental disabilities in affected subjects. Sildenafil is a selective inhibitor of the enzyme phosphodiesterase 5 (PDE5) that is able to cross the blood-brain barrier. Preclinical data suggest that sildenafil may be a good candidate for the prevention or repair of brain injury in both adults and neonates. The aim of this review is to summarize the evidence supporting the neuroprotective action of sildenafil and discuss the possible benefits of the association of sildenafil with current therapeutic strategies.
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Affiliation(s)
- Manuela Zinni
- Inserm UMR1141 NeuroDiderot, Université de Paris, 75019 Paris, France; (M.Z.); (J.P.); (M.E.K.)
| | - Julien Pansiot
- Inserm UMR1141 NeuroDiderot, Université de Paris, 75019 Paris, France; (M.Z.); (J.P.); (M.E.K.)
| | - Pierre-Louis Léger
- Pediatric and Neonatal Intensive Care Unit, Armand-Trousseau University Hospital, Assistance Publique-Hôpitaux de Paris, Sorbonne University, 75019 Paris, France;
| | - Marina El Kamouh
- Inserm UMR1141 NeuroDiderot, Université de Paris, 75019 Paris, France; (M.Z.); (J.P.); (M.E.K.)
- Laboratoire de Physiologie et Génomique des Poissons-INRAE, 35700 Rennes, France
| | - Olivier Baud
- Laboratory of Child Growth and Development, University of Geneva, 1211 Geneva, Switzerland
- Division of Neonatology and Pediatric Intensive Care, Children’s University Hospital of Geneva, 1211 Geneva, Switzerland
- Correspondence: ; Tel.: +41-795-534-204
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Sharma A. Randomized trial drug controlled compendious transcriptome analysis supporting broad and phase specific therapeutic potential of multiple candidates in COVID-19. Cytokine 2021; 148:155719. [PMID: 34597919 PMCID: PMC8463310 DOI: 10.1016/j.cyto.2021.155719] [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: 04/30/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 12/15/2022]
Abstract
Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed. Maladaptive hyperinflammation and excessive cytokine release underlie the disease severity, with antiinflammatory and cytokine inhibiting agents expected to exert therapeutic effects. A major present challenge is identification of appropriate phase of the illness for a given intervention to yield optimum outcomes. Considering its established disease biomarker and drug discovery potential, a compendious analysis of existing transcriptomic data is presented here toward addressing this gap. The analysis is based on COVID-19 data related to intensive care unit (ICU) and non-ICU admissions, discharged and deceased patients, ventilation and non-ventilation phases, and high oxygen supplementation. It integrates transcriptomic data related to the effects of, in various cellular treatment models, the COVID-19 randomized clinical trial (RCT) successful drug dexamethasone, and the failed drug, with a potential to harm, hydroxychloroquine/chloroquine. Similarly, effects of various COVID-19 candidate drugs/anticytokines as well as proinflammatory cytokines implicated in the illness are also examined. The underlying assumption was that compared to COVID-19, an effective drug/anticytokine and a disease aggravating agent would affect gene regulation in opposite and same direction, in that order. Remarkably, the assumption was supported with respect to both the RCT drugs. With this control validation, etanercept, followed by tofacitinib and adalimumab, showed transcriptomic effects predictive of benefits in both ventilation and non-ventilation ICU stages as well as in non-ICU phase. On the other hand, canakinumab showed potential for effectiveness in high oxygen supplementation phase. These findings may inform experimental and clinical studies toward drug repurposing in COVID-19.
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Affiliation(s)
- Abhay Sharma
- CSIR-Institute of Genomics and Integrative Biology, Sukhdev Vihar, Mathura Road, New Delhi 110025, India.
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12
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Sharma A. Inferring molecular mechanisms of dexamethasone therapy in severe COVID-19 from existing transcriptomic data. Gene 2021; 788:145665. [PMID: 33887367 PMCID: PMC8054526 DOI: 10.1016/j.gene.2021.145665] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/27/2021] [Accepted: 04/15/2021] [Indexed: 12/22/2022]
Abstract
Dexamethasone, a synthetic glucocorticoid, has previously shown mortality benefit in severe coronavirus disease 2019 (COVID-19) in a randomized controlled trial. As the illness is considered to reflect a hyperinflammatory state, this therapeutic effectiveness is presumably ascribed to broad anti-inflammatory activities of glucocorticoids. Here, an unbiased analysis of available transcriptomic data on lung and blood immune cells from severe COVID-19 patients and matching cellular models of dexamethasone treatment is presented that supports this presumption. Comparison of differentially expressed genes in severe COVID-19 with that in dexamethasone treated cells reveals a small set of genes that are regulated in opposite direction between the disease and the drug, and are enriched for genes and processes related to glucocorticoid pathway and receptor binding. This expression signature differentiates as a whole various cytokines from a set of anti-cytokine/anti-inflammatory agents, with the former resembling COVID-19 and the latter dexamethasone in gene regulation. The signature apparently relates to TNF- α, IL-1α, IL-1β, IFN-α, IFN-β, and IFN-γ signaling, but not IL-6 signaling, suggesting that therapeutic effect of dexamethasone in COVID-19 does not involve IL-6 pathway. However, as all these observations are purely based on bioinformatic analysis, experimental evidence will be required to validate the inferences drawn. In conclusion, the present analysis seems to provide a proof of concept for therapeutic mechanisms of dexamethasone in COVID-19.
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Affiliation(s)
- Abhay Sharma
- CSIR-Institute of Genomics and Integrative Biology, Sukhdev Vihar, Mathura Road, New Delhi 110025, India.
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13
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Rooney J, Ryan N, Liu J, Houtman R, van Beuningen R, Hsieh JH, Chang G, Chen S, Christopher Corton J. A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium. Chem Res Toxicol 2021; 34:313-329. [PMID: 33405908 PMCID: PMC10683854 DOI: 10.1021/acs.chemrestox.0c00243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Identification of chemicals that affect hormone-regulated systems will help to predict endocrine disruption. In our previous study, a 46 gene biomarker was found to be an accurate predictor of estrogen receptor (ER) α modulation in chemically treated MCF-7 cells. Here, potential ERα modulators were identified using the biomarker by screening a microarray compendium consisting of ∼1600 gene expression comparisons representing exposure to ∼1200 chemicals. A total of ∼170 chemicals were identified as potential ERα modulators. In the Connectivity Map 2.0 collection, 75 and 39 chemicals were predicted to activate or suppress ERα, and they included 12 and six known ERα agonists and antagonists/selective ERα modulators, respectively. Nineteen and eight of the total number were also identified as active in an ERα transactivation assay carried out in an MCF-7-derived cell line used to screen the Tox21 10K chemical library in agonist or antagonist modes, respectively. Chemicals predicted to modulate ERα in MCF-7 cells were examined further using global and targeted gene expression in wild-type and ERα-null cells, transactivation assays, and cell-free ERα coregulator interaction assays. Environmental chemicals classified as weak and very weak agonists were confirmed to activate ERα including apigenin, kaempferol, and oxybenzone. Novel activators included digoxin, nabumetone, ivermectin, and six progestins. Novel suppressors included emetine, mifepristone, niclosamide, and proscillaridin. Our strategy will be useful to identify environmentally relevant ERα modulators in future high-throughput transcriptomic screens.
