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Porukala M, Vinod PK. Network-level analysis of ageing and its relationship with diseases and tissue regeneration in the mouse liver. Sci Rep 2023; 13:4632. [PMID: 36944690 PMCID: PMC10030664 DOI: 10.1038/s41598-023-31315-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/09/2023] [Indexed: 03/23/2023] Open
Abstract
The liver plays a vital role in maintaining whole-body metabolic homeostasis, compound detoxification and has the unique ability to regenerate itself post-injury. Ageing leads to functional impairment of the liver and predisposes the liver to non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Mapping the molecular changes of the liver with ageing may help to understand the crosstalk of ageing with different liver diseases. A systems-level analysis of the ageing-induced liver changes and its crosstalk with liver-associated conditions is lacking. In the present study, we performed network-level analyses of the ageing liver using mouse transcriptomic data and a protein-protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To gain further insights, we also integrated the differentially expressed genes (DEGs) between young and different age groups with the PPI network and identified core modules and nodes associated with ageing. Finally, we computed the network proximity of the ageing network with different networks of liver diseases and regeneration to quantify the effect of ageing. Our analysis revealed the complex interplay of immune, cancer signalling, and metabolic genes in the ageing liver. We found significant network proximities between ageing and NAFLD, HCC, liver damage conditions, and the early phase of liver regeneration with common nodes including NLRP12, TRP53, GSK3B, CTNNB1, MAT1 and FASN. Overall, our study maps the network-level changes of ageing and their interconnections with the physiology and pathology of the liver.
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Affiliation(s)
- Manisri Porukala
- Centre for Computational Natural Sciences and Bioinformatics, IIIT, Hyderabad, 500032, India
| | - P K Vinod
- Centre for Computational Natural Sciences and Bioinformatics, IIIT, Hyderabad, 500032, India.
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2
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Lei X, Dai X, Wang Q, Long R, Xiang Z, Li H, Long Z, Zhang C, Zhu Z. RNA-seq transcriptome profiling of liver regeneration in mice identifies the miR-34b-5p/phosphoinositide-dependent protein kinase 1 axis as a potential target for hepatocyte proliferation. Biochem Biophys Res Commun 2022; 627:111-121. [PMID: 36030652 DOI: 10.1016/j.bbrc.2022.08.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/14/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022]
Abstract
Characterized by compensatory hyperplasia dependent on hepatocyte proliferation, the liver will initiate regeneration after partial hepatectomy (PH) and acute or chronic injuries. A variety of genes and noncoding RNAs play pivotal roles in these cell proliferation and growth processes. However, it is still unclear how competition endogenous RNAs (ceRNAs) modulate cellular activities during each phase of liver regeneration, and the specific mechanisms of posttranscriptional gene expression regulation in hepatocyte proliferation remain to be elucidated. To investigate the mechanism of liver regeneration through RNA-seq profiling and to determine the role of miR-34b-5p/PDK1 on hepatocyte proliferation, we established a 2/3 PH mouse model for whole transcriptome profiling based on high-throughput sequencing techniques. We subsequently constructed a lncRNA-miRNA-mRNA ceRNA regulatory network through integrative analyses of RNA interactions. Finally, plasmid transfection in NCTC 1469 cells, dual luciferase reporter gene assay, quantitative real-time PCR, western blotting, Cell Counting Kit-8, and EdU-DNA synthesis cell proliferation assay were used to demonstrate the role of the miR-34b-5p/PDK1 axis in hepatocyte proliferation in vitro. A total of 1443 mRNAs (962 up, 481 down), 48 miRNAs (35 up, 13 down), and 1955 lncRNAs (986 up, 969 down) were identified as significantly differentially expressed. We then successfully constructed a ceRNA regulatory network consisting of 7 lncRNAs, 15 miRNAs, and 347 mRNAs based on the predicted inverse interactions among ceRNAs. Additionally, miR-34b-5p/PDK1 was predicted to be closely related to hepatocyte proliferation. We further demonstrated that miR-34b-5p could bind specifically to the 3'-untranslated region (3'-UTR) of PDK1 using the dual luciferase reporter assay. Ectopic overexpression of miR-34b-5p significantly reduced the mRNA and protein expression of PDK1, while it markedly inhibited the proliferation of mouse NCTC 1469 cells in vitro. In contrast, knocking down miR-34b-5p exhibited the inverse effects on PDK1 expression and hepatocyte proliferation. Through analyzing the ceRNA network during mouse liver regeneration, this study reveals that miR-34b-5p can inhibit hepatocyte proliferation through negatively regulating PDK1 and may be a potential pharmacological intervention target.
