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Huang Y, Liu X, Wang HY, Chen JY, Zhang X, Li Y, Lu Y, Dong Z, Liu K, Wang Z, Wang Q, Fan G, Zou J, Liu S, Shao C. Single-cell transcriptome landscape of zebrafish liver reveals hepatocytes and immune cell interactions in understanding nonalcoholic fatty liver disease. FISH & SHELLFISH IMMUNOLOGY 2024; 146:109428. [PMID: 38325594 DOI: 10.1016/j.fsi.2024.109428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/27/2024] [Accepted: 02/03/2024] [Indexed: 02/09/2024]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is becoming the most common chronic liver disease in the world. Immunity is the major contributing factor in NAFLD; however, the interaction of immune cells and hepatocytes in disease progression has not been fully elucidated. As a popular species for studying NAFLD, zebrafish, whose liver is a complex immune system mediated by immune cells and non-immune cells in maintaining immune tolerance and homeostasis. Understanding the cellular composition and immune environment of zebrafish liver is of great significance for its application in NAFLD. Here, we established a liver atlas that consists of 10 cell types using single-cell RNA sequencing (scRNA-seq). By examining the heterogeneity of hepatocytes and analyzing the expression of NAFLD-associated genes in the specific cluster, we provide a potential target cell model to study NAFLD. Additionally, our analysis identified two subtypes of distinct resident macrophages with inflammatory and non-inflammatory functions and characterized the successive stepwise development of T cell subclusters in the liver. Importantly, we uncovered the possible regulation of macrophages and T cells on target cells of fatty liver by analyzing the cellular interaction between hepatocytes and immune cells. Our data provide valuable information for an in-depth study of immune cells targeting hepatocytes to regulate the immune balance in NAFLD.
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Affiliation(s)
- Yingyi Huang
- National Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 266072, Qingdao, Shandong, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, 266072, Qingdao, Shandong, China
| | - Xiang Liu
- National Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 266072, Qingdao, Shandong, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, 266072, Qingdao, Shandong, China
| | - Hong-Yan Wang
- National Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 266072, Qingdao, Shandong, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, 266072, Qingdao, Shandong, China
| | - Jian-Yang Chen
- BGI Research, 266555, Qingdao, Shandong, China; Qingdao Key Laboratory of Marine Genomics, BGI Research, 266555, Qingdao, Shandong, China
| | - Xianghui Zhang
- BGI Research, 266555, Qingdao, Shandong, China; Qingdao Key Laboratory of Marine Genomics, BGI Research, 266555, Qingdao, Shandong, China
| | - Yubang Li
- National Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 266072, Qingdao, Shandong, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, 266072, Qingdao, Shandong, China
| | - Yifang Lu
- BGI Research, 266555, Qingdao, Shandong, China; Qingdao Key Laboratory of Marine Genomics, BGI Research, 266555, Qingdao, Shandong, China
| | - Zhongdian Dong
- Key Laboratory of Aquaculture in South China Sea for Aquatic Economic Animal of Guangdong Higher Education Institutes, Fisheries College, Guangdong Ocean University, 524088, Zhanjiang, Guangdong, China
| | - Kaiqiang Liu
- National Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 266072, Qingdao, Shandong, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, 266072, Qingdao, Shandong, China
| | - Zhongduo Wang
- Key Laboratory of Aquaculture in South China Sea for Aquatic Economic Animal of Guangdong Higher Education Institutes, Fisheries College, Guangdong Ocean University, 524088, Zhanjiang, Guangdong, China
| | - Qian Wang
- National Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 266072, Qingdao, Shandong, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, 266072, Qingdao, Shandong, China
| | - Guangyi Fan
- BGI Research, 266555, Qingdao, Shandong, China; Qingdao Key Laboratory of Marine Genomics, BGI Research, 266555, Qingdao, Shandong, China; BGI Research, 518083, Shenzhen, China
| | - Jun Zou
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, 201306, Shanghai, China
| | - Shanshan Liu
- MGI Tech, 518083, Shenzhen, China; BGI Research, 518083, Shenzhen, China.
| | - Changwei Shao
- National Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 266072, Qingdao, Shandong, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, 266072, Qingdao, Shandong, China.