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Affiliation(s)
- John Rooney
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC 27711
- Present address: Integrated Lab Services, Research Triangle Park, NC
| | - Natalia Ryan
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC 27711
- Present address: Bayer Crop Science, Research Triangle Park, NC
| | - Jie Liu
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC 27711
| | - René Houtman
- PamGene International B.V., Den Bosch, The Netherlands
- Present address: Precision Medicine Lab, Oss, The Netherlands
| | | | - Jui-Hua Hsieh
- Kelly Government Solutions, Research Triangle Park, North Carolina
| | - Gregory Chang
- Department of Cancer Biology, Beckman Research Institute of the City of Hope, Duarte,California 91010
| | - Shiuan Chen
- Department of Cancer Biology, Beckman Research Institute of the City of Hope, Duarte,California 91010
| | - J. Christopher Corton
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC 27711
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Roy S, Dhaneshwar S, Bhasin B. Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery. Curr Drug Res Rev 2021; 13:101-119. [PMID: 33573567 DOI: 10.2174/2589977513666210211163711] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 09/07/2020] [Accepted: 10/26/2020] [Indexed: 11/22/2022]
Abstract
Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.
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Affiliation(s)
- Supriya Roy
- Amity Institute of Pharmacy, Amity University Uttar Pradesh, Lucknow Campus, India
| | - Suneela Dhaneshwar
- Amity Institute of Pharmacy, Amity University Uttar Pradesh, Lucknow Campus, India
| | - Bhavya Bhasin
- Poona College of Pharmacy, Bharati Vidyapeeth University, Pune, India
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15
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He S, Wen Y, Yang X, Liu Z, Song X, Huang X, Bo X. PIMD: An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:565-581. [PMID: 33075523 PMCID: PMC8377380 DOI: 10.1016/j.gpb.2018.10.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 09/21/2018] [Accepted: 10/10/2018] [Indexed: 11/28/2022]
Abstract
The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development. However, the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge. Here, we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning. In PIMD, drug similarity networks (DSNs) based on chemical, pharmacological, and clinical data are fused into an integrated DSN (iDSN) composed of many clusters. Rather than simple fusion, PIMD offers a systematic way to annotate clusters. Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses. PIMD provides new insights into the universality, individuality, and complementarity of different drug properties by evaluating the contribution of each property data. To test the performance of PIMD, we use chemical, pharmacological, and clinical properties to generate an iDSN. Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs. Within the top 20 recommended drug pairs, 7 drugs have been reported to be repurposed. The source code for PIMD is available at https://github.com/Sepstar/PIMD/.
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Affiliation(s)
- Song He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaoxi Yang
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Zhen Liu
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xinyu Song
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xin Huang
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China.
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16
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Wang L, Zhang W, Lu Z, Wang B, Li Y, Yang J, Li P, Zhao M. Functional Gene Module-Based Identification of Phillyrin as an Anticardiac Fibrosis Agent. Front Pharmacol 2020; 11:1077. [PMID: 32765276 PMCID: PMC7379486 DOI: 10.3389/fphar.2020.01077] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/02/2020] [Indexed: 11/25/2022] Open
Abstract
Cardiac fibrosis (CF) greatly influences the therapeutic effects of heart diseases and remains an urgent challenge in clinical therapy. Till now, only a few methods are used to find potential anti-CF drugs effectively. This study aimed to construct a gene functional module to represent the core pathological process of CF and screen antifibrotic agents capable of decreasing the expression of the gene functional module. First, three CF marker genes Postn, Ddr2, and Pdgfra were selected to identify the corresponding highest coexpressed genes in the genome-based transcriptional profiles of human hearts. Both the marker genes and the coexpressed genes formed the CF-related gene functional module. Second, the correlation of the module with the CF process was measured in a collection of gene expression profiles of heart diseases to evaluate the participation of the functional module in heart diseases. Third, the anti-CF effects of phillyrin were predicted by the enrichment analysis of the module in the phillyrin-induced transcriptional profile. Finally, the myocardial infarction animal model was used to validate the cardioprotective and anti-CF effects of phillyrin experimentally. The results showed that phillyrin was a novel antifibrotic agent in heart diseases.
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Affiliation(s)
- Lei Wang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Wuxia Zhang
- College of Arts and Sciences, ShanXi Agricultural University, Taigu, China
| | - Ziwen Lu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Baofu Wang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yang Li
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Jingjing Yang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Peng Li
- College of Arts and Sciences, ShanXi Agricultural University, Taigu, China
| | - Mingjing Zhao
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
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17
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Abstract
The never-ending explosion in the cost of new oncology drugs is reducing in many countries the access to the most recent, effective anticancer therapies and represents a significant obstacle to the design and realization of combinatorial trials. Already approved, anticancer and nonanticancer drugs can be considered for in silico, preclinical, and clinical repurposing approaches and offer the significant advantages of a potentially cheaper, faster, and safer validation. This review discusses recent advances and challenges in the field.
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18
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Li B, Dai C, Wang L, Deng H, Li Y, Guan Z, Ni H. A novel drug repurposing approach for non-small cell lung cancer using deep learning. PLoS One 2020; 15:e0233112. [PMID: 32525938 PMCID: PMC7289363 DOI: 10.1371/journal.pone.0233112] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 04/28/2020] [Indexed: 01/02/2023] Open
Abstract
Drug repurposing is an attractive and pragmatic way offering reduced risks and development time in the complicated process of drug discovery. In the past, drug repurposing has been largely accidental and serendipitous. The most successful examples so far have not involved a systematic approach. Nowadays, remarkable advances in drugs, diseases and bioinformatic knowledge are offering great opportunities for designing novel drug repurposing approach through comprehensive understanding of drug information. In this study, we introduced a novel drug repurposing approach based on transcriptomic data and chemical structures using deep learning. One strong candidate for repurposing has been identified. Pimozide is an anti-dyskinesia agent that is used for the suppression of motor and phonic tics in patients with Tourette's Disorder. However, our pipeline proposed it as a strong candidate for treating non-small cell lung cancer. The cytotoxicity of pimozide against A549 cell lines has been validated.
