1
|
Li S, Yan B, Wu B, Su J, Lu J, Lam TW, Boheler KR, Poon ENY, Luo R. Integrated modeling framework reveals co-regulation of transcription factors, miRNAs and lncRNAs on cardiac developmental dynamics. Stem Cell Res Ther 2023; 14:247. [PMID: 37705079 PMCID: PMC10500942 DOI: 10.1186/s13287-023-03442-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 08/07/2023] [Indexed: 09/15/2023] Open
Abstract
AIMS Dissecting complex interactions among transcription factors (TFs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are central for understanding heart development and function. Although computational approaches and platforms have been described to infer relationships among regulatory factors and genes, current approaches do not adequately account for how highly diverse, interacting regulators that include noncoding RNAs (ncRNAs) control cardiac gene expression dynamics over time. METHODS To overcome this limitation, we devised an integrated framework, cardiac gene regulatory modeling (CGRM) that integrates LogicTRN and regulatory component analysis bioinformatics modeling platforms to infer complex regulatory mechanisms. We then used CGRM to identify and compare the TF-ncRNA gene regulatory networks that govern early- and late-stage cardiomyocytes (CMs) generated by in vitro differentiation of human pluripotent stem cells (hPSC) and ventricular and atrial CMs isolated during in vivo human cardiac development. RESULTS Comparisons of in vitro versus in vivo derived CMs revealed conserved regulatory networks among TFs and ncRNAs in early cells that significantly diverged in late staged cells. We report that cardiac genes ("heart targets") expressed in early-stage hPSC-CMs are primarily regulated by MESP1, miR-1, miR-23, lncRNAs NEAT1 and MALAT1, while GATA6, HAND2, miR-200c, NEAT1 and MALAT1 are critical for late hPSC-CMs. The inferred TF-miRNA-lncRNA networks regulating heart development and contraction were similar among early-stage CMs, among individual hPSC-CM datasets and between in vitro and in vivo samples. However, genes related to apoptosis, cell cycle and proliferation, and transmembrane transport showed a high degree of divergence between in vitro and in vivo derived late-stage CMs. Overall, late-, but not early-stage CMs diverged greatly in the expression of "heart target" transcripts and their regulatory mechanisms. CONCLUSIONS In conclusion, we find that hPSC-CMs are regulated in a cell autonomous manner during early development that diverges significantly as a function of time when compared to in vivo derived CMs. These findings demonstrate the feasibility of using CGRM to reveal dynamic and complex transcriptional and posttranscriptional regulatory interactions that underlie cell directed versus environment-dependent CM development. These results with in vitro versus in vivo derived CMs thus establish this approach for detailed analyses of heart disease and for the analysis of cell regulatory systems in other biomedical fields.
Collapse
Affiliation(s)
- Shumin Li
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Bin Yan
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Binbin Wu
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Centre for Cardiovascular Genomics and Medicine, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Junhao Su
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Jianliang Lu
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Tak-Wah Lam
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Kenneth R Boheler
- The Division of Cardiology, Department of Medicine and The Whiting School of Engineering, Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, 21205, USA.
| | - Ellen Ngar-Yun Poon
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
- Centre for Cardiovascular Genomics and Medicine, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
- Hong Kong Hub of Paediatric Excellence (HK HOPE), The Chinese University of Hong Kong, Kowloon Bay, Hong Kong, China.
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China.
| |
Collapse
|
2
|
Zhen Z, Xue DJ, Chen YP, Li JH, Gao Y, Shen YB, Peng ZZ, Zhang N, Wang KX, Guan DG, Huang T. Decoding the underlying mechanisms of Di-Tan-Decoction in treating intracerebral hemorrhage based on network pharmacology. BMC Complement Med Ther 2023; 23:44. [PMID: 36765346 PMCID: PMC9912606 DOI: 10.1186/s12906-022-03831-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 12/29/2022] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Chinese medicine usually acts as "multi-ingredients, multi-targets and multi-pathways" on complex diseases, and these action modes reflect the coordination and integrity of the treatment process with traditional Chinese medicine (TCM). System pharmacology is developed based on the cross-disciplines of directional pharmacology, system biology, and mathematics, has the characteristics of integrity and synergy in the treatment process of TCM. Therefore, it is suitable for analyzing the key ingredients and mechanisms of TCM in treating complex diseases. Intracerebral Hemorrhage (ICH) is one of the leading causes of death in China, with the characteristics of high mortality and disability rate. Bring a significant burden on people and society. An increasing number of studies have shown that Chinese medicine prescriptions have good advantages in the treatment of ICH, and Ditan Decoction (DTT) is one of the commonly used prescriptions in the treatment of ICH. Modern pharmacological studies have shown that DTT may play a therapeutic role in treating ICH by inhibiting brain inflammation, abnormal oxidative stress reaction and reducing neurological damage, but the specific key ingredients and mechanism are still unclear. METHODS To solve this problem, we established PPI network based on the latest pathogenic gene data of ICH, and CT network based on ingredient and target data of DTT. Subsequently, we established optimization space based on PPI network and CT network, and constructed a new model for node importance calculation, and proposed a calculation method for PES score, thus calculating the functional core ingredients group (FCIG). These core functional groups may represent DTT therapy for ICH. RESULTS Based on the strategy, 44 ingredients were predicted as FCIG, results showed that 80.44% of the FCIG targets enriched pathways were coincided with the enriched pathways of pathogenic genes. Both the literature and molecular docking results confirm the therapeutic effect of FCIG on ICH via targeting MAPK signaling pathway and PI3K-Akt signaling pathway. CONCLUSIONS The FCIG obtained by our network pharmacology method can represent the effect of DTT in treating ICH. These results confirmed that our strategy of active ingredient group optimization and the mechanism inference could provide methodological reference for optimization and secondary development of TCM.