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2
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Krasniewski LK, Chakraborty P, Cui CY, Mazan-Mamczarz K, Dunn C, Piao Y, Fan J, Shi C, Wallace T, Nguyen C, Rathbun IA, Munk R, Tsitsipatis D, De S, Sen P, Ferrucci L, Gorospe M. Single-cell analysis of skeletal muscle macrophages reveals age-associated functional subpopulations. eLife 2022; 11:e77974. [PMID: 36259488 PMCID: PMC9629833 DOI: 10.7554/elife.77974] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 10/10/2022] [Indexed: 11/30/2022] Open
Abstract
Tissue-resident macrophages represent a group of highly responsive innate immune cells that acquire diverse functions by polarizing toward distinct subpopulations. The subpopulations of macrophages that reside in skeletal muscle (SKM) and their changes during aging are poorly characterized. By single-cell transcriptomic analysis with unsupervised clustering, we found 11 distinct macrophage clusters in male mouse SKM with enriched gene expression programs linked to reparative, proinflammatory, phagocytic, proliferative, and senescence-associated functions. Using a complementary classification, membrane markers LYVE1 and MHCII identified four macrophage subgroups: LYVE1-/MHCIIhi (M1-like, classically activated), LYVE1+/MHCIIlo (M2-like, alternatively activated), and two new subgroups, LYVE1+/MHCIIhi and LYVE1-/MHCIIlo. Notably, one new subgroup, LYVE1+/MHCIIhi, had traits of both M2 and M1 macrophages, while the other new subgroup, LYVE1-/MHCIIlo, displayed strong phagocytic capacity. Flow cytometric analysis validated the presence of the four macrophage subgroups in SKM and found that LYVE1- macrophages were more abundant than LYVE1+ macrophages in old SKM. A striking increase in proinflammatory markers (S100a8 and S100a9 mRNAs) and senescence-related markers (Gpnmb and Spp1 mRNAs) was evident in macrophage clusters from older mice. In sum, we have identified dynamically polarized SKM macrophages and propose that specific macrophage subpopulations contribute to the proinflammatory and senescent traits of old SKM.
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Affiliation(s)
- Linda K Krasniewski
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Papiya Chakraborty
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Chang-Yi Cui
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Krystyna Mazan-Mamczarz
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Christopher Dunn
- Flow Cytometry Core, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Yulan Piao
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Jinshui Fan
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Changyou Shi
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Tonya Wallace
- Flow Cytometry Core, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Cuong Nguyen
- Flow Cytometry Core, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Isabelle A Rathbun
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Rachel Munk
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Dimitrios Tsitsipatis
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Supriyo De
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Payel Sen
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
| | - Myriam Gorospe
- Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program, National Institutes of HealthBaltimoreUnited States
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Jo D, Kim HK, Kim YK, Song J. Transcriptome Profile of Thyroid Glands in Bile Duct Ligation Mouse Model. Int J Mol Sci 2022; 23:ijms23158244. [PMID: 35897811 PMCID: PMC9332885 DOI: 10.3390/ijms23158244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/18/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
Thyroid hormone (TH) contributes to multiple cellular mechanisms in the liver, muscle cells, adipose tissue, and brain, etc. In particular, the liver is an important organ in TH metabolism for the conversion of thyronine (T4) into triiodothyronine (T3) by the deiodinase enzyme. TH levels were significantly decreased and thyroid-stimulating hormone (TSH) levels were significantly increased in patients with liver failure compared with normal subjects. Among liver failure diseases, hepatic encephalopathy (HE) deserves more attention because liver damage and neuropathologies occur simultaneously. Although there is numerous evidence of TH dysregulation in the HE model, specific mechanisms and genetic features of the thyroid glands in the HE model are not fully understood. Here, we investigated the significantly different genes in the thyroid glands of a bile duct ligation (BDL) mouse model as the HE model, compared to the thyroid glands of the control mouse using RNA sequencing. We also confirmed the alteration in mRNA levels of thyroid gland function-related genes in the BDL mouse model. Furthermore, we evaluated the increased level of free T4 and TSH in the BDL mouse blood. Thus, we emphasize the potential roles of TH in liver metabolism and suggest that thyroid dysfunction-related genes in the HE model should be highlighted for finding the appropriate solution for an impaired thyroid system in HE.