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Affiliation(s)
- Bingrui Li
- Beijing Deep Intelligent Pharma Technologies Co., Ltd, Beijing, China
| | - Chan Dai
- Beijing Deep Intelligent Pharma Technologies Co., Ltd, Beijing, China
| | - Lijun Wang
- Beijing Deep Intelligent Pharma Technologies Co., Ltd, Beijing, China
| | - Hailong Deng
- Beijing Deep Intelligent Pharma Technologies Co., Ltd, Beijing, China
| | - Yingying Li
- Beijing Deep Intelligent Pharma Technologies Co., Ltd, Beijing, China
- * E-mail: (YL); (ZG); (HN)
| | - Zheng Guan
- Beijing Deep Intelligent Pharma Technologies Co., Ltd, Beijing, China
- * E-mail: (YL); (ZG); (HN)
| | - Haihong Ni
- Beijing Deep Intelligent Pharma Technologies Co., Ltd, Beijing, China
- * E-mail: (YL); (ZG); (HN)
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19
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Xie J, Ma A, Fennell A, Ma Q, Zhao J. It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data. Brief Bioinform 2020; 20:1449-1464. [PMID: 29490019 DOI: 10.1093/bib/bby014] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 01/16/2018] [Indexed: 12/12/2022] Open
Abstract
Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.
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20
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Krishnamoorthy PKP, Kamal MA, Warsi MK, Alnajeebi AM, Ali HA, Helmi N, Izhari MA, Mustafa S, Firoz A, Mobashir M. In-silico study reveals immunological signaling pathways, their genes, and potential herbal drug targets in ovarian cancer. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100422] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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21
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Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases. Nat Rev Rheumatol 2019; 16:32-52. [PMID: 31831878 DOI: 10.1038/s41584-019-0337-0] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2019] [Indexed: 02/08/2023]
Abstract
The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.
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22
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Liu Z, Zhu L, Thakkar S, Roberts R, Tong W. Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment? Chem Res Toxicol 2019; 33:271-280. [DOI: 10.1021/acs.chemrestox.9b00288] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Zhichao Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Liyuan Zhu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Shraddha Thakkar
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Ruth Roberts
- ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, U.K
- University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K
| | - Weida Tong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
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23
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A platform for target prediction of phenotypic screening hit molecules. J Mol Graph Model 2019; 95:107485. [PMID: 31836397 PMCID: PMC6983931 DOI: 10.1016/j.jmgm.2019.107485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 09/25/2019] [Accepted: 10/21/2019] [Indexed: 01/09/2023]
Abstract
Many drug discovery programmes, particularly for infectious diseases, are conducted phenotypically. Identifying the targets of phenotypic screening hits experimentally can be complex, time-consuming, and expensive. However, it would be valuable to know what the molecular target(s) is, as knowledge of the binding pose of the hit molecule in the binding site can facilitate the compound optimisation. Furthermore, knowing the target would allow de-prioritisation of less attractive chemical series or molecular targets. To generate target-hypotheses for phenotypic active compounds, an in silico platform was developed that utilises both ligand and protein-structure information to generate a ranked set of predicted molecular targets. As a result of the web-based workflow the user obtains a set of 3D structures of the predicted targets with the active molecule bound. The platform was exemplified using Mycobacterium tuberculosis, the causative organism of tuberculosis. In a test that we performed, the platform was able to predict the targets of 60% of compounds investigated, where there was some similarity to a ligand in the protein database. An algorithm to predict the molecular target(s) of phenotypic hits against TB. Uses information based on the ligand and protein structure. Allow visualisation of proposed binding pose. Web interface developed.
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24
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Yang G, Ma A, Qin ZS. An Integrated System Biology Approach Yields Drug Repositioning Candidates for the Treatment of Heart Failure. Front Genet 2019; 10:916. [PMID: 31608126 PMCID: PMC6773955 DOI: 10.3389/fgene.2019.00916] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 08/29/2019] [Indexed: 12/20/2022] Open
Abstract
Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein–protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates’ therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.
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Affiliation(s)
- Guodong Yang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| | - Aiqun Ma
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
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25
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Corton JC, Kleinstreuer NC, Judson RS. Identification of potential endocrine disrupting chemicals using gene expression biomarkers. Toxicol Appl Pharmacol 2019; 380:114683. [DOI: 10.1016/j.taap.2019.114683] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 07/05/2019] [Accepted: 07/15/2019] [Indexed: 02/07/2023]
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26
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Pulley JM, Rhoads JP, Jerome RN, Challa AP, Erreger KB, Joly MM, Lavieri RR, Perry KE, Zaleski NM, Shirey-Rice JK, Aronoff DM. Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data. Annu Rev Pharmacol Toxicol 2019; 60:333-352. [PMID: 31337270 DOI: 10.1146/annurev-pharmtox-010919-023537] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.
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Affiliation(s)
- Jill M Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jillian P Rhoads
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Rebecca N Jerome
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Anup P Challa
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kevin B Erreger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Meghan M Joly
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Robert R Lavieri
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kelly E Perry
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Nicole M Zaleski
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jana K Shirey-Rice
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - David M Aronoff
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.,Departments of Obstetrics and Gynecology, and Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
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27
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Ma J, Wang J, Ghoraie LS, Men X, Haibe-Kains B, Dai P. A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing. Front Pharmacol 2019; 10:109. [PMID: 30837876 PMCID: PMC6389713 DOI: 10.3389/fphar.2019.00109] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 01/28/2019] [Indexed: 12/29/2022] Open
Abstract
The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.
