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Park S, Kim OH, Lee K, Park IB, Kim NH, Moon S, Im J, Sharma SP, Oh BC, Nam S, Lee DH. Plasma and urinary extracellular vesicle microRNAs and their related pathways in diabetic kidney disease. Genomics 2022; 114:110407. [PMID: 35716820 DOI: 10.1016/j.ygeno.2022.110407] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/22/2022] [Accepted: 06/10/2022] [Indexed: 11/30/2022]
Abstract
To explore extracellular vesicle microRNAs (EV miRNAs) and their target mRNAs in relation to diabetic kidney disease (DKD), we performed paired plasma and urinary EV small RNA sequencing (n = 18) in patients with type 2 diabetes and DKD (n = 5) and healthy subjects (n = 4) and metabolic network analyses using our own miRNA and public mRNA datasets. We found 13 common differentially expressed EV miRNAs in both fluids and 17 target mRNAs, including RRM2, NT5E, and UGDH. Because succinate dehydrogenase B was suggested to interact with proteins encoded by these three genes, we measured urinary succinate and adenosine in a validation study (n = 194). These two urinary metabolite concentrations were associated with DKD progression. In addition, renal expressions of NT5E and UGDH proteins were increased in db/db mice with DKD compared to control mice. In conclusion, we profiled DKD-related EV miRNAs in plasma and urine samples and found their relevant target pathways.
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Affiliation(s)
- Sungjin Park
- Department of Genome Medicine and Science, AI Convergence Center for Medical Science, Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea
| | - Ok-Hee Kim
- Department of Physiology, Lee Gil Ya Cancer and Diabetes Institute, College of Medicine, Gachon University, Incheon, Republic of Korea
| | - Kiyoung Lee
- Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea; Department of Internal Medicine, Gachon University College of Medicine, Incheon, Republic of Korea
| | - Ie Byung Park
- Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea; Department of Internal Medicine, Gachon University College of Medicine, Incheon, Republic of Korea
| | - Nan Hee Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Seongryeol Moon
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
| | - Jaebeen Im
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
| | - Satya Priya Sharma
- Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Byung-Chul Oh
- Department of Physiology, Lee Gil Ya Cancer and Diabetes Institute, College of Medicine, Gachon University, Incheon, Republic of Korea
| | - Seungyoon Nam
- Department of Genome Medicine and Science, AI Convergence Center for Medical Science, Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea; Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea.
| | - Dae Ho Lee
- Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea; Department of Internal Medicine, Gachon University College of Medicine, Incheon, Republic of Korea; Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea.
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2
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Song K, Liu C, Zhang J, Yao Y, Xiao H, Yuan R, Li K, Yang J, Zhao W, Zhang Y. Integrated multi-omics analysis reveals miR-20a as a regulator for metabolic colorectal cancer. Heliyon 2022; 8:e09068. [PMID: 35284668 PMCID: PMC8914124 DOI: 10.1016/j.heliyon.2022.e09068] [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: 11/08/2021] [Revised: 12/21/2021] [Accepted: 03/03/2022] [Indexed: 11/28/2022] Open
Abstract
Single-driver molecular events specific to the metabolic colorectal cancer (CRC) have not been clearly elucidated. Herein, we identified 12 functional miRNAs linked to activated metabolism by integrating multi-omics features in metabolic CRC. These miRNAs exhibited significantly enriched CRC driver miRNAs, significant impacts on CRC cell growth and significantly correlated metabolites. Importantly, miR-20a is minimally expressed in normal colorectal tissues but highly expressed in metabolic CRC, suggesting the potential therapeutic target. Bioinformatics analyses further revealed miR-20a as the most powerful determinant that regulates a cascade of dysregulated events, including Wnt signaling pathway, core enzymes involved in FA metabolism program and triacylglycerol abundances. In vitro assays demonstrated that elevated miR-20a up-regulated FA synthesis enzymes via Wnt/β-catenin signaling, and finally promoted proliferative and migration of metabolic CRC cells. Overall, our study revealed that miR-20a promoted progression of metabolic CRC by regulating FA metabolism and served as a potential target for preventing tumor metastasis.
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Affiliation(s)
- Kai Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
- Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, Guangdong, 519000, China
| | - Chao Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150086, China
| | - Jiashuai Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Yang Yao
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150086, China
| | - Huiting Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Rongqiang Yuan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Keru Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Jia Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Wenyuan Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
- Corresponding author.
| | - Yanqiao Zhang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150086, China
- Corresponding author.