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Affiliation(s)
- Danbi Jo
- Department of Anatomy, Chonnam National University Medical School, Seoyangro 264, Hwasun 58128, Korea;
- Biomedical Science Graduate Program (BMSGP), Chonnam National University, Seoyangro 264, Hwasun 58128, Korea
| | - Hee Kyung Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Chonnam National University Medical School, Seoyangro 264, Hwasun 58128, Korea;
| | - Young-Kook Kim
- Department of Biochemistry, Chonnam National University Medical School, Seoyangro 264, Hwasun 58128, Korea;
| | - Juhyun Song
- Department of Anatomy, Chonnam National University Medical School, Seoyangro 264, Hwasun 58128, Korea;
- Biomedical Science Graduate Program (BMSGP), Chonnam National University, Seoyangro 264, Hwasun 58128, Korea
- Correspondence: ; Tel.: +82-61-379-2706
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Chen Y, Sun Y, Luo Z, Lin J, Qi B, Kang X, Ying C, Guo C, Yao M, Chen X, Wang Y, Wang Q, Chen J, Chen S. Potential Mechanism Underlying Exercise Upregulated Circulating Blood Exosome miR-215-5p to Prevent Necroptosis of Neuronal Cells and a Model for Early Diagnosis of Alzheimer's Disease. Front Aging Neurosci 2022; 14:860364. [PMID: 35615585 PMCID: PMC9126031 DOI: 10.3389/fnagi.2022.860364] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 03/21/2022] [Indexed: 02/05/2023] Open
Abstract
Exercise is crucial for preventing Alzheimer's disease (AD), although the exact underlying mechanism remains unclear. The construction of an accurate AD risk prediction model is beneficial as it can provide a theoretical basis for preventive exercise prescription. In recent years, necroptosis has been confirmed as an important manifestation of AD, and exercise is known to inhibit necroptosis of neuronal cells. In this study, we extracted 67 necroptosis-related genes and 32 necroptosis-related lncRNAs and screened for key predictive AD risk genes through a random forest analysis. Based on the neural network Prediction model, we constructed a new logistic regression-based AD risk prediction model in order to provide a visual basis for the formulation of exercise prescription. The prediction model had an area under the curve (AUC) value of 0.979, indicative of strong predictive power and a robust clinical application prospect. In the exercise group, the expression of exosomal miR-215-5p was found to be upregulated; miR-215-5p could potentially inhibit the expressions of IDH1, BCL2L11, and SIRT1. The single-cell SCENIC assay was used to identify key transcriptional regulators in skeletal muscle. Among them, CEBPB and GATA6 were identified as putative transcriptional regulators of miR-215. After "skeletal muscle removal of load," the expressions of CEBPB and GATA6 increased substantially, which in turn led to the elevation of miR-215 expression, thereby suggesting a putative mechanism for negative feedback regulation of exosomal homeostasis.
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Affiliation(s)
- Yisheng Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yaying Sun
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhiwen Luo
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinrong Lin
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Beijie Qi
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xueran Kang
- Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chenting Ying
- Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenyang Guo
- Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mengxuan Yao
- Department of Orthopaedic Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China
- Key Laboratory of Biomechanics of Hebei Province, Orthopaedic Research Institution of Hebei Province, Shijiazhuang, China
| | | | - Yi Wang
- Huashan Hospital, Fudan University, Shanghai, China
| | - Qian Wang
- Department of Central Laboratory, The Affiliated Taian City Central Hospital of Qingdao University, Tai’an, China
- *Correspondence: Qian Wang,
| | - Jiwu Chen
- Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Jiwu Chen,
| | - Shiyi Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Shiyi Chen,
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5
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Lu J, Chen W, Liu H, Yang H, Liu T. Transcription factor CEBPB inhibits the proliferation of osteosarcoma by regulating downstream target gene CLEC5A. J Clin Lab Anal 2019; 33:e22985. [PMID: 31364785 PMCID: PMC6868409 DOI: 10.1002/jcla.22985] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/26/2019] [Accepted: 07/03/2019] [Indexed: 12/28/2022] Open
Abstract
Objective To screen the gene related to osteosarcoma (OS) metastasis and the molecular mechanism. Methods GEO database and R2 chip analysis platform were used to screen genes related to OS metastasis. UCSC gene browser was used to find the transcription factor (TF) of CLEC5A. The mRNA level and protein expression of CLEC5A in OS tissues and normal tissues were determined by RT‐PCR and Western blotting, respectively. OS cell lines MG‐63 were transfected with CEBPB recombinant plasmid. After transfection, the expression of CLEC5A in MG‐63 cells was determined and the cell proliferation situation was determined by clone formation assay. Results Three genes CLEC5A, ALOX5AP, and RNASE3 were obtained, and CLEC5A has the highest correlation with OS. CLEC5A has screened the gene related to OS metastasis, and CEBPB can be taken as TF regulating downstream gene CLEC5A. CEBPB can regulate the downstream CLEC5A as transcription factor. The relative mRNA level and protein expression of CLEC5A in OS tissues were significantly higher than those in normal tissues. CLEC5A can prevent OS metastasis. Transfection of CEBPB increased the expression of CLEC5A in MG‐63 cells and also inhibited the proliferation of OS. Conclusion CEBPB can inhibit the proliferation of OS cells via regulating the expression of CLEC5A.