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Affiliation(s)
- Xiaohua Lei
- The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Xiaoming Dai
- The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Qian Wang
- The First Affiliated Hospital, Department of Reproductive Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Ruchang Long
- The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Zhiqiang Xiang
- The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Huajian Li
- The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Zhangtao Long
- The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Chao Zhang
- The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Zhu Zhu
- The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China; The First Affiliated Hospital, Department of Education and Training, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
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3
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Porukala M, Vinod PK. Systems-level analysis of transcriptome reorganization during liver regeneration. Mol Omics 2022; 18:315-327. [DOI: 10.1039/d1mo00382h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Tissue homeostasis and regeneration depend on the reversible transitions between quiescence (G0) and proliferation. The liver has a remarkable capacity to regenerate after injury or resection by cell growth and...
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4
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Christ B, Collatz M, Dahmen U, Herrmann KH, Höpfl S, König M, Lambers L, Marz M, Meyer D, Radde N, Reichenbach JR, Ricken T, Tautenhahn HM. Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function. Front Physiol 2021; 12:733868. [PMID: 34867441 PMCID: PMC8637208 DOI: 10.3389/fphys.2021.733868] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/26/2021] [Indexed: 01/17/2023] Open
Abstract
Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.
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Affiliation(s)
- Bruno Christ
- Cell Transplantation/Molecular Hepatology Lab, Department of Visceral, Transplant, Thoracic and Vascular Surgery, University of Leipzig Medical Center, Leipzig, Germany
| | - Maximilian Collatz
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
- Optisch-Molekulare Diagnostik und Systemtechnologié, Leibniz Institute of Photonic Technology (IPHT), Jena, Germany
- InfectoGnostics Research Campus Jena, Jena, Germany
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Sebastian Höpfl
- Faculty of Engineering Design, Production Engineering and Automotive Engineering, Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
| | - Matthias König
- Systems Medicine of the Liver Lab, Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Lena Lambers
- Faculty of Aerospace Engineering and Geodesy, Institute of Mechanics, Structural Analysis and Dynamics, University of Stuttgart, Stuttgart, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Daria Meyer
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Nicole Radde
- Faculty of Engineering Design, Production Engineering and Automotive Engineering, Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
| | - Jürgen R. Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Tim Ricken
- Faculty of Aerospace Engineering and Geodesy, Institute of Mechanics, Structural Analysis and Dynamics, University of Stuttgart, Stuttgart, Germany
| | - Hans-Michael Tautenhahn
- Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
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5
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Han M, Liu X, Zhang W, Wang M, Bu W, Chang C, Yu M, Li Y, Tian C, Yang X, Zhu Y, He F. TSMiner: a novel framework for generating time-specific gene regulatory networks from time-series expression profiles. Nucleic Acids Res 2021; 49:e108. [PMID: 34313778 PMCID: PMC8502000 DOI: 10.1093/nar/gkab629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/30/2021] [Accepted: 07/09/2021] [Indexed: 12/03/2022] Open
Abstract
Time-series gene expression profiles are the primary source of information on complicated biological processes; however, capturing dynamic regulatory events from such data is challenging. Herein, we present a novel analytic tool, time-series miner (TSMiner), that can construct time-specific regulatory networks from time-series expression profiles using two groups of genes: (i) genes encoding transcription factors (TFs) that are activated or repressed at a specific time and (ii) genes associated with biological pathways showing significant mutual interactions with these TFs. Compared with existing methods, TSMiner demonstrated superior sensitivity and accuracy. Additionally, the application of TSMiner to a time-course RNA-seq dataset associated with mouse liver regeneration (LR) identified 389 transcriptional activators and 49 transcriptional repressors that were either activated or repressed across the LR process. TSMiner also predicted 109 and 47 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly interacting with the transcriptional activators and repressors, respectively. These findings revealed the temporal dynamics of multiple critical LR-related biological processes, including cell proliferation, metabolism and the immune response. The series of evaluations and experiments demonstrated that TSMiner provides highly reliable predictions and increases the understanding of rapidly accumulating time-series omics data.