Collapse
Affiliation(s)
- Zheng Zhen
- grid.411866.c0000 0000 8848 7685The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Dao-jin Xue
- grid.411866.c0000 0000 8848 7685The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yu-peng Chen
- grid.284723.80000 0000 8877 7471Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China ,grid.484195.5Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou, Guangdong Province China
| | - Jia-hui Li
- grid.284723.80000 0000 8877 7471Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China ,grid.484195.5Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou, Guangdong Province China
| | - Yao Gao
- grid.263452.40000 0004 1798 4018Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, 030001 China
| | - You-bi Shen
- grid.411866.c0000 0000 8848 7685The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zi-zhuang Peng
- grid.411866.c0000 0000 8848 7685The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Nan Zhang
- grid.417404.20000 0004 1771 3058Neurosurgery Center, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, Department of Cerebrovascular Surgery, Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280 Guangdong China
| | - Ke-xin Wang
- grid.417404.20000 0004 1771 3058Neurosurgery Center, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, Department of Cerebrovascular Surgery, Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280 Guangdong China
| | - Dao-gang Guan
- grid.284723.80000 0000 8877 7471Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China ,grid.484195.5Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou, Guangdong Province China ,grid.284723.80000 0000 8877 7471Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Tao Huang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
| |
Collapse
|
3
|
Pan Y, Cao W, Mu Y, Zhu Q. Microfluidics Facilitates the Development of Single-Cell RNA Sequencing. BIOSENSORS 2022; 12:bios12070450. [PMID: 35884253 PMCID: PMC9312765 DOI: 10.3390/bios12070450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 12/12/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) technology provides a powerful tool for understanding complex biosystems at the single-cell and single-molecule level. The past decade has been a golden period for the development of single-cell sequencing, with scRNA-seq undergoing a tremendous leap in sensitivity and throughput. The application of droplet- and microwell-based microfluidics in scRNA-seq has contributed greatly to improving sequencing throughput. This review introduces the history of development and important technical factors of scRNA-seq. We mainly focus on the role of microfluidics in facilitating the development of scRNA-seq technology. To end, we discuss the future directions for scRNA-seq.
Collapse
Affiliation(s)
- Yating Pan
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Wenjian Cao
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
| | - Ying Mu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
| | - Qiangyuan Zhu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Huzhou Institute of Zhejiang University, Huzhou 313002, China
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
| |
Collapse
|
4
|
Chen YP, Wang KX, Cai JQ, Li Y, Yu HL, Wu Q, Meng W, Wang H, Yin CH, Wu J, Huang MB, Li R, Guan DG. Detecting Key Functional Components Group and Speculating the Potential Mechanism of Xiao-Xu-Ming Decoction in Treating Stroke. Front Cell Dev Biol 2022; 10:753425. [PMID: 35646921 PMCID: PMC9136080 DOI: 10.3389/fcell.2022.753425] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 02/25/2022] [Indexed: 02/05/2023] Open
Abstract
Stroke is a cerebrovascular event with cerebral blood flow interruption which is caused by occlusion or bursting of cerebral vessels. At present, the main methods in treating stroke are surgical treatment, statins, and recombinant tissue-type plasminogen activator (rt-PA). Relatively, traditional Chinese medicine (TCM) has widely been used at clinical level in China and some countries in Asia. Xiao-Xu-Ming decoction (XXMD) is a classical and widely used prescription in treating stroke in China. However, the material basis of effect and the action principle of XXMD are still not clear. To solve this issue, we designed a new system pharmacology strategy that combined targets of XXMD and the pathogenetic genes of stroke to construct a functional response space (FRS). The effective proteins from this space were determined by using a novel node importance calculation method, and then the key functional components group (KFCG) that could mediate the effective proteins was selected based on the dynamic programming strategy. The results showed that enriched pathways of effective proteins selected from FRS could cover 99.10% of enriched pathways of reference targets, which were defined by overlapping of component targets and pathogenetic genes. Targets of optimized KFCG with 56 components can be enriched into 166 pathways that covered 80.43% of 138 pathways of 1,012 pathogenetic genes. A component potential effect score (PES) calculation model was constructed to calculate the comprehensive effective score of components in the components-targets-pathways (C-T-P) network of KFCGs, and showed that ferulic acid, zingerone, and vanillic acid had the highest PESs. Prediction and docking simulations show that these components can affect stroke synergistically through genes such as MEK, NFκB, and PI3K in PI3K-Akt, cAMP, and MAPK cascade signals. Finally, ferulic acid, zingerone, and vanillic acid were tested to be protective for PC12 cells and HT22 cells in increasing cell viabilities after oxygen and glucose deprivation (OGD). Our proposed strategy could improve the accuracy on decoding KFCGs of XXMD and provide a methodologic reference for the optimization, mechanism analysis, and secondary development of the formula in TCM.