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Affiliation(s)
- Jianhua Lu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Weikai Chen
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hao Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Huilin Yang
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Tao Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
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6
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Yang F, Zhou L, Song J, WangJinMei A, Yang Y, Tang ZW, Huang QY. Liver CEBPβ Modulates the Kynurenine Metabolism and Mediates the Motility for Hypoxia-Induced Central Fatigue in Mice. Front Physiol 2019; 10:243. [PMID: 30930794 PMCID: PMC6428026 DOI: 10.3389/fphys.2019.00243] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Accepted: 02/25/2019] [Indexed: 12/29/2022] Open
Abstract
Central fatigue is defined as a failure of the central nervous system to adequately drive the muscle, manifesting limited development, and maintenance of locomotor activity. A plateau in hypoxia leads to central fatigue and followed by maximal motility recession. However, the underlying mechanism is still unclear. The present study describes a mechanism by which liver CEBPβ (CCAAT/enhancer-binding protein beta) induced by hypoxic environment alters the kynurenine (KYN) metabolism and causes the suppression of motility function recession. The activation of CEBPβ under hypoxia increases the liver expression of tryptophan dioxygenase, thereby enhancing the conversion of tryptophan into KYN; the KYN metabolite can traverse the blood-brain barrier and result in the suppression of motility function. However, the knockdown of CEBPβ by injecting pAAV-shRNA-CEBPβ via the hepatic portal vein reduces the KYN production and improves the motility function. KYN is a neurochemical that which restricts the exercise capacity after injection in the basal ganglia in mice. Reducing the plasma KYN protects the brain from hypoxia-induced changes associated with fatigue, and the knockdown liver of CEBPβ in mice renders resistance to fatigue post-acute hypoxia or tryptophan treatment. This study reveals resistance to central fatigue as a strategy for acclimatization to hypoxia mediated by transcription factor CEBPβ in the liver.
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Affiliation(s)
- Fan Yang
- Joint Surgery, General Hospital of Tibetan Military Command Lhasa, Lhasa, China.,Department of Cold Environment Medicine, College of High Altitude Military Medicine, Third Military Medical University and Key Laboratory of High Altitude Medicine, Ministry of Education, Key Laboratory of High Altitude Medicine, PLA, Chongqing, China
| | - Li Zhou
- Department of Pharmacy, General Hospital of Tibetan Military Command Lhasa, Lhasa, China
| | - Jun Song
- Joint Surgery, General Hospital of Tibetan Military Command Lhasa, Lhasa, China
| | - A WangJinMei
- Joint Surgery, General Hospital of Tibetan Military Command Lhasa, Lhasa, China
| | - Yuan Yang
- Joint Surgery, General Hospital of Tibetan Military Command Lhasa, Lhasa, China
| | - Zhong-Wei Tang
- Department of Cold Environment Medicine, College of High Altitude Military Medicine, Third Military Medical University and Key Laboratory of High Altitude Medicine, Ministry of Education, Key Laboratory of High Altitude Medicine, PLA, Chongqing, China
| | - Qing-Yuan Huang
- Department of Cold Environment Medicine, College of High Altitude Military Medicine, Third Military Medical University and Key Laboratory of High Altitude Medicine, Ministry of Education, Key Laboratory of High Altitude Medicine, PLA, Chongqing, China
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7
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Lee BP, Pilling LC, Emond F, Flurkey K, Harrison DE, Yuan R, Peters LL, Kuchel GA, Ferrucci L, Melzer D, Harries LW. Changes in the expression of splicing factor transcripts and variations in alternative splicing are associated with lifespan in mice and humans. Aging Cell 2016; 15:903-13. [PMID: 27363602 PMCID: PMC5013025 DOI: 10.1111/acel.12499] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/22/2016] [Indexed: 12/31/2022] Open
Abstract
Dysregulation of splicing factor expression and altered alternative splicing are associated with aging in humans and other species, and also with replicative senescence in cultured cells. Here, we assess whether expression changes of key splicing regulator genes and consequent effects on alternative splicing are also associated with strain longevity in old and young mice, across 6 different mouse strains with varying lifespan (A/J, NOD.B10Sn-H2(b) /J, PWD.Phj, 129S1/SvlmJ, C57BL/6J and WSB/EiJ). Splicing factor expression and changes to alternative splicing were associated with strain lifespan in spleen and to a lesser extent in muscle. These changes mainly involved hnRNP splicing inhibitor transcripts with most changes more marked in spleens of young animals from long-lived strains. Changes in spleen isoform expression were suggestive of reduced cellular senescence and retained cellular proliferative capacity in long-lived strains. Changes in muscle isoform expression were consistent with reduced pro-inflammatory signalling in longer-lived strains. Two splicing regulators, HNRNPA1 and HNRNPA2B1, were also associated with parental longevity in humans, in the InCHIANTI aging study. Splicing factors may represent a driver, mediator or early marker of lifespan in mouse, as expression differences were present in the young animals of long-lived strains. Changes to alternative splicing patterns of key senescence genes in spleen and key remodelling genes in muscle suggest that correct regulation of alternative splicing may enhance lifespan in mice. Expression of some splicing factors in humans was also associated with parental longevity, suggesting that splicing regulation may also influence lifespan in humans.
