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Li Z, Xu Z, Zhu L, Qin T, Ma J, Feng Z, Yue H, Guan Q, Zhou B, Han G, Zhang G, Li C, Jia S, Qiu Q, Hao D, Wang Y, Wang W. High-quality sika deer omics data and integrative analysis reveal genic and cellular regulation of antler regeneration. Genome Res 2025; 35:188-201. [PMID: 39542648 PMCID: PMC11789637 DOI: 10.1101/gr.279448.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 10/28/2024] [Indexed: 11/17/2024]
Abstract
The antler is the only organ that can fully regenerate annually in mammals. However, the regulatory pattern and mechanism of gene expression and cell differentiation during this process remain largely unknown. Here, we obtain comprehensive assembly and gene annotation of the sika deer (Cervus nippon) genome. We construct, together with large-scale chromatin accessibility and gene expression data, gene regulatory networks involved in antler regeneration, identifying four transcription factors, MYC, KLF4, NFE2L2, and JDP2, with high regulatory activity across the whole regeneration process. Comparative studies and luciferase reporter assay suggest the MYC expression driven by a cervid-specific regulatory element might be important for antler regenerative ability. We further develop a model called combinatorial TF Oriented Program (cTOP), which integrates single-cell data with bulk regulatory networks and find PRDM1, FOSL1, BACH1, and NFATC1 as potential pivotal factors in antler stem cell activation and osteogenic differentiation. Additionally, we uncover interactions within and between cell programs and pathways during the regeneration process. These findings provide insights into the gene and cell regulatory mechanisms of antler regeneration, particularly in stem cell activation and differentiation.
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Affiliation(s)
- Zihe Li
- New Cornerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ziyu Xu
- CEMS, NCMIS, HCMS, MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Zhu
- Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710054, China
- Shaanxi Key Laboratory of Spine Bionic Treatment, Xi'an, Shaanxi 710054, China
| | - Tao Qin
- New Cornerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jinrui Ma
- New Cornerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zhanying Feng
- CEMS, NCMIS, HCMS, MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford University, Stanford, California 94305, USA
| | - Huishan Yue
- New Cornerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
| | - Qing Guan
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Botong Zhou
- New Cornerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ge Han
- New Cornerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China
| | - Guokun Zhang
- Institute of Antler Science and Product Technology, Changchun Sci-Tech University, 130600 Changchun, China
| | - Chunyi Li
- Institute of Antler Science and Product Technology, Changchun Sci-Tech University, 130600 Changchun, China
| | - Shuaijun Jia
- Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710054, China
- Shaanxi Key Laboratory of Spine Bionic Treatment, Xi'an, Shaanxi 710054, China
| | - Qiang Qiu
- New Cornerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China;
| | - Dingjun Hao
- Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710054, China;
- Shaanxi Key Laboratory of Spine Bionic Treatment, Xi'an, Shaanxi 710054, China
| | - Yong Wang
- CEMS, NCMIS, HCMS, MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
| | - Wen Wang
- New Cornerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China;
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
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Ground M, Waqanivavalagi S, Park YE, Callon K, Walker R, Milsom P, Cornish J. Fibroblast growth factor 2 inhibits myofibroblastic activation of valvular interstitial cells. PLoS One 2022; 17:e0270227. [PMID: 35714127 PMCID: PMC9205485 DOI: 10.1371/journal.pone.0270227] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 06/06/2022] [Indexed: 12/30/2022] Open
Abstract
Heart valve disease is a growing problem worldwide. Though very common in older adults, the mechanisms behind the development of the disease aren't well understood, and at present the only therapeutic option is valve replacement. Valvular interstitial cells (VICs) may hold the answer. These cells can undergo pathological differentiation into contractile myofibroblasts or osteoblasts, leading to thickening and calcification of the valve tissue. Our study aimed to characterise the effect of fibroblast growth factor 2 (FGF-2) on the differentiation potential of VICs. We isolated VICs from diseased human valves and treated these cells with FGF-2 and TGF-β to elucidate effect of these growth factors on several myofibroblastic outcomes, in particular immunocytochemistry and gene expression. We used TGF-β as a positive control for myofibroblastic differentiation. We found that FGF-2 promotes a 'quiescent-type' morphology and inhibits the formation of α-smooth muscle actin positive myofibroblasts. FGF-2 reduced the calcification potential of VICs, with a marked reduction in the number of calcific nodules. FGF-2 interrupted the 'canonical' TGF-β signalling pathway, reducing the nuclear translocation of the SMAD2/3 complex. The panel of genes assayed revealed that FGF-2 promoted a quiescent-type pattern of gene expression, with significant downregulations in typical myofibroblast markers α smooth muscle actin, extracellular matrix proteins, and scleraxis. We did not see evidence of osteoblast differentiation: neither matrix-type calcification nor changes in osteoblast associated gene expression were observed. Our findings show that FGF-2 can reverse the myofibroblastic phenotype of VICs isolated from diseased valves and inhibit the calcification potential of these cells.
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Affiliation(s)
- Marcus Ground
- Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Steve Waqanivavalagi
- Green Lane Cardiothoracic Surgery Unit, Auckland City Hospital, Auckland District Health Board, Grafton, New Zealand
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Grafton, New Zealand
| | - Young-Eun Park
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Grafton, New Zealand
| | - Karen Callon
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Grafton, New Zealand
| | - Robert Walker
- Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Paget Milsom
- Green Lane Cardiothoracic Surgery Unit, Auckland City Hospital, Auckland District Health Board, Grafton, New Zealand
| | - Jillian Cornish
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Grafton, New Zealand
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Sun L, Chang Y, Jiang P, Ma Y, Yuan Q, Ma X. Association of gene polymorphisms in FBN1 and TGF-β signaling with the susceptibility and prognostic outcomes of Stanford type B aortic dissection. BMC Med Genomics 2022; 15:65. [PMID: 35307021 PMCID: PMC8935688 DOI: 10.1186/s12920-022-01213-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background This study is aimed at investigating the association of Fibrillin-1 (FBN1) and transforming growth factor β (TGF-β) signaling-related gene polymorphisms with the susceptibility of Stanford type B aortic dissection (AD) and its clinical prognostic outcomes. Methods Five single-nucleotide polymorphism (SNPs) (FBN1rs 145233125, rs201170905, rs11070646, TGFB1rs1800469, and TGFB2rs900) were analyzed in patients with Stanford type B AD (164) and healthy controls (317). Gene–gene and gene–environment interactions were assessed by generalized multifactor dimensionality reduction. A 4-year follow-up was performed for all AD patients. Results G carriers of FBN1 rs201170905 and TGFB1 rs1800469 have an increased risk of Stanford type B AD. The interaction of FBN1, TGFB1, TGFB2 and environmental promoted to the increased risk of type B AD (cross-validation consistency = 10/10, P = 0.001). Dominant models of FBN1rs145233125 TC + CC genotype (P = 0.028), FBN1 rs201170905 AG + GG (P = 0.047) and TGFB1 rs1800469 AG + GG (P = 0.052) were associated with an increased risk of death of Stanford type B AD. The recessive model of FBN1 rs145233125 CC genotype (P < 0.001), FBN1rs201170905 GG (P < 0.001), TGFB1 rs1800469 AG + GG genotype (P = 0.011) was associated with an increased risk of recurrence of chest pain in Stanford type B AD. Conclusions The interactions of gene–gene and gene–environment are related with the risk of Stanford type B AD. C carriers of rs145233125, G carriers of rs201170905 and G carriers of rs1800469 may be the poor clinical outcome indicators of mortality and recurrent chest pain in Stanford type B AD. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01213-z.
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