Collapse
Affiliation(s)
- Yu-peng Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Ke-xin Wang
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jie-qi Cai
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Yi Li
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hai-lang Yu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Qi Wu
- Department of Burns, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wei Meng
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Handuo Wang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Chuan-hui Yin
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Jie Wu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Mian-bo Huang
- Department of Histology and Embryology, Guangdong Provincial Key Laboratory of Construction and Detection in Tissue Engineering, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,*Correspondence: Mian-bo Huang, ; Rong Li, ; Dao-gang Guan,
| | - Rong Li
- Department of Cardiovascular Disease, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China,*Correspondence: Mian-bo Huang, ; Rong Li, ; Dao-gang Guan,
| | - Dao-gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China,*Correspondence: Mian-bo Huang, ; Rong Li, ; Dao-gang Guan,
| |
Collapse
|
5
|
Wu Q, Yin CH, Li Y, Cai JQ, Yang HY, Huang YY, Zheng YX, Xiong K, Yu HL, Lu AP, Wang KX, Guan DG, Chen YP. Detecting Critical Functional Ingredients Group and Mechanism of Xuebijing Injection in Treating Sepsis. Front Pharmacol 2021; 12:769190. [PMID: 34938184 PMCID: PMC8687625 DOI: 10.3389/fphar.2021.769190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Sepsis is a systemic inflammatory reaction caused by various infectious or noninfectious factors, which can lead to shock, multiple organ dysfunction syndrome, and death. It is one of the common complications and a main cause of death in critically ill patients. At present, the treatments of sepsis are mainly focused on the controlling of inflammatory response and reduction of various organ function damage, including anti-infection, hormones, mechanical ventilation, nutritional support, and traditional Chinese medicine (TCM). Among them, Xuebijing injection (XBJI) is an important derivative of TCM, which is widely used in clinical research. However, the molecular mechanism of XBJI on sepsis is still not clear. The mechanism of treatment of "bacteria, poison and inflammation" and the effects of multi-ingredient, multi-target, and multi-pathway have still not been clarified. For solving this issue, we designed a new systems pharmacology strategy which combines target genes of XBJI and the pathogenetic genes of sepsis to construct functional response space (FRS). The key response proteins in the FRS were determined by using a novel node importance calculation method and were condensed by a dynamic programming strategy to conduct the critical functional ingredients group (CFIG). The results showed that enriched pathways of key response proteins selected from FRS could cover 95.83% of the enriched pathways of reference targets, which were defined as the intersections of ingredient targets and pathogenetic genes. The targets of the optimized CFIG with 60 ingredients could be enriched into 182 pathways which covered 81.58% of 152 pathways of 1,606 pathogenetic genes. The prediction of CFIG targets showed that the CFIG of XBJI could affect sepsis synergistically through genes such as TAK1, TNF-α, IL-1β, and MEK1 in the pathways of MAPK, NF-κB, PI3K-AKT, Toll-like receptor, and tumor necrosis factor signaling. Finally, the effects of apigenin, baicalein, and luteolin were evaluated by in vitro experiments and were proved to be effective in reducing the production of intracellular reactive oxygen species in lipopolysaccharide-stimulated RAW264.7 cells, significantly. These results indicate that the novel integrative model can promote reliability and accuracy on depicting the CFIGs in XBJI and figure out a methodological coordinate for simplicity, mechanism analysis, and secondary development of formulas in TCM.
Collapse
Affiliation(s)
- Qi- Wu
- Department of Burns, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chuan-Hui Yin
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Province Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Yi Li
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jie-Qi Cai
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Province Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Han-Yun Yang
- The First Clinical Medical College of Southern Medical University, Guangzhou, China
| | - Ying-Ying Huang
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yi-Xu Zheng
- Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ke Xiong
- Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hai-Lang Yu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Province Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Ai-Ping Lu
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong China
| | - Ke-Xin Wang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Dao-Gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Province Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Yu-Peng Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Province Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| |
Collapse
|
6
|
Wang KX, Chen YP, Lu AP, Du GH, Qin XM, Guan DG, Gao L. A metabolic data-driven systems pharmacology strategy for decoding and validating the mechanism of Compound Kushen Injection against HCC. JOURNAL OF ETHNOPHARMACOLOGY 2021; 274:114043. [PMID: 33753143 DOI: 10.1016/j.jep.2021.114043] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 03/11/2021] [Accepted: 03/13/2021] [Indexed: 06/12/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Compound Kushen Injection (CKI) is a widely used TCM formula for treatment of carcinomatous pain and tumors of digestive system including hepatocellular carcinoma (HCC). However, the potential mechanisms of CKI for treatment of HCC have not been systematically and deeply studied. AIM OF STUDY A metabolic data-driven systems pharmacology approach was utilized to investigate the potential mechanisms of CKI for treatment of HCC. MATERIALS AND METHODS Based on phenotypic data generated by metabolomics and genotypic data of drug targets, a propagation model based on Dijkstra program was proposed to decode the effective network of key genotype-phenotype of CKI in treating HCC. The pivotal pathway was predicted by target propagation mode of our proposed model, and was validated in SMMC-7721 cells and diethylnitrosamine-induced rats. RESULTS Metabolomics results indicated that 12 differential metabolites, and 5 metabolic pathways might be involved in the anti-HCC effect of CKI. A total of 86 metabolic related genes that affected by CKI were obtained. The results calculated by propagation model showed that 6475 shortest distance chains might be involved in the anti-HCC effect of CKI. According to the results of propagation mode, EGFR was identified as the core target of CKI for the anti-HCC effect. Finally, EGFR and its related pathway EGFR-STAT3 signaling pathway were validated in vivo and in vitro. CONCLUSION The proposed method provides a methodological reference for explaining the underlying mechanism of TCM in treating HCC.