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Affiliation(s)
| | - Luke C. Pilling
- Epidemiology and Public Health; Institute of Biomedical and Clinical Sciences; University of Exeter Medical School; University of Exeter; Devon UK
| | | | - Kevin Flurkey
- The Jackson Laboratory Nathan Shock Centre of Excellence in the Basic Biology of Aging; Bar Harbor ME USA
| | - David E. Harrison
- The Jackson Laboratory Nathan Shock Centre of Excellence in the Basic Biology of Aging; Bar Harbor ME USA
| | - Rong Yuan
- The Jackson Laboratory Nathan Shock Centre of Excellence in the Basic Biology of Aging; Bar Harbor ME USA
| | - Luanne L. Peters
- The Jackson Laboratory Nathan Shock Centre of Excellence in the Basic Biology of Aging; Bar Harbor ME USA
| | - George A. Kuchel
- UConn Centre on Aging; University of Connecticut Health Centre; Farmington CT USA
| | | | - David Melzer
- Epidemiology and Public Health; Institute of Biomedical and Clinical Sciences; University of Exeter Medical School; University of Exeter; Devon UK
- UConn Centre on Aging; University of Connecticut Health Centre; Farmington CT USA
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8
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Sadtler K, Estrellas K, Allen BW, Wolf MT, Fan H, Tam AJ, Patel CH, Luber BS, Wang H, Wagner KR, Powell JD, Housseau F, Pardoll DM, Elisseeff JH. Developing a pro-regenerative biomaterial scaffold microenvironment requires T helper 2 cells. Science 2016; 352:366-70. [PMID: 27081073 DOI: 10.1126/science.aad9272] [Citation(s) in RCA: 463] [Impact Index Per Article: 57.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Accepted: 03/04/2016] [Indexed: 11/02/2022]
Abstract
Immune-mediated tissue regeneration driven by a biomaterial scaffold is emerging as an innovative regenerative strategy to repair damaged tissues. We investigated how biomaterial scaffolds shape the immune microenvironment in traumatic muscle wounds to improve tissue regeneration. The scaffolds induced a pro-regenerative response, characterized by an mTOR/Rictor-dependent T helper 2 pathway that guides interleukin-4-dependent macrophage polarization, which is critical for functional muscle recovery. Manipulating the adaptive immune system using biomaterials engineering may support the development of therapies that promote both systemic and local pro-regenerative immune responses, ultimately stimulating tissue repair.