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Affiliation(s)
- Mingfei Han
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Xian Liu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Wen Zhang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China.,Tianjin Key Laboratory of Food Science and Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, China
| | - Mengnan Wang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Wenjing Bu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Miao Yu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Yingxing Li
- Central Research Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Chunyan Tian
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Xiaoming Yang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
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6
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Davenport CF, Scheithauer T, Dunst A, Bahr FS, Dorda M, Wiehlmann L, Tran DDH. Genome-Wide Methylation Mapping Using Nanopore Sequencing Technology Identifies Novel Tumor Suppressor Genes in Hepatocellular Carcinoma. Int J Mol Sci 2021; 22:ijms22083937. [PMID: 33920410 PMCID: PMC8069345 DOI: 10.3390/ijms22083937] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 12/29/2022] Open
Abstract
Downregulation of multiple tumor suppressor genes (TSGs) plays an important role in cancer formation. Recent evidence has accumulated that cancer progression involves genome-wide alteration of epigenetic modifications, which may cause downregulation of the tumor suppressor gene. Using hepatocellular carcinoma (HCC) as a system, we mapped 5-methylcytosine signal at a genome-wide scale using nanopore sequencing technology to identify novel TSGs. Integration of methylation data with gene transcription profile of regenerated liver and primary HCCs allowed us to identify 10 potential tumor suppressor gene candidates. Subsequent validation led us to focus on functionally characterizing one candidate—glucokinase (GCK). We show here that overexpression of GCK inhibits the proliferation of HCC cells via induction of intracellular lactate accumulation and subsequently causes energy crisis due to NAD+ depletion. This suggests GCK functions as a tumor suppressor gene and may be involved in HCC development. In conclusion, these data provide valuable clues for further investigations of the process of tumorigenesis in human cancer.
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Affiliation(s)
- Colin F. Davenport
- Research Core Unit Genomics OE 9415, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, D-30623 Hannover, Germany; (C.F.D.); (T.S.); (M.D.); (L.W.)
| | - Tobias Scheithauer
- Research Core Unit Genomics OE 9415, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, D-30623 Hannover, Germany; (C.F.D.); (T.S.); (M.D.); (L.W.)
| | - Alessia Dunst
- Institut fuer Zellbiochemie, OE4310, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, D-30623 Hannover, Germany; (A.D.); (F.S.B.)
| | - Frauke Sophie Bahr
- Institut fuer Zellbiochemie, OE4310, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, D-30623 Hannover, Germany; (A.D.); (F.S.B.)
| | - Marie Dorda
- Research Core Unit Genomics OE 9415, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, D-30623 Hannover, Germany; (C.F.D.); (T.S.); (M.D.); (L.W.)
| | - Lutz Wiehlmann
- Research Core Unit Genomics OE 9415, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, D-30623 Hannover, Germany; (C.F.D.); (T.S.); (M.D.); (L.W.)
| | - Doan Duy Hai Tran
- Institut fuer Zellbiochemie, OE4310, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, D-30623 Hannover, Germany; (A.D.); (F.S.B.)
