1
|
Song Z, Yan A, Li Z, Shang Y, Chen R, Yang Z, Guo Z, Zhang Y, Wen T, Ogaji OD, Wang Y. Integrated metabolomic and transcriptomic analysis reveals the effects and mechanisms of Jinqi Jiangtang tablets on type 2 diabetes. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 134:155957. [PMID: 39181101 DOI: 10.1016/j.phymed.2024.155957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 06/30/2024] [Accepted: 08/14/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Type 2 diabetes (T2DM) is one of the major metabolic diseases and poses a serious challenge to human life and global economic development. Jinqi Jiangtang Tablets (JQJT) is effective in ameliorating the effects of T2DM, but the mechanism of JQJT is unclear. PURPOSE This study integrated metabolomics and transcriptomics to reveal the mechanism by which JQJT improves T2DM. METHODS The T2DM mouse model was established, and the effects of JQJT on improving T2DM were evaluated by determining the levels of blood lipids, fasting blood glucose (FBG), insulin metabolism and hepatic lipid accumulation in mice after JQJT administration for 8 weeks. Serum metabolites were detected using ultra-performance liquid chromatography/quadrupole time-of-flight-tandem mass spectrometry (UPLC-Q-TOF-MS) technology, and mouse liver differential genes were detected using transcriptomic technology. Correlation analysis was used to extract metabolites and RNA with correlations, and potential pathways were enriched and constructed using the common pathway analysis function of MetaboAnalyst 5.0. Finally, the expression of key target proteins and genes was verified by Western blot (WB) and Polymerase Chain Reaction (PCR) to further elucidate the mechanism by which JQJT improves T2DM. RESULTS JQJT reduced FBG and lipid levels, improved insulin resistance (IR) and hepatic lipoatrophy in mice. A total of 35 differentially abundant metabolites were identified by metabolomics, and 328 differential genes were detected by transcriptomics. The integrated metabolomics and transcriptomics results suggested that JQJT may ameliorate T2DM mainly by regulating glucose and lipid metabolic pathways. WB and PCR results showed that JQJT regulates the insulin signaling pathway, involved in fatty acid metabolism, glycogen synthesis and catabolism. CONCLUSIONS JQJT improved IR in T2DM mice by regulating the insulin signaling pathway, improving glycogen synthesis and glycolysis, and increasing hepatic triglyceride and fatty acid metabolism.
Collapse
Affiliation(s)
- Zhihui Song
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - An Yan
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Tianjin 300120, China
| | - Zhenzhen Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Ye Shang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Rui Chen
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zhihua Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zehui Guo
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yuhang Zhang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Tao Wen
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Omachi Daniel Ogaji
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yi Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
| |
Collapse
|
2
|
Matsukiyo Y, Tengeiji A, Li C, Yamanishi Y. Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design. J Chem Inf Model 2024; 64:5844-5852. [PMID: 39049516 DOI: 10.1021/acs.jcim.4c00531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Computational molecular generation methods that generate chemical structures from gene expression profiles have been actively developed for de novo drug design. However, most omics-based methods involve complex models consisting of multiple neural networks, which require pretraining. In this study, we propose a straightforward molecular generation method called GxRNN (gene expression profile-based recurrent neural network), employing a single recurrent neural network (RNN) that necessitates no pretraining for omics-based drug design. Specifically, our method utilizes the desired gene expression profile as input for the RNN, conditioning it to generate molecules likely to induce a similar profile. In a case study involving ten target proteins, GxRNN exhibited superior structural reproducibility of known ligands, surpassing several existing methods. This advancement positions our proposed method as a promising tool for facilitating de novo drug design.