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Liu Y, Cui Y, Bai X, Feng C, Li M, Han X, Ai B, Zhang J, Li X, Han J, Zhu J, Jiang Y, Pan Q, Wang F, Xu M, Li C, Wang Q. MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer. Front Genet 2020; 11:606940. [PMID: 33362865 PMCID: PMC7756031 DOI: 10.3389/fgene.2020.606940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 11/13/2020] [Indexed: 12/16/2022] Open
Abstract
Background Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients. Methods We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules. Results We identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets. Conclusions Our study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed.
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Affiliation(s)
- Yuejuan Liu
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Yuxia Cui
- School of Nursing, Harbin Medical University, Daqing, China
| | - Xuefeng Bai
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Chenchen Feng
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Meng Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Xiaole Han
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Bo Ai
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jian Zhang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Xuecang Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jiang Zhu
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Yong Jiang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Qi Pan
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Fan Wang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Mingcong Xu
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Chunquan Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Qiuyu Wang
- School of Medical Informatics, Harbin Medical University, Daqing, China
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Li M, Zhao J, Li X, Chen Y, Feng C, Qian F, Liu Y, Zhang J, He J, Ai B, Ning Z, Liu W, Bai X, Han X, Wu Z, Xu X, Tang Z, Pan Q, Xu L, Li C, Wang Q, Li E. HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures. Brief Bioinform 2020; 21:1411-1424. [PMID: 31350847 DOI: 10.1093/bib/bbz078] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/19/2019] [Accepted: 06/04/2019] [Indexed: 02/05/2023] Open
Abstract
With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.
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Affiliation(s)
- Meng Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Jianmei Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Xuecang Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Yang Chen
- Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Fengcui Qian
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Yuejuan Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Jianzhong He
- Institute of Oncologic Pathology, Shantou University Medical College
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Ziyu Ning
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Wei Liu
- Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - Xuefeng Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Xiaole Han
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Zhiyong Wu
- Departments of Oncology Surgery, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-Sen University
| | - Xiue Xu
- Institute of Oncologic Pathology, Shantou University Medical College
| | - Zhidong Tang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Qi Pan
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Liyan Xu
- Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Qiuyu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China
| | - Enmin Li
- Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
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Wang P, Li W, Zhai B, Jiang X, Jiang H, Zhang C, Sun X. Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis. J Cell Biochem 2020; 121:3090-3098. [PMID: 31886578 DOI: 10.1002/jcb.29576] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 12/11/2019] [Indexed: 12/17/2022]
Abstract
Pancreatic cancer is a malignancy of the digestive system characterized by poor prognosis. A number of prognostic messenger RNA (mRNA) signatures have been identified by using the high-throughput expression profiles. MicroRNAs (miRNA) play a critical role in regulating multiple cellular functions. However, no such integrated analysis of miRNAs and mRNAs for studying the prognostic mechanisms of pancreatic cancer has been reported. In this study, we first identified prognostic mRNAs and miRNAs based on The Cancer Genome Atlas datasets, and then performed an enrichment analysis to explore the underlying biological mechanisms involved in pancreatic cancer prognosis at the mRNA level. Furthermore, we performed an integrated analysis of mRNAs and miRNAs to identify prognostic subpathways, which were closely associated with pancreatic cancer genes and tumor hallmarks and involved in hypoxia, oxidative phosphyorylation and xenobiotic metabolisms. Meanwhile, we performed a random walk algorithm based on global network, prognostic mRNAs and miRNAs, and identified top risk mRNAs as the prognostic signature. Finally, an independent testing set was used to confirm the predictive power of the top mRNA signature, and most of these genes involved were known oncogenes. In conclusion, we performed a series of integrated analyses by comprehensively exploring pancreatic cancer prognosis and systematically optimized the prognostic signature for clinical use.