Collapse
Affiliation(s)
- Ke-Xin Wang
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.
| | - Yu-Peng Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Ai-Ping Lu
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China.
| | - Guan-Hua Du
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China; Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Xue-Mei Qin
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.
| | - Dao-Gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China; Guangdong Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China.
| | - Li Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.
| |
Collapse
|
7
|
Analysis of Molecular Mechanism of Erxian Decoction in Treating Osteoporosis Based on Formula Optimization Model. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2021; 2021:6641838. [PMID: 34239693 PMCID: PMC8238601 DOI: 10.1155/2021/6641838] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 03/05/2021] [Accepted: 04/07/2021] [Indexed: 12/21/2022]
Abstract
Osteoporosis (OP) is a highly prevalent orthopedic condition in postmenopausal women and the elderly. Currently, OP treatments mainly include bisphosphonates, receptor activator of nuclear factor kappa-B ligand (RANKL) antibody therapy, selective estrogen receptor modulators, teriparatide (PTH1-34), and menopausal hormone therapy. However, increasing evidence has indicated these treatments may exert serious side effects. In recent years, Traditional Chinese Medicine (TCM) has become popular for treating orthopedic disorders. Erxian Decoction (EXD) is widely used for the clinical treatment of OP, but its underlying molecular mechanisms are unclear thanks to its multiple components and multiple target features. In this research, we designed a network pharmacology method, which used a novel node importance calculation model to identify critical response networks (CRNs) and effective proteins. Based on these proteins, a target coverage contribution (TCC) model was designed to infer a core active component group (CACG). This approach decoded the mechanisms underpinning EXD's role in OP therapy. Our data indicated that the drug response network mediated by the CACG effectively retained information of the component-target (C-T) network of pathogenic genes. Functional pathway enrichment analysis showed that EXD exerted therapeutic effects toward OP by targeting PI3K-Akt signaling (hsa04151), calcium signaling (hsa04020), apoptosis (hsa04210), estrogen signaling (hsa04915), and osteoclast differentiation (hsa04380) via JNK, AKT, and ERK. Our method furnishes a feasible methodological strategy for formula optimization and mechanism analysis and also supplies a reference scheme for the secondary development of the TCM formula.
Collapse
|
8
|
Wang KX, Gao Y, Gong WX, Ye XF, Fan LY, Wang C, Gao XF, Gao L, Du GH, Qin XM, Lu AP, Guan DG. A Novel Strategy for Decoding and Validating the Combination Principles of Huanglian Jiedu Decoction From Multi-Scale Perspective. Front Pharmacol 2020; 11:567088. [PMID: 33424585 PMCID: PMC7789881 DOI: 10.3389/fphar.2020.567088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 11/12/2020] [Indexed: 12/12/2022] Open
Abstract
Traditional Chinese medicine (TCM) formulas treat complex diseases through combined botanical drugs which follow specific compatibility rules to reduce toxicity and increase efficiency. "Jun, Chen, Zuo and Shi" is one of most used compatibility rules in the combination of botanical drugs. However, due to the deficiency of traditional research methods, the quantified theoretical basis of herbal compatibility including principles of "Jun, Chen, Zuo and Shi" are still unclear. Network pharmacology is a new strategy based on system biology and multi-disciplines, which can systematically and comprehensively observe the intervention of drugs on disease networks, and is especially suitable for the research of TCM in the treatment of complex diseases. In this study, we systematically decoded the "Jun, Chen, Zuo and Shi" rules of Huanglian Jiedu Decoction (HJD) in the treatment of diseases for the first time. This interpretation method considered three levels of data. The data in the first level mainly depicts the characteristics of each component in single botanical drug of HJD, include the physical and chemical properties of component, ADME properties and functional enrichment analysis of component targets. The second level data is the characterization of component-target-protein (C-T-P) network in the whole protein-protein interaction (PPI) network, mainly include the characterization of degree and key communities in C-T-P network. The third level data is the characterization of intervention propagation properties of HJD in the treatment of different complex diseases, mainly include target coverage of pathogenic genes and propagation coefficient of intervention effect between target proteins and pathogenic genes. Finally, our method was validated by metabolic data, which could be used to detect the components absorbed into blood. This research shows the scientific basis of "Jun-Chen-Zuo-Shi" from a multi-dimensional perspective, and provides a good methodological reference for the subsequent interpretation of key components and speculation mechanism of the formula.
Collapse
Affiliation(s)
- Ke-Xin Wang
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.,Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
| | - Yao Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.,Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
| | - Wen-Xia Gong
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Xiao-Feng Ye
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
| | - Liu-Yi Fan
- Department of Orthopaedics and Traumatology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chun Wang
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xue-Fei Gao
- Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Guan-Hua Du
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China.,Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xue-Mei Qin
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Ai-Ping Lu
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
| | - Dao-Gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Key Laboratory of Biochip Technology, Southern Medical University, Guangzhou, China
| |
Collapse
|
9
|
Gao Y, Wang KX, Wang P, Li X, Chen JJ, Zhou BY, Tian JS, Guan DG, Qin XM, Lu AP. A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus. Front Pharmacol 2020; 11:512877. [PMID: 33117150 PMCID: PMC7562735 DOI: 10.3389/fphar.2020.512877] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 09/11/2020] [Indexed: 01/26/2023] Open
Abstract
Complex disease is a cascade process which is associated with functional abnormalities in multiple proteins and protein-protein interaction (PPI) networks. One drug one target has not been able to perfectly intervene complex diseases. Increasing evidences show that Chinese herb formula usually treats complex diseases in the form of multi-components and multi-targets. The key step to elucidate the underlying mechanism of formula in traditional Chinese medicine (TCM) is to optimize and capture the important components in the formula. At present, there are several formula optimization models based on network pharmacology has been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. How to select the key group of effective components (KGEC) from the formula of TCM based on the optimal space which link pathogenic genes and drug targets is a bottleneck problem in network pharmacology. To address this issue, we designed a novel network pharmacological model, which takes Lang Chuang Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case. We used the weighted gene regulatory network and active components targets network to construct disease-targets-components network, after filtering through the network attribute degree, the optimization space and effective proteins were obtained. And then the KGEC was selected by using contribution index (CI) model based on knapsack algorithm. The results show that the enriched pathways of effective proteins we selected can cover 96% of the pathogenetic genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, KGEC with 82 components were obtained, and 105 enriched pathways of KGEC targets were consistent with enriched pathways of pathogenic genes (80.15%). Finally, the key components in KGEC of LCW were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the KGEC in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.