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Affiliation(s)
- Jun Ma
- National Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi'an, China.,Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Jenny Wang
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | | | - Xin Men
- Shaanxi Microbiology Institute, Xi'an, China
| | | | - Penggao Dai
- National Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi'an, China
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28
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Taškova K, Fontaine JF, Mrowka R, Andrade-Navarro MA. Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets. PLoS One 2019; 14:e0210467. [PMID: 30640953 PMCID: PMC6331104 DOI: 10.1371/journal.pone.0210467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 12/24/2018] [Indexed: 11/30/2022] Open
Abstract
The study of drug toxicity in human organs is complicated by their complex inter-relations and by the obvious difficulty to testing drug effects on biologically relevant material. Animal models and human cell cultures offer alternatives for systematic and large-scale profiling of drug effects on gene expression level, as typically found in the so-called toxicogenomics datasets. However, the complexity of these data, which includes variable drug doses, time points, and experimental setups, makes it difficult to choose and integrate the data, and to evaluate the appropriateness of one or another model system to study drug toxicity (of particular drugs) of particular human organs. Here, we define a protocol to integrate drug-wise rankings of gene expression changes in toxicogenomics data, which we apply to the TG-GATEs dataset, to prioritize genes for association to drug toxicity in liver or kidney. Contrast of the results with sets of known human genes associated to drug toxicity in the literature allows to compare different rank aggregation approaches for the task at hand. Collectively, ranks from multiple models point to genes not previously associated to toxicity, notably, the PCNA clamp associated factor (PCLAF), and genes regulated by the master regulator of the antioxidant response NFE2L2, such as NQO1 and SRXN1. In addition, comparing gene ranks from different models allowed us to evaluate striking differences in terms of toxicity-associated genes between human and rat hepatocytes or between rat liver and rat hepatocytes. We interpret these results to point to the different molecular functions associated to organ toxicity that are best described by each model. We conclude that the expected production of toxicogenomics panels with larger numbers of drugs and models, in combination with the ongoing increase of the experimental literature in organ toxicity, will lead to increasingly better associations of genes for organism toxicity.
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Affiliation(s)
| | | | - Ralf Mrowka
- Experimentelle Nephrologie, Universitätsklinikum Jena, KIM III, Jena, Germany
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29
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Zhou Y, Lutz P, Ibrahim EC, Courtet P, Tzavara E, Turecki G, Belzeaux R. Suicide and suicide behaviors: A review of transcriptomics and multiomics studies in psychiatric disorders. J Neurosci Res 2018; 98:601-615. [DOI: 10.1002/jnr.24367] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 11/23/2018] [Accepted: 11/26/2018] [Indexed: 12/11/2022]
Affiliation(s)
- Yi Zhou
- McGill Group for Suicide Studies Douglas Mental Health University Institute, McGill University Montréal Canada
| | - Pierre‐Eric Lutz
- Centre National de la Recherche Scientifique Institut des Neurosciences Cellulaires et Intégratives, CNRS UPR 3212 Strasbourg France
| | - El Chérif Ibrahim
- Institut de Neurosciences de la Timone ‐ UMR7289,CNRS Aix‐Marseille Université Marseille France
- Fondamental, Fondation de Recherche et de Soins en Santé Mentale Créteil France
| | - Philippe Courtet
- Fondamental, Fondation de Recherche et de Soins en Santé Mentale Créteil France
- CHRU Montpellier, University of Montpellier, INSERM unit 1061 Montpellier France
| | - Eleni Tzavara
- Fondamental, Fondation de Recherche et de Soins en Santé Mentale Créteil France
- INSERM, UMRS 1130, CNRS, UMR 8246, Sorbonne University UPMC, Neuroscience Paris‐Seine Paris France
| | - Gustavo Turecki
- McGill Group for Suicide Studies Douglas Mental Health University Institute, McGill University Montréal Canada
| | - Raoul Belzeaux
- Institut de Neurosciences de la Timone ‐ UMR7289,CNRS Aix‐Marseille Université Marseille France
- Fondamental, Fondation de Recherche et de Soins en Santé Mentale Créteil France
- AP‐HM, Pôle de Psychiatrie Marseille France
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30
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Schyman P, Printz RL, Estes SK, Boyd KL, Shiota M, Wallqvist A. Identification of the Toxicity Pathways Associated With Thioacetamide-Induced Injuries in Rat Liver and Kidney. Front Pharmacol 2018; 9:1272. [PMID: 30459623 PMCID: PMC6232954 DOI: 10.3389/fphar.2018.01272] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 10/18/2018] [Indexed: 12/25/2022] Open
Abstract
Ingestion or exposure to chemicals poses a serious health risk. Early detection of cellular changes induced by such events is vital to identify appropriate countermeasures to prevent organ damage. We hypothesize that chemically induced organ injuries are uniquely associated with a set (module) of genes exhibiting significant changes in expression. We have previously identified gene modules specifically associated with organ injuries by analyzing gene expression levels in liver and kidney tissue from rats exposed to diverse chemical insults. Here, we assess and validate our injury-associated gene modules by analyzing gene expression data in liver, kidney, and heart tissues obtained from Sprague-Dawley rats exposed to thioacetamide, a known liver toxicant that promotes fibrosis. The rats were injected intraperitoneally with a low (25 mg/kg) or high (100 mg/kg) dose of thioacetamide for 8 or 24 h, and definite organ injury was diagnosed by histopathology. Injury-associated gene modules indicated organ injury specificity, with the liver being most affected by thioacetamide. The most activated liver gene modules were those associated with inflammatory cell infiltration and fibrosis. Previous studies on thioacetamide toxicity and our histological analyses supported these results, signifying the potential of gene expression data to identify organ injuries.
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Affiliation(s)
- Patric Schyman
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, United States
| | - Richard L Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Shanea K Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Kelli L Boyd
- Division of Comparative Medicine, Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Anders Wallqvist
- DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, United States
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31
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Liu W, Tu W, Li L, Liu Y, Wang S, Li L, Tao H, He H. Revisiting Connectivity Map from a gene co-expression network analysis. Exp Ther Med 2018; 16:493-500. [PMID: 30112021 PMCID: PMC6090433 DOI: 10.3892/etm.2018.6275] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 12/08/2017] [Indexed: 12/16/2022] Open
Abstract
The Connectivity Map (CMap) is a tool that has been extensively utilized to study drug repositioning and side-effect prediction. However, most of these analyses rely on signature genes, ignoring the pathways by which those genes are regulated, as well as the functional overlap of redundant genes. The present study utilized a systems biology approach referred to as Weighted Gene Co-expression Network Analysis (WGCNA) to dissect the transcriptional profiles of CMap and reveal these hidden factors. Seven common modules associated with protein binding, extracellular matrix organization and translation were identified. Furthermore, drugs were clustered based on module expression to infer their mechanism of action (MoA) based on common activity profiles. As an extension of this, an example of disease-based module projection to identify novel drugs was provided. The analysis developed in the present study may provide a novel framework for drug repositioning or discovering MoAs.