Collapse
Affiliation(s)
- Yuki Matsukiyo
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, Japan
| | - Atsushi Tengeiji
- Modality Research Laboratories I, Daiichi Sankyo Co., Ltd., 1-2-58 Hiromachi, Shinagawa, Tokyo 140-8710, Japan
| | - Chen Li
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, Japan
| |
Collapse
|
3
|
Li J, Huang HY, Lin YCD, Zuo H, Tang Y, Huang HD. Cinnamomi ramulus inhibits cancer cells growth by inducing G2/M arrest. Front Pharmacol 2023; 14:1121799. [PMID: 37007025 PMCID: PMC10063822 DOI: 10.3389/fphar.2023.1121799] [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: 12/12/2022] [Accepted: 02/13/2023] [Indexed: 03/19/2023] Open
Abstract
Introduction: Cinnamomi ramulus (CR) is one of the most widely used traditional Chinese medicine (TCM) with anti-cancer effects. Analyzing transcriptomic responses of different human cell lines to TCM treatment is a promising approach to understand the unbiased mechanism of TCM. Methods: This study treated ten cancer cell lines with different CR concentrations, followed by mRNA sequencing. Differential expression (DE) analysis and gene set enrichment analysis (GSEA) were utilized to analyze transcriptomic data. Finally, the in silico screening results were verified by in vitro experiments. Results: Both DE and GSEA analysis suggested the Cell cycle pathway was the most perturbated pathway by CR across these cell lines. By analyzing the clinical significance and prognosis of G2/M related genes (PLK1, CDK1, CCNB1, and CCNB2) in various cancer tissues, we found that they were up-regulated in most cancer types, and their down-regulation showed better overall survival rates in cancer patients. Finally, in vitro experiments validation on A549, Hep G2, and HeLa cells suggested that CR can inhibit cell growth by suppressing the PLK1/CDK1/ Cyclin B axis. Discussion: This is the first study to apply transcriptomic analysis to investigate the cancer cell growth inhibition of CR on various human cancer cell lines. The core effect of CR on ten cancer cell lines is to induce G2/M arrest by inhibiting the PLK1/CDK1/Cyclin B axis.
Collapse
Affiliation(s)
- Jing Li
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
| | - Hsi-Yuan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
| | - Yang-Chi-Dung Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
| | - Huali Zuo
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
| | - Yun Tang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
| | - Hsien-Da Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
| |
Collapse
|
4
|
Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives. Cancers (Basel) 2022; 14:cancers14225545. [PMID: 36428637 PMCID: PMC9688479 DOI: 10.3390/cancers14225545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/07/2022] [Accepted: 11/09/2022] [Indexed: 11/15/2022] Open
Abstract
Colorectal cancer (CRC) is common Cancer as well as the third leading cause of mortality around the world; its exact molecular mechanism remains elusive. Although CRC risk is significantly correlated with genetic factors, the pathophysiology of CRC is also influenced by external and internal exposures and their interactions with genetic factors. The field of CRC research has recently benefited from significant advances through Omics technologies for screening biomarkers, including genes, transcripts, proteins, metabolites, microbiome, and lipidome unbiasedly. A promising application of omics technologies could enable new biomarkers to be found for the screening and diagnosis of CRC. Single-omics technologies cannot fully understand the molecular mechanisms of CRC. Therefore, this review article aims to summarize the multi-omics studies of Colorectal cancer, including genomics, transcriptomics, proteomics, microbiomics, metabolomics, and lipidomics that may shed new light on the discovery of novel biomarkers. It can contribute to identifying and validating new CRC biomarkers and better understanding colorectal carcinogenesis. Discovering biomarkers through multi-omics technologies could be difficult but valuable for disease genotyping and phenotyping. That can provide a better knowledge of CRC prognosis, diagnosis, and treatments.