Collapse
Affiliation(s)
- Yao Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Ke-xin Wang
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Peng Wang
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Xiao Li
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Jing-jing Chen
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong
- Zhijiang College, Zhejiang University of Technology, Shaoxing, China
| | - Bo-ya Zhou
- Department of Ultrasound, Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Jun-sheng Tian
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Dao-gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Xue-mei Qin
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Ai-ping Lu
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong
| |
Collapse
|
10
|
Sun K, Wang H, Sun H. NAMS webserver: coding potential assessment and functional annotation of plant transcripts. Brief Bioinform 2020; 22:5906158. [PMID: 33080021 PMCID: PMC8138890 DOI: 10.1093/bib/bbaa200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 07/23/2020] [Accepted: 08/04/2020] [Indexed: 11/16/2022] Open
Abstract
Recent advances in transcriptomics have uncovered lots of novel transcripts in plants. To annotate such transcripts, dissecting their coding potential is a critical step. Computational approaches have been proven fruitful in this task; however, most current tools are designed/optimized for mammals and only a few of them have been tested on a limited number of plant species. In this work, we present NAMS webserver, which contains a novel coding potential classifier, NAMS, specifically optimized for plants. We have evaluated the performance of NAMS using a comprehensive dataset containing more than 3 million transcripts from various plant species, where NAMS demonstrates high accuracy and remarkable performance improvements over state-of-the-art software. Moreover, our webserver also furnishes functional annotations, aiming to provide users informative clues to the functions of their transcripts. Considering that most plant species are poorly characterized, our NAMS webserver could serve as a valuable resource to facilitate the transcriptomic studies. The webserver with testing dataset is freely available at http://sunlab.cpy.cuhk.edu.hk/NAMS/.
Collapse
Affiliation(s)
- Kun Sun
- Corresponding authors: Kun Sun, Shenzhen Bay Laboratory, Shenzhen 518132, China. Tel.: +86-0755-2641-9310; Fax: +86-755-8696-7710. E-mail: ; Hao Sun, Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong SAR 999077, China. Tel.: +852-3763-6048; Fax: +852-2636-5090. E-mail:
| | | | | |
Collapse
|
11
|
Guo Q, Li L, Zheng K, Zheng G, Shu H, Shi Y, Lu C, Shu J, Guan D, Lu A, He X. Imperatorin and β-sitosterol have synergistic activities in alleviating collagen-induced arthritis. J Leukoc Biol 2020; 108:509-517. [PMID: 32392637 PMCID: PMC7496114 DOI: 10.1002/jlb.3ma0320-440rr] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 03/14/2020] [Accepted: 03/22/2020] [Indexed: 12/13/2022] Open
Abstract
Rheumatoid arthritis (RA) is a chronic disease with complex molecular network of pathophysiology, single drug is usually not full satisfactory because it is almost impossible to target the whole molecular network of the disease. Drug combinations that act synergistically with each another is an effective strategy in RA therapy. In this study, we aimed to establish a new strategy to search effective synergized compounds from Chinese herbal medicine (CHM) used in RA. Based on multi‐information integrative approaches, imperatorin (IMP) and β‐sitosterol (STO) were predicted as the most effective pair for RA therapy. Further animal experiments demonstrated that IMP+STO treatment ameliorated arthritis severity of collagen‐induced arthritis (CIA) rats in a synergistic manner, whereas IMP or STO administration separately had no such effect. RNA sequencing and IPA analysis revealed that the synergistic mechanism of IMP+STO treatment was related to its regulatory effect on 5 canonical signaling pathways, which were not found when IMP or STO used alone. Moreover, LTA, CD83, and SREBF1 were 3 important targets for synergistic mechanism of IMP+STO treatment. The levels of these 3 genes were significantly up‐regulated in IMP+STO group compared to model group, whereas IMP or STO administration separately had no effect on them. In conclusion, this study found that IMP and STO were 2 synergistic compounds from the CHM in RA therapy, whose synergistic mechanism was closely related to regulate the levels of LTA, CD83, and SREBF1.
