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Srimadh Bhagavatham SK, Pulukool SK, Pradhan SS, R S, Ashok Naik A, V M DD, Sivaramakrishnan V. Systems biology approach delineates critical pathways associated with disease progression in rheumatoid arthritis. J Biomol Struct Dyn 2022:1-22. [PMID: 36047508 DOI: 10.1080/07391102.2022.2115555] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease leading to inflammation, cartilage cell death, synoviocyte proliferation, and increased and impaired differentiation of osteoclasts and osteoblasts leading to joint erosions and deformities. Transcriptomics, proteomics, and metabolomics datasets were analyzed to identify the critical pathways that drive the RA pathophysiology. Single nucleotide polymorphisms (SNPs) associated with RA were analyzed for the functional implications, clinical outcomes, and blood parameters later validated by literature. SNPs associated with RA were grouped into pathways that drive the immune response and cytokine production. Further gene set enrichment analysis (GSEA) was performed on gene expression omnibus (GEO) data sets of peripheral blood mononuclear cells (PBMCs), synovial macrophages, and synovial biopsies from RA patients showed enrichment of Th1, Th2, Th17 differentiation, viral and bacterial infections, metabolic signalling and immunological pathways with potential implications for RA. The proteomics data analysis presented pathways with genes involved in immunological signaling and metabolic pathways, including vitamin B12 and folate metabolism. Metabolomics datasets analysis showed significant pathways like amino-acyl tRNA biosynthesis, metabolism of amino acids (arginine, alanine aspartate, glutamate, glutamine, phenylalanine, and tryptophan), and nucleotide metabolism. Furthermore, our commonality analysis of multi-omics datasets identified common pathways with potential implications for joint remodeling in RA. Disease-modifying anti-rheumatic drugs (DMARDs) and biologics treatments were found to modulate many of the pathways that were deregulated in RA. Overall, our analysis identified molecular signatures associated with the observed symptoms, joint erosions, potential biomarkers, and therapeutic targets in RA. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Sujith Kumar Pulukool
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Anantapur, A.P., India
| | - Sai Sanwid Pradhan
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Anantapur, A.P., India
| | - Saiswaroop R
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Anantapur, A.P., India
| | - Ashwin Ashok Naik
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Anantapur, A.P., India
| | - Datta Darshan V M
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Anantapur, A.P., India
| | - Venketesh Sivaramakrishnan
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Anantapur, A.P., India
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Yan J, Hu Z, Li ZW, Sun S, Guo WF. Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer. Front Oncol 2022; 12:891676. [PMID: 35712516 PMCID: PMC9195174 DOI: 10.3389/fonc.2022.891676] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022] Open
Abstract
Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.
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Affiliation(s)
- Jipeng Yan
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Zhuo Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Zong-Wei Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiren Sun
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
| | - Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
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Wang N, Du N, Peng Y, Yang K, Shu Z, Chang K, Wu D, Yu J, Jia C, Zhou Y, Li X, Liu B, Gao Z, Zhang R, Zhou X. Network Patterns of Herbal Combinations in Traditional Chinese Clinical Prescriptions. Front Pharmacol 2021; 11:590824. [PMID: 33551800 PMCID: PMC7854460 DOI: 10.3389/fphar.2020.590824] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/15/2020] [Indexed: 12/13/2022] Open
Abstract
As a well-established multidrug combinations schema, traditional Chinese medicine (herbal prescription) has been used for thousands of years in real-world clinical settings. This paper uses a complex network approach to investigate the regularities underlying multidrug combinations in herbal prescriptions. Using five collected large-scale real-world clinical herbal prescription datasets, we construct five weighted herbal combination networks with herb as nodes and herbal combinational use in herbal prescription as links. We found that the weight distribution of herbal combinations displays a clear power law, which means that most herb pairs were used in low frequency and some herb pairs were used in very high frequency. Furthermore, we found that it displays a clear linear negative correlation between the clustering coefficients and the degree of nodes in the herbal combination network (HCNet). This indicates that hierarchical properties exist in the HCNet. Finally, we investigate the molecular network interaction patterns between herb related target modules (i.e., subnetworks) in herbal prescriptions using a network-based approach and further explore the correlation between the distribution of herb combinations and prescriptions. We found that the more the hierarchical prescription, the better the corresponding effect. The results also reflected a well-recognized principle called “Jun-Chen-Zuo-Shi” in TCM formula theories. This also gives references for multidrug combination development in the field of network pharmacology and provides the guideline for the clinical use of combination therapy for chronic diseases.
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Affiliation(s)
- Ning Wang
- Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Ninglin Du
- Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Yonghong Peng
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | - Kuo Yang
- Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Zixin Shu
- Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Kai Chang
- Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Di Wu
- Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States.,Department of Biostatistics, UNC Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Jian Yu
- Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Caiyan Jia
- Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Yana Zhou
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Xiaodong Li
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Baoyan Liu
- China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhuye Gao
- National Clinical Research Center for Chinese Medicine Cardiology, Beijing, China.,Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Runshun Zhang
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.,Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xuezhong Zhou
- Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
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