Collapse
|
5
|
Amano Y, Yamane M, Honda H. RAID: Regression Analysis–Based Inductive DNA Microarray for Precise Read-Across. Front Pharmacol 2022; 13:879907. [PMID: 35935858 PMCID: PMC9354856 DOI: 10.3389/fphar.2022.879907] [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: 02/20/2022] [Accepted: 05/30/2022] [Indexed: 12/02/2022] Open
Abstract
Chemical structure-based read-across represents a promising method for chemical toxicity evaluation without the need for animal testing; however, a chemical structure is not necessarily related to toxicity. Therefore, in vitro studies were often used for read-across reliability refinement; however, their external validity has been hindered by the gap between in vitro and in vivo conditions. Thus, we developed a virtual DNA microarray, regression analysis–based inductive DNA microarray (RAID), which quantitatively predicts in vivo gene expression profiles based on the chemical structure and/or in vitro transcriptome data. For each gene, elastic-net models were constructed using chemical descriptors and in vitro transcriptome data to predict in vivo data from in vitro data (in vitro to in vivo extrapolation; IVIVE). In feature selection, useful genes for assessing the quantitative structure–activity relationship (QSAR) and IVIVE were identified. Predicted transcriptome data derived from the RAID system reflected the in vivo gene expression profiles of characteristic hepatotoxic substances. Moreover, gene ontology and pathway analysis indicated that nuclear receptor-mediated xenobiotic response and metabolic activation are related to these gene expressions. The identified IVIVE-related genes were associated with fatty acid, xenobiotic, and drug metabolisms, indicating that in vitro studies were effective in evaluating these key events. Furthermore, validation studies revealed that chemical substances associated with these key events could be detected as hepatotoxic biosimilar substances. These results indicated that the RAID system could represent an alternative screening test for a repeated-dose toxicity test and toxicogenomics analyses. Our technology provides a critical solution for IVIVE-based read-across by considering the mode of action and chemical structures.
Collapse
|
6
|
Namba S, Iwata M, Yamanishi Y. From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures. Bioinformatics 2022; 38:i68-i76. [PMID: 35758779 PMCID: PMC9235496 DOI: 10.1093/bioinformatics/btac240] [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] [Indexed: 11/17/2022] Open
Abstract
Motivation A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem. Results In this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases. Predictions were performed by a trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown of 4345 genes and overexpression of 3114 genes) and disease-specific gene transcriptomic signatures of 79 diseases. The trans-disease method, which takes into account similarities among diseases, enabled us to distinguish the inhibitory from activatory targets and to predict the therapeutic targetability of not only proteins with known target–disease associations but also orphan proteins without known associations. Our proposed method is expected to be useful for understanding the commonality of mechanisms among diseases and for therapeutic target identification in drug discovery. Availability and implementation Supplemental information and software are available at the following website [http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/]. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Satoko Namba
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| |
Collapse
|
7
|
Radhakrishnan A, Stefanakis G, Belkin M, Uhler C. Simple, fast, and flexible framework for matrix completion with infinite width neural networks. Proc Natl Acad Sci U S A 2022; 119:e2115064119. [PMID: 35412891 PMCID: PMC9169779 DOI: 10.1073/pnas.2115064119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 01/17/2022] [Indexed: 11/18/2022] Open
Abstract
Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. Remarkably, taking the width of a neural network to infinity allows for improved computational performance. In this work, we develop an infinite width neural network framework for matrix completion that is simple, fast, and flexible. Simplicity and speed come from the connection between the infinite width limit of neural networks and kernels known as neural tangent kernels (NTK). In particular, we derive the NTK for fully connected and convolutional neural networks for matrix completion. The flexibility stems from a feature prior, which allows encoding relationships between coordinates of the target matrix, akin to semisupervised learning. The effectiveness of our framework is demonstrated through competitive results for virtual drug screening and image inpainting/reconstruction. We also provide an implementation in Python to make our framework accessible on standard hardware to a broad audience.