- Correspondence: ; Tel.: +49-511-532-2857; Fax: +49-511-532-2847
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7
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Zhang W, Wang L, Sun XH, Liu X, Xiao Y, Zhang J, Wang T, Chen H, Zhan YQ, Yu M, Ge CH, Li CY, Ren GM, Yin RH, Yang XM. Toll-like receptor 5-mediated signaling enhances liver regeneration in mice. Mil Med Res 2021; 8:16. [PMID: 33622404 PMCID: PMC7901072 DOI: 10.1186/s40779-021-00309-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 02/10/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Toll-like receptor 5 (TLR5)-mediated pathways play critical roles in regulating the hepatic immune response and show hepatoprotective effects in mouse models of hepatic diseases. However, the role of TLR5 in experimental models of liver regeneration has not been reported. This study aimed to investigate the role of TLR5 in partial hepatectomy (PHx)-induced liver regeneration. METHODS We performed 2/3 PHx in wild-type (WT) mice, TLR5 knockout mice, or TLR5 agonist CBLB502 treated mice, as a model of liver regeneration. Bacterial flagellin content was measured with ELISA, and hepatic TLR5 expression was determined with quantitative PCR analyses and flow cytometry. To study the effects of TLR5 on hepatocyte proliferation, we analyzed bromodeoxyuridine (BrdU) incorporation and proliferating cell nuclear antigen (PCNA) expression with immunohistochemistry (IHC) staining. The effects of TLR5 during the priming phase of liver regeneration were examined with quantitative PCR analyses of immediate early gene mRNA levels, and with Western blotting analysis of hepatic NF-κB and STAT3 activation. Cytokine and growth factor production after PHx were detected with real-time PCR and cytometric bead array (CBA) assays. Oil Red O staining and hepatic lipid concentrations were analyzed to examine the effect of TLR5 on hepatic lipid accumulation after PHx. RESULTS The bacterial flagellin content in the serum and liver increased, and the hepatic TLR5 expression was significantly up-regulated in WT mice after PHx. TLR5-deficient mice exhibited diminished numbers of BrdU- and PCNA-positive cells, suppressed immediate early gene expression, and decreased cytokine and growth factor production. Moreover, PHx-induced hepatic NF-κB and STAT3 activation was inhibited in Tlr5-/- mice, as compared with WT mice. Consistently, the administration of CBLB502 significantly promoted PHx-mediated hepatocyte proliferation, which was correlated with enhanced production of proinflammatory cytokines and the recruitment of macrophages and neutrophils in the liver. Furthermore, Tlr5-/- mice displayed significantly lower hepatic lipid concentrations and smaller Oil Red O positive areas than those in control mice after PHx. CONCLUSION We reveal that TLR5 activation contributes to the initial events of liver regeneration after PHx. Our findings demonstrate that TLR5 signaling positively regulates liver regeneration and suggest the potential of TLR5 agonist to promote liver regeneration.
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Affiliation(s)
- Wen Zhang
- Department of Pharmaceutical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Lei Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Xue-Hua Sun
- Department of Pharmaceutical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Xian Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yang Xiao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Jie Zhang
- Department of Pharmaceutical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Ting Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.,School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, Anhui Province, China
| | - Hui Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yi-Qun Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Miao Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Chang-Hui Ge
- Beijing Institute of Radiation Medicine, Beijing, 100850, China
| | - Chang-Yan Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Guang-Ming Ren
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Rong-Hua Yin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Xiao-Ming Yang
- Department of Pharmaceutical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China. .,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
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8
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Alibardi L. Appendage regeneration in anamniotes utilizes genes active during larval-metamorphic stages that have been lost or altered in amniotes: The case for studying lizard tail regeneration. J Morphol 2020; 281:1358-1381. [PMID: 32865265 DOI: 10.1002/jmor.21251] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 07/20/2020] [Accepted: 07/25/2020] [Indexed: 12/17/2022]
Abstract
This review elaborates the idea that organ regeneration derives from specific evolutionary histories of vertebrates. Regenerative ability depends on genomic regulation of genes specific to the life-cycles that have differentially evolved in anamniotes and amniotes. In aquatic environments, where fish and amphibians live, one or multiple metamorphic transitions occur before the adult stage is reached. Each transition involves the destruction and remodeling of larval organs that are replaced with adult organs. After organ injury or loss in adult anamniotes, regeneration uses similar genes and developmental process than those operating during larval growth and metamorphosis. Therefore, the broad presence of regenerative capability across anamniotes is possible because generating new organs is included in their life history at metamorphic stages. Soft hyaluronate-rich regenerative blastemas grow in submersed or in hydrated environments, that is, essential conditions for regeneration, like during development. In adult anamniotes, the ability to regenerate different organs decreases in comparison to larval stages and becomes limited during aging. Comparisons of genes activated during metamorphosis and regeneration in anamniotes identify key genes unique to these processes, and include thyroid, wnt and non-coding RNAs developmental pathways. In the terrestrial environment, some genes or developmental pathways for metamorphic transitions were lost during amniote evolution, determining loss of regeneration. Among amniotes, the formation of soft and hydrated blastemas only occurs in lizards, a morphogenetic process that evolved favoring their survival through tail autotomy, leading to a massive although imperfect regeneration of the tail. Deciphering genes activity during lizard tail regeneration would address future attempts to recreate in other amniotes regenerative blastemas that grow into variably completed organs.