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Affiliation(s)
- Ping Wang
- The Hepatosplenic Surgery Center, Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of Interventional Radiology, The Third Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Weidong Li
- The Hepatosplenic Surgery Center, Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of General Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Bo Zhai
- The Hepatosplenic Surgery Center, Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of General Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xian Jiang
- The Hepatosplenic Surgery Center, Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hongchi Jiang
- The Hepatosplenic Surgery Center, Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chunlong Zhang
- Division of Computer and Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xueying Sun
- The Hepatosplenic Surgery Center, Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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6
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Tian S, Mi W, Zhang M, Xing L, Zhang C. Comprehensive analysis of mRNA-level and miRNA-level subpathway activities for identifying robust ovarian cancer prognostic signatures. J Cell Mol Med 2020; 24:2582-2592. [PMID: 31957240 PMCID: PMC7028850 DOI: 10.1111/jcmm.14968] [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: 06/02/2019] [Revised: 12/16/2019] [Accepted: 12/21/2019] [Indexed: 12/20/2022] Open
Abstract
Ovarian cancer (OvCa) causes the highest mortality among all gynaecologic cancers. A large number of mRNA‐ or miRNA‐based signatures were identified for OvCa patient prognosis. However, the comprehensive analysis of function‐level prognostic signatures is currently not considered in OvCa. In the present study, we respectively inferred subpathway activities from mRNA and miRNA levels based on high‐throughput expression profiles and reconstructed subpathways. Firstly, the activities of two tumour pathways were calculated and the difference between normal and tumour samples were analysed using multiple tumour types. Then, we calculated subpathway activities for OvCa based on the expression profiles from both mRNA and miRNA levels. Furthermore, based on these subpathway activity matrices, we performed bootstrap analysis to obtain sub‐training sets and utilized univariate method to identify robust OvCa prognostic subpathways. A comprehensive comparison of subpathway results between these two levels was performed. As a result, we observed subpathway mutual exclusion trend between the levels of mRNA and miRNA, which indicated the necessary of combining mRNA‐miRNA levels. Finally, by using ICGC data as testing sets, we utilized two strategies to verify survival predictive power of the mRNA‐miRNA combined subpathway signatures and performed comparisons with results from individual levels. It was confirmed that our framework displayed application to identify robust and efficient prognostic signatures for OvCa, and the combined signatures indeed exhibited advantages over individual ones. In the study, we took a step forward in relevant novel integrated functional signatures for OvCa prognosis.
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Affiliation(s)
- Songyu Tian
- Department of Gynecological Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wanqi Mi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingyue Zhang
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Linan Xing
- Department of Gynecological Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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7
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Zeidler M, Hüttenhofer A, Kress M, Kummer KK. Intragenic MicroRNAs Autoregulate Their Host Genes in Both Direct and Indirect Ways-A Cross-Species Analysis. Cells 2020; 9:cells9010232. [PMID: 31963421 PMCID: PMC7016697 DOI: 10.3390/cells9010232] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 01/10/2020] [Accepted: 01/14/2020] [Indexed: 12/15/2022] Open
Abstract
MicroRNAs (miRNAs) function as master switches for post-transcriptional gene expression. Their genes are either located in the extragenic space or within host genes, but these intragenic miRNA::host gene interactions are largely enigmatic. The aim of this study was to investigate the location and co-regulation of all to date available miRNA sequences and their host genes in an unbiased computational approach. The majority of miRNAs were located within intronic regions of protein-coding and non-coding genes. These intragenic miRNAs exhibited both increased target probability as well as higher target prediction scores as compared to a model of randomly permutated genes. This was associated with a higher number of miRNA recognition elements for the hosted miRNAs within their host genes. In addition, strong indirect autoregulation of host genes through modulation of functionally connected gene clusters by intragenic miRNAs was demonstrated. In addition to direct miRNA-to-host gene targeting, intragenic miRNAs also appeared to interact with functionally related genes, thus affecting their host gene function through an indirect autoregulatory mechanism. This strongly argues for the biological relevance of autoregulation not only for the host genes themselves but, more importantly, for the entire gene cluster interacting with the host gene.
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Affiliation(s)
- Maximilian Zeidler
- Institute of Physiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Alexander Hüttenhofer
- Institute of Genomics and RNomics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Michaela Kress
- Institute of Physiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Kai K. Kummer
- Institute of Physiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Correspondence: ; Tel.: +43-650-970-0514; Fax: +43-512-9003-73800
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Alaimo S, Micale G, La Ferlita A, Ferro A, Pulvirenti A. Computational Methods to Investigate the Impact of miRNAs on Pathways. Methods Mol Biol 2019; 1970:183-209. [PMID: 30963494 DOI: 10.1007/978-1-4939-9207-2_11] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.