Collapse
Affiliation(s)
- Adityanarayanan Radhakrishnan
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - George Stefanakis
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Mikhail Belkin
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA 92093
| | - Caroline Uhler
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142
| |
Collapse
|
8
|
Pham TH, Qiu Y, Liu J, Zimmer S, O’Neill E, Xie L, Zhang P. Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing. PATTERNS 2022; 3:100441. [PMID: 35465231 PMCID: PMC9023899 DOI: 10.1016/j.patter.2022.100441] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/13/2021] [Accepted: 01/12/2022] [Indexed: 12/18/2022]
Abstract
Chemical-induced gene expression profiles provide critical information of chemicals in a biological system, thus offering new opportunities for drug discovery. Despite their success, large-scale analysis leveraging gene expressions is limited by time and cost. Although several methods for predicting gene expressions were proposed, they only focused on imputation and classification settings, which have limited applications to real-world scenarios of drug discovery. Therefore, a chemical-induced gene expression ranking (CIGER) framework is proposed to target a more realistic but more challenging setting in which overall rankings in gene expression profiles induced by de novo chemicals are predicted. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics. Furthermore, a drug screening pipeline based on CIGER is proposed to identify potential treatments of drug-resistant pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision medicine. A new deep-learning method (CIGER) for chemical-induced gene expression ranking CIGER can predict gene expression for de novo chemicals from chemical structures We discovered drugs for the treatment of drug-resistant pancreatic cancer
In recent years, a phenotype-based drug discovery approach using chemical-induced gene expressions has shown to be effective in drug discovery and precision medicine. However, it is not feasible to experimentally determine chemical-induced gene expressions for all available chemicals of interest, thereby hindering the application of gene expression-based compound screening on a large scale. Thus, it is crucial to design a computational approach that can generate gene expression information for any chemicals. We proposed a new, deep-learning framework named chemical-induced gene expression ranking (CIGER) to predict a landmark gene expression profile (i.e., gene ranking) induced by de novo chemicals based on their chemical structures. Leveraging CIGER, we predicted and experimentally validated that several existing drugs can increase the therapeutic response on drug-resistant pancreatic cancer. Our results demonstrated the effectiveness of CIGER for precision drug discovery in practice.
Collapse
Affiliation(s)
- Thai-Hoang Pham
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Yue Qiu
- Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, NY 10016, USA
| | - Jiahui Liu
- Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK
| | | | - Eric O’Neill
- Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK
- EpiCombi.AI Therapeutics, Oxford OX7 3SB, UK
| | - Lei Xie
- Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, NY 10016, USA
- Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, USA
- Ph.D. Program in Computer Science and Biochemistry, The Graduate Center, The City University of New York, New York, NY 10016, USA
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Ping Zhang
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
- Corresponding author
| |
Collapse
|
9
|
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.
Collapse
Affiliation(s)
- Abhay Sharma
- CSIR-Institute of Genomics and Integrative Biology, Sukhdev Vihar, Mathura Road, New Delhi 110025, India.
| |
Collapse
|
10
|
Kaitoh K, Yamanishi Y. TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning. J Chem Inf Model 2021; 61:4303-4320. [PMID: 34528432 DOI: 10.1021/acs.jcim.1c00967] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.
Collapse
Affiliation(s)
- Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| |
Collapse
|
11
|
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] [MESH Headings] [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.
Collapse
Affiliation(s)
- Abhay Sharma
- CSIR-Institute of Genomics and Integrative Biology, Sukhdev Vihar, Mathura Road, New Delhi 110025, India.
| |
Collapse
|
12
|
Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
Collapse
Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| |
Collapse
|
13
|
Pham TH, Qiu Y, Zeng J, Xie L, Zhang P. A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing. NAT MACH INTELL 2021; 3:247-257. [PMID: 33796820 PMCID: PMC8009091 DOI: 10.1038/s42256-020-00285-9] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 12/15/2020] [Indexed: 12/15/2022]
Abstract
Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism. Chemical-induced gene expression profile provides a mechanistic signature of phenotypic response. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Few methods can perform phenotype-based de novo chemical compound screening. Here, we propose a mechanism-driven neural network-based method DeepCE, which utilizes graph neural network and multi-head attention mechanism to model chemical substructure-gene and gene-gene associations, for predicting the differential gene expression profile perturbed by de novo chemicals. Moreover, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves superior performances to state-of-the-art methods. The effectiveness of gene expression profiles generated from DeepCE is further supported by comparing them with observed data for downstream classification tasks. To demonstrate the value of DeepCE, we apply it to drug repurposing of COVID-19, and generate novel lead compounds consistent with clinical evidence. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data and screening novel chemicals for the modulation of a systemic response to disease.