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Affiliation(s)
- Wei Liu
- Department of Bioinformatics, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, P.R. China
| | - Wei Tu
- Department of Molecular and Cellular Medicine, Texas A&M Health Science Center, College Station, TX 77843-1114, USA
| | - Li Li
- Department of Medical Informatics, Institute of Health Service and Medical Information, Academy of Military Medical Sciences, Beijing 100850, P.R. China
| | - Yingfu Liu
- Department of Cell Biology, Logistics University of Chinese Armed Police Forces, Tianjin 300309, P.R. China
| | - Shaobo Wang
- Department of Bioinformatics, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, P.R. China
| | - Ling Li
- Department of Bioinformatics, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, P.R. China
| | - Huan Tao
- Department of Bioinformatics, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, P.R. China
| | - Huaqin He
- Department of Bioinformatics, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, P.R. China
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32
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Yella JK, Yaddanapudi S, Wang Y, Jegga AG. Changing Trends in Computational Drug Repositioning. Pharmaceuticals (Basel) 2018; 11:E57. [PMID: 29874824 PMCID: PMC6027196 DOI: 10.3390/ph11020057] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 06/01/2018] [Accepted: 06/02/2018] [Indexed: 12/12/2022] Open
Abstract
Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.
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Affiliation(s)
- Jaswanth K Yella
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Suryanarayana Yaddanapudi
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Yunguan Wang
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.
- Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH 45219, USA.
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A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Artery Disease. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:108-117. [PMID: 29888052 PMCID: PMC5961807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Currently, drug discovery approaches focus on the design of therapies that alleviate an index symptom by reengineering the underlying biological mechanism in agonistic or antagonistic fashion. For example, medicines are routinely developed to target an essential gene that drives the disease mechanism. Therapeutic overloading where patients get multiple medications to reduce the primary and secondary side effect burden is standard practice. This single-symptom based approach may not be scalable, as we understand that diseases are more connected than random and molecular interactions drive disease comorbidities. In this work, we present a proof-of-concept drug discovery strategy by combining network biology, disease comorbidity estimates, and computational drug repositioning, by targeting the risk factors and comorbidities of peripheral artery disease, a vascular disease associated with high morbidity and mortality. Individualized risk estimation and recommending disease sequelae based therapies may help to lower the mortality and morbidity of peripheral artery disease.
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KalantarMotamedi Y, Eastman RT, Guha R, Bender A. A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria. Malar J 2018; 17:160. [PMID: 29642892 PMCID: PMC5896032 DOI: 10.1186/s12936-018-2294-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 03/24/2018] [Indexed: 01/01/2023] Open
Abstract
Background Nearly half of the world’s population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. Methods The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. Results One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6 ± 136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. Conclusions Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner. Electronic supplementary material The online version of this article (10.1186/s12936-018-2294-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yasaman KalantarMotamedi
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Richard T Eastman
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, MD, 20852, USA
| | - Rajarshi Guha
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, MD, 20852, USA.
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
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Ishii T, Kimura Y, Ichise M, Takahata K, Kitamura S, Moriguchi S, Kubota M, Zhang MR, Yamada M, Higuchi M, Okubo Y, Suhara T. Anatomical relationships between serotonin 5-HT2A and dopamine D2 receptors in living human brain. PLoS One 2017; 12:e0189318. [PMID: 29220382 PMCID: PMC5722317 DOI: 10.1371/journal.pone.0189318] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 11/22/2017] [Indexed: 11/18/2022] Open
Abstract
Serotonin 2A (5-HT2A) receptors and dopamine D2 receptors are intimately related to the physiology and pathophysiology of neuropsychiatric disorders. A large number of studies have reported the effectiveness of psychotropic agents targeting 5-HT2A and D2 receptors in these disorders. In addition to the individual functions of these receptors, the interaction between the two neurotransmitter systems has been studied in the living brain. However, little is known about their regional relationship in individual human brains. We investigated regional relationships between 5-HT2A and D2 receptors using positron emission tomography (PET) and a bicluster analysis of the correlation matrix of individual variation in the two receptor densities to identify groups of distinctive regional correlations between the two receptors.
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Affiliation(s)
- Tatsuya Ishii
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
- Department of Neuropsychiatry, Nippon Medical School, Tokyo, Japan
| | - Yasuyuki Kimura
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
- Department of Clinical and Experimental Neuroimaging, Center for Development of Advanced Medicine for Dementia, National Center for Geriatrics and Gerontology, Obu, Japan
- * E-mail:
| | - Masanori Ichise
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Keisuke Takahata
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Soichiro Kitamura
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Sho Moriguchi
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Manabu Kubota
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Ming-Rong Zhang
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Makiko Yamada
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Makoto Higuchi
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Yoshinori Okubo
- Department of Neuropsychiatry, Nippon Medical School, Tokyo, Japan
| | - Tetsuya Suhara
- Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
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36
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Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines. Cell Syst 2017; 5:604-619.e7. [PMID: 29226804 PMCID: PMC5747350 DOI: 10.1016/j.cels.2017.11.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 08/17/2017] [Accepted: 11/02/2017] [Indexed: 01/06/2023]
Abstract
In individuals, heterogeneous drug-response phenotypes result from a complex interplay of dose, drug specificity, genetic background, and environmental factors, thus challenging our understanding of the underlying processes and optimal use of drugs in the clinical setting. Here, we use mass-spectrometry-based quantification of molecular response phenotypes and logic modeling to explain drug-response differences in a panel of cell lines. We apply this approach to cellular cholesterol regulation, a biological process with high clinical relevance. From the quantified molecular phenotypes elicited by various targeted pharmacologic or genetic treatments, we generated cell-line-specific models that quantified the processes beneath the idiotypic intracellular drug responses. The models revealed that, in addition to drug uptake and metabolism, further cellular processes displayed significant pharmacodynamic response variability between the cell lines, resulting in cell-line-specific drug-response phenotypes. This study demonstrates the importance of integrating different types of quantitative systems-level molecular measurements with modeling to understand the effect of pharmacological perturbations on complex biological processes.
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37
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Delavan B, Roberts R, Huang R, Bao W, Tong W, Liu Z. Computational drug repositioning for rare diseases in the era of precision medicine. Drug Discov Today 2017; 23:382-394. [PMID: 29055182 DOI: 10.1016/j.drudis.2017.10.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/19/2017] [Accepted: 10/11/2017] [Indexed: 12/12/2022]
Abstract
There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.