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Affiliation(s)
- Salvatore Alaimo
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Giovanni Micale
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | | | - Alfredo Ferro
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
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9
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Das SS, Saha P, Chakravorty N. miRwayDB: a database for experimentally validated microRNA-pathway associations in pathophysiological conditions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4915493. [PMID: 29688364 PMCID: PMC5829561 DOI: 10.1093/database/bay023] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 02/07/2018] [Indexed: 01/31/2023]
Abstract
MicroRNAs (miRNAs) are well-known as key regulators of diverse biological pathways. A series of experimental evidences have shown that abnormal miRNA expression profiles are responsible for various pathophysiological conditions by modulating genes in disease associated pathways. In spite of the rapid increase in research data confirming such associations, scientists still do not have access to a consolidated database offering these miRNA-pathway association details for critical diseases. We have developed miRwayDB, a database providing comprehensive information of experimentally validated miRNA-pathway associations in various pathophysiological conditions utilizing data collected from published literature. To the best of our knowledge, it is the first database that provides information about experimentally validated miRNA mediated pathway dysregulation as seen specifically in critical human diseases and hence indicative of a cause-and-effect relationship in most cases. The current version of miRwayDB collects an exhaustive list of miRNA-pathway association entries for 76 critical disease conditions by reviewing 663 published articles. Each database entry contains complete information on the name of the pathophysiological condition, associated miRNA(s), experimental sample type(s), regulation pattern (up/down) of miRNA, pathway association(s), targeted member of dysregulated pathway(s) and a brief description. In addition, miRwayDB provides miRNA, gene and pathway score to evaluate the role of a miRNA regulated pathways in various pathophysiological conditions. The database can also be used for other biomedical approaches such as validation of computational analysis, integrated analysis and prediction of computational model. It also offers a submission page to submit novel data from recently published studies. We believe that miRwayDB will be a useful tool for miRNA research community. Database URL: http://www.mirway.iitkgp.ac.in
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Affiliation(s)
- Sankha Subhra Das
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal, 721302, India
| | - Pritam Saha
- Cryogenic Engineering Centre, Indian Institute of Technology, Kharagpur, West Bengal, 721302, India
| | - Nishant Chakravorty
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal, 721302, India
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10
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Han J, Liu S, Jiang Y, Xu C, Zheng B, Jiang M, Yang H, Su F, Li C, Zhang Y. Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer. J Cell Mol Med 2018; 22:4304-4316. [PMID: 29971923 PMCID: PMC6111825 DOI: 10.1111/jcmm.13720] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 05/13/2018] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.
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Affiliation(s)
- Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Siyao Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ying Jiang
- College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Baotong Zheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Minghao Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Fei Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Harbin, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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11
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Identification of a robust subpathway-based signature for acute myeloid leukemia prognosis using an miRNA integrated strategy. PLoS One 2018; 13:e0194245. [PMID: 29570744 PMCID: PMC5865743 DOI: 10.1371/journal.pone.0194245] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 02/27/2018] [Indexed: 01/22/2023] Open
Abstract
Acute myeloid leukemia (AML) is a heterogeneous disease, and survival signatures are urgently needed to better monitor treatment. MiRNAs displayed vital regulatory roles on target genes, which was necessary involved in the complex disease. We therefore examined the expression levels of miRNAs and genes to identify robust signatures for survival benefit analyses. First, we reconstructed subpathway graphs by embedding miRNA components that were derived from low-throughput miRNA-gene interactions. Then, we randomly divided the data sets from The Cancer Genome Atlas (TCGA) into training and testing sets, and further formed 100 subsets based on the training set. Using each subset, we identified survival-related miRNAs and genes, and identified survival subpathways based on the reconstructed subpathway graphs. After statistical analyses of these survival subpathways, the most robust subpathways with the top three ranks were identified, and risk scores were calculated based on these robust subpathways for AML patient prognoses. Among these robust subpathways, three representative subpathways, path: 05200_10 from Pathways in cancer, path: 04110_20 from Cell cycle, and path: 04510_8 from Focal adhesion, were significantly associated with patient survival in the TCGA training and testing sets based on subpathway risk scores. In conclusion, we performed integrated analyses of miRNAs and genes to identify robust prognostic subpathways, and calculated subpathway risk scores to characterize AML patient survival.