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Affiliation(s)
- Kaitlyn Sadtler
- Translational Tissue Engineering Center, Wilmer Eye Institute and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenneth Estrellas
- Translational Tissue Engineering Center, Wilmer Eye Institute and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brian W Allen
- Translational Tissue Engineering Center, Wilmer Eye Institute and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthew T Wolf
- Translational Tissue Engineering Center, Wilmer Eye Institute and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hongni Fan
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ada J Tam
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chirag H Patel
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brandon S Luber
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hao Wang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kathryn R Wagner
- Hugo W. Moser Research Institute at Kennedy Krieger Institute, Baltimore, MD 21205, USA, and Departments of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jonathan D Powell
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Franck Housseau
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Drew M Pardoll
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jennifer H Elisseeff
- Translational Tissue Engineering Center, Wilmer Eye Institute and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA. Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Pilling LC, Joehanes R, Kacprowski T, Peters M, Jansen R, Karasik D, Kiel DP, Harries LW, Teumer A, Powell J, Levy D, Lin H, Lunetta K, Munson P, Bandinelli S, Henley W, Hernandez D, Singleton A, Tanaka T, van Grootheest G, Hofman A, Uitterlinden AG, Biffar R, Gläser S, Homuth G, Malsch C, Völker U, Penninx B, van Meurs JBJ, Ferrucci L, Kocher T, Murabito J, Melzer D. Gene transcripts associated with muscle strength: a CHARGE meta-analysis of 7,781 persons. Physiol Genomics 2016; 48:1-11. [PMID: 26487704 PMCID: PMC4757025 DOI: 10.1152/physiolgenomics.00054.2015] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Accepted: 10/08/2015] [Indexed: 12/22/2022] Open
Abstract
Lower muscle strength in midlife predicts disability and mortality in later life. Blood-borne factors, including growth differentiation factor 11 (GDF11), have been linked to muscle regeneration in animal models. We aimed to identify gene transcripts associated with muscle strength in adults. Meta-analysis of whole blood gene expression (overall 17,534 unique genes measured by microarray) and hand-grip strength in four independent cohorts (n = 7,781, ages: 20-104 yr, weighted mean = 56), adjusted for age, sex, height, weight, and leukocyte subtypes. Separate analyses were performed in subsets (older/younger than 60, men/women). Expression levels of 221 genes were associated with strength after adjustment for cofactors and for multiple statistical testing, including ALAS2 (rate-limiting enzyme in heme synthesis), PRF1 (perforin, a cytotoxic protein associated with inflammation), IGF1R, and IGF2BP2 (both insulin like growth factor related). We identified statistical enrichment for hemoglobin biosynthesis, innate immune activation, and the stress response. Ten genes were associated only in younger individuals, four in men only and one in women only. For example, PIK3R2 (a negative regulator of PI3K/AKT growth pathway) was negatively associated with muscle strength in younger (<60 yr) individuals but not older (≥ 60 yr). We also show that 115 genes (52%) have not previously been linked to muscle in NCBI PubMed abstracts. This first large-scale transcriptome study of muscle strength in human adults confirmed associations with known pathways and provides new evidence for over half of the genes identified. There may be age- and sex-specific gene expression signatures in blood for muscle strength.
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Affiliation(s)
- L C Pilling
- Epidemiology and Public Health Group, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, United Kingdom
| | - R Joehanes
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts; Population Studies Branch, National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - T Kacprowski
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany
| | - M Peters
- Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands; The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, the Netherlands
| | - R Jansen
- Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - D Karasik
- Hebrew SeniorLife Institute for Aging Research, Boston, Massachusetts
| | - D P Kiel
- Hebrew SeniorLife Institute for Aging Research, Boston, Massachusetts
| | - L W Harries
- RNA mechanisms of complex diseases group, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, United Kingdom
| | - A Teumer
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany
| | - J Powell
- Centre for Neurogenetics and Statistical Genomics, Queensland Brain Institute, University of Queensland, St. Lucia, Brisbane, Australia
| | - D Levy
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts; Population Studies Branch, National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - H Lin
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts; Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - K Lunetta
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - P Munson
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts; The Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, Maryland
| | - S Bandinelli
- Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy
| | - W Henley
- Institute for Health Services Research, University of Exeter Medical School, Exeter, United Kingdom
| | - D Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland
| | - A Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland
| | - T Tanaka
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - G van Grootheest
- Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - A Hofman
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, the Netherlands; Department of Epidemiology, Erasmus Medical Center Rotterdam, the Netherlands
| | - A G Uitterlinden
- Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands; The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, the Netherlands; Department of Epidemiology, Erasmus Medical Center Rotterdam, the Netherlands
| | - R Biffar
- Department of Prosthetic Dentistry, Gerostomatology and Dental Materials, University Medicine Greifswald, Greifswald, Germany
| | - S Gläser
- Department of Internal Medicine B - Cardiology, Intensive Care, Pulmonary Medicine and Infectious Diseases, University of Greifswald, Greifswald, Germany
| | - G Homuth
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany
| | - C Malsch
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany
| | - U Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany
| | - B Penninx
- Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - J B J van Meurs
- Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands; The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, the Netherlands
| | - L Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - T Kocher
- Unit of Periodontology, Department of Restorative Dentistry, Periodontology and Endodontology, University Medicine Greifswald, Greifswald, Germany; and
| | - J Murabito
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts; General Internal Medicine Section, Boston University, Boston, Massachusetts
| | - D Melzer
- Epidemiology and Public Health Group, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, United Kingdom;
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