Collapse
Affiliation(s)
- Qingqing Guo
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,Law Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Li Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Kang Zheng
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,Law Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Guang Zheng
- School of Information Science & Engineering, Lanzhou University, Lanzhou, Gansu, China
| | - Haiyang Shu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yingjie Shi
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,Shanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Cheng Lu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jun Shu
- Institute of Clinical Medical Science, China-Japan Friendship Hospital, Beijing, China
| | - Daogang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, and Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou, Guangdong Province, China.,Institute of Integrated Bioinfomedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Aiping Lu
- Law Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinfomedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiaojuan He
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| |
Collapse
|
12
|
Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM. Gene regulatory network inference resources: A practical overview. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194430. [PMID: 31678629 DOI: 10.1016/j.bbagrm.2019.194430] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 02/08/2023]
Abstract
Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
Collapse
Affiliation(s)
- Daniele Mercatelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Laura Scalambra
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Luca Triboli
- Centre for Integrative Biology (CIBIO), University of Trento, Italy
| | - Forest Ray
- Department of Systems Biology, Columbia University Medical Center, New York, NY, United States
| | - Federico M Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
| |
Collapse
|
13
|
Poon ENY, Hao B, Guan D, Jun Li M, Lu J, Yang Y, Wu B, Wu SCM, Webb SE, Liang Y, Miller AL, Yao X, Wang J, Yan B, Boheler KR. Integrated transcriptomic and regulatory network analyses identify microRNA-200c as a novel repressor of human pluripotent stem cell-derived cardiomyocyte differentiation and maturation. Cardiovasc Res 2019; 114:894-906. [PMID: 29373717 DOI: 10.1093/cvr/cvy019] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/22/2018] [Indexed: 11/12/2022] Open
Abstract
Aims MicroRNAs (miRNAs) are crucial for the post-transcriptional control of protein-encoding genes and together with transcription factors (TFs) regulate gene expression; however, the regulatory activities of miRNAs during cardiac development are only partially understood. In this study, we tested the hypothesis that integrative computational approaches could identify miRNAs that experimentally could be shown to regulate cardiomyogenesis. Methods and results We integrated expression profiles with bioinformatics analyses of miRNA and TF regulatory programs to identify candidate miRNAs involved with cardiac development. Expression profiling showed that miR-200c, which is not normally detected in adult heart, is progressively down-regulated both during cardiac development and in vitro differentiation of human embryonic stem cells (hESCs) to cardiomyocytes (CMs). We employed computational methodologies to predict target genes of both miR-200c and five key cardiac TFs to identify co-regulated gene networks. The inferred cardiac networks revealed that the cooperative action of miR-200c with these five key TFs, including three (GATA4, SRF and TBX5) targeted by miR-200c, should modulate key processes and pathways necessary for CM development and function. Experimentally, over-expression (OE) of miR-200c in hESC-CMs reduced the mRNA levels of GATA4, SRF and TBX5. Cardiac expression of Ca2+, K+ and Na+ ion channel genes (CACNA1C, KCNJ2 and SCN5A) were also significantly altered by knockdown or OE of miR-200c. Luciferase reporter assays validated miR-200c binding sites on the 3' untranslated region of CACNA1C. In hESC-CMs, elevated miR-200c increased beating frequency, and repressed both Ca2+ influx, mediated by the L-type Ca2+ channel and Ca2+ transients. Conclusions Our analyses demonstrate that miR-200c represses hESC-CM differentiation and maturation. The integrative computation and experimental approaches described here, when applied more broadly, will enhance our understanding of the interplays between miRNAs and TFs in controlling cardiac development and disease processes.
Collapse
Affiliation(s)
- Ellen Ngar-Yun Poon
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Baixia Hao
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Daogang Guan
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Mulin Jun Li
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.,Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong. Hong Kong, China
| | - Jun Lu
- School of Biomedical Sciences, LSK Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Yong Yang
- Laboratory for Food Safety and Environmental Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave, Shenzhen, Guangdong 518055, China
| | - Binbin Wu
- Laboratory for Food Safety and Environmental Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave, Shenzhen, Guangdong 518055, China
| | - Stanley Chun-Ming Wu
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sarah E Webb
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Yan Liang
- Laboratory for Food Safety and Environmental Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave, Shenzhen, Guangdong 518055, China
| | - Andrew L Miller
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.,Marine Biology Laboratory, Woods Hole, MA 02543, USA
| | - Xiaoqiang Yao
- School of Biomedical Sciences, LSK Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Junwen Wang
- Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong. Hong Kong, China.,Center for Individualized Medicine, Department of Health Sciences Research, Mayo Clinic, Scottsdale, AZ 85259, USA and Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA
| | - Bin Yan
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong. Hong Kong, China.,Laboratory for Food Safety and Environmental Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave, Shenzhen, Guangdong 518055, China
| | - Kenneth R Boheler
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| |
Collapse
|
14
|
Li X, Shi L, Zhang K, Wei W, Liu Q, Mao F, Li J, Cai W, Chen H, Teng H, Li J, Sun Z. CirGRDB: a database for the genome-wide deciphering circadian genes and regulators. Nucleic Acids Res 2019; 46:D64-D70. [PMID: 29059379 PMCID: PMC5753205 DOI: 10.1093/nar/gkx944] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/03/2017] [Indexed: 01/16/2023] Open
Abstract
Circadian rhythms govern various kinds of physiological and behavioral functions of the living organisms, and disruptions of the rhythms are highly detrimental to health. Although several databases have been built for circadian genes, a resource for comprehensive post-transcriptional regulatory information of circadian RNAs and expression patterns of disease-related circadian RNAs is still lacking. Here, we developed CirGRDB (http://cirgrdb.biols.ac.cn) by integrating more than 4936 genome-wide assays, with the aim of fulfilling the growing need to understand the rhythms of life. CirGRDB presents a friendly web interface that allows users to search and browse temporal expression patterns of interested genes in 37 human/mouse tissues or cell lines, and three clinical disorders including sleep disorder, aging and tumor. More importantly, eight kinds of potential transcriptional and post-transcriptional regulators involved in the rhythmic expression of the specific genes, including transcription factors, histone modifications, chromatin accessibility, enhancer RNAs, miRNAs, RNA-binding proteins, RNA editing and RNA methylation, can also be retrieved. Furthermore, a regulatory network could be generated based on the regulatory information. In summary, CirGRDB offers a useful repository for exploring disease-related circadian RNAs, and deciphering the transcriptional and post-transcriptional regulation of circadian rhythms.