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Computational Inferring of Risk Subpathways Mediated by Dysfunctional Non-coding RNAs. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1094:87-95. [DOI: 10.1007/978-981-13-0719-5_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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13
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Zhang CL, Xu YJ, Yang HX, Xu YQ, Shang DS, Wu T, Zhang YP, Li X. sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses. Sci Rep 2017; 7:15322. [PMID: 29127397 PMCID: PMC5681640 DOI: 10.1038/s41598-017-15631-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 10/30/2017] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.
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Affiliation(s)
- Chun-Long Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yan-Jun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hai-Xiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Ying-Qi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - De-Si Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Tan Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yun-Peng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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Alaimo S, Marceca GP, Ferro A, Pulvirenti A. Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach. Noncoding RNA 2017; 3:ncrna3020020. [PMID: 29657291 PMCID: PMC5831934 DOI: 10.3390/ncrna3020020] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Revised: 03/28/2017] [Accepted: 04/10/2017] [Indexed: 12/14/2022] Open
Abstract
In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states.
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Affiliation(s)
- Salvatore Alaimo
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, c/o Dipartimento di Matematica e Informatica, Viale A. Doria 6, 95125 Catania, Italy.
| | - Gioacchino Paolo Marceca
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, c/o Dipartimento di Matematica e Informatica, Viale A. Doria 6, 95125 Catania, Italy.
| | - Alfredo Ferro
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, c/o Dipartimento di Matematica e Informatica, Viale A. Doria 6, 95125 Catania, Italy.
| | - Alfredo Pulvirenti
- Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, c/o Dipartimento di Matematica e Informatica, Viale A. Doria 6, 95125 Catania, Italy.
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15
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Park DI, Dournes C, Sillaber I, Uhr M, Asara JM, Gassen NC, Rein T, Ising M, Webhofer C, Filiou MD, Müller MB, Turck CW. Purine and pyrimidine metabolism: Convergent evidence on chronic antidepressant treatment response in mice and humans. Sci Rep 2016; 6:35317. [PMID: 27731396 PMCID: PMC5059694 DOI: 10.1038/srep35317] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 08/24/2016] [Indexed: 12/13/2022] Open
Abstract
Selective Serotonin Reuptake Inhibitors (SSRIs) are commonly used drugs for the treatment of psychiatric diseases including major depressive disorder (MDD). For unknown reasons a substantial number of patients do not show any improvement during or after SSRI treatment. We treated DBA/2J mice for 28 days with paroxetine and assessed their behavioral response with the forced swim test (FST). Paroxetine-treated long-time floating (PLF) and paroxetine-treated short-time floating (PSF) groups were stratified as proxies for drug non-responder and responder mice, respectively. Proteomics and metabolomics profiles of PLF and PSF groups were acquired for the hippocampus and plasma to identify molecular pathways and biosignatures that stratify paroxetine-treated mouse sub-groups. The critical role of purine and pyrimidine metabolisms for chronic paroxetine treatment response in the mouse was further corroborated by pathway protein expression differences in both mice and patients that underwent chronic antidepressant treatment. The integrated -omics data indicate purine and pyrimidine metabolism pathway activity differences between PLF and PSF mice. Furthermore, the pathway protein levels in peripheral specimens strongly correlated with the antidepressant treatment response in patients. Our results suggest that chronic SSRI treatment differentially affects purine and pyrimidine metabolisms, which may explain the heterogeneous antidepressant treatment response and represents a potential biosignature.