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Affiliation(s)
- Brian Delavan
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; University of Arkansas at Little Rock, Little Rock, AR 72204, USA
| | - Ruth Roberts
- ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health Rockville, MD 20850, USA
| | | | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Zhichao Liu
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
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38
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Zheng T, Ni Y, Li J, Chow BKC, Panagiotou G. Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference. Front Physiol 2017; 8:753. [PMID: 29033850 PMCID: PMC5625024 DOI: 10.3389/fphys.2017.00753] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 09/19/2017] [Indexed: 12/14/2022] Open
Abstract
Background: A range of computational methods that rely on the analysis of genome-wide expression datasets have been developed and successfully used for drug repositioning. The success of these methods is based on the hypothesis that introducing a factor (in this case, a drug molecule) that could reverse the disease gene expression signature will lead to a therapeutic effect. However, it has also been shown that globally reversing the disease expression signature is not a prerequisite for drug activity. On the other hand, the basic idea of significant anti-correlation in expression profiles could have great value for establishing diet-disease associations and could provide new insights into the role of dietary interventions in disease. Methods: We performed an integrated analysis of publicly available gene expression profiles for foods, diseases and drugs, by calculating pairwise similarity scores for diet and disease gene expression signatures and characterizing their topological features in protein-protein interaction networks. Results: We identified 485 diet-disease pairs where diet could positively influence disease development and 472 pairs where specific diets should be avoided in a disease state. Multiple evidence suggests that orange, whey and coconut fat could be beneficial for psoriasis, lung adenocarcinoma and macular degeneration, respectively. On the other hand, fructose-rich diet should be restricted in patients with chronic intermittent hypoxia and ovarian cancer. Since humans normally do not consume foods in isolation, we also applied different algorithms to predict synergism; as a result, 58 food pairs were predicted. Interestingly, the diets identified as anti-correlated with diseases showed a topological proximity to the disease proteins similar to that of the corresponding drugs. Conclusions: In conclusion, we provide a computational framework for establishing diet-disease associations and additional information on the role of diet in disease development. Due to the complexity of analyzing the food composition and eating patterns of individuals our in silico analysis, using large-scale gene expression datasets and network-based topological features, may serve as a proof-of-concept in nutritional systems biology for identifying diet-disease relationships and subsequently designing dietary recommendations.
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Affiliation(s)
- Tingting Zheng
- Systems Biology and Bioinformatics Group, Faculty of Sciences, School of Biological Sciences, The University of HongKong, Hong Kong, Hong Kong
| | - Yueqiong Ni
- Systems Biology and Bioinformatics Group, Faculty of Sciences, School of Biological Sciences, The University of HongKong, Hong Kong, Hong Kong
| | - Jun Li
- Systems Biology and Bioinformatics Group, Faculty of Sciences, School of Biological Sciences, The University of HongKong, Hong Kong, Hong Kong
| | - Billy K C Chow
- Faculty of Science, School of Biological Sciences, The University of Hong Kong, Hong Kong, Hong Kong
| | - Gianni Panagiotou
- Systems Biology and Bioinformatics Group, Faculty of Sciences, School of Biological Sciences, The University of HongKong, Hong Kong, Hong Kong.,Department of Systems Biology and Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
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39
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Li B, Liu J, Yu Y, Wang P, Zhang Y, Ni X, Liu Q, Zhang X, Wang Z, Wang Y. Network-Wide Screen Identifies Variation of Novel Precise On-Module Targets Using Conformational Modudaoism. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 7:16-25. [PMID: 28925077 PMCID: PMC5784734 DOI: 10.1002/psp4.12253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 08/30/2017] [Accepted: 09/13/2017] [Indexed: 01/30/2023]
Abstract
Modular targeting is promising in drug research at the network level, but it is challenging to quantificationally identify the precise on-modules. Based on a proposed Modudaoism (MD), we defined conserved MD (MDc) and varied MD (MDv) to quantitatively evaluate the conformational and energy variations of modules, and thereby identify the conserved and discrepant allosteric modules (AMs). Compared to the Zsummary , MDc/MDv got an optimized result of module preserved ratio and modular structure. In the mice anti-ischemic networks, 3, 5, and 1 conserved AMs as well as 4, 1, and 3 on-modules of baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) were identified by MDc and MDv, 5 unique AMs and their characteristic actions were revealed. Besides, co-immunoprecipitation (Co-IP) experiments validated the representative modular structure. MDc/MDv method can quantitatively define the conformational variations of modules and screen the precise on-modules network-wide, which may provide a promising strategy for drug discovery.
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Affiliation(s)
- Bing Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,ShanXi Buchang Pharmaceutical Co., Ltd., Heze, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanan Yu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Pengqian Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yingying Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, China
| | - Qiong Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaoxu Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yongyan Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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40
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Boezio B, Audouze K, Ducrot P, Taboureau O. Network-based Approaches in Pharmacology. Mol Inform 2017; 36. [PMID: 28692140 DOI: 10.1002/minf.201700048] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 06/21/2017] [Indexed: 12/23/2022]
Abstract
In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example.
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Affiliation(s)
- Baptiste Boezio
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
| | - Karine Audouze
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Olivier Taboureau
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
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41
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Kohonen P, Parkkinen JA, Willighagen EL, Ceder R, Wennerberg K, Kaski S, Grafström RC. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury. Nat Commun 2017; 8:15932. [PMID: 28671182 PMCID: PMC5500850 DOI: 10.1038/ncomms15932] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 05/15/2017] [Indexed: 01/17/2023] Open
Abstract
Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion'-concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.
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Affiliation(s)
- Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Box 210, SE-17177 Stockholm, Sweden
| | - Juuso A Parkkinen
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Konemiehentie 2, P.O. Box 15400, 00076 Aalto, Finland
| | - Egon L Willighagen
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Box 210, SE-17177 Stockholm, Sweden.,Department of Bioinformatics-BiGCaT, Maastricht University, Universiteitssingel 50, P.O. Box 616, UNS 50 Box19, NL-6200 MD Maastricht, The Netherlands
| | - Rebecca Ceder
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Box 210, SE-17177 Stockholm, Sweden
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Tukholmankatu 8, P.O. Box 20, FI-00014 Helsinki, Finland
| | - Samuel Kaski
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Konemiehentie 2, P.O. Box 15400, 00076 Aalto, Finland.,Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, P.O. Box 68, FI-00014 Helsinki, Finland
| | - Roland C Grafström
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Box 210, SE-17177 Stockholm, Sweden
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42
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March-Vila E, Pinzi L, Sturm N, Tinivella A, Engkvist O, Chen H, Rastelli G. On the Integration of In Silico Drug Design Methods for Drug Repurposing. Front Pharmacol 2017; 8:298. [PMID: 28588497 PMCID: PMC5440551 DOI: 10.3389/fphar.2017.00298] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 05/10/2017] [Indexed: 11/13/2022] Open
Abstract
Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug–target, target–disease, and ultimately drug–disease associations.