Collapse
Affiliation(s)
- Thai-Hoang Pham
- Department of Computer Science and Engineering, The Ohio State University, Columbus, 43210, USA
| | - Yue Qiu
- Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, 10016, USA
| | - Jucheng Zeng
- Department of Biomedical Informatics, The Ohio State University, Columbus, 43210, USA
| | - Lei Xie
- Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, 10016, USA
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
- Ph.D. Program in Computer Science and Biochemistry, The Graduate Center, The City University of New York, New York, 10016, USA
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, 10021, USA
| | - Ping Zhang
- Department of Computer Science and Engineering, The Ohio State University, Columbus, 43210, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, 43210, USA
- Translational Data Analytics institute, The Ohio State University, Columbus, 43210, USA
| |
Collapse
|
14
|
Supplitt S, Karpinski P, Sasiadek M, Laczmanska I. Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine. Int J Mol Sci 2021; 22:1422. [PMID: 33572595 PMCID: PMC7866970 DOI: 10.3390/ijms22031422] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 12/12/2022] Open
Abstract
Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.
Collapse
Affiliation(s)
- Stanislaw Supplitt
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1, 50-368 Wroclaw, Poland; (P.K.); (M.S.); (I.L.)
| | - Pawel Karpinski
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1, 50-368 Wroclaw, Poland; (P.K.); (M.S.); (I.L.)
- Laboratory of Genomics and Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Weigla 12, 53-114 Wroclaw, Poland
| | - Maria Sasiadek
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1, 50-368 Wroclaw, Poland; (P.K.); (M.S.); (I.L.)
| | - Izabela Laczmanska
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1, 50-368 Wroclaw, Poland; (P.K.); (M.S.); (I.L.)
| |
Collapse
|
15
|
Pham TH, Qiu Y, Zeng J, Xie L, Zhang P. A deep learning framework for high-throughput mechanism-driven phenotype compound screening. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 32743586 PMCID: PMC7386506 DOI: 10.1101/2020.07.19.211235] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.
Collapse
Affiliation(s)
- Thai-Hoang Pham
- The Ohio State University, Department of Computer Science and Engineering, Columbus, 43210, USA
| | - Yue Qiu
- The City University of New York, Ph.D. Program in Biology, The Graduate Center, New York, 10016, USA
| | - Jucheng Zeng
- The Ohio State University, Department of Biomedical Informatics, Columbus, 43210, USA
| | - Lei Xie
- The City University of New York, Ph.D. Program in Biology, The Graduate Center, New York, 10016, USA.,Hunter College, The City University of New York, Department of Computer Science, New York, 10065, USA.,The City University of New York, Ph.D. Program in Computer Science and Biochemistry, The Graduate Center, New York, 10016, USA.,Weill Cornell Medicine, Cornell University, Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain Mind Research Institute, New York, 10021, USA
| | - Ping Zhang
- The Ohio State University, Department of Computer Science and Engineering, Columbus, 43210, USA.,The Ohio State University, Department of Biomedical Informatics, Columbus, 43210, USA
| |
Collapse
|
16
|
Iida M, Iwata M, Yamanishi Y. Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. Bioinformatics 2020; 36:i516-i524. [PMID: 32657408 PMCID: PMC7355285 DOI: 10.1093/bioinformatics/btaa439] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
MOTIVATION Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease-disease relationships remain uncharacterized. RESULTS In this study, we proposed a novel approach for network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease-disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Midori Iida
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| |
Collapse
|