Collapse
Affiliation(s)
- Xianfeng Li
- State Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China
| | - Leisheng Shi
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.,Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Kun Zhang
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.,Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Wenqing Wei
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Qi Liu
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Fengbiao Mao
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jinchen Li
- State Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China
| | - Wanshi Cai
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Huiqian Chen
- Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Huajing Teng
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Jiada Li
- State Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China
| | - Zhongsheng Sun
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| |
Collapse
|
15
|
Yang B, Chen Y, Zhang W, Lv J, Bao W, Huang DS. HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model. Int J Mol Sci 2018; 19:E3178. [PMID: 30326663 PMCID: PMC6214043 DOI: 10.3390/ijms19103178] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 10/08/2018] [Accepted: 10/10/2018] [Indexed: 11/17/2022] Open
Abstract
Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT). The regulations of each target gene can be obtained by iteratively performing HSCVFNT. For each target gene, the HSCVFNT algorithm utilizes a novel scoring method based on time-delayed mutual information (TDMI), time-delayed maximum information coefficient (TDMIC) and time-delayed correlation coefficient (TDCC), to reduce the redundancy of regulatory relationships and obtain the candidate regulatory factor set. Then, the TDCC method is utilized to create time-delayed gene expression time-series matrix. Finally, a complex-valued flexible neural tree model is proposed to infer the time-delayed regulations of each target gene with the time-delayed time-series matrix. Three real time-series expression datasets from (Save Our Soul) SOS DNA repair system in E. coli and Saccharomyces cerevisiae are utilized to evaluate the performance of the HSCVFNT algorithm. As a result, HSCVFNT obtains outstanding F-scores of 0.923, 0.8 and 0.625 for SOS network and (In vivo Reverse-Engineering and Modeling Assessment) IRMA network inference, respectively, which are 5.5%, 14.3% and 72.2% higher than the best performance of other state-of-the-art GRN inference methods and time-delayed methods.
Collapse
Affiliation(s)
- Bin Yang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan 250002, China.
| | - Wei Zhang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
| | - Jiaguo Lv
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
| | - Wenzheng Bao
- School of Computer Science, China University of Mining and Technology, Xuzhou 221000, China.
| | - De-Shuang Huang
- Institute of Machine Learning and Systems Biology, Tongji University, Shanghai 200092, China.
| |
Collapse
|
16
|
Yan B, Wang P, Wang J, Boheler KR. Discovery of Surface Target Proteins Linking Drugs, Molecular Markers, Gene Regulation, Protein Networks, and Disease by Using a Web-Based Platform Targets-search. Methods Mol Biol 2018; 1722:331-344. [PMID: 29264813 DOI: 10.1007/978-1-4939-7553-2_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Integration and analysis of high content omics data have been critical to the investigation of molecule interactions (e.g., DNA-protein, protein-protein, chemical-protein) in biological systems. Human proteomic strategies that provide enriched information on cell surface proteins can be utilized for repurposing of drug targets and discovery of disease biomarkers. Although several published resources have proved useful to the analysis of these interactions, our newly developed web-based platform Targets-search has the capability of integrating multiple types of omics data to unravel their association with diverse molecule interactions and disease. Here, we describe how to use Targets-search, for the integrated and systemic exploitation of surface proteins to identify potential drug targets, which can further be used to analyze gene regulation, protein networks, and possible biomarkers for diseases and cancers. To illustrate this process, we have taken data from Ewing's sarcoma to identify surface proteins differentially expressed in Ewing's sarcoma cells. These surface proteins were then analyzed to determine which ones were known drug targets. The information suggested putative targets for drug repurposing and subsequent analyses illustrated their regulation by the transcription factor EWSR1.
Collapse
Affiliation(s)
- Bin Yan
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.,Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Panwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA.,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA
| | - Junwen Wang
- Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China. .,Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA. .,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
| | - Kenneth R Boheler
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China. .,Stem Cell & Regenerative Medicine Consortium and School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.
| |
Collapse
|
17
|
Ferdous MM, Bao Y, Vinciotti V, Liu X, Wilson P. Predicting gene expression from genome wide protein binding profiles. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.09.094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
18
|
An integrative method to decode regulatory logics in gene transcription. Nat Commun 2017; 8:1044. [PMID: 29051499 PMCID: PMC5715098 DOI: 10.1038/s41467-017-01193-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 08/25/2017] [Indexed: 12/27/2022] Open
Abstract
Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF–TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems. Existing transcriptional regulatory networks models fall short of deciphering the cooperation between multiple transcription factors on dynamic gene expression. Here the authors develop an integrative method that combines gene expression and transcription factor-DNA binding data to decode transcription regulatory logics.
Collapse
|
19
|
Qin J, Yan B, Hu Y, Wang P, Wang J. Applications of integrative OMICs approaches to gene regulation studies. QUANTITATIVE BIOLOGY 2016. [DOI: 10.1007/s40484-016-0085-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
20
|
BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research. Sci Rep 2016; 6:37140. [PMID: 27876826 PMCID: PMC5120305 DOI: 10.1038/srep37140] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 10/24/2016] [Indexed: 02/06/2023] Open
Abstract
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.