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Affiliation(s)
- Eric March-Vila
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
| | - Luca Pinzi
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
| | - Noé Sturm
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
| | - Annachiara Tinivella
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
| | - Ola Engkvist
- Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D GothenburgMölndal, Sweden
| | - Hongming Chen
- Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D GothenburgMölndal, Sweden
| | - Giulio Rastelli
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
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43
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Thiel C, Cordes H, Fabbri L, Aschmann HE, Baier V, Smit I, Atkinson F, Blank LM, Kuepfer L. A Comparative Analysis of Drug-Induced Hepatotoxicity in Clinically Relevant Situations. PLoS Comput Biol 2017; 13:e1005280. [PMID: 28151932 PMCID: PMC5289425 DOI: 10.1371/journal.pcbi.1005280] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 12/03/2016] [Indexed: 11/18/2022] Open
Abstract
Drug-induced toxicity is a significant problem in clinical care. A key problem here is a general understanding of the molecular mechanisms accompanying the transition from desired drug effects to adverse events following administration of either therapeutic or toxic doses, in particular within a patient context. Here, a comparative toxicity analysis was performed for fifteen hepatotoxic drugs by evaluating toxic changes reflecting the transition from therapeutic drug responses to toxic reactions at the cellular level. By use of physiologically-based pharmacokinetic modeling, in vitro toxicity data were first contextualized to quantitatively describe time-resolved drug responses within a patient context. Comparatively studying toxic changes across the considered hepatotoxicants allowed the identification of subsets of drugs sharing similar perturbations on key cellular processes, functional classes of genes, and individual genes. The identified subsets of drugs were next analyzed with regard to drug-related characteristics and their physicochemical properties. Toxic changes were finally evaluated to predict both molecular biomarkers and potential drug-drug interactions. The results may facilitate the early diagnosis of adverse drug events in clinical application. Liver toxicity may occur at drug levels above the therapeutic range and is thus a crucial problem in clinical care. However, the cellular changes induced by drug administration of therapeutic and toxic doses in humans are still not well understood. We here coupled patient-specific drug concentration-time profiles following oral administration of therapeutic and toxic doses with in vitro drug response data to predict toxic changes that quantitatively reflect the transition from desired drug effects to undesired toxic reactions. These toxic changes were comparatively evaluated for fifteen hepatotoxic drugs to identify subsets of drugs, which show similar drug effects on key cellular processes, functional classes of genes, and individual genes, respectively. In addition, analyzing toxic changes for individual genes allowed the prediction of molecular biomarkers and potential drug-drug interactions. Our results may hence support the early diagnosis of liver toxicity in clinical care in the future and may, moreover, help to assess potential risks of drug combination therapies.
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Affiliation(s)
- Christoph Thiel
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Henrik Cordes
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Lorenzo Fabbri
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Hélène Eloise Aschmann
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Vanessa Baier
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Ines Smit
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Francis Atkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Lars Mathias Blank
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Lars Kuepfer
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
- * E-mail:
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44
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Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics. Sci Rep 2017; 7:40164. [PMID: 28071740 PMCID: PMC5223214 DOI: 10.1038/srep40164] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 12/01/2016] [Indexed: 12/17/2022] Open
Abstract
The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.
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45
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Scharaw S, Iskar M, Ori A, Boncompain G, Laketa V, Poser I, Lundberg E, Perez F, Beck M, Bork P, Pepperkok R. The endosomal transcriptional regulator RNF11 integrates degradation and transport of EGFR. J Cell Biol 2016; 215:543-558. [PMID: 27872256 PMCID: PMC5119934 DOI: 10.1083/jcb.201601090] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 09/07/2016] [Accepted: 10/17/2016] [Indexed: 12/24/2022] Open
Abstract
Maintenance of EGFR plasma membrane levels is critical for cell functioning. Scharaw et al. demonstrate that endosomal RNF11 is required for transcriptional up-regulation of COPII components, specifically facilitating EGFR transport in response to its lysosomal degradation after EGF stimulation. Stimulation of cells with epidermal growth factor (EGF) induces internalization and partial degradation of the EGF receptor (EGFR) by the endo-lysosomal pathway. For continuous cell functioning, EGFR plasma membrane levels are maintained by transporting newly synthesized EGFRs to the cell surface. The regulation of this process is largely unknown. In this study, we find that EGF stimulation specifically increases the transport efficiency of newly synthesized EGFRs from the endoplasmic reticulum to the plasma membrane. This coincides with an up-regulation of the inner coat protein complex II (COPII) components SEC23B, SEC24B, and SEC24D, which we show to be specifically required for EGFR transport. Up-regulation of these COPII components requires the transcriptional regulator RNF11, which localizes to early endosomes and appears additionally in the cell nucleus upon continuous EGF stimulation. Collectively, our work identifies a new regulatory mechanism that integrates the degradation and transport of EGFR in order to maintain its physiological levels at the plasma membrane.
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Affiliation(s)
- Sandra Scharaw
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Murat Iskar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Alessandro Ori
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Gaelle Boncompain
- Institut Curie, Paris Sciences et Lettres Research University, 75248 Paris, France.,Institut Curie, Centre National de la Recherche Scientifique UMR144, 75248 Paris, France
| | - Vibor Laketa
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Ina Poser
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
| | - Emma Lundberg
- Science for Life Laboratory, KTH Royal Institute of Technology, 17121 Solna, Sweden
| | - Franck Perez
- Institut Curie, Paris Sciences et Lettres Research University, 75248 Paris, France.,Institut Curie, Centre National de la Recherche Scientifique UMR144, 75248 Paris, France
| | - Martin Beck
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.,Max Delbrueck Center for Molecular Medicine, 13125 Berlin, Germany.,Department of Bioinformatics, Biocenter, University of Wuerzburg, 97074 Wuerzburg, Germany
| | - Rainer Pepperkok
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
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46
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Luo H, Zhang P, Cao XH, Du D, Ye H, Huang H, Li C, Qin S, Wan C, Shi L, He L, Yang L. DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome. Sci Rep 2016; 6:35996. [PMID: 27805045 PMCID: PMC5090963 DOI: 10.1038/srep35996] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 10/10/2016] [Indexed: 02/06/2023] Open
Abstract
The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational “drug candidate positioning” or “drug positioning”, to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/.