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Oliva-Vilarnau N, Vorrink SU, Ingelman-Sundberg M, Lauschke VM. A 3D Cell Culture Model Identifies Wnt/ β-Catenin Mediated Inhibition of p53 as a Critical Step during Human Hepatocyte Regeneration. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:2000248. [PMID: 32775153 PMCID: PMC7404138 DOI: 10.1002/advs.202000248] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 06/01/2020] [Indexed: 05/14/2023]
Abstract
The liver is a highly regenerative organ. While mature hepatocytes under homeostatic conditions are largely quiescent, upon injury, they rapidly enter the cell cycle to recover the damaged tissue. In rodents, a variety of injury models have provided important insights into the molecular underpinnings that govern the proliferative activation of quiescent hepatocytes. However, little is known about the molecular mechanisms of human hepatocyte regeneration and experimental methods to expand primary human hepatocytes (PHH). Here, a 3D spheroid model of PHH is established to study hepatocyte regeneration and integrative time-lapse multi-omics analyses show that upon isolation from the native liver PHH acquire a regenerative phenotype, as seen in vivo upon partial hepatectomy. However, proliferation is limited. By analyzing global promoter motif activities, it is predicted that activation of Wnt/β-catenin and inhibition of p53 signaling are critical factors required for human hepatocyte proliferation. Functional validations reveal that activation of Wnt signaling through external cues alone is sufficient to inhibit p53 and its proliferative senescence-inducing target PAI1 (SERPINE1) and drive proliferation of >50% of all PHH. A scalable 3D culture model is established to study the molecular and cellular biology of human hepatocyte regeneration. By using this model, an essential role of Wnt/β-catenin signaling during human hepatocyte regeneration is identified.
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Affiliation(s)
- Nuria Oliva-Vilarnau
- Department of Physiology and Pharmacology Karolinska Institutet Stockholm 171 77 Sweden
| | - Sabine U Vorrink
- Department of Physiology and Pharmacology Karolinska Institutet Stockholm 171 77 Sweden
| | | | - Volker M Lauschke
- Department of Physiology and Pharmacology Karolinska Institutet Stockholm 171 77 Sweden
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Dubois V, Gheeraert C, Vankrunkelsven W, Dubois‐Chevalier J, Dehondt H, Bobowski‐Gerard M, Vinod M, Zummo FP, Güiza F, Ploton M, Dorchies E, Pineau L, Boulinguiez A, Vallez E, Woitrain E, Baugé E, Lalloyer F, Duhem C, Rabhi N, van Kesteren RE, Chiang C, Lancel S, Duez H, Annicotte J, Paumelle R, Vanhorebeek I, Van den Berghe G, Staels B, Lefebvre P, Eeckhoute J. Endoplasmic reticulum stress actively suppresses hepatic molecular identity in damaged liver. Mol Syst Biol 2020; 16:e9156. [PMID: 32407006 PMCID: PMC7224309 DOI: 10.15252/msb.20199156] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 04/09/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023] Open
Abstract
Liver injury triggers adaptive remodeling of the hepatic transcriptome for repair/regeneration. We demonstrate that this involves particularly profound transcriptomic alterations where acute induction of genes involved in handling of endoplasmic reticulum stress (ERS) is accompanied by partial hepatic dedifferentiation. Importantly, widespread hepatic gene downregulation could not simply be ascribed to cofactor squelching secondary to ERS gene induction, but rather involves a combination of active repressive mechanisms. ERS acts through inhibition of the liver-identity (LIVER-ID) transcription factor (TF) network, initiated by rapid LIVER-ID TF protein loss. In addition, induction of the transcriptional repressor NFIL3 further contributes to LIVER-ID gene repression. Alteration to the liver TF repertoire translates into compromised activity of regulatory regions characterized by the densest co-recruitment of LIVER-ID TFs and decommissioning of BRD4 super-enhancers driving hepatic identity. While transient repression of the hepatic molecular identity is an intrinsic part of liver repair, sustained disequilibrium between the ERS and LIVER-ID transcriptional programs is linked to liver dysfunction as shown using mouse models of acute liver injury and livers from deceased human septic patients.