Collapse
|
21
|
Wang P, Qin J, Qin Y, Zhu Y, Wang LY, Li MJ, Zhang MQ, Wang J. ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks. Nucleic Acids Res 2015; 43:W264-9. [PMID: 25916854 PMCID: PMC4489297 DOI: 10.1093/nar/gkv398] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Accepted: 04/15/2015] [Indexed: 12/17/2022] Open
Abstract
Transcription factors (TFs) play an important role in gene regulation. The interconnections among TFs, chromatin interactions, epigenetic marks and cis-regulatory elements form a complex gene transcription apparatus. Our previous work, ChIP-Array, combined TF binding and transcriptome data to construct gene regulatory networks (GRNs). Here we present an enhanced version, ChIP-Array 2, to integrate additional types of omics data including long-range chromatin interaction, open chromatin region and histone modification data to dissect more comprehensive GRNs involving diverse regulatory components. Moreover, we substantially extended our motif database for human, mouse, rat, fruit fly, worm, yeast and Arabidopsis, and curated large amount of omics data for users to select as input or backend support. With ChIP-Array 2, we compiled a library containing regulatory networks of 18 TFs/chromatin modifiers in mouse embryonic stem cell (mESC). The web server and the mESC library are publicly free and accessible athttp://jjwanglab.org/chip-array.
Collapse
Affiliation(s)
- Panwen Wang
- Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, China
| | - Jing Qin
- Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, China
| | - Yiming Qin
- Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yun Zhu
- Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, China
| | - Lily Yan Wang
- Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, China
| | - Mulin Jun Li
- Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, China
| | - Michael Q Zhang
- Bioinformatics Division, TNLIST, Tsinghua University, Beijing 100084, China Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas at Dallas, Dallas, TX 75080, USA
| | - Junwen Wang
- Centre for Genomic Sciences and Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, China
| |
Collapse
|
22
|
He K, Lv W, Zheng D, Cheng F, Zhou T, Ye S, Ban Q, Ying Q, Huang B, Chen L, Wu G, Liu D. The stromal genome heterogeneity between breast and prostate tumors revealed by a comparative transcriptomic analysis. Oncotarget 2015; 6:8687-97. [PMID: 25826086 PMCID: PMC4496176 DOI: 10.18632/oncotarget.3478] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 02/12/2015] [Indexed: 11/25/2022] Open
Abstract
Stromal microenvironment increases tumor cell survival, proliferation and migration, and promotes angiogenesis. In order to provide comprehensive information on the stromal heterogeneity of diverse tumors, here we employed the microarray datasets of human invasive breast and prostate cancer-associated stromals and applied Gene Set Enrichment Analysis (GSEA) to compare the gene expression profiles between them. As a result, 8 up-regulated pathways and 73 down-regulated pathways were identified in the breast tumor stroma, while 32 up-regulated pathways and 18 down-regulated pathways were identified in the prostate tumor stroma. Only 9 pathways such as tryptophan metabolism were commonly up or down regulated, but most of them (including ABC transporters) were specific for these two tumors. Several essential tumors stromal marker genes were also significantly identified. For example, CDH3 was significantly up-regulated in the stromals of both breast and prostate tumors, however EGFR was only significantly down-regulated in the stromal of breast tumor. Our study would be helpful for future therapeutic and predictive applications in breast and prostate cancers.
Collapse
Affiliation(s)
- Kan He
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| | - Wenwen Lv
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| | - Dongni Zheng
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| | - Fei Cheng
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| | - Tao Zhou
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| | - Shoudong Ye
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Department of Cell and Neurobiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Molecular Genetics, Shanghai Medical School, Fudan University, Shanghai, China
| | - Qian Ban
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| | - Qilong Ying
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Department of Cell and Neurobiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Bei Huang
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| | - Lei Chen
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| | - Guohua Wu
- Laboratory of Quality & Safety Risk Assessment for Sericultural Products and Edible Insects, Ministry of Agriculture, College of Biotechnology and Sericultural Research Institute, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Dahai Liu
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei City, Anhui, China
| |
Collapse
|
23
|
Alharbi BA, Alshammari TH, Felton NL, Zhurkin VB, Cui F. nuMap: a web platform for accurate prediction of nucleosome positioning. GENOMICS PROTEOMICS & BIOINFORMATICS 2014; 12:249-53. [PMID: 25220945 PMCID: PMC4411418 DOI: 10.1016/j.gpb.2014.08.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2014] [Revised: 08/03/2014] [Accepted: 08/05/2014] [Indexed: 11/27/2022]
Abstract
Nucleosome positioning is critical for gene expression and of major biological interest. The high cost of experimentally mapping nucleosomal arrangement signifies the need for computational approaches to predict nucleosome positions at high resolution. Here, we present a web-based application to fulfill this need by implementing two models, YR and W/S schemes, for the translational and rotational positioning of nucleosomes, respectively. Our methods are based on sequence-dependent anisotropic bending that dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and parameters for threading calculation and provides multiple layout formats. The nuMap is implemented in Java/Perl/MySQL and is freely available for public use at http://numap.rit.edu. The user manual, implementation notes, description of the methodology and examples are available at the site.
Collapse
Affiliation(s)
- Bader A Alharbi
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Thamir H Alshammari
- B. Thomas Golisano College of Computing & Information Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Nathan L Felton
- Information Technology Services, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Victor B Zhurkin
- Laboratory of Cell Biology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Feng Cui
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA.
| |
Collapse
|