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Affiliation(s)
- Heng Luo
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Ping Zhang
- Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | - Xi Hang Cao
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA 19122, USA
| | - Dizheng Du
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hao Ye
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hui Huang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Can Li
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Shengying Qin
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Chunling Wan
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Leming Shi
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Lin He
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Lun Yang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
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47
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Li B, Liu J, Zhang YY, Wang PQ, Yu YN, Kang RX, Wu HL, Zhang XX, Wang Z, Wang YY. Quantitative Identification of Compound-Dependent On-Modules and Differential Allosteric Modules From Homologous Ischemic Networks. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:575-584. [PMID: 27758049 PMCID: PMC5080653 DOI: 10.1002/psp4.12127] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 07/28/2016] [Accepted: 08/22/2016] [Indexed: 12/13/2022]
Abstract
Module‐based methods have made much progress in deconstructing biological networks. However, it is a great challenge to quantitatively compare the topological structural variations of modules (allosteric modules [AMs]) under different situations. A total of 23, 42, and 15 coexpression modules were identified in baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) in a global anti‐ischemic mice network, respectively. Then, we integrated the methods of module‐based consensus ratio (MCR) and modified Zsummary module statistic to validate 12 BA, 22 JA, and 8 UA on‐modules based on comparing with vehicle. The MCRs for pairwise comparisons were 1.55% (BA vs. JA), 1.45% (BA vs. UA), and 1.27% (JA vs. UA), respectively. Five conserved allosteric modules (CAMs) and 17 unique allosteric modules (UAMs) were identified among these groups. In conclusion, module‐centric analysis may provide us a unique approach to understand multiple pharmacological mechanisms associated with differential phenotypes in the era of modular pharmacology.
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Affiliation(s)
- B Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - J Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Y Y Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - P Q Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Y N Yu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - R X Kang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - H L Wu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - X X Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Z Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
| | - Y Y Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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48
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Yu Y, Zhang X, Li B, Zhang Y, Liu J, Li H, Chen Y, Wang P, Kang R, Wu H, Wang Z. Entropy-based divergent and convergent modular pattern reveals additive and synergistic anticerebral ischemia mechanisms. Exp Biol Med (Maywood) 2016; 241:2063-2074. [PMID: 27480252 DOI: 10.1177/1535370216662361] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Module-based network analysis of diverse pharmacological mechanisms is critical to systematically understand combination therapies and disease outcomes. We first constructed drug-target ischemic networks in baicalin, jasminoidin, ursodeoxycholic acid, and their combinations baicalin and jasminoidin as well as jasminoidin and ursodeoxycholic acid groups and identified modules using the entropy-based clustering algorithm. The modules 11, 7, 4, 8 and 3 were identified as baicalin, jasminoidin, ursodeoxycholic acid, baicalin and jasminoidin and jasminoidin and ursodeoxycholic acid-emerged responsive modules, while 12, 8, 15, 17 and 9 were identified as disappeared responsive modules based on variation of topological similarity, respectively. No overlapping differential biological processes were enriched between baicalin and jasminoidin and jasminoidin and ursodeoxycholic acid pure emerged responsive modules, but two were enriched by their co-disappeared responsive modules including nucleotide-excision repair and epithelial structure maintenance. We found an additive effect of baicalin and jasminoidin in a divergent pattern and a synergistic effect of jasminoidin and ursodeoxycholic acid in a convergent pattern on "central hit strategy" of regulating inflammation against cerebral ischemia. The proposed module-based approach may provide us a holistic view to understand multiple pharmacological mechanisms associated with differential phenotypes from the standpoint of modular pharmacology.
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Affiliation(s)
- Yanan Yu
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
| | - Xiaoxu Zhang
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
| | - Bing Li
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
| | - Yingying Zhang
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
| | - Jun Liu
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
| | - Haixia Li
- 2 Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Yinying Chen
- 2 Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Pengqian Wang
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
| | - Ruixia Kang
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
| | - Hongli Wu
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
| | - Zhong Wang
- 1 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimennei, Beijing 100700, China
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49
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Kusebauch U, Campbell DS, Deutsch EW, Chu CS, Spicer DA, Brusniak MY, Slagel J, Sun Z, Stevens J, Grimes B, Shteynberg D, Hoopmann MR, Blattmann P, Ratushny AV, Rinner O, Picotti P, Carapito C, Huang CY, Kapousouz M, Lam H, Tran T, Demir E, Aitchison JD, Sander C, Hood L, Aebersold R, Moritz RL. Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome. Cell 2016; 166:766-778. [PMID: 27453469 PMCID: PMC5245710 DOI: 10.1016/j.cell.2016.06.041] [Citation(s) in RCA: 240] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Revised: 04/28/2016] [Accepted: 06/21/2016] [Indexed: 02/08/2023]
Abstract
The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.
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Affiliation(s)
| | | | | | | | | | | | - Joseph Slagel
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Zhi Sun
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | | | | | | | - Peter Blattmann
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Alexander V Ratushny
- Institute for Systems Biology, Seattle, WA 98109, USA; Center for Infectious Disease Research, Seattle, WA 98109, USA
| | - Oliver Rinner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; Biognosys AG, 8952 Schlieren, Switzerland
| | - Paola Picotti
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Christine Carapito
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | | | | | - Henry Lam
- Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Tommy Tran
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Emek Demir
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - John D Aitchison
- Institute for Systems Biology, Seattle, WA 98109, USA; Center for Infectious Disease Research, Seattle, WA 98109, USA
| | - Chris Sander
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; Faculty of Science, University of Zurich, 8006 Zurich, Switzerland.
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50
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Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. Mol Pharm 2016; 13:2524-30. [PMID: 27200455 DOI: 10.1021/acs.molpharmaceut.6b00248] [Citation(s) in RCA: 262] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.
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Affiliation(s)
- Alexander Aliper
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Sergey Plis
- Datalytic Solutions , 1101 Yale Boulevard NE, Albuquerque, New Mexico 87106, United States.,The Mind Research Network , Albuquerque, New Mexico 87106, United States
| | - Artem Artemov
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Alvaro Ulloa
- The Mind Research Network , Albuquerque, New Mexico 87106, United States
| | - Polina Mamoshina
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Alex Zhavoronkov
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States.,The Biogerontology Research Foundation , Oxford, U.K
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