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Affiliation(s)
- Dong Ik Park
- Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, 80804, Munich, Germany
| | - Carine Dournes
- Max Planck Institute of Psychiatry, Department of Stress Neurobiology and Neurogenetics, 80804 Munich, Germany
| | | | - Manfred Uhr
- Max Planck Institute of Psychiatry, Department of Clinical Research, 80804 Munich, Germany
| | - John M Asara
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Nils C Gassen
- Max Planck Institute of Psychiatry, Department of Stress Neurobiology and Neurogenetics, 80804 Munich, Germany
| | - Theo Rein
- Max Planck Institute of Psychiatry, Department of Stress Neurobiology and Neurogenetics, 80804 Munich, Germany
| | - Marcus Ising
- Max Planck Institute of Psychiatry, Department of Clinical Research, 80804 Munich, Germany
| | - Christian Webhofer
- Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, 80804, Munich, Germany
| | - Michaela D Filiou
- Max Planck Institute of Psychiatry, Department of Stress Neurobiology and Neurogenetics, 80804 Munich, Germany
| | - Marianne B Müller
- Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, 80804, Munich, Germany.,Experimental Psychiatry, Department of Psychiatry and Psychotherapy &Focus Program Translational Neuroscience, Johannes Gutenberg University Medical Center, 55128 Mainz, Germany
| | - Christoph W Turck
- Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, 80804, Munich, Germany
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Pinweha P, Rattanapornsompong K, Charoensawan V, Jitrapakdee S. MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers. Comput Struct Biotechnol J 2016; 14:223-33. [PMID: 27358718 PMCID: PMC4915959 DOI: 10.1016/j.csbj.2016.05.005] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 05/25/2016] [Accepted: 05/27/2016] [Indexed: 12/15/2022] Open
Abstract
Altered cellular metabolism is a fundamental adaptation of cancer during rapid proliferation as a result of growth factor overstimulation. We review different pathways involving metabolic alterations in cancers including aerobic glycolysis, pentose phosphate pathway, de novo fatty acid synthesis, and serine and glycine metabolism. Although oncoproteins, c-MYC, HIF1α and p53 are the major drivers of this metabolic reprogramming, post-transcriptional regulation by microRNAs (miR) also plays an important role in finely adjusting the requirement of the key metabolic enzymes underlying this metabolic reprogramming. We also combine the literature data on the miRNAs that potentially regulate 40 metabolic enzymes responsible for metabolic reprogramming in cancers, with additional miRs from computational prediction. Our analyses show that: (1) a metabolic enzyme is frequently regulated by multiple miRs, (2) confidence scores from prediction algorithms might be useful to help narrow down functional miR-mRNA interaction, which might be worth further experimental validation. By combining known and predicted interactions of oncogenic transcription factors (TFs) (c-MYC, HIF1α and p53), sterol regulatory element binding protein 1 (SREBP1), 40 metabolic enzymes, and regulatory miRs we have established one of the first reference maps for miRs and oncogenic TFs that regulate metabolic reprogramming in cancers. The combined network shows that glycolytic enzymes are linked to miRs via p53, c-MYC, HIF1α, whereas the genes in serine, glycine and one carbon metabolism are regulated via the c-MYC, as well as other regulatory organization that cannot be observed by investigating individual miRs, TFs, and target genes.
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Key Words
- 2-HG, 2-hydroxyglutarate
- ACC, acetyl-CoA carboxylase
- ACL, ATP-citrate lyase
- BRCA1, breast cancer type 1 susceptibility protein
- Cancer
- FAS, fatty acid synthase
- FH, fumarate hydratase
- G6PD, glucose-6-phosphate dehydrogenase
- GDH, glutamate dehydrogenase
- GLS, glutaminase
- GLUT, glucose transporter
- HIF1α, hypoxia inducible factor 1α
- HK, hexokinase
- IDH, isocitrate dehydrogenase
- MCT, monocarboxylic acid transporter
- ME, malic enzyme
- Metabolism
- MicroRNA
- Oncogene
- PC, pyruvate carboxylase
- PDH, pyruvate dehydrogenase
- PDK, pyruvate dehydrogenase kinase
- PEP, phosphoenolpyruvate
- PEPCK, phosphoenolpyruvate carboxykinase
- PFK, phosphofructokinase
- PGK, phosphoglycerate kinase (PGK)
- PHGDH, phosphoglycerate dehydrogenase
- PKM, muscle-pyruvate kinase
- PPP, pentose phosphate pathway
- PSAT, phosphoserine aminotransferase
- PSPH, phosphoserine phosphatase
- SDH, succinate dehydrogenase
- SHMT, serine hydroxymethyl transferase
- SREBP1, sterol regulatory element binding protein 1
- TCA, tricarboxylic acid
- TFs, transcription factors
- Transcriptional regulation network
- c-MYC, V-myc avian myelocytomatosis viral oncogene homolog
- miR/miRNA, LDH, lactate dehydrogenase micro RNA
- p53, tumor protein p53
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Affiliation(s)
- Pannapa Pinweha
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | | | - Varodom Charoensawan
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom 73170, Thailand
| | - Sarawut Jitrapakdee
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
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