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Affiliation(s)
- Vanessa Dubois
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
- Present address:
Clinical and Experimental EndocrinologyDepartment of Chronic Diseases, Metabolism and Ageing (CHROMETA)KU LeuvenLeuvenBelgium
| | - Céline Gheeraert
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Wouter Vankrunkelsven
- Clinical Division and Laboratory of Intensive Care MedicineDepartment of Cellular and Molecular MedicineKU LeuvenLeuvenBelgium
| | | | - Hélène Dehondt
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | | | - Manjula Vinod
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | | | - Fabian Güiza
- Clinical Division and Laboratory of Intensive Care MedicineDepartment of Cellular and Molecular MedicineKU LeuvenLeuvenBelgium
| | - Maheul Ploton
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Emilie Dorchies
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Laurent Pineau
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Alexis Boulinguiez
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Emmanuelle Vallez
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Eloise Woitrain
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Eric Baugé
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Fanny Lalloyer
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Christian Duhem
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Nabil Rabhi
- UMR 8199 ‐ EGIDCNRSInstitut Pasteur de LilleUniversity of LilleLilleFrance
| | - Ronald E van Kesteren
- Center for Neurogenomics and Cognitive ResearchNeuroscience Campus AmsterdamVU UniversityAmsterdamThe Netherlands
| | - Cheng‐Ming Chiang
- Simmons Comprehensive Cancer CenterDepartments of Biochemistry and PharmacologyUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Steve Lancel
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Hélène Duez
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | | | - Réjane Paumelle
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Ilse Vanhorebeek
- Clinical Division and Laboratory of Intensive Care MedicineDepartment of Cellular and Molecular MedicineKU LeuvenLeuvenBelgium
| | - Greet Van den Berghe
- Clinical Division and Laboratory of Intensive Care MedicineDepartment of Cellular and Molecular MedicineKU LeuvenLeuvenBelgium
| | - Bart Staels
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Philippe Lefebvre
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
| | - Jérôme Eeckhoute
- Inserm, CHU LilleInstitut Pasteur de LilleU1011‐EGIDUniversity of LilleLilleFrance
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Chromatin dynamics during liver regeneration. Semin Cell Dev Biol 2020; 97:38-46. [DOI: 10.1016/j.semcdb.2019.03.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/12/2019] [Accepted: 03/28/2019] [Indexed: 12/15/2022]
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12
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Yeganeh M, Praz V, Carmeli C, Villeneuve D, Rib L, Guex N, Herr W, Delorenzi M, Hernandez N. Differential regulation of RNA polymerase III genes during liver regeneration. Nucleic Acids Res 2019; 47:1786-1796. [PMID: 30597109 PMCID: PMC6393285 DOI: 10.1093/nar/gky1282] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 11/22/2018] [Accepted: 12/14/2018] [Indexed: 12/23/2022] Open
Abstract
Mouse liver regeneration after partial hepatectomy involves cells in the remaining tissue synchronously entering the cell division cycle. We have used this system and H3K4me3, Pol II and Pol III profiling to characterize adaptations in Pol III transcription. Our results broadly define a class of genes close to H3K4me3 and Pol II peaks, whose Pol III occupancy is high and stable, and another class, distant from Pol II peaks, whose Pol III occupancy strongly increases after partial hepatectomy. Pol III regulation in the liver thus entails both highly expressed housekeeping genes and genes whose expression can adapt to increased demand.
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Affiliation(s)
- Meghdad Yeganeh
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Viviane Praz
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Cristian Carmeli
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland.,Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Dominic Villeneuve
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Leonor Rib
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Nicolas Guex
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Winship Herr
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Mauro Delorenzi
- Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.,Department of Fundamental Oncology and the Ludwig Center for Cancer research, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Nouria Hernandez
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
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